esa-sceva/satcom-chunk-collection huggingface dataset:
# Approaching Breakthrough: Resource-Efficient Micropolitan Removal with MBR-GAC Configuration
## 2 Materials and Methods
### Syvb's Wastewater Treatment Plant (WWTP) Himmerfjardsverket  
Syvb's WWTP Himmerfjardsverket, located south of Stockholm, Sweden, has been in operation since 1974 and treats the wastewater of approximately 250,000 PE (110,000 m\({}^{3}\)/d). In 2020, a fundamental reconstruction of the plant was started, aiming to increase the treatment capacity to approximately 350,000 PE and to meet new effluent requirements (5 mg/L of biological oxygen demand, 7 days (BOD?), 6 mg/L of total nitrogen (TN) and 0.2 mg/L of total phosphorus (TP)). The chosen MBR process configuration was selected not only to reach these stricter treatment requirements but also to produce an effluent quality that is highly suitable for resource-efficient quaternary treatment for the removal of OMPs, focusing on pharmaceutical residues and PFASs. Since 2013, Syvab (Grodinge, Sweden) has been involved in several research projects regarding quaternary treatment. Following a pre-design study in 2019, a complementary GAC filtration was designed to be combined with the future MBR configuration [35]. Mapping of OMPs and related risk assessment of the receiving coastal water identified risks, especially for pharmaceuticals such as diclofenac and PFASs like perfluorooctanesulfonic acid (PFOS).
## 5 Conclusions  
In this paper, a new non-destructive measuring method for rock surface strength is presented based on the DNN technique and spectrogram analysis. The process comprises four steps: (1) collect hammering sounds and strength data, (2) produce spectrograms of the hammering sounds and remove invalid spectrograms using a modified K-means algorithm, (3) re-train the Inception-ResNet-v2 model by taking the spectrograms and strength data as the inputs and labels respectively, and (4) use regression algorithms to make the prediction results continuous. The validation shows that the strengths (represented by rebound values) of almost all the samples can be predicted within an error of [4; 5; 6; 7].  
Moreover, the combination of the re-trained DNN and KNN has the highest fitting accuracy, and the combination of the re-trained DNN and SVM has the strongest generalization ability. Therefore, if the size and the number of clusters of training samples are large, we recommend KNN for regression. Otherwise, SVM is recommended.  
The proposed method was accomplished using Python and Tensorflow programming; however, it should be noted that the contribution of this research is not about a new deep neural network, and both the Inception-ResNet-v2 model and transfer learning are well-established techniques. This research is an application of them, and is aimed at presenting an effective and simple method for field survey. Overall, the proposed method offers great potential in supporting the implementation for efficient rock strength measurement methods in the field.  
A noted limitation of this research is related to the use of a rebound device to capture the raw rock strength data, under experimental conditions. The precision of the rebound method is low, and the measurement is easily affected by hitting directions and hitting speed. In the subsequent work,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strength**} & \multicolumn{3}{c}{**Mean Errors**} & \multicolumn{3}{c}{**Variance**} \\ \cline{2-7}  & **KNN** & **SVM** & **RF** & **KNN** & **SVM** & **RF** \\ \hline
48.00 & \(-1.75\) & \(-1.11\) & \(-1.96\) & \(2.19\) & \(1.23\) & \(2.33\) \\
61.00 & \(1.68\) & \(1.17\) & \(1.49\) & \(2.14\) & \(2.31\) & \(2.41\) \\
64.00 & \(-1.75\) & \(-0.58\) & \(-1.79\) & \(6.30\) & \(4.66\) &higher precision techniques, such as acoustic emission techniques, will be considered to optimize the performance of the method further.  
[PERSON] collected and analyzed the data, wrote the code and the paper; [PERSON] gave professional geological guidance; [PERSON] provided the idea and edited the manuscript; [PERSON] gave professional geological guidance.  
This research was funded by the National Natural Science Foundation for Excellent Young Scientists of China (Grant no. 51622904), the Tianjin Science Foundation for Distinguished Young Scientists of China (Grant no. 17)CJQJC44000), and the Research Grants Council of Hong Kong (PolyU 152093/14E).  
The authors declare no conflict of interest.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### The Establishment of the Initial Model  
The pitch shaft hole of the U-frame in the spaceborne photoelectric tracking turntable is linked to the pitch shaft system, facilitating the support for the photoelectric detection equipment. The azimuth shaft hole connects to the satellite via the azimuth shaft system and the flange. Five convex platforms are secured with five pyrotechnical bolts to restrict the rotation of the turntable in two directions. Hole features on the side wall were eliminated, while preserving the shape features of the three shaft holes. Finally, the bolt hole was eliminated to finalize the geometric cleaning, yielding the initial model as depicted in Figure 5.
## 5 Conclusions  
In this paper, a spatial proximity-based geographically weighted regression (S-GWR) model is proposed for assessing the landslide susceptibility. The presented model solves the issues of the spatial non-stationarity between landslide factors and its occurrence that usually neglected in previous landslide susceptibility assessment studies. In order to express spatial proximity properly, slope units are adopted. The multicollinearity between the data is eliminated through VIF method and principal component analysis.  
The Qingchuan area in China after the 2008 Ms. 8.0 Wenchuan earthquake is selected as a case study to illustrate the effect and validity of the proposed S-GWR model. The result shows that the four influencing factors (including geological factor, slope shape factor, hydrographic factor, and  
Figure 8: ROC curves and area under the curves (AUC) for the susceptibility maps produced by GWR, ANN, I, OLS. (**a**) Slope unit; (**b**) grid unit.  
Figure 9: Success rate curves and area under the curves (AUC) for the susceptibility maps produced by GWR, ANN, I, OLS. (**a**) Slope unit; (**b**) grid unit.  
aspect factor) in the S-GWR model all have noticeable spatial non-stationary effects on the landslide susceptibility. By quantitatively testing, the missing rate of S-GWR is 12.37%, which is much lower than that of G-GWR of 20.96%, and the AUC values of ROC curve and success rate curve of S-GWR (0.859 and 0.850, respectively) are both higher than those of G-GWR (0.837 and 0.827, respectively). Besides, the AUC values of the ROC curve and success rate curve of the GWR models are higher than those of ANN, I, and OLS models, and the accuracy of each model using slope unit is higher than those of the grid unit with similar cell size.  
Our study verifies the importance of considering the spatial non-stationary and the applicability of the GWR model in the landslide susceptibility assessment. It also suggests that slope unit can better express the spatial relationship between landslide data and make the evaluation results more accurate.  
Conceptualization and methodology, Y.L. and Z.H.; Writing--validation, X.L.; Review and editing, J.D.; All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Key R&D Program of China (grant no. 2018 YED100401); the National Natural Science Foundation of China (grant no. 41702310); the Natural Science Foundation of Hunan (grant no. 2018 JJ3638); and the Innovation Driven Program of Central South University (grant no. 2019 CX011). These financial supports are gratefully acknowledged.  
We would like to thank [PERSON] in Central South University, China, for his constructive suggestions. We also extend our gratitude to editor-in-chief and three reviewers for their insightful comments during the review stage while we submitted the manuscript.  
The authors declare no conflict of interest.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 4 Optimization of Cable Distribution
### Optimization Algorithm
The dashed line passing through \(f_{m}\), \(f_{0}\), and \(\tau_{d}\) denotes the set of all the tension solutions corresponding to the desired \(\tau_{d}\); the blue part of this dashed line in the tension space denotes the feasible tension solutions, \(f_{m}\) denotes the feasible tension solution closest to \(f_{0}\), and \(f_{p}\) represents the solution that is nearest to \(f_{0}\) in tension space. If we fix \(\hat{L}_{a}\) and only update \(\hat{S}\) following Equation (34), the tension value converges to \(f_{p}\), reducing \(E_{2}\) to the minimum. Moreover, even when we update the original fixed \(\hat{L}_{a}\) with Equation (33), when \(\hat{S}\) changes the value of the tension may deviate from the feasible tension solutions related to the desired control wrench. Thus, in order to manage this situation, tracking of the desired wrench with an appropriate \(\alpha_{1}\) is required to ensure that tensions are only selected from the points on the blue part of the dashed line; by updating the law of Equation (34), \(E_{2}\) becomes the minimum value on the blue part of the dashed line. The total calculation and control process is shown in Algorithm 1. To guarantee the convergence of \(E_{1}\), in the following section we examine its dynamic change and define the tunable range of \(\alpha_{1}\). ```
0:\(q,\dot{q},q_{d},\hat{L}_{a0},\hat{S}_{0},\lambda\)
0:\(f\) LOOP Process
1:for\(t=0\) to \(t_{f}\)do
2: Calculate \(\tau_{d}\) following Equation (11). 3: Calculate the Updating law of Equations (33) and (34). 4: Update the integral of \(\hat{L}_{a},\hat{S}\). 5: Calculate the tension following Equation (28) and use the calculation result to control the robot. 6:endfor
```  
**Algorithm 1** tanh(:)-based Optimal Tension Distribution
###### Abstract  
In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain.  
S +
Footnote †: journal: Applied sciences
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Comparisons with the Contextualized Word Embedding Models and the BERT with/without the S-EWEs
Table 8 shows the accuracies and macro-F1 values of the BERT and CoSE-T models on SemEval-2013 and SST-2 datasets. From these results, we can observe that: (1) Contextualized word embedding can achieve better results than feature-based word embedding (shown in Table 5 and 6) in sentiment analysis tasks. Specifically, CoSE-T achieved 90.80% accuracy on the SemEval-2013 dataset and 89.80% accuracy on the SST-2 dataset; BERT achieved 90.80% accuracy and 88.88% macro-F1 on the SemEval-2013 dataset, and 90.38% accuracy and 90.38% macro-F1 on the SST-2 dataset. (2) BERT with S-EWE embeddings performs worse than BERT and CoSE-T without S-EWE embeddings. Specifically, on the SemEval-2013 dataset, BERT\({}_{\text{S-EWE}_{c}}\) was 9.14% less accurate than BERT and CoSE-T, and 11.34% lower than BERT in macro-F1; BERT\({}_{\text{S-EWE}_{c}}\) was 14.97% less accurate than BERT and CoSE-T and 20.21% lower than BERT in macro-F1. On the SST-2 dataset, BERT\({}_{\text{S-EWE}_{t}}\) was 10.39% lower than CoSE-T and 10.97% lower than BERT in accuracy, and 11.34% lower than BERT in macro-F1; BERT\({}_{\text{S-EWE}_{c}}\) was 17.14% lower than CoSE-T and 17.72% lower than BERT in accuracy, and 17.73% lower than BERT in macro-F1. From these observations, we can conclude that the S-EWE method is unsuitable for enhancing contextualized word embedding. The main reasons are that the S-EWE method only enhances the embedding layer of the model and has no effect on the model itself.
# Spatial Shape-Aware Network for Elongated Target Detection
## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Contribution** & **Limitation** & **Improvement Stage** \\ \hline
[13] & A new convolution kernel sampling pattern is designed to enhance feature extraction. Flexible adjustment of receptive field; Dynamic modeling for target shapes and orientations. & May introduce unnecessary computational costs in simple scenarios. Increased computational complexity due to dynamic modules, resulting in slower inference speed. & Improvement in feature extraction for remote sensing detection. \\ \hline
[14] & Fused multi-level feature maps, enabling the network to handle targets of varying sizes in remote sensing images. & No significant improvement in simpler scenarios, possibly increasing algorithm complexity. & Improvement in feature extraction for remote sensing detection. \\ \hline
[16] & Introduced feature refinement module (FRM), addressing sensitivity to rotation angles in traditional IoU methods. Introduced regularizers to shallow features, enhancing the extraction of rotation-invariant features and class-specific information. & Sensitive to hyperparameter settings. \\ \hline
[17] & Introduced rotational anchor box generation mechanism in two-stage detection. Generated sparser and shape-variable anchor boxes. Enhanced feature alignment ability in single-stage detectors. & Limited rotation parameters, excessive redundant rotational boxes. Performance suffers with scale-imbalanced detection objects. & Improvement in candidate region generation and feature alignment. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A review of the research on rotating object detection.
# A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location
## 3 Weight Definition Method of Weighted Bus Network
### Improved Calculation Method for Bus Driving Efficiency  
In actual situations, when we calculate passengers' time from station \(i\) to station \(j\), in addition to the bus travel time, we should also consider the waiting time of passengers [14]. The waiting time of passengers is related to the number of bus routes, but also to the passenger flow \(x_{ij}\); when a bus line tends to be fully loaded, the probability of passengers missing the bus is greater, resulting in more waiting time. Therefore, an improved calculation method for bus driving efficiency is put forward:  
\[W_{ij}=\frac{x_{ij}}{t_{ij}\Big{(}1+\lambda\Big{(}\frac{x_{ij}}{x_{max}}\Big{)} ^{\omega}\Big{)}} \tag{4}\]  
\(x_{max}\) is the upper limit for bus passenger volume; \(\omega\) and \(\lambda\) are correction coefficients; according to the actual situation of bus line operation in the city of Jinan, this paper set up \(\lambda\) = 1, \(\omega\) = 10. The improved \(W_{ij}\) is used as the weight of the edge.  
The improved results are shown in Figure 3. As can be seen from the figure, in the traditional algorithm, the bus driving efficiency linearly increases with the increase of passenger load rate; however, in the improved algorithm, after adjusting passenger volume and other factors, a clear line of demarcation could be seen: when the passenger load rate is below 80%, the bus driving efficiency will increase linearly with the increase of passenger load rate; when the passenger load rate reaches 80%, the bus driving efficiency will decrease gradually with the increase of passenger load rate.
## 4 Hardware Description
### Description of Hardware Components
#### 4.2.6 RF Module  
This module operates in a half-duplex mode that is at an instance it can either transmit or receive [42]. After each transmission, the module switches to receiver mode automatically. The TX and RX LED shows receiving or transmitting of data. During transmission of data, if any data is input to the transmitter, the data is kept in the buffer for the next transmission cycle. The incoming data is stored in the incoming buffer so that the data is not lost when the module is transmitting data actively. at the time of submission, please state that they will be provided.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 2 Intrusion Detection in the Internet of Things  
This part examines various literature sources on IDS solutions tailored for the IoT. The intrusion detection systems (IDSs) taxonomy for the Internet of Things (IoT) offers a structured framework to categorize and understand various IDS solutions tailored to IoT environments. Table 1 shows the different categories of IDS categories and subcategories. Here is the breakdown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IDS Proposals for IoT** & **Subcategories** & **Advantages** & **Disadvantage** \\ \hline \multirow{4}{*}{Placement strategy} & Edge-based IDS & Low latency, bandwidth efficiency, & Resource constraints, \\  & & data privacy & Scalability \\ \cline{2-4}  & \multirow{2}{*}{Cloud-based IDS} & Computational power, & Latency, \\  & Cloud-based IDS & scalability, & Bandwidth consumption, \\  & & Centralized management. & Data privacy concerns \\ \cline{2-4}  & \multirow{2}{*}{Hybrid IDS} & Flexibility, & \\  & & Optimized bandwidth, & Complexity \\  & & Enhanced coverage & \\ \hline \multirow{4}{*}{Detection method} & Signature-based & High accuracy in detecting known threats, & \\  & & fast response & \\  & & Anomaly-based & Detects unknown threats, offers a holistic view & Resource-intensive may miss subtle \\  & & & attacks \\ \cline{2-4}  & \multirow{2}{*}{Specification-based} & Highly customizable, effective for enforcing & Limited to known specifications, Can be \\  & & compliance & complex to configure \\ \cline{2-4}  & \multirow{2}{*}{Hybrid} & Improved accuracy, greater flexibility & Increased complexity, resource-intensive, \\  & & & costly \\ \hline \multirow{4}{*}{Security threat} & Physical attacks & Immediate detection & Limited to physical proximity, \\  & & & False alarms \\ \cline{2-4}  & Network attacks & Broad coverage, scalability & Limited visibility, potential false positives \\ \cline{2-4}  & Software attacks & Detects common threats, fast response & Limited to known threats, may not \\  & & prevent initial infection \\ \cline{2-4}  & Side-channel attacks & Protection against specialized attacks & Specialized knowledge required \\ \hline \multirow{4}{*}{Validation strategy} & \multirow{4}{*}{Simulation} & Cost efficiency & Model accuracy \\  & & Scalability & Limited realism \\  & & Risk mitigation & Validation gap \\ \cline{2-4}  & \multirow{2}{*}{Testbed} & Real-world environment & Costly setup \\  & & Integration testing & Limited scalability \\  & & Controlled conditions & Resource constraints \\  & & Repeatability issues \\ \hline \multirow{4}{*}{Validation strategy} & Real-world & Authentic validation & High risk \\  & & User feedback & Costly fixes \\ \cline{2-4}  & deployment & Scalability testing & Limited control \\ \cline{2-4}  & \multirow{2}{*}{Theoretical analysis} & Early assessment & \\ \cline{1-1}  & & Low cost & \\ \cline{1-1}  & & Insight & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intrusion detection in the Internet of Things.
# Using Plastic Waste in a Circular Economy Approach to Improve the Properties of Bituminous Binders
## 3 Results and Discussion  
### Thermogravimetric Analysis (DSC)  
Figure 4 shows the normalized results for the wax formed in pyrolysis, under the conditions described in Section 2.2.1, after testing on the DSC identified in Section 2.2.2.  
The heat flow curve showed two endothermic peaks: the first band, which appeared at intermediate temperature (\(\approx\)114 \({}^{\circ}\)C), was attributed to wax melting, and the second band (around 500 \({}^{\circ}\)C) indicated that a degradation step occurred when the conversion took place (the thermal degradation initiation temperature, T\({}_{\text{onset}}\), is about 450 \({}^{\circ}\)C). The melting temperature peak (at \(\approx\)114 \({}^{\circ}\)C) was higher than the range pointed out in [7] (70-80 \({}^{\circ}\)C) for waves obtained from HDPE at 500 \({}^{\circ}\)C or even for FT waves (\(\approx\)100 \({}^{\circ}\)C) [25], but it was smaller than the ones published by [PERSON] et al. [38] (129 \({}^{\circ}\)C) and [PERSON] et al. [57] (128.5 \({}^{\circ}\)C). So, when this wax was added to bitumen, the manufacturing temperature of  
Figure 4: Wax—thermogravimetric analysis (normalized): (a) heat flow; (b) weight loss; (c) derivative weight.  
Figure 3: Dynamic rheology: (a) dynamic shear rheometer; (b) its operating mode.  
the bituminous mixture could be substantially lower when compared to that specified for traditional binders (35/50 and PMB), generally above 160 \({}^{\circ}\)C. In addition, its residue (char) after degradation (weight curve) was almost nil.  
In Figure 5, the normalized results for three studied binders are shown, measured on the same DSC test equipment identified in Section 2.2.2 (under conditions described in Section 2.2.1).  
In these thermograms, only minor differences could be observed in the melting behaviour of all samples, while the enthalpy energy in degradation (for temperatures ranging between 450 \({}^{\circ}\)C and 500 \({}^{\circ}\)C) had substantial dissimilarities. These last peaks, where the maximum mass loss occurred, were related to the volatilization of hydrocarbons. The differences observed in the heat flow and residue of the PMB + wax, compared to PMB, were also partially due to the functional additives included. In the case of PMB + wax, the difference in residue at 800 \({}^{\circ}\)C could also have been affected by the shear rate and the mixing time used (conditions that affect the char content, as proved by [PERSON] et al. [58] for SBS/binder blends).