esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 6 Discussion of Findings and Consideration for Processing Approaches
### Constrains of Actual Non-Uniform and Non-Rectangular Target Returns
The principle is based on the idea that a constant linear movement with a constant target speed over ground \(|v_{\text{trg}}|\) is still a valid model within the considered CPI duration \(T_{\text{cov}}\). Only the effectively measurable velocity \(|v_{\text{eff}}|\leq|v_{\text{trg}}|\) as expressed in Equation (8) might simply deviate due to the position dependence of the bistatic angle pairs in a bi- or, respectively, multistatic radar geometry. This leads to a geometry-induced acceleration even in the case of a straight-line trajectory without target acceleration. Most compensation methods that were shown in Section 6.3 can however not flexibly account for non-constant and time-dependent scale factors that change within the processing interval.
## 4 Case Study--Numerical Experiments
### Inter-Facility Material Transfer Process
#### 4.1.5 Comparison of Analytical and Simulation Methodologies  
Analytical and simulation methodologies offer unique advantages when comparing methodologies for studying buffer allocations and system performance. Analytical methodologies provide fast and cost-effective analysis, with mathematical equations and formulas enabling quick calculations of system performance measures. They offer closed-form solutions and accessible interpretation, making them suitable for straightforward systems and providing valuable insights. In contrast, simulation methodologies excel at modelling complex and dynamic systems, capturing real-world complexities and uncertainties more accurately. Although more time consuming and resource intensive, simulation models allow for detailed process flows, interactions, and random variations, providing flexibility and realism. The choice between methodologies depends on research goals, system complexity, available resources, and the trade-off between accuracy, time, and cost. Researchers often employ both methods to gain comprehensive insights into buffer allocations and system performance. This integrated approach ensures a balance between efficiency, accuracy, and the ability to capture the intricacies of the studied systems.
## 1 Introduction
A nonlinear sliding mode controller has been proposed in [25] by coupling humidity and temperature for energy efficient working of HVAC. The proposed technique has surpassed the conventional PID controller in terms of steady state, settling and rise time. The performance of model predictive control has been compared to the PID controllers as well. In [26], the model of a house considering climatic data of a Portuguese city for the functioning of an air conditioner has been simulated for the demand response. Six ToU rates have been considered and an optimal solution was selected on the basis of expenses incurred. Similar research has been conducted by the same authors in [27] while considering the thermal comfort. However, it has been seen that previous efforts in energy optimization often results in jeopardizing the user comfort as the major focus of these studies were mainly on energy consumption minimization at the cost of user comfort. Recent research work on an energy management system for residential buildings is concentrated on the scheduling of appliances to off-peak hours, which can cause delay in working of appliances and increase of PAR. These approaches are also non-adaptive according to the user schedule. There are different environment variables that constitute the user comfort e.g., indoor temperature, outdoor temperature, humidity, etc. Therefore, in order to efficiently utilize the benefits of DR incentive while maintaining user comfort, there is a need to consider more parameters that directly affect the user Our proposed methodology is considering the objectives of user thermal comfort maintenance along with energy consumption minimization, which were not considered in the previous studies [17; 28]. Using a humidity parameter allows us to set the thermostat set-points that not only lie in the resident's comfort zone but also result in energy conservation. In order to evaluate the energy consumption of HVAC for all the possible scenarios that occur due to considered input parameters, a large number of rules to be defined in an FIS rule base pose a difficulty. In this paper, a method is proposed that uses all of the combinations of the input parameters membership function and define all the rules in a less cumbersome manner. In addition to this, a feedback loop is added to the proposed FIS, which constantly checks the change in the indoor room temperature and controls the working of the heater specifically. The proposed system in this paper requires less memory to evaluate the rules in the FIS rule base (approximately 2 kB). The proposed fuzzy controller also requires less power and makes it possible for the controller to be integrated at the home level as a working mechanism using RIOT. The remainder of paper is structured as follows: state-of-the-art work is categorized and tabulated in Section 2; Section 3 elaborates the problem formulation; in Section 4, the system model based on the fuzzy logic system is presented; simulation results in Section 5 is followed by Section 6 where conclusion of the paper is discussed. Table 1 describes the variables and the abbreviations used throughout this paper.
## 1 Introduction  
Unmanned aerial base stations (UABSs)-based communications and networking provide deployment flexibility in three-dimensional (3D) space and enable ubiquitous and non-line-of-sight (NLOS) connectivity. More specifically, UABS are deployed to meet mobile data and coverage needs. Furthermore, by restoring any damaged infrastructure and relieving the pressure on the terrestrial networks, it reduces the cost of dense small cell deployments [1; 2; 3; 4; 5]. During one such emergency, AT&T deployed a cell-on-wings (COW) drone to restore Long Term Evolution (LTE) cell coverage in Puerto Rico [6; 7; 8] in the aftermath of Hurricane Maria. This application demonstrates the capability of UABSs to support mission-critical communication and enables high-speed real-time data, video, and multimedia services.  
A challenge while designing an LTE-Advanced public safety communications (PSC) network is to ensure seamless and ubiquitous coverage. Especially during and after an emergency, trying to achieve the ubiquitous broadband coverage criteria would be difficult, using only the limited terrestrial infrastructure. One of the main design criteria for a PSC network, such as the first responder's network authority (FirstNet) in the United States, is to attain at least a 95% coverage of the geographical area and human population enabled with broadband rates [9]. Another critical aspect during and after an emergency is that the general public (civilian cellular users) tends to cluster at various safe and secure locations. However, this clustering of users could lead to network congestion over the limited terrestrial network in the region. To this end, several academic research articles have focused on investigating the role of UABSs in improving spectral efficiency (SE) and wireless coverage in [4; 5; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19].
## 1 Introduction
Although there is currently no universally accepted standard for these dimensions, we adopted these terms based on prior EMDR and VR-based research [16]. Speed (s\({}_{\text{sp}}\)) represents the velocity of stimulus movement, distance (d) corresponds to the spatial gap between the participant and the stimulus, and size (s\({}_{\text{sz}}\)) denotes the size or area of the presented stimulus. Deep learning-based regression analysis was then applied to validate the model's effectiveness. We leverage deep learning methods--particularly artificial neural networks (ANNs)--to process complex data and optimize key EMDR parameters [17]. Deep learning refers to multilayered neural network architectures capable of learning hierarchical representations from large-scale datasets. Accordingly, we employ a Python and TensorFlow-based algorithm, which offers a flexible platform for rapid prototyping and parameter tuning. Our goal is to demonstrate the feasibility of this approach in an applied context rather than propose an original theoretical model. The second experiment involved EEG measurements to assess display effects on participants diagnosed with depression. Specifically, this study measured right frontal beta wave activity, as previous research has associated elevated beta waves in this area with depressive symptoms [18; 19; 20]. By focusing on beta wave reduction, this study aims to validate the impact of optimized EMDR video stimuli on reducing depression and stress symptoms [21]. To address the gaps in optimizing EMDR video stimuli parameters specifically for treating depression, this study investigates key questions to guide effective EMDR application. The following research questions were formulated:  
1. What are the optimal parameters--speed (s\({}_{\text{sp}}\)), distance (d), and size (s\({}_{\text{sz}}\))--of video stimuli that enhance the effectiveness of EMDR therapy for treating depression? 2. How does an artificial neural network (ANN) model contribute to determining and predicting the ideal values for video stimuli parameters in EMDR therapy aimed at alleviating depression? 3. To what extent do optimized video stimuli parameters affect EEG-measured therapeutic outcomes, specifically in terms of beta wave reduction in the right frontal lobe for individuals with depression?
# State of the Art of Tactile Micro Coordinate Metrology
## 2 Probe Developments
Issues of drift and stylus tip displacement while switching the stiffness yet need to be addressed. Further probe developments still in the research phase are conducted in Asia: The Hefei University of Technology has been working on several concepts, all based on glass or tungsten styli attached to a floating plate, the latter being suspended on wires or leaf springs [16; 17; 18]. The deflection is measured with laser focus sensors or with a laser interferometer combined with an autocollimator based optical sensor for the angular motion. Their latest development includes fiber Bragg grating sensors to measure the deflection of the three arc-shaped cantilevers holding the stylus [19]. The University of Kitakyushu is working on a vibrating fiber probing system [20]. It consists of an extremely fine optical fiber with a glass tip diameter of 5 \(\upmu\)m, but it is limited to probing in \(x/y\)-direction only. METAS has developed, in collaboration with the Swiss Federal Institute of Technology EPFL and the industrial partner Mecartex, a 3D probe manufactured out of a single piece of aluminum by conventional milling and electro-discharge machining [4; 21]. Its filigree parallel kinematic structure (Figure 1) with 60 \(\upmu\)m thick flexure hinges minimizes the moving mass and leaves the probing sphere exactly three translational degrees of freedom. It has a stiffness of 20 N/m and exhibits perfectly isotropic probing forces below 0.5 mN at 20 \(\upmu\)m deflection. The probe head allows for exchangeable styli with probe sphere diameters ranging from 0.1 mm to 1 mm. The probe deflection, _i.e._, the \(x/y/z\) components of the translational motion, is measured by three inductive sensors. The tilted orientation of the probe head coordinate system (Figure 1) greatly facilitates the access to the probe head for exchangeable styli and also implicates, that all three axes are affected by the gravitational force in an identical way, allowing for isotropic probing forces even when changing the stylus weight.
## 2 Time-Gated AQ-SPAD Receiver Modeling  
SPAD is very sensitive to the arrival of photons. When the photon arrives and triggers the avalanche event, the counting pulse output by SPAD can be detected directly. However, the SPAD cannot respond to incident photons within the dead time after the photon arrival, resulting in counting loss. The dead time of the active quenching single photon avalanche diode (AQ-SPAD) is constant. The photons arrival within the dead time will prolong the dead time of passive quenching single photon avalanche diode (PQ-SPAD). Therefore, the AQ-SPAD has a higher count rate than the PQ-SPAD. The modeling and research of this paper is based on AQ-SPAD.  
The difference between the free-running scheme and the time-gated scheme is whether there are additional time-gated signals. For the time-gated SPAD, photon detection only occurs in the gate-ON time, and the photons cannot be detected at the gate-OFF time. The output of the free-running SPAD and the time-gated SPAD is shown in Figure 1a, the blue dashed portion represents the missed photons. It is worth noting that for AQ-SPAD, regardless of whether the external gating signal is turned on, it cannot detect photons before the end of the dead time. Setting gate-ON time can effectively reduce the error count caused by background photons and alleviate ISI distortion [20].
## 5 Discussion
### Interpretation of the Proposed Model  
Deep learning models, often considered black boxes, require explanation techniques like LIME to understand their decision-making processes. LIME helps us analyze how the model arrives at specific predictions and how different features contribute to those outcomes [61]. Building trust in deep learning models for HAR necessitates examining how they classify individual data points across various categories. This approach can reduce the number of features considered, potentially leading to faster training times and improved accuracy by focusing on the most influential elements. To establish clinical trust in deep learning approaches for activity prediction, it is crucial to understand the role of motion features captured by sensors like accelerometers, gyroscopes, and magnetometers. Explainable AI (XAI) can be valuable in exploring how these features contribute to the model's classification decisions.  
To enhance our model's understandability and transparency, we leveraged a technique called Grad-CAM (Gradient-weighted Class Activation Mapping) [62]. This technique helps us visualize and pinpoint the specific regions within the sensor data that significantly influence the model's predictions.  
For time-series data, Grad-CAM identified the most crucial moments within the sequence that influenced the network's classification decision. Figure 13 showcases an example sequence, with a colormap highlighting these critical regions [63]. In this study, we extracted and normalized the activation features generated by Grad-CAM to a range between 0 and 1. Figure 13 displays the Grad-CAM activations for randomly chosen segments on the Opportunity, PAMAP2, and REALWORLD16 datasets using our proposed Att-ResBiGRU models. Lighter colors indicate higher activation values, signifying a more significant impact on the prediction. The results for each dataset (Opportunity, PAMAP2, and REALWORLD16) are presented in rows 1, 2, and 3, respectively.  
Figure 12: Number of trainable parameters of deep learning models used in this work.
###### Abstract  
In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM\({}_{2.5}\) (particulate matter of diameter less than 2.5 \(\upmu\)m) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM\({}_{2.5}\) daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM\({}_{2.5}\) concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.  
PM\({}_{2.5}\); DFA (detrended fluctuation analysis); MFDFA (multi fractal DFA); DCCA (detrended cross-correlation analysis); surrogate data hypothesis testing; ARFIMA (auto-regressive fractionally integrated moving average)
## 3 Results and Discussion
### Implications for the Start-Up of Nitritation Processes in Mainstream WWTPs  
The results indicate that starting a PN reactor with a high FA concentration (50 mg/L) and gradually reducing it to a low FA concentration (10 mg/L) effectively suppresses NOB and efficiently enriches AOB. Previous studies have employed various strategies to suppress NOB for mainstream nitrogen treatment. For example, [PERSON] et al. (2020) combined low DO (0.2 mg/L) and a high influent NH4+-N concentration (175 mg/L) with a high FA strategy in a CSTR, achieving stable PN at an influent NH4+-N concentration of 50 mg/L in approximately 80 days [30]. At this point, _Nitrosomonas_ reached 4.8%, and _Nitrospira_ was at 0.4%. Additionally, [PERSON] et al. (2019) intermittently applied high FNA (400-500 mg/L) and FA (210 mg/L) inhibition for over 400 days [23]. With an influent NH4+-N concentration of 40 mg/L, the effluent NO2-N was 12 mg/L, and NH4+-N and NO3-N was less than 2 mg/L, indicating effective NOB suppression and NO2- accumulation. Although the proportion of _Nitrosomonas_ was not mentioned, NOB genera _Nitrospira_ and _Nitrobacter_ were detected at less than 0.5%. While various FA concentrations and strategies have been attempted in previous studies, low AOB proportions and long stabilization periods have posed challenges for practical applications. According to this study, starting nitritation with an influent NH4+-N concentration of 300 mg/L (FA of 50 mg/L), and then gradually reducing the influent concentration, can quickly suppress NOB and enrich AOB to high proportions.  
In real wastewater treatment plants, achieving high FA levels for mainstream nitrogen treatment may not be feasible. However, integrating the sidestream treatment of anaerobic digester liquor (>500 mg NH4+-N/L) can enable the application of FA inhibition strategies to start a nitritation reactor with suppressed NOB. Furthermore, this strategy can be applied if NO3- production and the proportion of NOB increase during reactor operation. However, further investigation is needed, as information on practical application and controllable strategies remains limited.
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 1 Introduction  
Landslides are one of the most frequent geohazards in mountain regions, often causing severe damage to facilities and threatening human lives [1, 2]. There are two critical landslide triggers, earthquakes and heavy rains, which occur frequently in Taiwan due to the subtropical monsoon climate and the location on the convergent boundary between the Eurasian plate and the Philippine Sea plate [3, 4, 5]. In previous studies of landslides, whether focused on the collapse mechanism or the rainfall conditions for landslides, the occurrence time of a landslide event was a critical, but hardly accessible, piece of information [6]. Nevertheless, it was indicated that seismic activities causing ground vibrations, such as earthquakes, landslides, and volcano-tectonics, could be recorded as seismic signals by a seismograph, and the features of a seismic signal originating from different seismic sources are distinctive and discriminable for seismic event classification [7, 8, 9]. Note that the occurrence time of a landslide event has generally been estimated by interviewing local witnesses in a field survey [6].  
If the characteristics of a landslide event could be detected and identified from its seismic signal, a more precise and objective estimation of the occurrence time could be acquired [10], and uninhabited areas would have a higher possibility of being investigated.  
Consider that large sets of seismic signals, manual interpretation, and classification of seismic records can be time-consuming and may be highly subjective. Consequently, an intelligent alternative, such as an automatic classifier utilizing machine learning algorithms for seismic event classification, would be helpful in characterizing the seismic signals for understanding the seismic activities.  
In this study, the Broadband Array in Taiwan for Seismology (BATS) was utilized to distinguish landslide events from cases of earthquakes and noise. First, three kinds of seismograms, those containing signals of landslides, earthquakes, and noise, were collected for a training dataset. Then the attributes of the training dataset were calculated from the waveforms or frequency spectra to construct and to train an automatic classification model. Finally, seismic network data collected during Typhoon Morakot in 2009 and Typhoon Soudelor in 2015 were analyzed to examine the model, and the attribute performance was evaluated to determine which attributes are the most discriminant for the classification.
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
###### Abstract  
The application of power to gas (P2G) provides a new way to absorb intermittent renewable energy generation, which improves the efficiency of renewable energy utilization and provides the necessary flexibility for operating the integrated energy system. The electro-thermal integrated energy system with P2G is a new form of using energy efficiently. In this paper, we first introduce the technology and application of P2G. On the basis of considering the characteristics of P2G facilities, power systems, natural gas systems and heating systems, an optimal dispatching model of electro-thermal multi-energy system with P2G facilities is proposed. Particle swarm optimization is used to solve the optimal scheduling model. The simulation results are discussed for the six-bus and six-node integration system and show that when the volume fraction of hydrogen does not exceed 20% in the gas network, for the same operating mode, an integrated energy grid with P2G function will save about 20 tons of standard coal per day and the abandoned wind rate can be regarded as 0.  
power to gas; hydrogen storage; wind power; HCNG; combined heat and power +
Footnote †: journal: Applied Sciences