esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Structural Analysis and Evaluation of the Backfilling Hydraulic Support
### Compaction Characteristics  
The compaction characteristics of the backfilling hydraulic support refer to the basic parameters when compaction is performed using filling materials, including compaction force and angles, the distance from the top, and the blanking clearance [36].  
Compaction force is the compaction pressure of the compaction mechanism on the filling material. The ZC4800/16/30 backfilling hydraulic support compaction mechanism is designed for single-stage compaction. Combined with engineering experience, the compaction mechanism should provide a compaction strength of not less than 2.5 MPa to ensure the compaction effect of the filling material.  
Figure 17: Variation of dip angles of columns of the ZC4800/16/30 backfilling hydraulic support.  
Figure 18: Variation of offset of the top beam of the ZC4800/16/30 backfilling hydraulic support.  
The compaction angle refers to the angle between the compaction mechanism and the base when the compaction mechanism is extended. In Figure 19, the compaction angle of the ZC4800/16/30 backfilling hydraulic support is at 10-35\({}^{\circ}\). The maximum compaction angle increases linearly with the increased mining height, and the maximum compaction angle is obtained at the maximum mining height. The compaction mechanism has a sufficient moving range for compaction materials.  
The distance from the top refers to the vertical distance between the compaction head at the front end of the compaction mechanism and the top plate after the compaction mechanism of the backfilling hydraulic support is fully extended. In Figure 20, the distance from the top changes in a smiling curve, and the bottom of the smiling curve is the most suitable working height area for the support. It roughly coincides with the mining height range of 2.0-2.5 m, indicating that the distance from the top is more reasonable. The sensitive area of the distance from the top is located on the right side of the most suitable working height area, just avoiding the influence of the change at the mining height of 2.0-2.5 m.  
Figure 19: Compaction angle of the ZC4800/16/30 backfilling hydraulic support.  
Figure 20: Variation in the distances from the top of the ZC4800/16/30 backfilling hydraulic support.  
The blanking clearance affects the accumulation state of the filling material. In Figure 21, the blanking clearance of the ZC4800/16/30 backfilling hydraulic support first increases and then decreases. When the mining height is 2.0-2.5 m, the maximum blanking clearance can be obtained, that is, the maximum blanking space can be created. The rear of the stand also has enough working space at this point.  
Based on the compaction force, compaction angle, distance from the top, and blanking clearance of the ZC4800/16/30 backfilling hydraulic support, its compaction characteristics meet the basic requirements.
## 2 Experimental Section and Material and Methods
### Oil Samples  
In-service oil samples were considered to verify the response of the POF-MIP sensor in used oil. As a typical insulating oil, a Nytro Libra insulating oil from Nynas S.r.L. (Stockholm, Sweden) was considered. The used oil was collected from serviced electrical equipment, _i.e._, two 145 kV oil insulated  
Figure 1: Optical Sensor system with MIP layer. Inset: Cross section of the MIP sensing region.  
current transformer CTs (CT.134 and CT.729 respectively), which operated in different substations of the Italian Transmission System Operator. The transformers had been operating for about 30 years. The samples were taken from gas-tight syringes used for dissolved gas analysis (DGA) and for furan analysis, and were analyzed by an external laboratory. Furan concentrations were measured by means of high pressure liquid chromatography according to the IEC61189 method. Results confirmed the presence of degradation compounds generated over the years of transformer operation (Table 1). These products originate both from the oxidation of the oil and from the degradation of cellulose. The same oil, but a new sample, was used for sensor calibration.  
As reported in Table 1, the two samples are very similar, and both CT.134 and CT.729 show a remarkable furfural (2-FAL) concentration (0.112 ppm and 0.114 ppm respectively). No other furan derivatives were detected. From Table 1 significant methanol and ethanol concentrations emerge as well. These compounds, like furans, are oil soluble by-products of the aging of oil-impregnated cellulose [4; 5; 6] and their presence could potentially interfere with the furan detection by the sensor.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges
#### 3.5.5 Diverse User Requirements  
In HetNets, handovers can be challenging due to diverse user requirements. As, HetNets consists of a combination of different networks, which have different coverage areas and capacities so they may have different requirements for handovers, such as high throughput, low latency and minimal interruption time, etc. Additionally, the varying characteristics of the different network types can make it difficult to ensure that handovers are performed seamlessly and efficiently. Therefore, it is important to design and optimize handover algorithms to meet the diverse user requirements in HetNets. For example, some advanced handover techniques such as proactive handover, conditional handovers, multi-attribute decision-based handovers and hybrid handover, etc, can be used to improve handover performance in HetNets [147; 100].
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 3 Proposed Protocol Description  
Following the above-mentioned introduction to well-known protocols for the avoidance and or control of congestion in WSNs, COALA (_COngestion ALleviation and Avoidance_), which is the proposed in this article protocol of this kind, is introduced in this section.  
COALA protocol, operating proactively, uses simple static information of network nodes in order to perform data routing through the paths that are less probable to be congested. In the face of imminent congestion, COALA acting reactively, uses implicit notification of congestion and applies resource control with the intention of preventing the further diffusion of congestion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Metric Considered** & **Congestion Notification** & **Congestion** \\  & & & **Mitigation/Avoidance** \\ \hline CODA [7] & Buffer occupancy and channel load & Explicit & Traffic Control \\ \hline Ahmad and Turgut [8] & Buffer occupancy and characteristic ratio & Implicit & Traffic Control \\ \hline PACA [9] & Buffer occupancy, characteristic ratio, distance, time of use & Implicit & Resource Control \\ \hline DALPaS [10] & Buffer occupancy, channel load, energy & Implicit & Resource Control \\ \hline TALONet [11] & Buffer occupancy & Implicit & Traffic and Resource Control \\ \hline HTAP [12] & Buffer occupancy, energy & Implicit & Resource Control \\ \hline TADR [13] & Buffer occupancy & Implicit & Resource Control \\ \hline ECODA [14] & Dual buffer threshold and weighted buffer difference & Implicit & Traffic Control \\ \hline TARA [15] & Buffer occupancy and channel load & Explicit & Resource Control \\ \hline PASCCC [16] & Buffer occupancy, type of content & Implicit & Traffic Control \\ \hline COALA & Buffer occupancy, popularity index, energy, distance, vicinity index & Implicit & Resource Control \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic features of congestion avoidance and control protocols.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 4 Searching Strategy for Multiple DLSH Clusters on Multiple GPGPU Devices
### Multi-Stage Search  
To overcome **Problem 1** (Blind search), we propose to divide the search processing for queries into small stages. We create memory space for the Master and slave's \(k\)NN results before the search process starts. After each stage, these memory spaces are synchronized among the MPI processes. The new \(k\)NN results are added to the empty slots after synchronization. Therefore, the Multi-stage search of a query can be stopped when the \(k\)NN result memory is filled. This helps to eliminate **Problem 1**. An example is shown in Figure 8, where the search processes of \(q_{5}\) and \(q_{6}\) can be stopped after stages 0 and 1, respectively. The \(k\)NN result array \(K\) is the collection of \(k\)NN results for every query \(q\) in \(Q\):  
\[K=\{k\text{NN}_{\text{LSH}}(q_{1}),...,k\text{NN}_{\text{LSH}}(q_{m})\}. \tag{7}\]  
Figure 7: The LSH pre-clustering before generating the StagedLSH hash table of 2 data clusters.  
However, there is still the problem of the worst-case scenario happening in an early stage, which causes the search time to be non-optimal (Query 3 in Figure 8 is an example). We can greatly reduce this problem by using a heuristic data cluster-selection scheme, which tries to search for the \(k\)NN results of a query in the slaves with a high likelihood of obtaining good \(k\)NN results first. The first search stage in the multi-stage search always takes part in the data cluster that has the same hash value as the query. In this case, we reuse the family of hash function \(\hat{H}_{0}\) for data clustering to calculate this Level 1 LSH. After the first search state, the query uses probes in the nearest data clusters to the data cluster that was searched first.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Asynchronous Stream-Sensing Network
#### 5.2.3 Linear Extractor 1  
This extractor fetches four features, \(\mathbf{z_{R}}\), \(\mathbf{z_{I}}\), \(\delta_{R}\), and \(\delta_{I}\), from the AutoEncoder Extractor and the Inception-layer-based extractor. Then, \(\mathbf{z_{R}}\) and \(\delta_{R}\) are concatenated, while \(\mathbf{z_{I}}\) and \(\delta_{I}\) are attached. Each concatenated feature is passed through a 2D convolution layer. Both resultant feature vectors are concatenated into a single vector, and then several fully connected layers are followed. Then, the fully connected layers produce a vector \(\mathbf{w_{c}}\).
# Advances in Mechanical Systems Dynamics 2020
## 3 Fields of Application
### Cars and Heavy Vehicles  
This Special Issue includes six papers dealing with cars and heavy vehicles.  
The modeling and simulations of tires are still classic, especially when it comes to the modeling of the effect of temperature on the tire performance. In [1], an extension of the well-known Magic Formula is derived in order to include the effect of temperature on car tires: new coefficients are added to account for changes in longitudinal and cornering stiffness as well as peak factors. In addition, the thermal model is extended.  
The handling of trucks is considered in [18], with a focus on the effects of taper-leaf vs. multi-leaf suspensions on handling. The drift test, the ramp steer test, the step steer, and brake-in-turn tests are simulated in a multi-body environment, after validation of the numerical model in steady-turning conditions.  
Independent active rear steering is the most flexible approach to rear steering of road vehicles. A two-level hierarchical synchronization control strategy is proposed in [12]. The upper controller adopts a virtual synchronization controller based on a dynamic model of the virtual rear axle steering mechanism to reduce the synchronization error between the rear wheel steering angles, while the lower controller is designed to realize an accurate tracking control of the steering angle for each wheel and reject disturbances. Experimental results on the prototype vehicle are shown.  
Heavy-wheeled vehicles with articulated hydraulic steering systems are widely used (e.g., in construction and road building). However, they often suffer from poor directional stability. A driver-aid system aimed at improving the directional stability is presented in [13], based on the steering wheel rotation, a frame articulation sensor, and an additional hydraulic circuit for straightening the frame.  
Farming tracked vehicles on deformable soils are considered in [19]. A multibody approach is employed. The two flexible tracks are modeled as groups of rigid links constrained by bushing elements able to replicate the elastic behavior of the track. A soil model is used to describe the terrain behavior subjected to the grouser's action.  
The interaction of the driver with the vehicle's steering has been investigated in the past both in the case of cars and motorcycles. In [14], the focus on an instrumented steering wheel (ISW) is employed to investigate the driver's response to a lateral disturbance (the so-called \"kick-plate\" maneuver) on the vehicle dynamics. A notable feature of the work is that the ISW employed is capable of measuring the force and moment applied by each hand separately. An involuntary torque peak is observed after the disturbance before the voluntary (recovery) torque is applied, both in one- and two-hands maneuvers.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 3 Direct Wave Signal Parameter Estimation Method Based on Template Matching
### Signal Model  
The PAR transmits the LFM signal, which can be expressed as  
\[s_{t}\left(t_{m},\hat{t}\right)=A\:\text{Rect}\left(\frac{\hat{t}}{\tau_{m}} \right)\exp\left[j2\pi f_{m}\left(t_{m}+\hat{t}\right)\right]\exp\left(j\pi\mu _{m}\hat{t}^{2}\right) \tag{3}\]  
where A is the amplitude of transmitted signal, \(t_{m}\) is the slow time, \(m\) indicates the pulse number, \(\hat{t}\) is the fast time, \(\text{Rect}(\cdot)\) is the window function, \(\tau_{m}\) is the pulse width, \(f_{m}\) is the carrier frequency, and \(\mu_{m}=B_{m}/\tau_{m}\) is the chirp rate with bandwidth \(B_{m}\). Then, the direct wave signal received by the reference antenna can be represented as  
\[s_{d}\left(t_{m},\hat{t}\right)=\sigma_{d}A\:\text{Rect}\left(\frac{\hat{t}- L/c}{\tau_{m}}\right)\exp\left[j2\pi f_{m}\left(t_{m}+\hat{t}-L/c\right) \right]\exp\left[j\pi\mu_{m}(\hat{t}-L/c)^{2}\right] \tag{4}\]  
where \(\sigma_{d}\) is the attenuation coefficient and \(L\) is the distance between the PAR transmitter and reference antenna. The parameters to be estimated are \(f_{m}\), \(\tau_{m}\), \(B_{m}\), and the time of arrival.  
Based on the a priori knowledge of the waveform of PAR, we can build a template library of radar parameters, including CF template library noted as \(\left\{f^{(k)}|\text{k}=1,2,\ldots,N_{f}\right\}\), PW template library noted as \(\left\{\tau^{(k)}|\text{k}=1,2,\ldots,N_{\text{r}}\right\}\), and BW template library noted as \(\left\{B^{(k)}|\text{k}=1,2,\ldots,N_{B}\right\}\), where \(f^{(k)}\), \(\tau^{(k)}\), and \(B^{(k)}\) are the specific templates in the signal parameter template library and \(N_{f}\), \(N_{\text{r}}\), and \(N_{B}\) are the number of parameters of CF, PW, and BW respectively. Based on these prior information, we can use a method based on template matching to estimate the parameters of direct wave signals in real time.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 4 Conflict-Detection Algorithms Using GPU
### Workload Scheduling Problem  
To achieve optimized performance, we defined a workload scheduling problem regarding how to assign filters to threads evenly according to their workload.  
Description of workload scheduling problem: Assuming that we perform conflict detection for \(n\) filters, the workloads of \(n\) filters are \(\alpha_{1},\ \ldots,\ \alpha_{n}\), which are assigned to \(m\) threads with equal computing capability. The issue is to perform workload scheduling to minimize the execution time for conflict detection.  
We can convert the above problem to the well-known deterministic scheduling problem as follows: \(n\) independent tasks \((I_{1},\ \ldots,\ I_{n})\), which require execution times \((t_{1},\ \ldots,\ I_{n})\), respectively, are assigned to \(m\) processors with equal computing capability, and tasks will not be interrupted during their execution. The goal is to obtain a scheduling result so that the finish time is the shortest. During workload scheduling, the conflict detection of \(n\) filters is considered as an independent task. When a filter executes conflict detection, it cannot be interrupted or reassigned to another thread, and \(m\) threads with equal capabilities are similar to \(m\) processors with identical capabilities. Unfortunately, the deterministic scheduling problem has been proven to be an NP-complete problem [37; 38], indicating that an optimum workload scheduling algorithm cannot be found within a limited time. Therefore, we attempted to propose a near-optimum workload scheduling algorithm. The longest processing time (LPT) algorithm [39] has been analyzed and proven to be the near-optimum algorithm closest to the optimum result.  
Figure 5: Filters dispatch of the GPCDA.  
The concept of the LPT algorithm is to arrange the task to be executed from long-to-short execution times, followed by its designed dispatch algorithm assigning tasks to the processors. The LPT algorithm guarantees that the difference in performance does not exceed \(4/3-1/3\ m\), compared with the optimum result; that is, the lower-bound performance of the LPT algorithm is 1.33 of the optimum performance. Therefore, we propose an EPCDA based on the LPT concept. In the EPCDA, a filter list is assigned to each thread to achieve workload balancing.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 2 Assessment of Hidden Ecological Factors
### Factor Analysis  
Exploratory factor analysis (EFA) is one of the techniques for factor analysis (FA). It is primarily used in statistics to describe variance among observed correlated variables in terms of potentially a smaller number of unobserved variables, usually referred to as factors [27]. In this work, EFA was employed to search for confounding ecological factors that are latent [8; 27] from the set of observed meteorological variables.  
We demonstrate the FA technique using simple mathematical sketches; the observed variables can be expressed as linear combinations of the potential factors plus the residual terms. Consider the following observed variables \(Y_{1},Y_{2},\cdots,Y_{M}\) of size \(M\), and assume they are linearly related to a small number of unobservable (latent variables) factors \(F_{1},F_{2},\cdots,F_{N}\), with \(N\ll M\) such that:  
\[\begin{split} Y_{1}&=\psi_{10}+\psi_{11}F_{1}+\cdots +\psi_{1N}F_{N}+e_{1}\\ Y_{2}&=\psi_{20}+\psi_{21}F_{1}+\cdots+\psi_{1N}F_{ N}+e_{2}\\ \vdots&\\ Y_{M}&=\psi_{M0}+\psi_{M1}F_{1}+\cdots+\psi_{MN}F_{ N}+e_{M}\end{split} \tag{1}\]  
where \(e_{1},\cdots,e_{M}\) are the residual terms, assuming that \(E(e_{i})=0\), and \(Var(e_{i})=\delta_{i}^{2}\). While the unobservable factors \(F_{i}\) are independent from each other and \(E(F_{j})=0\) and \(Var(F_{j})=1\). These two assumptions stand as the robust pre-conditions for the application of structural equation modelling (SEM). The loading scores can be obtained from covariance and variance of any two observed variables using the following formula presented in Equation (2)  
\[\begin{split} Cov(Y_{i},Y_{j})&=\sum_{i\
eq j}^{N }\psi_{iN}\psi_{jN}\\ Var(Y_{i})&=\sum_{i=1}^{N}\psi_{iK}+\delta_{i}^{2} \end{split} \tag{2}\]  
where the summation sign in Equation (2) denotes communality of the variables, the variance of which is explained by the common factors \(F_{N}\).
###### Abstract  
A theoretical model is presented for the accurate detection of a gas leak source through a pinhole in a cylindrical storage vessel using the acoustic emission (AE) technique. Pinholes of various diameters ranging from 0.20 to 1.2 mm were installed as leak sources, and safe N\({}_{2}\) was used as a filler gas. AE signals were measured and analyzed in terms of AE parameters (such as frequency, amplitude and RMS) as a function of angle and axial distance. Among them, the amplitude characteristic was the most important parameter to determine the leakage dynamics of AE with a continuous waveform. The simulation of AE amplitude was performed using the theoretical model for AE. For practical applications, the theoretical formula was modified into two semi-empirical equations by introducing the normalization method to fit the angular and axial characteristics of the observed AE amplitude, respectively. The main finding of this study is that the semi-empirical equations provide an accurate solution for leak source localization in the cylindrical vessel. As a priori knowledge, the value of \(\kappa_{\eta}\) in Green's function, which determines the angular and axial dependence of the AE amplitude, was determined by applying external excitation to the cylinder surface. The proposed formulas provide a suitable approach for practical application in the localization of leak sources in cylindrical storage tanks.  
acoustic emission; amplitude; analytical modeling; semi-empirical equation; leak source localization Article  
Academic Editor: [PERSON]