esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### ARIMA-Based Residual Energy Prediction Model
A d-order differencing is used to transform it into a stationary time sequence, where d is the number of times that the residual energy of the node has been subtracted from past values. Then, we need to check whether the residual energy sequence is a white noise sequence. If it is not, we need to determine the parameters p and q of the ARIMA model by the autocorrelation coefficient (ACF) and the partial autocorrelation coefficient (PACF), respectively. \(p\) is the order of the auto-regression model, which represents the relationship between the remaining energy of the node at the next moment and the remaining energy of the node in the previous \(p\) moments. \(q\) is the order of the moving average model, which describes the relationship between the remaining energy of the nodes at the next moment and the white noise for the previous \(q\) moments. The Akaike information criterion is used to specify the optimal order \((p,q)\) of the model. After the model order is determined, the mean square error method or the maximum likelihood method is used to obtain the weight coefficients \(\eta\) and \(\varphi\) of the autoregressive-term and the sliding-average-term. According to the ARIMA model established above, node \(i\) can predict the residual energy of the neighboring node by the following formula  
\[\widehat{E}_{j}^{res}(t)=\sum_{v=1}^{p}\eta_{v}\widehat{E}_{j}^{res}(t-v)+w(t) +\sum_{l=1}^{q}\varphi_{l}w(t-l) \tag{4}\]  
where \(\widehat{E}_{j}^{res}(t)\) is the predicted residual energy at the next hello moment \(t\). \(\widehat{E}_{j}^{res}(t-v)\) is the residual energy at the previous \(vth\) hello moment. \(w(t)\) is the Gaussian white noise of residual energy of the node \(j\) at the next moment t. \(w(t-l)\) is the Gaussian white noise at previous \(lth\) hello moment. \(\eta_{v}\) and \(\varphi_{l}\) is the weight coefficients of residual energy and Gaussian white noise at the previous \(vth\) and \(lth\) hello moments, respectively.
## 2 Geological Setting  
The Malay Basin is situated at offshore in the South China Sea, within the north-central region of first-order Sunda Block [13] (Figure 1). This basin is mainly a tertiary-fill basin that is one of the deepest continental extensional basins located in the South China Sea and the Gulf of Thailand, which has an asymmetric NW-SE to NNW-SSE trending and parallel to Peninsular Malaysia (about 500 km long and 200 km wide). It is classified into two parts: the northern part, i.e., oriented NNW-SSE and the southern part, i.e., oriented NW-SE [14]. The study area is in the northern part of the Malay Basin, which has NNW-SSE to N-S fault dominant direction.  
The main reservoir targets have occurred at D and E formations [14]. These reservoirs deposited at the fluvial-delta system (Figure 2). Group D and E were deposited by progradation stacking of dominantly alluvial/estuarine channels and culminated with localized erosional unconformity [15]. Group E was occurred in a slightly influenced by a marine at the coastal plain environment, with the sandstone being deposited in delta front and delta plain including distributary mouth bar, shoreface, and channels. Group E has estimated to have 300 m thickness that was deposited by sandstone, siltstone, carbonaceous shale, and coal. Group D, with a range thickness of 300-650 m, comprises siltstone, shale, sandstone, and coal. The most effective source rock in the Northern Malay basin has occurred in groups H and I. Their present-day depth of burial, maturity, and source rock quality related to the effectiveness. The groups of E and D reservoirs are predicted to be charged from the group I source rocks, which reached the gas generation phase by the time the anticlinal traps were generated.  
Figure 1: Geographical map of Peninsula Malaysia. The red circle is the study area location which is located in the Malay Basin.  
Figure 2: Regional stratigraphy column of the Malay Basin and the reservoir targets which is highlighted by red rectangular. The reservoir targets are focused on D and E groups (modified from) [16].
## 5 Performance Evaluation
### Indoor Positioning Error Comparison  
We perform an indoor positioning error comparison based on the most cited depth estimation model in the past decade. Figure 10 records their changing trends. Compared with the model proposed by [PERSON] et al. in 2014 [PERSON] et al. (2014) and 2015 [PERSON] et al. (2015), the performance of LBP-FFT-CNNs is improved by 66.7% and 65.2%, respectively. This is mainly because the multi-scale deep network model can only improve accuracy by increasing the amount of training data, but its feature extraction ability is weak. However, the LBP module in our model can better extract image features. Even if the model proposed by [PERSON] in 2016 [PERSON] et al. (2016) adds encoded super pixel information, it still has a 51.1% performance improvement compared to that proposed by [PERSON] in 2015 [PERSON] et al. (2016). [PERSON] et al. (2017) used stereo image pairs to achieve excellent performance, but there was still a 64.9% gap with the LBP-FFT-CNNs model. Stereoscopic image pairs may suffer from viewpoint inconsistencies because the camera's position and orientation may not be precisely aligned or due to dynamic elements in the scene. This can lead to errors in tasks such as depth estimation. [PERSON] et al. (2016) presents a deep convolutional neural network framework combined with conditional random fields to predict scene depth or the normal surface from single monocular images. It achieved competitive results on the Make3D and NYU Depth V2 datasets. [PERSON] (2017) inverts the  
Figure 9: Confusion matrix based on the LBP-FFT-CNNs model for classification performance.  
parameters of Huber loss function, making it more sensitive to smaller errors but less robust to outliers. Compared with LRA method, which is also based on the MC4L device, its performance is improved by 64.2%. [PERSON] [30] replaced the use of explicit depth data during training with easier-to-obtain binocular stereo footage. This method saves a lot of manpower without explicit depth labels and enables real-time depth perception. However, binocular stereo lenses usually rely on texture and lighting information. Therefore, the accuracy of depth estimation may decrease for scenes that lack texture or have large lighting changes. Experimental results show that combining advanced feature extraction techniques such as LBP and FFT with CNNs can significantly improve positioning accuracy. Another important reason is the use of inertial-based indoor positioning algorithms, which greatly reduces the unstable positioning problem under complex environments with too many obstacles.
## 2 Methodology
### Ensemble Model Creation
\[\text{min}Z=a\sum_{t=1}^{h}\Big{\{}P_{t}^{D}\times E_{t}^{D}+P_{t}^{R}\times E _{t}^{R}+P_{t}^{F}\times E_{t}^{F}-P_{t}^{DR}\times E_{t}^{DR}+\lambda_{t} \times S_{t}\Big{\}}+b\sum_{t=1}^{h}\Big{\{}z_{t}^{T}Cov\ z_{t}\Big{\}} \tag{3}\]  
Constrained to the following:  
\[E_{t}^{D}+E_{t}^{R}+E_{t}^{F}-E_{t}^{DR}=F(t),\forall\tau\exists h \tag{4}\]  
\[E_{t}^{D}\geq 0,E_{t}^{R}\geq 0,E_{t}^{F}\geq 0,E_{t}^{DR}\geq 0 \tag{5}\]  
\[a+b=1 \tag{6}\]  
where \(Cov\) is the covariance matrix of prices; \(z_{t}^{T}=\big{[}E_{t}^{D}E_{t}^{R}E_{t}^{F}E_{t}^{DR}\big{]}\) is the vector of energy allocation of each market; \(t:\) the time index (for each hour on market time unit); \(h:\) the number of hours in the optimization horizon; \(F(t):\) the energy demand for hour \(t\) output of the AutoML (MWhr); \(P_{t}^{D}:\) the price of energy in the Day-Ahead Market (DAM) at hour \(t\) (in EUR/MWh); \(P_{t}^{R}:\) the price of energy in the Real-Time Market (RTM) at hour \(t\) (in EUR/MWh); \(P_{t}^{F}:\) the price of energy in the Futures Market at hour \(t\) (in EUR/MWh); \(P_{t}^{DR}:\) the revenue from the price of energy in the Futures Market at hour \(t\) (in EUR/MWh); \(P_{t}^{DR}:\) the revenue from Demand Response at hour \(t\) (in EUR/MWh reduction); \(E_{t}^{F}:\) the energy provided through the Futures Market at hour \(t\) (in MWh); \(E_{t}^{P}:\) the energy procured in the Day-Ahead Market at hour \(t\) (in MWh); \(E_{t}^{R}:\) the energy procured in the Real-Time Market at hour \(t\) (in MWh); \(E_{t}^{DR}:\) the demand reduced through Demand Response at hour \(t\) (in MWh); \(\lambda_{t}:\) the penalty for energy shortfall or deviation from demand at hour \(t\) (in EUR/MWh); \(S_{t}:\) the energy shortfall at hour \(t\) (in EUR/MWh); \(a\): weight for the cost of across markets and shortfall components; \(b:\) weight for the risk component; \(o_{t}^{F}:\) standard deviation (or variance) of the forecasted price in the Futures Market at hour \(t;\)\(o_{t}^{R}:\) standard deviation (or variance) of the forecasted price in the Real-Time Market at hour \(t;\)\(o_{t}^{D}:\) standard deviation (or variance) of the forecasted price in the Day-Ahead Market at hour \(t;\)\(o_{t}^{DR}:\) standard deviation (or variance) of the forecasted price in the Demand Response Market at hour \(t\). The next flowchart, in Figure 1, proposes an energy aggregation framework for the automated bidding mechanism [38] to the energy market:Each round of forecasting and optimizing the energy quantities on the market is conducted after the publication of new raw data.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Applications of Fog Computing in IoT  
IoT applications--one which store data in terabytes (TB) or higher, that have fast or a significant quantity of essential data processing requirements, and that must send data to the cloud back and forth--become successful candidates for the deployment and implementation of fog computing. With a vast number of IoT applications, there is a large opportunity for fog computing to play a critical role.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 4 Experimental Results
### Flight Experiment  
Flight experiments were carried out at middle latitudes. The duration was 4 h, including a half-hour's alignment. Firstly, the experiment was carried out based on the n-frame. After flying for half an hour, the navigation frame was changed to the G-frame until the end of the flight.  
In order to evaluate the effects caused by navigation frame transformation, the results that are based on a local horizontal geographic frame are used as reference. Because the flight experiments are conducted at middle latitudes, there is no algorithm error introduced by the choice of navigation frame. The reference result is reliable. The navigation results,  
Figure 2: The process of the covariance transformation method.  
based on the covariance transformation and non-covariance transformation, are shown in Figure 3.  
As shown in Figure 3a, the change of the filter structure results in a fluctuation of the relative attitude error. Among the attitude errors, the relative yaw error reaches the maximum value of 2.2 'without covariance transformation. As a comparison, the covariance transformation method reduces this error to 0.3'. Figure 3b shows that the relative position error is less than 10 m, regardless of whether the covariance transformation is used. The INS/GNSS-integrated navigation filter uses the position information provided by GNSS as observations to correct the INS results, resulting in less position error. As shown in Figure 3c,d, the maximum bias error of the gyroscope with and without covariance transformation reaches 0.003'/h and 0.01'/h, respectively. The maximum bias error of the accelerometer with and without covariance transformation reaches 6 \(\upmu\)g and 50 \(\upmu\)g, respectively. Because of the non-zero values of the non-diagonal elements in the covariance matrix, the bias estimates of the gyroscope and accelerometer are affected by the cross-coupling of other error states. As a result, the bias estimates of the gyroscope and accelerometer also show instability.  
The flight experiment was repeated six times. The results of the experiments are shown in Tables 1 and 2.  
Figure 3: The navigation results, based on the covariance transformation and non-covariance transformation. (**a**) The relative error of attitude; (**b**) the relative error of position; (**c**) the relative error of gyro bias estimation; (**d**) the relative error of accelerometer bias estimation.
## Abbreviations  
* SACS & Structural Analysis Computer System U.C. & Unity checks API & American Petroleum Institute API RP 2A-WSD & Planning, Designing and Constructing Fixed Offshore Platforms--Working Stress Design AISC & American Institute of Steel Construction IMCA & Instituto Mexicano de la Construccion del Acero Pemex & Petroleos Mexicanos Cr & Ratio between the ultimate lateral load resisting the platform before collapse RSR & Resistance Reserve Factor S-N & Curve stress interval vs. number of cycles DNV GL & DNV (Det Norske Veritas) y GL (Germanischer Lloyd) FE & Finite element ARMAX & Autoregressive moving average model with exogenous inputs SCFs & Concentration factors WAVEWATCH III & Wave data simulator PoF & Probability of Failure N & Order of the stream function k & Wave number TLP & Tension Leg Platform CQC & Complete quadratic combination SRSS & Square root of the sum of the squares H(f) & Mechanical transfer function Ki & Generalized stiffness fn & Natural frequency c & Percent damping STRN & Strength ISO & International Organization for Standardization D & Fatigue damage
## 2 Synthesis and Characterization of MIP
### Polymerization Parameter in MIP Synthesis
#### 2.1.2 Template Molecule
If the analyte/template molecule is costly and complicated to prepare, a dummy template can be employed as a structural counterpart to replace it [40]. However, it must first be proven that the signal of the dummy molecule does not impede any measurements [37]. The leakage should then be reduced to tolerable levels by minimizing solvent variations, drying the sorbent, or performing thermal post-treatments [41]. MIP sensors used fluorescence and potentiometric methods to detect the presence of cocaine, according to [PERSON] [42]. The former used a MIP made by combining a divinylbenzene functional monomer with EGDMA, as well as PEG-coated, Mn-doped ZnS quantum dots (QDs). The MIP particles were made on a solid substrate, and histamine was templated into a control polymer instead of the more common NIP or nonimprinted polymer. In comparison to the usual technique, real samples of human blood serum were assessed and determined to be accurately analysed. The successful use of a \"dummy template\", a chemical replacement for the target of interest, was a distinctive aspect of this MIP.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 2 Constellation Design
### Model Establishment
The constellation period is calculated as follows. \[T_{C}=\frac{T_{S}P}{T} \tag{9}\]  
where \(T_{s}=2\pi\sqrt{\frac{\pi^{3}}{\mu}}\), \(T_{C}\) is the constellation period, \(T_{s}\) is the satellite period, and \(\mu\) is the Earth's gravity parameter. Here, the value is 398,600.4.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Tbs  
TBRS or the time-variant load balancing scheme proposed in [58] aims to extend network lifetime by designing a balanced distribution of network traffic among nodes. Sensor nodes are deployed based on a distinct deployment scheme. The deployed nodes are anchored with the help of long ropes, and can be pushed to the water surface with the help of buoys. Each sensor node can alter its Tx power up to three different levels, thus achieving different ranges. This flexibility enables a transmitting sensor node to reach a certain next hop node having related weight and Tx power, resulting in fair energy depletion, and consequently mitigating the energy sinkhole issue. To achieve this, the authors have developed a comprehensive analytical model. Optimal weights that define traffic load on each candidate next hop node are derived taking into account the mobility pattern of sensor nodes. The node with the most optimal weight is selected for transmission. Simulation results show that data traffic can be fairly distributed among nodes, so long as the nodes can switch between three different transmission power levels. Moreover, the proposed scheme achieves an extended network lifetime and improved energy efficiency, compared to the nominal range-based data forwarding.
# What Advanced Treatments Can Be Used to Minimize the Production of Sewage Sludge in WWTPs?
## 3 Advanced Treatments and Technologies
### Biological Treatments
#### 3.2.2 Granular Sludge Systems (GSS)  
GSS includes anaerobic granular sludge systems (AnGSS) and aerobic granular sludge systems (AeGSS). AnGSS have been largely applied and have achieved good results in the treatment of high concentration organic wastewater [84]. AeGSS are currently under study [84; 85; 86; 87; 88]. In granular systems, a specific production of sludge was observed in the order of 0.07-0.15 kgTSS kgCOD\({}_{\text{removed}}\)\({}^{-1}\), compared to 0.27-0.35 kgTSS kgCOD\({}_{\text{removed}}\)\({}^{-1}\) of the traditional activated sludge [25]. These results were confirmed by [PERSON] et al. [89; 90; 91; 92], who found a specific production of sludge of 0.16 kgTSS kgCOD\({}_{\text{removed}}\)\({}^{-1}\) in treating municipal wastewater. An example of a plant of this type has been set by the CNR and is known as the sequencing batch biofilter granular reactor (SBBGR) system [89; 90].  
The experimental results obtained showed that the plant, fed with the effluent exiting the primary sedimentation of an urban WWTP, presented high yields of COD removal, of total suspended solids and of nitrogen (all higher 80%), allowing compliance with the discharge limits established by current legislation. The process was characterized by a very low sludge production (0.12-0.14 kgTSS kgCOD\({}_{\text{removed}}\)\({}^{-1}\)), with respect to CAS, due to the high SRT in the system (\(>\)120 d) [91]. A significant advantage of the SBR configuration is that the use of a traditional or integrated settler is not necessary. The separation of sludge and effluent occurs within the reactor during a short settling phase [93]. Therefore, the SBBGR system does not require the presence of a secondary decanter [91; 93]. The main disadvantage of these systems is the relatively high investment costs. In fact, [PERSON] et al. [93] studied the possible alternative to CAS represented by AeGSS. From an economic point of view, they found that it is very promising technology, and should therefore be further developed, but at the moment has higher costs than CAS. [PERSON] et al. [91] found that this process allows us to reduce TSS, with yields ranging from 80% to 90%.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 3 Analysis Method of Two-Stage Finger Seal Based on FEM/CFD Coupling
### CFD Model of Two-Stage Finger Seal  
#### 3.3.1 Numerical Model  
In view of the cyclic symmetric structural characteristics of 2 SFS, the circumferential region corresponding to one finger stick is taken as the computational domain of the CFD model of 2 SFS. As shown in Figure 4, the CFD model of 2 SFS includes the fluid zone composed of high-pressure, middle-pressure, and low-pressure cavities, pressure balanced cavities and radial hysteresis clearance of each 1 SFS, as well as the porous zones transformed from the finger stick bundles. Porous zones A and B are used to simulate the flow thorough the finger foot bundles, and porous zones C and D are used to simulate the flow thorough the finger stick stem bundles.  
In this paper, the ANSYS-FLUENT software package is used in the CFD analysis. It is assumed that the sealing medium is an ideal compressible gas and the gas flow is turbulent (the maximum Reynolds number is up to \(\sim\)10\({}^{6}\)). The realizable \(k\)-\(\epsilon\) model with enhanced wall treatment is used to simulate the turbulent flow. The main boundary conditions of the CFD model of 2 SFS are as follows: (a) The pressure-inlet condition is applied in the inlet section of high-pressure cavity and pressure balanced cavities; (b) the pressure-outlet condition is applied in the outlet section of low-pressure cavity; (c) the moving wall is applied in the rotor surface and the rotor speed is specified; (e) the periodic boundary conditions are defined in the two sides in the circumferential direction; (f) the interfaces between fluid zone and porous zone, as well as the interfaces between adjacent porous zones are set to be interior to realize the mutual flow; (g) the rest surfaces are set to be a static wall boundary condition. In addition, the resistance coefficients (viscous and inertial resistance coefficients) are defined in all the porous zones to characterize additional flow resistances when fluid flows through the porous media, so as to simulate the flow through the two finger stick bundles of 2 SFS. The calculation of resistance coefficients of porous media will be elaborated in Section 3.3.2.  
The mesh generation is carried out using ANSYS Meshing software. As shown in Figure 5, in addition to the porous zone D, the hexahedral mesh element is used in the other computational domains. The meshes near the regions of the rotor surface are very fine to ensure that the y+ values are within their acceptable range, the range of the rotor  
Figure 4: Schematic diagram of the CFD model of 2 SFS.  
surface y+ is 0.58-4.35 in the analysis. The number of mesh layers in radial direction of radial hysteresis clearance is set to be 10, and the meshes in porous zones are also finer than that in the fluid zone to better perform the numerical simulations. Finally, several meshes with different densities are tested, and results showed that the mesh with 2.64 million of elements can guarantee the mesh independent solution.
## 4 Protocol Description  
We have designed our proprietary end-to-end networking stack for large-scale WSN deployments with modular components that are configurable, extensible, as well as plug-and-playable, depending on the sensing application requirements and the physical environment of the sensing region of interest. It is designed for scalability through the use of lightweight protocols that minimize both communication and storage overheads. Existing networking protocols are often designed in silo and, thus, unable to support our multi-faceted sensor network testbed requirements. In the following, we briefly describe its basics, the route establishment, the data transmission and the packet format.  
The indoor testbed we use in this paper serves as an evaluation platform for our actual sensor-based smart system deployed in outdoor urban areas. As said before, our passive testing technique can be applied all along the development lifecycle, helping thus the normalization, as well as the deployment phase. Our outdoor sensor networks necessitate the use of multiple gateways in the network, so that timely and reliable data delivery can be achieved. Commercially available network protocols, such as ZigBee, are based on the _Ad hoc_ On-Demand Distance Vector (AODV) routing protocol [35] and do not have inherent support for the use of multiple gateways in the network. The routing protocol in use is multi-hop and opportunistic in nature. It associates a metric called a gradient to each sensor towards the gateway of the network. According to prevailing link quality, a dynamic update of the routing gradients is performed.