esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 1 Introduction
The single weather variable of temperature, wind speed, relative humidity, and rainfall, as well as their various combinations, were separately assembled with the temporal data and historical passenger flow data, to build a series of station-level forecasting models to comprehensively analyze the impacts of different weather conditions on hourly metro passenger flow forecasting. Researchers have widely applied a deep LSTM_NN in building a passenger flow forecasting model, but rarely have they used it to build a forecasting model to comprehensively analyze the impacts of different weather conditions on passenger flow forecasting in such detail. (2) The majority of weather data present general variations, which do not have much impact on passengers' daily commuting, even in the case of heavy rain. This indicates that adding the weather data as inputs to build a forecasting model may not be helpful in improving the forecasting results, because the weather data are likely noisy data in comparison with the temporal data and historical passenger flow data. Furthermore, the study of weather impacts on hourly passenger flow forecasting may be more meaningful than daily, monthly, or seasonal passenger flows. In these cases, how to select the suitable weather data to develop a short-term passenger forecasting model with higher performance is challenging. The experimental results powerfully verified that adding the previous variables of one-hour temperature and wind speed could indeed improve the results in comparison with the model using no weather data for hourly metro passenger flow forecasting at Taipei Main Station on rainy days. The remainder of this paper is organized as follows. Section 2 describes the station-level passenger demand in the Taipei Mass Rapid Transit (MRT). The input variables used and the impacts of weather on hourly passenger flow are also analyzed in this section. Section 3 introduces the architecture of a cell in LSTM and the dataflow of the deep LSTM_NN in this paper. A case study of Taipei Main Station is presented in Section 4 in detail, and the experimental results are analyzed comprehensively against RF and the other three deep neural networks. Section 5 concludes the paper and suggests directions for future study.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 3 Asynchronous Flipped SCMA Scheme  
This paper proposes a novel uplink asynchronous multiple-access scheme named AF-SCMA for the mMTC scenario in the future SAT-IoT communication networks. This scheme makes SCMA truly asynchronous, which reduces the signaling overhead for keeping time synchronous. Moreover, by exploiting the concept of flipped diversity and SIC technique, AF-SCMA can mitigate the negative impact of codebook collision in grant-free SCMA systems, which can further improve the network throughput. In this section, we will present the design of AF-SCMA in detail.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 4 Segmenting the Maps and Building the Anchor Sets
### Finding Unique Regions in a Segmented Map  
Given a set of regions from a segmented map, the first step of the URSIM method is to find the most unique regions, i.e., the most distinguishable ones. To do so, each region is associated with a set of \(N\) attributes with values \(a_{n}\), with \(0\leq n<N\), representing different characteristics such as the size or shape of each region. All attributes are standardized to have comparable values and avoid the effect of outliers. While normalizing would reduce the differences between \(a_{n}\) in the presence of outliers, standardizing, which gives the data a mean of 0 and a standard deviation of 1, mitigates the effect of outliers. Using the set of all attributes \(i\) for a given map, the set is standardized using the standard score:  
\[a_{n}^{s}=\frac{a_{n}-\mu}{\sigma} \tag{1}\]  
with \(\mu\) being the mean of all regions' \(a_{n}\) and \(\sigma\) being the standard deviation. If the value of any \(a_{n}^{s}\) of a region is at least 1 standard deviation away from the mean, we consider the region's attribute, and thus the region, to be unique.  
In our method, we use two simple attributes based on the way MAORIS uses free space to segment maps: the size of each region and their general shape. The size of a region corresponds to the number of pixels in it. The shape of a region is roughly estimated by using the eigenvalues of its two first principal components (PC): more circular regions will have similar eigenvalues, while elongated regions will have different eigenvalues. To obtain a scalar parameter from the PCs describing the shape of a region, we first calculate the eigenvalues of the two first PCs. We then normalize the eigenvalues between 0 and 1 by dividing both of them by the larger one. Finally, the attribute representing the shape is the difference between both normalized eigenvalues. An example of unique regions in a sketch map is shown in Figure 3. However, it should be noted that the method to find unique regions is independent of the set of attributes used. One could use different attributes depending on the type of map or segmentation. We left the study of additional suitable attributes for future work.
## 3 Parameter Identification Method and Process
### Parameter Sensitivity Analysis  
The parameters should be kept in a suitable range to ensure system transient stability. The parameter influence on the system dynamic response was revealed through the sub-module parameter sensitivity analysis under different input conditions, which gave reference for parameter identification.  
Taking the core fuel and coolant temperature module for example, Figure 12 shows the trajectory sensitivity analysis results with changes of \(\pm 10\%\) to \(\Omega/\mu_{c}\) under a step change of 0.01 for \(N_{\rm r}\), or a positive step change of 10 degrees for \(T_{\theta 1}\).  
The parameter influence of \(\Omega/\mu_{c}\) on output variables (i.e., \(T_{\rm F}\), \(T_{\rm av}\) and \(T_{\theta 2}\)) existed constantly as \(N_{\rm r}\) changed. The trajectory sensitivities eventually dropped to zero after about 30 s under step change of \(T_{\theta 1}\) (see Figure 12). Thus, there was a significant difference in the sensitivity of \(\Omega/\mu_{c}\) to each output variable under two cases. The change of \(N_{\rm r}\) was more beneficial for the parameter identification of \(\Omega/\mu_{c}\). Similarly, some other structural and thermal parameters for the steam generator module also had a constant effect on the model output variables under given input conditions.
# Pharmaceuticals Removal from Wastewater with Microalgae: A Pilot Study
## 2 Materials and Methods  
### Experimental Conditions, Sampling Site, and Collection  
The experiments were conducted outdoors in the WWTP of Quinta do Lago, Algarve, in the South of Portugal, between late July and October. Microalgae cultivation was conducted in experimental pilot GreenDune photobioreactors (GD PBRs) developed by Bluemater, S.A., a Portuguese company, which were designed for wastewater treatment. The experimental setup of the photobioreactors and their efficiency in the tertiary treatment of wastewater has been described elsewhere [15]. Briefly, the GD PBRs are open modular systems, and, in this case, they were connected in lines of 3 modules, giving a total treatment volume of 1440 L. The system was fed with partially treated water (after biological secondary treatment of activated sludge with phosphorus precipitation) coming from the secondary settlers of the WWTP. No inoculation was performed; instead, a natural forming consortium of microalgae was allowed to develop in the PBRs for one week prior to the beginning of the experiments. Two experiments of five weeks were conducted, the first in the summer (2 August 2021-31 August 2021) and the second in the autumn (27 September 2021-26 October 2021). The PBRs were operated in continuous mode with an HRT of 24 h. During that time, the concentration of nitrogen and phosphorus nutrients and organic matter was determined in the inflowing water in composed samples of 24 h, collected every week. Biomass concentration was also measured. Pharmaceuticals' concentration was measured both in the influent and effluent of the microalgae system to assess removal.
## 3 Numerical Model for Short-Wall Spacing Strip Filling
### Constitutive Model and Mechanical Parameters  
Employing the Mohr-Coulomb yield criterion, inclusive of tensile truncation, for each distinct layer of overburden rock, the corresponding yield function is as follows [21]:  
\[\begin{split} F_{\text{s}}&=\sigma_{1}-\sigma_{3}\Phi+2C \sqrt{\Phi}\\ F_{\text{t}}&=\sigma_{3}-\sigma_{\text{t}}\end{split} \tag{1}\]  
where \(\Phi=(1+\sin\varphi)/(1-\sin\varphi)\); \(F_{\text{S}}\) is the shear yield function; \(F_{\text{t}}\) is the tensile yield function; \(C\), \(\varphi\), \(\sigma_{\text{t}}\) represents the cohesion, internal friction angle, and tensile strength of the rock layer.  
Figure 3: Mining and filling schematic diagram.  
Figure 2: Numerical calculation model.  
The coal seam utilizes a strain-softening model, with the stress-strain curve illustrated in Figure 4. Figure 5 delineates the diminution of the coal's post-peak shear strength parameters as they correlate with plastic strain.  
Characterized as a low-cohesion granular substance, the filling paste experiences an irreversible volume reduction when bearing the load imposed by the overburden rock stratum. Consequently, the double-yield plastic model [23; 24] is employed to scrutinize its yielding behavior. This model accounts for volumetric yield stemming from enduring volumetric alterations, in addition to considering shear and tensile yields. The yield criterion is expressed as follows:  
\[\begin{split} f^{\text{s}}&=\sigma_{1}-\sigma_{3} \Phi_{\text{b}}+2C_{\text{b}}\sqrt{\Phi_{\text{b}}}\\ f^{\text{t}}&=\sigma_{\text{fb}}-\sigma_{3}\\ f^{\text{N}}&=\tfrac{1}{3}(\sigma_{1}+\sigma_{2}+ \sigma_{3})+p_{\text{c}}\end{split} \tag{2}\]  
where \(\Phi_{\text{b}}=(1+\sin\!\varphi_{\text{b}})/(1-\sin\!\varphi_{\text{b}});f^{ \text{t}}\) is the shear yield function; \(f^{\text{t}}\) is the tensile yield function; \(C_{\text{b}}\), \(\varphi_{\text{b}}\), \(\sigma_{\text{fb}}\), and \(p_{\text{c}}\) are the cohesion, friction angle, tensile strength, and isotropic consolidation pressure of filling body, respectively.  
Figure 4: Strain softening model stress–strain relationship [22].  
Figure 5: Relations between plastic strain, friction angle, and cohesion of coal.  
The hardening attributes of filling materials correlate with volumetric plasticity, and their hardening conduct is ascertained through the subsequent empirical formula [24]:  
\[p_{\text{c}}=1.15\times 10^{7}\bigg{(}\frac{\text{e}^{\text{PV}}}{0.28-\text{e}^{ \text{PV}}}\bigg{)}^{1.5}+10^{4} \tag{3}\]  
where \(\text{e}^{\text{PV}}\) is the plastic volume strain.  
According to the geological survey results, the parameter values of each layer are shown in Table 1.
# Experimental Evaluation of Rigidity Center
## 3 Results and Discussion
### Results of Group C Buildings  
Group C buildings had rectangular floor plans. C1 had symmetrical shear walls, and little torsion was seen. C2 had symmetrical shear walls in the N-S direction, and little or no torsion was observed in the N-S direction, but significant torsion in the W-E direction was visible. Maximum displacement was at the opposite side of the shear walls, resulting in clockwise rotation. In C3 and C4, torsion was seen depending on the strong axis of the shear walls (Figure 19).  
Figure 20 indicates the rigidity center locations comparatively, and Table 9 summarizes the results of the second floor comparatively. In the symmetrical C1 building, the RC and E were close to each other, and the maximum difference was 2.55%. In C4, the maximum difference was 1.4%, indicating that the RC and E were almost the same. They were again close to each other in the other buildings, but the difference was at a maximum of 7.85% in C2 and 6.56% in C3. In C2, the RC was calculated at the symmetry line of the shear walls, whereas E found the rigidity center near the east corner of the  
Figure 19: Test results of Group C.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RC from ETABS**} & \multicolumn{2}{c}{**RC from Test**} & \multicolumn{2}{c}{**Difference, \%**} \\ \cline{2-7}  & \(x_{r}\)**mm** & \(y_{r}\)**mm** & \(x_{r}\)**mm** & \(y_{r}\)**mm** & \(\Delta x_{r}\) & \(\Delta y_{r}\) \\ \hline B1 & 149.7 & 149.5 & 144.6 & 155.3 & 1.72 & 0.93 \\ B2 & 125.1 & 260.8 & 123.8 & 269.6 & 0.42 & 2.48 \\ B3 & 146.7 & 169.8 & 161.3 & 180.7 & 4.87 & 3.65 \\ B4 & 159.7 & 183.5 & 200.8 & 225.0 & 13.70 & 13.84 \\ B5 & 150.1 & 149.8 & 150.1 & 112.0 & 0.01 & 12.61 \\ B6 & 146.2 & 139.2 & 134.1 & 146.3 & 4.03 & 2.36 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Rigidity center comparisons of Group B.  
shear wall located in the west. In C3, although the RC and E were close to each other on the ground and first floor, they differed in the second floor and difference was a bit higher when compared to other buildings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RC from ETABS**} & \multicolumn{2}{c}{**RC from Test**} & \multicolumn{2}{c}{**Difference, \%**} \\ \cline{2-7}  & \(x_{r}\)**, mm** & \(y_{r}\)**, mm** & \(x_{r}\)**, mm** & \(y_{r}\)**, mm** & \(\Delta x_{r}\) & \(\Delta y_{r}\) \\ \hline C1 & 125.9 & 159.3 & 125.6 & 166.9 & 0.10 & 2.55 \\ C2 & 115.1 & 126.6 & 99.4 & 136.9 & 7.85 & 3.45 \\ C3 & 233.0 & 121.1 & 252.6 & 126.4 & 6.56 & 2.14 \\ C4 & 42.9 & 111.9 & 43.1 & 108.6 & 0.10 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Rigidity center comparisons of Group C.  
Figure 20: Rigidity centers of Group C.
## 3 Problem Statement and Input Data Representation Methods
### Common Forms of Input Representations
#### 3.2.1 Time Series
Then, an LSTM model is used to predict traffic trends. Traditional time series forecasting models perform poorly when encountering missing data in the dataset. Regarding temporal dependencies, all roadways undergo seasonal variations characterized by long-term temporal dependencies and missing data. Traditional time series forecasting models perform poorly when encountering missing data in the dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Representations** & **Reference** & **Techniques** \\ \hline \multirow{3}{*}{Time Series} & [51] & RNN \\  & [52] & RNN \\  & [53] & LSTM \\  & [54] & LSTM \\ \hline \multirow{3}{*}{Matrix/Grid-based Sequence} & [55] & ResNet \\  & [56] & LSTM + CNN \\  & [57] & LSTM + CNN \\  & [58] & LSTM + CNN \\ \hline \multirow{3}{*}{Graph-based Sequence} & [59] & RNN + GNN \\  & [60] & LSTM + GNN \\ \cline{1-1}  & [61] & GRU + GCN \\ \cline{1-1}  & [62] & GAT + Attention \\ \cline{1-1}  & [63] & GCN + Attention \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different input representations.
###### Abstract  
This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model's adaptability and robustness, facilitating precise fault identification under varied conditions. Key to ATASML's approach is its novel use of adversarial examples and data-augmentation techniques, including noise injection and temporal warping, which extend the model's exposure to diverse operational scenarios and fault manifestations. This enriched training environment significantly boosts the model's ability to generalize from limited data, a critical advantage in industrial applications where anomaly patterns frequently vary. The framework's performance was rigorously evaluated on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Skoltech Anomaly Benchmark (SKAB), which are representative of complex industrial systems. The results indicate that ATASML outperforms conventional meta-learning models, particularly in scenarios characterized by few-shot learning requirements. Notably, ATASML demonstrated superior accuracy and F1 scores, validating its effectiveness in enhancing fault-diagnosis capabilities. Furthermore, ATASML's strategic incorporation of task sequencing and adversarial tasks optimizes the training process, which not only refines learning outcomes but also improves computational efficiency. This study confirms the utility of the ATASML framework in significantly enhancing the accuracy and reliability of fault-diagnosis systems under diverse and challenging conditions prevalent in industrial processes.  
fault diagnosis; meta-learning; adversarial training; data augmentation; industrial processes; anomaly detection; few-shot learning Article
## 2 Background Work
### Disaster Relief Use Case  
To illustrate the use of opportunistic systems, the example of a disaster relief scenario related to a flood is shown in Figure 2. In such a situation, the environment is rapidly affected by the flood, so quick decisions need to be made. To tackle these issues, it is assumed that there are IoT sensors deployed across the scenario to detect the water level. Such sensors monitor risk conditions characterized by high water levels or heavy rains and are connected to gateways that enable sending the collected information to servers located in a remote Cloud, where it is analyzed to send alerts. However, in the case of a flood, the connectivity of the deployed IoT water level sensors can be affected, making it not possible to send the data to the Cloud (step 1 in Figure 2). Nonetheless, when using an opportunistic system, devices like drones, wearables, or mobile phones can act opportunistically as mobile OEC Gateways to collect information from the sensors (step 2) or provide them with Edge Computing services. It is important to note that, in the event that one of the OEC gateways fails or when a connection cannot be established with an OEC gateway, it would be necessary to wait for another mobile node to appear opportunistically to reestablish the connection. Thus, once one OEC gateway collects the information, it is routed through the network infrastructure, being replicated among all the gateways (step 3) until reaching the one that can connect to the Cloud (step 4). The Cloud can then carry out the necessary actions, such as activating the flood alert system (step 5) or sending alerts to the remote users closest to the affected area (step 6) (these latter actions can also be performed in an opportunistic way, but, for the sake of clarity of Figure 2, they are represented as direct actions).  
Figure 2: OEC in a disaster relief scenario.
## 4 Discussion
### Strengths and Limitations  
The study included consecutive subjects from the general population referred for evaluation of bariatric surgery. The study population with a mean age of 43 years, a female predominance (82%), and a BMI reduction of 11 kg/m2 seemed to be representative of subjects undergoing bariatric surgery [38]. There is no consensus for the handling of subjects undergoing bariatric surgery. In this study, the interventions were performed according to international recommendations. There was a conservative lifestyle intervention for six months and an energy-restricted diet in the last weeks before surgery. Surgery was performed with accepted methods, and there was a follow-up six months after surgery [39; 40]. The psychosomatic symptoms were registered with validated questionnaires. Because the data were collected six months before and not immediately before surgery, the observed changes were the results of combined conservative and surgical treatment. The follow-up period of six months might have been too short for complete stabilisation after surgery. The gender distribution was skewed in favour of females, which is common, but it reduces external validity [38]. The downregulation of the KP metabolites after surgery was judged as normalisation, but reference values for non-obese subjects with the same method for comparison with this slightly overweight/obese group six months after surgery were not available. The accordance with other studies regarding downregulation of the KP after surgery indirectly substantiates the other findings, e.g., the overall negative associations between the KP and psychosomatic disorders. The KP in the cerebrospinal fluid might reflect the psychosomatic symptoms better than the KP in blood. The mixed model linear regression analyses allowed using data from all subjects and not only from subjects with complete data sets. The high risk of type I errors because of multiple testing was counteracted by adjusting for multiple testing ad modum Benjamini-Hochberg. The relatively small sample size increased the possibility of type II errors.
# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
###### Abstract  
Hip fracture incidence is life-threatening and has an impact on the person's physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person's physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.  
Internet of Things (IoT); rehabilitation; hip fracture model; remote movement monitoring; activity recognition; wearable intelligent sensor; edge computing +
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