esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 5 Computational Results  
### Experiment Settings  
Five measures are available for evaluating the robustness of a schedule: Monte Carlo sampling (MC), the analytical approximation method (AC) proposed by us, and three surrogate robustness measures (\(RM_{1},RM_{2},\ and\ RM_{3}\)). All of these algorithms were coded and implemented in Python on an Intel(R) Core(TM) i5-8350U @CPU 1.60 GHz with RAM 4.00 GB.  
To demonstrate the effectiveness of our surrogate measure, this paper presents a variety of comparison SMs that were tested on [31] (ft10, ft20), [PERSON] [32] (la01, la06, la11, la16, la21, la26, la31, la35, la40), [PERSON], [PERSON] [33] (swv01, swv06, swv11), [PERSON] [34] (tai01, ta11, tai21, tai31, tai41, tai51), and [PERSON] and [PERSON] [35] (yn1). We focus on the performance comparison of different surrogate measures of robustness. The genetic algorithm [36] was used to generate the initial scheduling of all benchmarks. For comparability and ease of implementation reasons, all the parameters of the genetic algorithm are set to the same values. The population size (\(P_{size}\)) and the evolutionary generation (\(gen\)) are both set to 300, the operation sequence crossover rate was 0.9, and the operation sequence mutation rate was 0.1.  
We inserted the PMs to each machine as late as possible, while keeping the constraint that the machine's age is always smaller than \(T_{PM}=\theta\big{\{}t_{p}/t_{c}(\beta-1)\big{\}}^{1/\beta}\). Then, we obtained the schedule with PMs.  
The parameters of instances are set as follows. We set the preventive maintenance time of machine \(tp=10\) and corrective maintenance \(tc=\{20,40,60,80\}\); It is assumed that the failure time is subject to a Weibull distribution with shape parameter \(\beta=2\) and scale parameter \(\theta=\{0.5,1.0,1.5\}\times C_{max}\big{(}\sigma_{p}\big{)}\). Then, we obtain 252 cases.
# Distinct Bacterial Consortia Established in ETBE-Degrading Enrichments from a Polluted Aquifer
###### Abstract  
Ethyl tert-butyl ether (ETBE) is a gasoline additive that became an important aquifer pollutant. The information about natural bacterial consortia with a capacity for complete ETBE degradation is limited. Here we assess the taxonomical composition of bacterial communities and diversity of the _ethB_ gene (involved in ETBE biodegradation) in ETBE-enrichment cultures that were established from a gasoline-polluted aquifer, either from anoxic ETBE-polluted plume water (PW), or from an upstream non-polluted water (UW). We used a 165 rRNA microarray, and 16S rRNA and _ethB_ gene sequencing. Despite the dissimilar initial chemical conditions and microbial composition, ETBE-degrading consortia were obtained from both PW and UW. The composition of ETBE-enrichment cultures was distinct from their initial water samples, reflecting the importance of the rare biosphere as a reservoir of potential ETBE degraders. No convergence was observed between the enrichment cultures originating from UW and PW, which were dominated by _Mesorhizobium_ and _Hydrogenophaga_, respectively, indicating that distinct consortia with the same functional properties may be present at one site. Conserved _ethB_ genes were evidenced in both PW and UW ETBE-enrichment cultures and in PW water. Our results suggest that the presence of _ethB_ genes rather than the taxonomical composition of in situ bacterial communities indicate the potential for the ETBE degradation at a given site.  
Keywords:ethyl tert-butyl ether; ETBE biodegradation; bacterial community; polluted aquifer; fuel oxygenates; _ethB_ gene +
Footnote †: journal: Applied Sciences
## 3 System Development: Methodologies and Analysis
### Alert Thresholds  
Flood thresholds used to trigger an alert were primarily based on the rainfall and water-level thresholds described in Sections 3.2 and 3.3. In addition, rainfall thresholds identified from IDF curves derived for American Samoa [22; 33] were used to compensate for data gaps or short available records within the Vaisigano catchment. A simplified schema showing the analytical data flow from real-time observations and forecasts to alert triggers are shown in Figure 4.  
Observed water level alerts are determined directly from in-catchment water level gauges at Lelata, Alaoa East and Alaoa West. The thresholds (5-, 20-, 100-, 1000-year return periods) at which an alert is signalled are based on those identified within the most recent flood modelling of the catchment presented in [23]. Thresholds for the Alaoa East and Alaoa West site were derived by rescaling the estimates for the Lelata using flow per unit area.  
Observed rainfall alerts are based on information taken from [22], i.e., from data that have been derived for American Samoa [33]. Rainfall thresholds can be either a rainfall intensity or a total amount of rain over a specified period. Observed rainfall is also used to predict water level at each river gauging station, enabling advanced warning of up to 1 h or more depending on rainfall intensity, of the likelihood of water level thresholds being exceeded at those locations.  
Figure 4: Schematic of alerting criteria used to indicate increased flood risk. This is pre-configured and displayed on the predicted ‘Flood Status’ and ‘Peak Expected Time’ tools on the flood decision support system (FDSS) web-portal.  
Two NWP sources provide medium-term rainfall forecasts:
* Weather Research and Forecasting Model (WRF) (3 km resolution) 1-h rainfall, available at 00 UTC, for a 7-day window; and
* NOAA Global forecast systems (GFS) forecast (~20 km resolution) 3-h rainfall, available at 00, 06, 12, 18 UTC, for a 10 day window.  
Alert signal thresholds for forecast rainfall in addition to observed rain and water levels, include:  
* A threshold of 5 mm/10-min to indicate risk of flooding;
* A threshold of 25 mm/12-h for two consecutive timeframes is also used to indicate increased flood risk;
* A threshold of 75 mm/24-h used to indicate increased flood risk.  
Flood warnings issued by MNRE are conveyed via available communications outlets (e.g., website, national media stations, social media), and guided by the MNRE Forecast Operations Plan [35].
## 3 The Proposed Methods
### Target Localization in the Unknown Transmit Power Case
Figure 2: Absorption coefficient, \(\alpha_{f}\), versus frequency. In fact, we can further improve localization performance in the unknown transmit power case. To do so, first, we solve Equation (22) to obtain the initial location estimate of target node \(\mathbf{x}\), \(\widehat{\mathbf{x}}_{1}\), and then use this estimate to search for the ML estimate of the transmit power \(P_{0}\), \(\widehat{P}_{0}\). Finally, with the estimated \(\widehat{P}_{0}\), the problem is transformed into the localization in the known transmit power case. Hence, we form the following three-step procedure:  
1. Solve Equation (22) to obtain an initial estimate of \(\mathbf{x}\), \(\widehat{\mathbf{x}}_{1}\). 2. Use \(\widehat{\mathbf{x}}_{1}\) to search for the ML estimate of \(P_{0}\), \(\widehat{P}_{0}\) as \[\widehat{P}_{0}=\frac{\sum_{i=1}^{N}(P_{i}+10\gamma\log_{10}\frac{\widehat{d}_ {i}}{\widehat{d}_{0}}+\alpha_{f}(\widehat{d}_{i}-d_{0}))}{N}\] (24) where \(\widehat{d}_{i}=\|\widehat{\mathbf{x}}_{1}-\mathbf{s}_{i}\|\). 3. Use \(\widehat{P}_{0}\) to calculate the \(\widehat{\eta}=10^{\frac{\widehat{P}_{0}}{10\gamma}}\), \(\widehat{\lambda}_{i}=\beta_{i}+\frac{\widehat{\eta}\alpha_{f}\ln 10}{10\gamma}\) and use this estimate to solve the WLS-K in Equation (12), then obtain the new estimate of \(\mathbf{x}\), \(\widehat{\mathbf{x}}\). We label this method as \"WLS-U\"(weighted least square-unknown).
## 5 Anomaly Detection Model Test Results
### Multi-Layer Perceptron (MLP)  
Unlike other architectures such as LSTM, CNN, and one-class SVM, the MLP-based anomaly detection models showed a rather large performance difference each time they were trained, even in the same hidden layers and hidden units. Therefore, we trained the MLP-based anomaly detection models for each configuration in Table 4 many times and compared the results. Figure 13(a) below shows the TPR and TNR results, and Figure 13(b) shows the TNR results according to training iteration for each model. In Figure 13(a), we can see that the TPR was mostly derived high, but the TNR was often derived low. Additionally,  
Figure 12: An example of one training example for each input/output data structure type.  
Figure 13: Derivation process of signal duration.  
in Figure 14b, we can see that TNR tended to appear low when the training iteration was small. That is, models with a low TNR can be viewed as local optimal. According to these results, we can conclude that the MLP-based anomaly detection models are capable of adequately distinguishing anomalies from normal, but local optimal models with a low TNR can be easily derived. Table 9 shows the average values of the training results for each model. We can see that the TNRs and accuracies are lower than the TPRs because low TNR cases were often derived, as shown in Figure 14a.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods  
We collected Turkish financial tweets discretely between 13 January 2019 and 10 March 2020 using Python, Tweepy library, Twitter API and MySQL. Collected tweets were manually tagged as positive, negative, neutral and irrelevant using our Java-based tagging program.  
In the tweet pre-processing phase, using our Python code, we removed unnecessary sections of tweets, transformed tweet text to lowercase, and fixed spelling/writing errors (normalization) and restored popular abbreviations to their full forms (e.g., mrb to merhaba). ITU Turkish NLP Web Service API [7] was used for the normalization process.  
Word embedding and fastText's pre-trained word embedding [12] were used as feature extractors. Deep learning algorithms--Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN--were used for sentiment classification. The configuration of Neural Networks, encompassing factors such as the number of hidden layers, the dimensions of layers and the choice of activation functions, was contingent upon the unique requirements posed by the task at hand and the characteristics of the dataset. Nevertheless, Table 4 furnishes broad insights into the prevalent architecture commonly employed across different categories of Neural Networks.
## 3 Results
### Optimal Sample Size Determination  
The results from the bootstrapping showed increasing standard errors as the sample size decreased for both forest area and canopy cover estimates, regardless of site and image type (Figure 8). Resampling with a sample size of 200 observations resulted in estimates that were very close to the original sample size of 228, followed by 150, 100, and 50 as the second, third, and fourth closest estimates, respectively. On average, the increase in standard error when using 200 instead of 228 sample observations was 6.7%, 6.5%, and 4.9% for forest area and 8.7%, 6.0%, and 4.2% for canopy cover estimates from PlanetScope, RapidEye, and Sentinel-2, respectively. Similarly, samples of 150 observations on average generated estimates with a standard error inflation of less than 24.0% for all of the image types and both forest area and canopy cover estimations. Furthermore, when the sample size was reduced to 100 observations, the standard error increased almost by 50%.  
The standard errors were consistently smaller for site F than for the rest of the study sites. In addition, the standard error values of sites A and F were entirely below the average curve, while those of sites D and E were constantly above the average curve for both the forest area and canopy cover estimates of the three image types (Figure 8). Reducing the sample size to only 50 observations resulted in a steeper curve and increased the standard errors by more than 100% in all of the cases. In general, considering the sample sizes, overall estimates of PlanetScope and RapidEye were found to be less sensitive to the reduction of sample size for forest area and canopy cover estimations, and the ranges of percentage increment across the sites were smaller in each respective case. Meanwhile, the Sentinel-2 estimates were highly sensitive in both cases with larger range values across the sites, regardless of having some smaller values.  
Figure 7: Average standard error for forest area and canopy cover estimates by sites as well as for canopy cover estimates by LULC classes from the three image types.
## 4 Modeling the Relationship between Cellular Microwave-Links' (CMLs) Length and Density  
The findings in Table 2 suggest that there may be an underling relationship between the CMLs' density and their mean length for a given region. Following that intuition, an analysis on a larger scale was performed. Given the set of CMLs over Israel, a set of 3626 sub-regions was generated. A moving window with varying dimensions scanned the area of Israel. For every iteration, meaning, for every one of the 3626 windows, there was a subset of CMLs that were captured within the window's bounds. Two features were captured for each such iteration, the CMLs' density and the CMLs' mean length. Thus, 3626 observations of pairs {density, mean length} were observed. Figure 5 presents the scatter plot of those observations in blue.
With the diversification of modern power grid systems, an increasing number of factors, including weather, holidays, real-time electricity prices, and even the degree of urban development and human being behaviors, have various impacts on load demand. Traditional load forecasting methods are unable to provide forecasting models with sufficient predictive accuracy. Therefore, deep learning methods are widely used in the field of load forecasting. [PERSON] and [PERSON] [16] used Recurrent neural networks (RNN) for the first time to establish a short-term load forecasting model. However, the conventional RNN would suffer from the gradient vanishing problem and the long short term memory (LSTM) network is an effective approach to relieve the issue. More recently, [PERSON] et al. [17] proposed an LSTM architecture to forecast the load of individual residences [18]. [PERSON] et al. [19] combined the energy consumption of a residence with the behavior of a resident, converted the behavior patterns of energy consumers into a sequence of input features to the network, thanks to which the accuracy of the load forecasting was improved. Some other studies [20; 21; 22; 23] also used LSTM to load forecasting, whereas [PERSON] et al. [24] used gated recurrent units and achieved more accurate results. [PERSON], [PERSON] et al. [25] used the niche immunity lion algorithm and Convolutional neural networks (CNN) for the one step short-term EVs charging load forecasting, where competitive forecasting accuracy could be obtained. Experimental results of these studies show that deep learning models have higher accuracy in load forecasting than the traditional methods. Unlike normal industrial or household loads, EV loads are unique in both periodicity and fluctuation. The significant penetration of EVs would pose a large pressure to the power system particular the distributed network, which calls for more powerful tools for establishing accurate prediction models. In this paper, four deep learning models are preliminarily used to forecast EV charging station load, where real-world EV charging station datasets are adopted in the numerical study. The rest of the paper is organized as follow: Section 2 briefly describes three deep learning models; Section 3 introduces the dataset and proposes the data pre-processing method as well as load forecasting framework; the experimental results are shown in Section 4, followed by Section 5 concludes the paper and outlooks the future research.
## 1 Introduction
In order to effectively extract the required features for human detection from occlusion and differential illumination, an optimized network structure called the generalized separated and enhancement aggregation network (GSEAN) was designed to replace the C2f module in the YOLOv8 neck network. A lightweight convolution called global spatial and channel reconstruction convolution (GSCConv) was also added to the neck network to compress spatial and channel redundancy. Then, the spatial pyramid pool was combined with effective partial self-attention (SPPEPSA) to ensure the network's ability to extract features at different scales. Finally, the proposed neural network was trained using a self-built dataset consisting entirely of indoor human body images (Human in Complex Indoor Environments Dataset, HCIE dataset). The experimental results show that the model significantly improves the detection accuracy of indoor human bodies without increasing floating-point operations. It can detect targets of different scales and recognize human bodies under low illumination. The main contributions of this paper are as follows:  
1. A new method for human detection through vision named CIHD-YOLO has been developed based on deep learning. The method is optimized and adapted based on the YOLOv8 architecture, which can significantly improve detection accuracy in complex indoor environments. 2. Due to the lack of a dedicated dataset for indoor human detection, the HCIE dataset was created. The new dataset combines multiple dimensions, such as different camera angles, subtle differences in lighting, indoor obstacles, and diverse populations composed of different age groups, forming a comprehensive resource. 3. The combination of spatial pyramid pooling and the efficient partial self-attention mechanism (SPPEPSA) allows the network to extract features at different scales and aggregate them locally, enhancing the model's ability to capture critical information. This improves the model's detection capability for human subjects at various scales. 4. An optimized network architecture GSEAM was proposed, which compensates for the losses caused by occlusion and illumination level differences by combining depth-wise separable convolutions and residual connections, enabling it to extract effective features from visual data with poor illumination levels in indoor environments.
## 1 Introduction
### Contributions  
Gas field surface investment accounts for about 30% to 40% of the total development investment cost, and the proportion of investment in the construction of surface-level gathering and transportation pipeline networks often accounts for 60% to 70% of the surface investment cost. It can be seen that a natural gas-gathering and transmission pipeline network occupies an important position in the construction of gas field ground engineering, and its design optimization can significantly improve the economic benefits. At present, some traditional optimization methods are not sufficient for the study of multi-level structures of pipeline networks and the discovery of an effective solution. Therefore, this paper proposes a set of optimization methods for the more complex star-tree gas-gathering pipeline network in discrete spaces.  
Based on the literature reviewed, scholars have made significant strides in the design optimization of gas pipeline networks. However, their focus has primarily been on optimizing single-network topologies, neglecting the complexity introduced by multiple topological combinations, particularly the star-tree multi-level configurations prevalent in oil and gas fields. The inclusion of diverse network types significantly escalates the complexity of solving these models. We approach this issue as follows:  
1. We establish an MINLP model for the optimal design of star-tree networks in discrete space.
2. A hierarchical optimization framework is proposed. The first stage focuses on optimizing the design of the star topology pipeline network connecting wells to stations. The second stage involves optimizing the design of the tree topology pipeline network connecting stations to plants.
3. An improved genetic algorithm (IGA) is proposed, and the Kruskal algorithm is used to achieve optimal layout solutions for the tree network.  
Section 2 delineates the mathematical model. Section 3 introduces a hierarchical optimization framework. Section 4 presents a practical case and examines the optimization result. Finally, Section 5 provides a summary.
## 5 Discussion  
### Current Earthquake Risk of the Weihe Fault  
To evaluate the earthquakes risk of the Weihe Fault, the variations of slip rate and locking depth along the profiles in Figure 6d were inverted by the improved dip-slip model, as shown in Figure 7. Gray dots and red lines in Figure 7 are secular vertical observations and the corresponding best-fitting models, and the best-fitting parameters (\(S\), \(d\) and \(\delta\)) are labeled on each subfigure. It can be seen that most observations are well fitted by the improved dip-slip model. The slip rate of the Weihe Fault shows a decreasing trend from west (\(\sim\)4 mm/yr) to east (\(\sim\)3 mm/yr), suggesting different activities at different sections of the Weihe Fault. This finding is consistent with prior research. However, the slip rate obtained in this study is higher than that of the geological results, which may be caused by the fact that the slip rate obtained by the geological method spans multiple seismic cycles while the rate obtained by the geodetic method is the current crustal deformation rate. The dip of the Weihe Fault is generally increasing from west (\(\sim\)50\({}^{\circ}\)) to east (\(\sim\)80\({}^{\circ}\)), which is slightly smaller than the previous results in the western section (65-80\({}^{\circ}\)) and slightly larger than those in the eastern section (70\({}^{\circ}\)). This difference may be related to the fact that the Weihe Fault is a shovel-type fault with a large dip in the shallow part (within 5 km) and a gradually smaller dip in the deeper part, while the dip-slip model used in this study did not consider the variation of the fault dip with different depths. For the locking depth, the results showed that the locking depth was gradually increasing from west (\(\sim\)5 km) to east (\(\sim\)14 km). It should be noted that the locking depth for profile 6 was very large, indicating that the slip rate in this profile was not significant.  
Figure 6: Multiscale vertical velocity field, using scales (**a**) \(q\) = 5–7, (**b**) \(q\) = 5–8, (**c**) \(q\) = 5–9, (**d**) \(q\) = 5–10, (**e**) \(q\) = 5–12, (**f**) \(q\) = 11–12. The white lines in (**d**) represent locations of profiles.  
According to the earthquake catalogues of the Weihe Fault, most earthquakes are distributed on the eastern section of the Weihe Fault, including the 1161 M5, 280 BC M5.5, 1487 M6.3, and the 1568 M6.8 events. Taking into account the higher locking depth and historical earthquake record, attention should be paid to the earthquake risk of the eastern section of the Weihe Fault.