esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since deepwater drilling is a high-risk and high-investment operation with a daily expense of approximately $1,000,000, problems such as lost circulation and leakage of drilling fluid lead to huge economic loss, and increase security risks. The ideal deepwater drilling fluid should have both excellent shale hydration inhibition and low-temperature rheological properties. Therefore, at present, it is essential to solve the contradiction that the encapsulator with high-molecular-weight causes thickening of drilling fluid at a low temperature, while that with low-molecular-weight provides poor encapsulation performance. Experimental study showed that, when the molecular weight of conventional encapsulator was lower than 2,000,000, the encapsulation effect was not satisfactory [22]. As currently reported, in consideration of both the encapsulation and low-temperature rheological properties, the low-molecular-weight encapsulator product was developed and its molecular weight was about 900,000 [21; 23]. Field applications showed that it mitigated the thickening of WBDF at low temperatures to some extent. However, at present, the rheology-related problem has not been completely solved and lost circulation of drilling fluid occursfrequently in deepwater drilling operations. Moreover, it should be noted that the latest encapsulator is a cationic polymer, which can neutralize the negative charge on clay surface and increase the stability of shale. However, there are concerns about the compatibility of cationic polymer with other components of WBDF, because sometimes serious flocculation of the drilling fluid occurs when cationic polymer is added [24; 25]. Therefore, the molecular structure and molecular weight of the encapsulator need to be further optimized. In this paper, to simultaneously realize the good encapsulation performance, low-temperature rheological property and compatibility of drilling fluid, a zwitterionic polymer P(AM-DMC-AMPS) with low-molecular-weight for deepwater drilling was developed. It was synthesized using acrylamide (AM), methacrylatoethyl trimethyl ammonium chloride (DMC), and 2-acrylamido-2-methylpropane sulfonic acid (AMPS) as the monomers, and was named SDCap as a commercial product for potential application in the future. Its feasibility on deepwater drilling was investigated through experimental evaluations of the low-temperature rheological property, shale inhibition and filtration property.
## 3 Methodology
### Multimodal Fusion Model  
The framework of the proposed multimodal fusion is illustrated in Figure 8. Initially, a multi-scale spatiotemporal feature fusion network model is employed for the representation learning of video signal data. Concurrently, our proposed channel attention-based temporal convolutional network model is used for the representation learning of PPG signal data. Secondly, the representations learned by the two models are integrated at the model-level fusion. Finally, the fused data representation is utilized to recognize learners' learning emotions.  
Figure 7: Confusion matrix and ROC curve of temporal convolutional network model based on channel attention. (**a**) Results for the scale dimension attention compression module following the TCN; (**b**) Results for the scale dimension attention compression module preceding the TCN.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 4 Cooperative Relative Positioning
### GNSS-Based Relative Localization
#### 4.2.4 Differential Carrier Phase Ambiguity Resolution  
A number of groups have addressed the relative positioning problem of vehicles by solving differenced GNSS carrier phase ambiguities rather than using GNSS differenced pseudorange techniques. The potential of this approach comes from the fact that noise in the Phase-Locked Loop (PLL) of the GNSS receiver is smaller by several orders of magnitude [58]. In order to determine the range towards the satellite, the integer number of cycles has to be resolved. This task is especially difficult in vehicular environments due to signal disturbances, satellite blockage and multipath, which lead to cycle slips that will reset the resolution algorithm.  
[PERSON] et al. built a test platform for relative positioning yielding range errors of less than 1 m 99% of the time in open sky and 90% on obscured roads in [97]. [PERSON] et al. propose a particle filter design that samples from the relative position domain to solve the carrier phase ambiguity [98]. The use case they are focusing on is relative positioning of low orbit satellites. [PERSON] et al. have worked on a trajectory duplication using carrier phase-based relative positioning in [99; 100]. [PERSON] et al. have investigated cooperative relative positioning exchanging Real-Time Kinematic (RTK) position solutions between vehicles [101]. Further on, they looked at integrity concepts based on the same methods in [102]. By exploiting the redundancy when integrating measurements from different vehicles, [PERSON] et al. have shown that the time to integer fixing can be decreased at the same time that the reliability is increased [103]. [PERSON] et al. conclude that it is possible to fix ambiguities with single-frequency, low-cost receivers in benign road environments and to obtain a centimeter-level relative positioning in [104]. While the previous work output the relative position in Earth-fixed coordinates, [PERSON] et al. propose a GNSS carrier phase-based ground truth system for evaluating the performance of radar sensors [105]. They conclude that centimeter-level accuracy quickly degrades when less than four satellites are in view of both vehicles.  
GNSS carrier phase relative positioning offers extremely accurate full three-dimensional relative position and relative velocity at the output rate of a GNSS receiver. If coupled with inertial on-board sensors, the update rate can be enhanced to meet the requirements of safety-critical applications. The main disadvantage is its sensitivity and, consequently, the limited continuity in its performance. Trees, bridges and highway gantries cause momentary drops in the signal level, loss of lock on the carrier phase and cycle slips. Low cost receivers are especially sensitive, and a continuous tracking of the carrier phase is practically impossible in usual scenarios.
# Prediction of Building's Thermal Performance Using LSTM and MLP Neural Networks
## 1 Introduction
They encompass cyclic connections that make them, in principle, more suitable for modeling time sequence data than forward neural networks [35; 36]. Some of the numerous fields where LSTM neural networks were applied are language modeling [36; 37], speech recognition [35], tourism flow [38; 39], sequential diagnosis [32; 40] and energy analysis in buildings [21; 41; 42]. The aim of this work is to study the thermal inertia of a building by developing a methodology using MLP and LSTM neural networks. This procedure was applied to a public library building located in the north of Spain [43; 44]. The available data are hourly observations of thermal demand and indoor temperatures of the building. Moreover, hourly observations of two weather variables (outdoor temperatures and solar radiation) and three time variables (hour of the day, day of the week and hour of the year) are also needed. A comparison between the prediction errors according to the number of time lags (each lag is one hour) introduced into the models, with respect to the studied variables, has been carried out. In addition, the accuracy of the models was measured using the CV(RMSE) as advised by AHSRAE. The novelty of this work lies in the analysis of the building inertia through two different artificial intelligence algorithms observing the information provided by the introduction of more time lags in the models.
## 3 Methodology
After going through the calibration procedures (detailed in Section 4.1), the intrinsic parameters (e.g., the vertical and horizontal field of view, and the focal length of the camera) are determined and utilized for the next stage. Testing. In the final stage, testing is the experimental implementation of NFD for detecting objects and estimating their distances from the visual content. Testing images, in which the objects' distances were measured, and demo videos were uploaded to the cloud computing platform for detection. In the smartphone application, the trained deep learning model is assumed to download to a smartphone to perform the tasks. Once the target objects were detected and framed by bounding boxes, NFD combined computer vision techniques and the parameters of the camera to estimate the distances of near-front public objects based on the distance estimation model.
## 3 Circularly Polarized 5G Antenna Design and Optimization  
In this section, the design and development of CP 5G NR Sub-6 GHz RDRA are explained in detail. Moreover, the antenna optimization, along with results, is discussed. The lengths and widths of the feeding strip were very critical in this design configuration which is herein demonstrated and discussed. It is a well-known fact that a circularly polarized wave can be generated if the feeding network is capable of exciting the orthogonal degenerate modes, since such excitation generates two far-field components that are equal in magnitude with the quadrature phase shift necessary for CP generation [25]. In this section an initially linearly polarized rectangular DRA was designed using a single conformal strip. The next feeding mechanism was modified step by step to design a circularly polarized DRA without adding any complicated changes. Five different design geometries for the development of CP 5G Sub-6 GHz RDRA are presented and discussed in detail.
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 5 Performance Comparison and Discussion
### Ablation Experiments for Residual Connections within DMSAM  
This section explores the impact of residual connections within DMSAM on the prediction performance of MSA-FCN. Four sets of ablation experiments were conducted on the FD002 and FD004 datasets. The neural networks involved in the experiments include the following: MSA-FCN with full dual residual connections, a neural network adopting the original Transformer residual connection (TFormer Connection), a neural network with only the residual connection within DMSAM_Data (Connect_1), a neural network with only the residual connection within DMSAM_Features (Connection_2), and a neural network without any residual connections within DMSAM (W/O Connection). The relevant experimental results are summarized in Table 11.  
Firstly, although the average RMSE of TFormer Connection on FD004 is slightly lower than that of MSA-FCN, the average RMSE of MSA-FCN on FD002 is the smallest among all variant neural networks. Secondly, despite MSA-FCN having a slightly higher average Score on FD002 compared with Connect_2, its average Score on FD004 is the lowest among all variant neural networks. Finally, MSA-FCN has the lowest STD values among all variant neural networks across all metrics.  
The partial improvement in MSA-FCN's prediction performance due to the residual connections within DMSAM can be attributed to the dual residual connections effectively alleviating the training burden of the network. This accelerates the decrease in loss during the training process and effectively avoids the problem of gradient vanishing. These experimental results can provide new insights for further optimizing neural network structures and improving RUL prediction performance.
## 3 Joint Cooperative Spectrum-Sharing Scheme
### Analysis of the Proposed Two-Stage Stackelberg Game
#### 3.3.2 \(E\!U_{m}\)'s Strategy in Stage \(I\)  
Given the \(DT_{n}\)'s optimal power allocation coefficient \(\alpha_{mn}^{*}(\tau_{mn}^{*})\) which is calculated in Stage \(II\), the \(E\!U_{m}\) derives the optimal time allocation coefficient for maximizing its utility. Based on the \(DT_{n}\)'s optimal power allocation coefficient \(\alpha_{mn}^{*}(\tau_{mn})\) which is calculated in Stage \(II\), the \(E\!U_{m}\)'s optimal time allocation coefficient of \(\tau_{mn}^{*}\) may be expressed as:  
\[\tau_{mn}^{*}=arg\ \max_{\tau_{mn}}U_{E\!U}^{nm}(\tau_{mn},\alpha^{*} (\tau_{mn}^{*})) \tag{19}\] \[s.t. U_{mn}^{DD2}\geq 0\] (20) \[0<\tau_{mn}<1\] (21) \[0<\alpha_{mn}^{*}<1\] (22) \[\forall m\in\{1,\dots,M\},\forall n\in\{1,\dots,N\} \tag{23}\]  
where \(U_{mn}^{DD2D}\) is the utility of \(DT_{n}\) as shown in Equation (12). The first constraint guarantees that \(DT_{n}\) is willing to cooperate. The utility of \(DT_{n}\) is at least zero. \(U_{mn}^{DD}=0\) is the minimum requirements when \(DT_{n}\) cooperatives with \(E\!U_{m}\).  
**Proposition 1**.: _Given the \(DT_{n}\)'s optimal power allocation coefficient of \(\alpha_{mn}^{*}(\tau_{mn}^{*})\), \(E\!U_{m}\)'s optimal time allocation coefficient, namely \(\tau_{mn}^{*}\) is the unique solution of the following equation:_  
\[(1-\tau_{mn}^{*})\log_{2}\left\{1+\frac{h_{mn}\alpha^{*}(\tau_{mn}^{*})P_{n}}{ bh_{mn}\left[1-\alpha^{*}(\tau_{mn}^{*})\right]+N_{0}}\right\}-\left[1-\alpha^{*}( \tau_{mn}^{*})\right]=0 \tag{24}\]  
See proof in Appendix A.  
Let us now prove the existence and the uniqueness of the Nash Equilibrium for the proposed two-stage Stackelberg game.  
**Definition 1**.: _A strategy profile \((\tau_{mn}^{*},\alpha_{mn}^{*})\) is a Nash Equilibrium of the proposed two-stage Stackelberg game, if:_  
\[U_{mn}^{E\!U}(\tau_{mn}^{*},\alpha_{mn}^{*})\geq U_{mn}^{E\!U}( \tau_{mn}^{*},\alpha_{mn}^{*}), \tag{25}\] \[U_{mn}^{DD2D}(\tau_{mn}^{*},\alpha_{mn}^{*})\geq U_{mn}^{DD2D}( \tau_{mn}^{*},\alpha_{mn}^{*}). \tag{26}\]  
As above discussion, Equation (12) is convex. Hence, the optimal power allocation coefficient \(\alpha^{*}\) exists and is unique. Then we have \(U_{DT}(\tau,\alpha^{*})\geq U_{DT}(\tau,\alpha)\). Furthermore, based on the Proposition 1,we have \(U_{EUI}(\tau^{*},\alpha^{*})\geq U_{EUI}(\tau,\alpha^{*})\). Therefore, according to the Definition 1, strategy profile \((\tau^{*},\alpha^{*})\) is the Nash Equilibrium of the proposed two-stage Stackelberg game. The proposed two-stage Stackelberg game algorithm to determine the Stackelberg equilibrium is shown in Table 2.
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 6 Security Analysis
### Non-Repudiation  
Every message generated by the vehicle \(VID_{j}\) or \(RSU_{i}\) uses the digital signature as evidence of non-repudiation. Moreover, digital certificates, which are issued by \(LTCA\), contain an expiration date. Therefore, \(Sign_{VID_{j}}\) already involves the timestamp \(t_{j}\) and the receiver checks whether the \(Cert_{VID_{j}}\) is valid or not for the pseudo-identity \(PS_{j}\). Thus, if \(VID_{j}\) or \(RSU_{i}\) behaves maliciously, \(RA\) can easily detect and revoke their certificates. Hence, non-repudiation is guaranteed.
## 2 Materials and Methods
### Sleeping Posture Classification  
The pressure mapping system generates a image (800 \(\times\) 600 pixels) in which each pixel is associated to the pressure applied to the specific area of the mattress. Starting from the  
Figure 4: The PML is composed of a 13 \(\times\) 15 sensing elements structure in which the 15 columns are connected to the digital port of the Arduino Board while the 13 rows are connected to the analog port through the front-end unit.  
195 sensing areas of the PML, the pressure image is built by linear spatial interpolation of the values obtained in each sensing area. Figure 5 shows the pressure image for typical sleeping postures of 4 of the 7 available subjects.  
To perform automatic classification of the sleeping postures, we extracted a set of 9 features obtained by reducing the sensing area density from \(15\times 13\) to \(3\times 3\) by topological averaging. The reduced 9 topological area were obtained linearly by diving each axes of a sensing area in 3 intervals. The 9 features associated with each reduced topological area are the average values of the subtended PML signals.  
The position feature vectors obtained (9 elements) are the input of the machine learning classifiers with six predefined classes associated with six typical sleeping conditions: (i) not on bed, (ii) supine position, (iii) lying on the left side, (iv) lying on the right side, (v) prone position, and (vi) movement. We used 80% of the sample for training, 10% for the validation set, and 10% for testing. We tested the following five classifiers:  
* Artificial Neural Network (ANN): We employed a two-layer ANN; the size of the hidden layer was set to 10. For the training process, we applied a backward propagation algorithm with the scaled conjugate gradient method.
* K-nearest neighbors (KNN): we set the number of neighbors (k) to 5 and the distance metric as Euclidean.
* Support Vector Machines (SVM): a Gaussian kernel function was set for the SVM.
* Decision Tree: a decision tree algorithm with Adaptive Boosting (AdaBoost).
* Naive Bayes: a Kernel-based naive Bayes with Gaussian kernel.  
Figure 5: The pressure images obtained by spatial linear interpolation of the data obtained from the 195 sensing areas in typical sleeping postures of four of the seven subjects: supine, prone, left side, and right side. Each row of the figure represents a specific subject. The arms, trunk, limbs, and head are clearly recognizable. The units are the logical levels of analog to digital converter.
# Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
## 3 Analysis of Test Results
### Effect of Lubrication State on Shear Stress  
Under the condition of normal stress of 60 kPa, the peak shear stress and stability of sand with different particle sizes under different lubrication conditions are shown in Table 1. In the analysis obtained, the peak shear stress in each group with slurry lubrication is about 74% of that without slurry lubrication, and the stable value of shear stress is about 70% of that of the group without slurry lubrication. Considering continuous grouting in practical engineering, a complete slurry sleeve can be formed and the permeability of the stratum is smaller than that of the sand. The test results show that injecting lubricating slurry between the soil and steel pipe jacking during steel pipe jacking construction can effectively reduce the friction between the pipe and soil.
## 3 Results and Discussion  
### Estimation of Avocado Oil Yield  
SE and UAE are classical methods that are commonly used for avocado oil extraction [7]. These processes have total solvent penetration into the oil membranes of avocado fruit [7]. Furthermore, these have been widely used to determine the theoretical maximum oil yield of avocado [41]. However, some factors, including variety, drying method, organic solvent, and temperature, should be taken into account in oil recovery. For each instance, the current data concur with previous publications [7]. Avocado oils were weighed to measure the oily mass, and all yields were calculated from 46.76 to 66.22% (_w/w_) for SE and 31.97 to 54.54% (_w/w_) for the UAE method. Similarly to our results, in a previous study, SE produced a 64.76% (_w/w_) oil yield and UAE produced a 54.63% (_w/w_) oil yield [19].
# A Master-Slave Separate Parallel Intelligent Mobile Robot Used for Autonomous Pallet Transportation
## 4 Experiment and Analysis
### Steady State Error Analysis of Actual Motions
#### 4.1.4 Steady State Error Factors  
As we know, in the ideal case, the zero offset error means that slave robot follows master robot absolutely according to calibrated data, and there is no error. So the most significant factor that affects the stability of the system is the accuracy of calibrated data. (1) Camera installation error: The installation angle deviation of camera and driving unit is unavoidable. The camera should be calibrated first to reduce the initial angle deviation. (2) Encoder calibration offset: The error of encoder deviation would be transferred from inner-loop to the outer-loop. Thus the encoder calibration offset would directly lead to the error of system stability. (3) Accuracy of driving control: In actual motions, the precision of each driving motor is up to 1 rpm. As the result, the precision of the following velocity and position control could not be well guaranteed. (4) Mechanical installation error: The mechanical installation error represents installation tolerance error such as driving unit and other mechanical parts. All these errors would result in the difference between calibrated zero position and actual zero position.  
As we known, the errors of actual motions of these two sub robots cannot reach zero. This section analyzes most of the factors that may affect the steady state errors of the following motions. The two driving units of slave robot may jitter during the adjusting control. Surface roughness of the road and the curve degree of vision-based color bar on the road can both have significant effect on the stability of the master-slave parallel robot' motion. As discussed above, various factors affect the steady state error. The steady state error for the master-slave mobile robot measured in the experiment is about 3 mm, which is not easy to eliminate.
## 3 Results and Discussion
### Creep Behavior Modeling and Lifetime Prediction for the Target Shipping Container
The lifetime (time to failure) estimated from the FS and average creep rate of the container gradually slowed as the applied load increased at all relative humidity levels (Table 7, Figure 6b). However, as the applied load level increased, the difference with respect to the relative humidity tended to decrease. Overall, the effect of the applied load level was greater than that of relative humidity. For example, in the case of Container B, when applied loads of approximately 10.4%, 17.8%, and 25.2% of the CS in the standard condition (relative humidity 50%) were applied, the predicted lifetimes decreased by approximately 24%, 27%, and 31%, respectively, compared to the lifetime of the standard condition at 70% relative humidity, and by approximately 34%, 39%, and 45%, respectively, at 90% relative humidity.