esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this work was to develop bacterial communities to effectively degrade petroleum hydrocarbons (PHs). We investigated the biotic and abiotic contributors to differences in PHs degradation efficacy between two bacterial strains, _Klebsiella pneumoniae_ (Kp) and _Pseudomonas aeruginosa_ (Pa), screened out from the activated sludge of a petroleum refinery. We characterized the temporal variations in degradation efficacy for diesel and its five major constituents as a sole carbon source and identified more constituents they degraded. The growth characteristics, surface tension, hydrophobicity and emulsifiability of these two strains were measured. We further estimated the relationships between their degradation efficacy and all the biotic and abiotic factors. Results showed that the Pa strain had higher diesel degradation efficacy (58% on Day 14) and utilized more diesel constituents (86%) compared to Kp. Additionally, the growth of the Pa strain in diesel medium was faster than that of the Kp strain. The Pa strain had a lower surface tension and higher hydrophobicity and emulsifiability than Kp, while the surfactant produced by Pa was identified as rhamn. Degradation of PHs was positively related to bacterial growth, hydrophobicity and emulsification but negatively related to surface tension. Overall, differences in degrading capacity for diesel constituents, relative growth rate, and biosurfactant production contributed to the variation in the PHs degradation efficacy of these two bacterial strains.  
biodegradation; bacteria; diesel constituents; biosurfactant; degradation efficacy; growth rate +
Footnote †: journal: Applied Physics Letters B
## 3 Technologies for Treating Landfill Leachate
### Advanced Oxidation Progresses
#### 3.2.4 Photocatalytic Oxidation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **pH** & **Parameter** & \begin{tabular}{c} **Concentration** \\ **(mg/L)** \\ \end{tabular} & \begin{tabular}{c} **Removal** \\ **Rate (\%)** \\ \end{tabular} & **Ref.** \\ \hline EO & 7.3 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 3400.0 & & 50.0 & [113] \\ EO & 3.0 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 3015.7 & & 90.1 & [115] \\ O\({}_{3}\) & 7.2 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 2417.0 & 80.4 & [118] \\ O\({}_{3}\) & 7.6 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 1062.0 & & 55.8 & [120] \\  & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 469.0 & & 70.0 & [124] \\ Fenton & 2.0 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 1851.0 & & 97.4 & [126] \\ Fenton & 5.0 & \begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 600.0 & & 85.0 & [130] \\ Photocatalytic & 6.0 & \
\begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 90.0 & & 72.0 & [131] \\ Photocatalytic & 6.0 &
\begin{tabular}{c} COD\({}_{1}\) \\ \end{tabular} & 13,185.0 & & 72.0 & [131] \\ \hline \hline \end{tabular} Note: i refers to initial; f refers to final; EO refers to electrooxidation.  
\end{table} 
Table 4: Efficiency of advanced oxidation processes at laboratory scale. dosage critically influence the process. These variables can be optimized by establishing reaction kinetics models to enhance overall performance [29; 133]. AOPs demonstrate exceptional technical merits in landfill leachate remediation, particularly in three operational domains: (1) effective mineralization of refractory organic compounds [46], (2) complete decolorization and odor elimination [123; 125], and (3) broad-spectrum applicability to leachates of varying stabilization stages [132]. However, their industrial implementation encounters three principal constraints: (i) elevated operational costs [46], (ii) high energy consumption [35], and (iii) catalyst deactivation issues [133].
## 1 Introduction
This implies that locating a sufficient number of emission outlets in certain areas will be more challenging and will require higher costs to cover a broader range. The issue of insufficient data necessitates models to be more accurate and generalizable. In response to the aforementioned issue, we have created a dataset to recognize sewage outlet and sewage color, as well as nocturnal sewage discharge behavior. Additionally, an enhanced network with attention-guided features has been developed to detect common types of sewage outlets, sewage colors, and nocturnal sewage behaviors in camera images. The dataset comprises sewage discharge data from 11 different locations, processed using image enhancement techniques and the Cycle GAN generation method, resulting in a total of 10,035 image data samples. The proposed sewage outlet dataset includes a variety of representative outlet types with significant appearance variation, expanded through data augmentation to increase the diversity of sewage outlet samples. By introducing BiFPN into the YOLOv5 network, the multi-scale target recognition capability has been enhanced without increasing computational costs, thereby improving the detection of small targets in sewage images. The sewage discharge images contain numerous unimportant features (such as natural scenes) that can affect the robustness of the detection model. To address this issue and ensure the model focuses on relevant features in the images, we have employed the CBAM attention mechanism. All in all, the main contributions of this study are as follows:A dataset of sewage outlet camera images in a multi-class, complex environment has been established, including images of sewage discharge from outlets at night under low-light conditions, aiming to advance the development of an intelligent recognition technology for detecting sewage outlets in rivers. 2. A network improvement was proposed that utilizes the C3 Ghost Module, BiFPN, and CBAM attention mechanism to guide the enhancement of valuable features and reduce model size, thereby improving recognition accuracy and efficiency. The results indicate that the YOLOV5-BIFC model has a size of 7.8 MB, representing a 44.2% reduction compared to the original YOLOv5 model. The FPS of this model is 26.7, enabling real-time discharge monitoring. The F1 score and mAP achieved by YOLOv5-BiFC are 89.8% and 87.3%. 3. The proposal aims to enhance the Cycle GAN to increase the quantity and diversity of emission data from outlets. The rest of the paper is organized as follows. Section 2 introduces the methods which have already been used. Section 3 introduces the proposed detection method and improvement module. Section 4 describes the sewage discharge dataset and the experimental results. Finally, Section 5 provides a summary of our research work.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 1 Introduction  
The rapid growth of tourism demand and the digital transformation of tourism social platforms have led to an exponential increase in user-generated content [1, 2, 3]. Tourist reviews not only reflect individual assessments of attraction services and experiences [4] but also reveal latent thematic similarity, sentiment coherence, and other implicit semantic associations through cross-scenic mentions [5, 6, 7]. In addition, attractions also exhibit spatial correlations based on their geographic proximity and transit cost [8]. Collectively, these spatial and semantic relationships shape tourists' route planning behaviors [9]. Despite this, existing tourism recommendation systems predominantly rely on single-dimension optimization strategies, often resulting in suboptimal routes characterized by high transit costor low thematic relevance. Such approaches fail to address the dual requirements of spatial efficiency and experiential coherence, underscoring the necessity for a unified framework that synergistically integrates spatial and semantic associations in route recommendation.  
Tourism review texts have emerged as a critical resource for understanding tourist behavior, sentiment orientation, and regional tourism dynamics [10, 11]. Nevertheless, their unstructured nature and fragmented content pose substantial challenges for semantic extraction and analysis [12]. While advances have been made in topic classification and sentiment polarity analysis, prevailing methodologies often suffer from misaligned analytical granularity and a lack of integration between thematic and emotional dimensions. Furthermore, current tourism route recommendation systems--whether attribute-driven or behavior-driven--are limited in their capacity to systematically model spatial and semantic correlations among attractions.  
In summary, while recent studies have made significant progress in analyzing the semantic features of tourist attractions and improving route recommendation systems, existing approaches often neglect the interplay between thematic and sentiment features. Furthermore, there is a lack of systematic modeling of spatial-semantic associations among attractions. To achieve a balanced trade-off among transit cost, user behavior, and experiential quality, route recommendations should simultaneously consider spatial proximity, associative co-occurrence, and thematic-sentiment similarity among attractions. This study proposes the Tourism Sentiment Chain (TSC) model that integrates spatial and semantic information to quantify multidimensional relationships between attractions. Based on graph propagation and random walk strategies, the model generates tourism routes that jointly optimize geographical accessibility and experiential coherence, offering travelers cost-effective and coherent travel experiences.
# A Distributed RF Threat Sensing Architecture
## 3 Proposed Approach
### Sensor Expected Performance and Alarm Threshold Level
Hence the capacity of the channel is degraded, affecting several network parameters such as the packet delivery ratio (PDR), network throughput, and collision rate, until the total loss of communication. Based on the above, an \(ATL_{\text{(f/t)}}\) can be defined as such a level that, if exceeded, the wireless network will face severe difficulty in medium access, implying a possible jamming attack against it. Hence, in our proposed system, the \(ATL_{\text{(f/t)}}\) is automatically set after the profiling period by taking into consideration the specific environment profiling parameters, for each specific band \(i\) and time slot \(t\) under no jamming events (\(RSS_{\text{Env}_{\text{max}}}\), \(RSS_{\text{Env}_{\text{min}}}\), \(RSS_{\text{Env}_{\text{ave}}}\)) and \(J_{\text{tol}}\). The calculation is provided by Equation (4) for band \(i\) per time slot \(t\). \[ATL_{band_{i}(t)}=RSS_{env_{\text{ave}}(i)(t)}+J_{tol}+k \tag{4}\]  
where \(k\) is a manually set guard value. For example, as described in [13], for WiFi communications a constant jamming signal is effective when exceeds it the legitimate signal by 4 dB. To avoid false-positive alarms occurring due to RSS fluctuations in each band, we force:  
\[I_{tol}>(RSS_{env_{max(i)(t)}}-RSS_{env_{\text{ave}(i)(t)}}) \tag{5}\]  
To consider a sensing scenario, we assume the existence of a low-to-moderate RF jammer with an EIRP power of 48 dBm at 2.4 GHz and an EIRP of 45 dBm at 5.8 GHz, operating in a typical average noise suburban environment, up to 3 km away from the sensor. It can be theoretically calculated, using the free space loss Equation (6), that the path loss is 109.5 dB at 2.4 GHz and 117.2 dB at 5.8 GHz. This means that the expected signal level arriving at the Vivaldi antenna would be \(-61.6\) dBm at 2.4 GHz and \(-72.3\) dBm at 5.8 GHz, which are within the detected ranges of the \(\pm 1\) dB log conformance error of the specific log amps. \[FSL=\left(\frac{4\pi d}{\lambda}\right)^{2} \tag{6}\]  
where \(d\) is the distance between the transmitter and receiver in meters and \(\lambda\) the wavelength of the signal. The proposed algorithm is presented with the following pseudocode (Algorithm 1):
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 6 Experimental Results and Analysis
### Ablation Studies and Hyperparameter Optimization  
To address the critical need for methodological transparency, we conducted systematic ablation studies and hyperparameter optimization experiments. Specifically, we compared four preprocessing configurations: (1) raw data, (2) Kalman filtering only, (3) normalization only, and (4) a combination of Kalman filtering and normalization. The complete pipeline consistently outperformed partial implementations by 8.2-14.7%, highlighting the synergistic effect of noise reduction and feature normalization.  
For hyperparameter optimization, we employed grid search with cross-validation across all models. For CNN, we systematically evaluated filter sizes \(\in 32,64,128\), kernel sizes \(\in 3,5,7\), dense units \(\in 50,100,200\), and learning rates \(\in 0.01,0.001,0.0001\). The best performance was observed when filters were set to 64, kernel size to 3, dense units to 100, and learning rate to 0.001.  
Similar optimization procedures were applied to the LSTM model, where the number of units \(\in 50,100,200\), dropout rates \(\in 0.2,0.3,0.4\), and learning rates \(\in 0.01,0.001,0.0001\) were explored. The Transformer-based models were also tuned following a similar approach. These comprehensive hyperparameter analyses improved model performance and offered insights into the sensitivity of different architectures to variations in parameter settings.
# Assessment of the Water Quality of WWTPs' Effluents through the Use of Wastewater Quality Index
## 2 Materials and Methods
### Principal Component Analysis  
Multivariate analysis and visualization of wastewater effluents' datasets are achieved through the application of Principal Component Analysis (PCA) [48; 49]. The PCA is used for dimensionality reduction of interrelated variables, keeping the variation in the original data as much as possible [50]. The latent factors (principal components) identify the variance sources and data structure, where the first few components, those with eigenvalues higher than 1, preserve the significant part of dataset variation [51]. The principal components (PCs) are calculated from the original input data matrix as a product of two orthogonal factor matrices: factor loadings and factor scores. Factor loadings present the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{2-10}
**WWTP** & **COD** & **TSS** & **TN** & **TP** & **EC** & **Cd** & **Cr** & **Ni** & **Pb** \\  & **mg/L O\({}_{2}\)** & **mg/L** & **mg/L** & **mg/L** & **pH** & **\#S/cm** & **\#g/L** & **\#g/L** & **\#g/L** \\ \hline   LOV & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   MON & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   MZD & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   PAZ & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   PBN & 125 & 60 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   PDV & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   PER & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   POP & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   PVN & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   RDN & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   SEV & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   SMK & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   SOF & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   SZG & 125 & 35 & 10 & 1 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\   TRO & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._weights of original variables in the formation of new variables (factors or PCs) and give information about the principal component origin. Factor scores present the projections of the original data on PCs and could be used for the identification of similarity groups between investigated samples.  
Before the PCA analysis, the input data were auto-scaled and Venetian blinds as a cross-validation procedure was applied. All multivariate statistics models were performed in MATLAB R2021a using PLS Toolbox 9.0 (Eigenvector Research Inc., Manson, WA, USA).
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Evaluation Metrics
#### 3.3.3 Precision  
Precision is an evaluation metric which calculates the proportion of true positive predictions made by a model out of all positive predictions. The formula to compute the precision of a classifier is given below in Equation (2).  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{2}\]  
Precision is the number of instances of a class that were correctly predicted as a member of that class divided by the total number of instances predicted to belong to that class. Precision is a very critical measure especially in a situation where false positives are desired to be minimized. For example, in medical diagnosis, it is better to have low false positive rates to avoid unnecessary treatments. On the other hand, a high false negative rate is more acceptable in this case because it may result in missed diagnoses that can be corrected later.
## 5 Results and Discussion
The VAD and VJD feature combinations perform approximately the same as VAJD. In addition, experiments were conducted with combinations without velocity or jerk. A combination of VAD and AJD features was used for the experiment. The results in Figure 6a show that the absence of jerk in the VAD combination does not decrease performance. Regarding the effect of velocity, the AJD in Figure 6b shows that the absence of velocity significantly affects the overall performance. The results imply that combining acceleration and jerk with direction (and with the absence of velocity) does not improve overall performance for all event types. Figure 4: The confusion matrix for AD and VD feature combinations. Figure 5: The confusion matrix for VJD and VAJD feature combinations.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology
### Definitions of Algorithm Parameters  
Before executing the developed algorithms, some dependent parameters are set based on expert knowledge in the field, as is the case with other density-based clustering algorithms such as BOCEDS, BOCEDS Inc, CEDAS, and MuDi-Stream. The developed algorithms define the following clustering parameters.  
1. Inc Step for Quantization: The step that is used for increasing the quantization at any dimension to fulfill the condition given in the inequality of Equation (1).
2. Initial quantization level (\(q_{0}\)): The value that is given as the initial quantization level when starting the algorithm.
3. Decay: Decay is the number of data points that arrive per unit time from the data stream. It is the data transfer rate. A decay of 50 indicates that 50 data points are extracted consecutively from the data stream on average per unit time (second, minute). It is utilized to keep the energy of micro-clusters up to date. This value is determined based on the application's expert knowledge.
4. Maximum (\(R_{max}\)) and Minimum (\(R_{min}\)): The maximum and minimum radii of micro-clusters are determined using professional knowledge of the application. The maximum radius demonstrates the separation and smoothness of micro-clusters, whilst the minimum radius demonstrates the development of micro-clusters with enough data points.
5. CM Threshold: The density threshold that is used to convert the data arrived at a certain grid to a core micro-cluster.
6. Cell Threshold: The threshold used to determine when the Tempo Spatial Hyper Cube has converged, and no additional quantization level increments are required in any dimension.
7. Outlier Threshold: The density threshold that is used to convert a \(CM\) to an outlier for removing it.
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 2 Methodology
### Line Segment Feature Algorithm  
Point features are mainly corner points in the image. At present, there are a lot of widely used point feature detection and extraction algorithms such as scale invariant feature transform (SIFT), speeded up robust features (SURF) and oriented FAST and rotated brief (ORB). Compared to other algorithms, ORB has a high extraction speed and good real-time performance while maintaining the invariance of feature sub-rotation and scale. Since it is likely that sufficient point features of the splicing registration algorithm cannot be effectively extracted in underwater environments, the robustness of the algorithm decline. In contrast, line features are mainly the edges of objects in the image. The depth information in the line segment feature changes less and thus the line segment feature is easier to extract underwater. Thus, the robustness of the algorithm is improved.  
Line segment detection is an important and frequently used application in computer vision. In the traditional method, the Canny edge detector is used to extract the edge information and then the line segments consisting of edge points exceeding the set threshold are extracted by Hough transform. Finally, the length thresholds are used to select these line segments. There are serious defects in extracting straight lines by Hough transformation, and error detection will occur at a high edge density. In addition, this method has high time complexity and cannot be used as the line segment extraction algorithm for underwater real-time positioning.  
As shown in Figure 1, LSD is an extraction algorithm that extracts sub-pixel precision line segments in linear time. It uses heuristic search and inverse verification methods to achieve sub-pixel precision in linear time without setting any parameters. It defines a line segment as the image region, called the line-support region, which is a straight region the points of which have roughly the same image gradient angle. Finally, the judgement as to whether the line-support region is a line segment is conducted by counting the number of aligned points.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 5 Results and Discussion
### Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal  
#### 5.2.1 Pressure Distribution of Two-Stage Finger Seal  
When the radial displacement excitation of rotor is 30 \(\upmu\)m, the RMS roughness of finger element is 1 \(\upmu\)m, and the rotor speed is 10,000 \(\uptau\)/min, Figure 16 shows the pressure contour of 2 SFS under the axial pressure difference of 0.6 MPa, and there exist similar pressure contours under other axial pressure differences. As shown in Figure 16, the fluid pressure in the high-pressure cavity, middle-pressure cavity, and low-pressure cavity changes little, however, when the fluid flows successively through the 1 st finger seal and the 2 nd finger seal, the pressure decreases step by step due to the high resistances of finger stick bundles. In addition, the fluid pressure changes sharply at the fence height region, especially near the corner of the inner edge of the art cover plate. The reason is as follows: When the fluid flows out of each finger stick bundle, the flow resistance suddenly decreases, and the fluid  
Figure 14: Effect of side leakage flow on the leakage of 2 SFS.  
Figure 15: Effect of radial displacement excitation of rotor on the leakage of 2 SFS.  
accelerates and expands near the corner of inner edge of the aft cover plate, so the fluid pressure changes violently.  
In Figure 16, as the axial pressure difference is 0.6 MPa, the pressure drop of the 1 st finger seal is 0.233 MPa, while the pressure drop of the 2 nd finger seal is 0.367 MPa, so the pressure drop of the 2 nd finger seal is 1.575 times of that of the 1 st finger seal, which indicates that the 2 SFS has a significant pressure drop imbalance between stages