esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
###### Abstract  
Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.  
gear pump; wavelet packet denoising; kernel principal component analysis (KPCA); just in time learning (JITL); remaining useful life (RUL) prediction +
Footnote †: journal: Applied Sciences
# Nucleotides as Bacterial Second Messengers
## 4 The cAMP Nucleotide
### Mycobacterium tuberculosis  
Unlike other pathogenic bacteria, _Mycobacterium tuberculosis_ is distinguished by high levels of cAMP signaling. For example, _M. tuberculosis_ has 16 AC-like proteins and 10 confirmed AC, indicating that cAMP function is essential in this bacterium. Another difference is that low levels of glucose do not produce a significant effect on cAMP levels. In contrast, the AC activity is controlled by various host conditions; for example, fatty acids, carbon dioxide (CO\({}_{2}\)), and pH affects the activity of _M. tuberculosis_ ACs, while hypoxia and starvation affect the expression of genes encoding ACs [61]. _M. tuberculosis_ within the macrophage is known to produce and secrete cAMP. Production is by the AC, Rv0386, which has been corroborated by deleting the corresponding gene of Rv0386, and results in a phenotype of decreased virulence and pathology of _M. tuberculosis_ in a murine infection model [69].  
In addition to the different ACs enzymes of _M. tuberculosis_, ten putative cAMP-binding proteins have been identified as receptors. Among them are two transcription factors of the CRP family and a protein lysine acetylase. Of the two transcription factors, one Crp induces the expression of a regulon, where it simultaneously controls the expression of about 100 genes. An _M. tuberculosis_ strain with Crp deletion significantly reduces its virulence in a murine model [70]. The second Crp transcription factor, Cmr, is involved in macrophage response and in the expression of genes in response to cAMP levels [71].
Though the reserved capacity for a wind turbine may be not sufficient enough, the reserved capacity for a wind-turbine cluster is still impressive and can be used to stabilize the cluster power output and support grid frequency. In China, some active power limiting strategy has begun to implement in wind farms to keep wind farms have reserved power for power regulation and frequency support, such as Gansu province. There will be large freedom degrees for the wind power for stabilized power output and supporting the power system stability. Thirdly, a complete wind turbine model may be helpful when considering the turbine load characteristic, but a simplified model could be useful to reduce the problem order in the optimal wind farm active power control. The dynamic characteristic for a wind turbine will be changed under continuous operations and cause a difference between wind turbines. Such property should also be taken into consideration during active power control. In addition, many model predictive control (MPC) related strategies are used in wind farm active power distribution. From the control theory perspective, there still exist potential improvements to further develop the control method. Like the state assessment based variable weight MPC strategy proposed in [17], the further improvement based on current model predictive control framework is also a focused problem. It should be noted that the wake effect is not considered in this paper, but the proposed strategy still can be used in a waked wind farm. Reasons for this will be explained in the main text. In this paper, a wind turbine active power control strategy is proposed to optimize the active power set-point from a wind-turbine cluster level controller. The power tracking dynamic characteristic for each wind turbine is modeled as a first-order inertial link to describe the wind-turbine cluster power tracking dynamic response, and the model predictive control strategy is adopted to optimize the power tracking error between the cluster power demand and output. The rest of this paper is organized as follows: in Section 2, the simplified NREL 5 MW wind turbine model is introduced. Section 3 discusses the wind turbine power tracking dynamic characteristics, which are combined into the active power model predictive controller in Section 4. Time-varying constraints are proposed in Section 5 to limit and balance the power set-point for each wind turbine. In Section 6, the proposed strategy is tested under different cases. Section 7 concludes this paper.
## 3 Results and Discussion
### The Mechanism of Bayer Red Mud Admixture on the Compressive Strength of AASR
#### 3.2.4 Micromorphology Analysis  
Figure 9 shows the 28-day results of BSE/EDS of AASR. It can be seen that the dissolution of silica-alumina precursors is not complete, and there are unhydrated slag and red mud particles in their hardened bodies. It can also be seen that with a proper content of red mud, the microstructure of AASR is denser, so a higher strength can be obtained. However, when the RM content increases to more than 50%, the microstructure becomes loose, and the degree of structural densification is reduced, leading to more cracks (Figure 10).  
Figure 10: BSE images of alkali slag-red with different additions of RM.  
Figure 9: BSE/EDS images of alkali slag-red with different additions of RM.  
Additionally, according to the surface scanning diagram in Figure 11, it is found that the distributions of Si, Ca, Na, and Mg elements are uniform, while Al elements show different degrees of enrichment, which is mainly caused by incomplete cleaning due to the embedding of polishing powder in surface pores during the process of polishing. Another important feature is that the distribution of Fe in the control group is small and uniform, while Fe elements show different degrees of enrichment and distribution in the C-A-S-H after adding red mud in AASR, which indicates that these hematites are difficult to dissolve into the hydration products.  
Given the results of EDS, combined with the XRD analysis, it is plausible that the dissolved ionic species, including aluminates, calcium, silicates, and alkali ions, can form various types of amorphous phases, including calcium-silicate-hydrate (C-S-H), calcium-aluminosilicate-hydrate (C-A-S-H), and alkali aluminosilicate-hydrate (N-A-S-H), which provide the binding capabilities in AASR, so the decrease of compressive strength is more significant in the AASR blended with 70% red mud.  
Figure 11: Elemental distribution of BSE-EDS surface sweep of RM with different additions.
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 3 Methodology Description  
Over time, the underwater vehicles and their equipment are updated due to mission requirements, to improve their performance, or by obsolescence. The changes could affect the software, the equipment, and even the vehicle itself, and including more precise sensors, more capable actuators, or modifying other physical aspects can be quite common. All those changes have an impact on the real vehicle, changing its behavior. To avoid the divergence in results over time between the simulation and the reality, the digital twin needs to evolve in parallel with the real vehicle. This evolution requires the incorporation of some changes into the digital twin. This can be translated into the need to perform specific tests and measurements over the real vehicle to adequately evaluate the effects.  
One of the key aspects that is affected in most cases is the vehicle's hydrodynamic behavior, particularly in the case of open-frame vehicles, whose geometry is highly dependent on the onboard equipment. In order to adjust the hydrodynamic model and accurately represent the real behavior, we propose a methodology based on evolutionary algorithms, which is described in this section. Figure 1 shows an overall diagram of the proposed process that is going to be explained in detail in the next subsections.
## 3 Results and Discussion
### Investigation of PVC Photodegradation Using Weight Loss  
The PVC irradiation led to the elimination of volatiles, such as HCl gas, and a decrease in the polymer weight [57]. The polymeric material weight loss can be used as a tool for assessing the level of damage that the irradiation caused. Thus, the PVC films were weighed (\(W_{1}\)) and irradiated followed by a measurement of weight after every 50 h (\(W_{2}\)). The weight loss (%) was calculated using Equation (2). The results are presented in Figure 5. Complexes **1-4** led to a significant PVC photodabilization, in which **1** was the most effective additive. The weight loss (%) was highest (1.39%) for pure PVC and lowest (0.27%) for the blend containing **1** after 300 h irradiation. Complex **1** provided five-fold protection for PVC against photodegradation.  
\[\text{Weight loss }(\%)\ =\ \left[(W_{1}-W_{2})/W_{0}\right]\ \times\ 100 \tag{2}\]  
Figure 4: The IC=C of the PVC films versus time.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 4 Experimental Results
### TCP and UDP Performance Assessment  
As a baseline for the subsequent evaluation of Modbus-TCP and Modbus-UDP performance, some preliminary tests were performed with the goal of assessing the maximum throughput the experimental boards could achieve when sending raw TCP and UDP payloads. To this purpose the standard iperf3 test program, version 3.9, was used with a measurement window of 60 s and offered bandwidth limit of 100 Mb/s, that is, the same as the link speed. Layer-4 (L4) payload sizes of 16, 32, 64, 128, and 256 bytes were used, consistently with the range of L4 payload sizes used in the Modbus experiments to be presented in Sections 4.3 and 4.4.  
Figure 6 shows the results. Generally speaking, TCP performs better than UDP in terms of throughput because, regardless of the payload size used when transmitting data, it is allowed to aggregate multiple payloads into a single TCP segment, thus achieving better efficiency. The presence of automatic back pressure in TCP causes the receive-side throughput (measured at the receiver ingress port) to always be equal to the offered throughput (measured at the transmitter egress port) with no data loss. On the contrary, UDP exerts no back pressure, which leads to datagram loss when the offered throughput exceeds what the receiver is able to handle.
# Integration of BIM Tools for the Facility Management of Railway Bridges
## 6 Discussion
### Limitations  
Despite the advances and benefits of the designed tool, and in light of specific objective (c) stated at the beginning of this paper, it is necessary to consider the following limitations of this work:  
* The first limitation focuses on the damage intensities. This information available for each bridge element can be exported from Autodesk(r) Revi(r) to MS Excel(r), but not the other way around. This is reflected in the proposed workflow of the tool because the spreadsheet contains all the damage intensities marked from the BIM model during the on-site visit, and the damage severity level value for each element is obtained, which is then imported into the model and changes the color of the elements. This is designed with the idea that during the on-site monitoring visit, it is more convenient to perform it with the help of software that allows the visualization of the BIM model, enabling the selection of damage intensities, rather than with the help of the automated spreadsheet on-site. However, if a change in intensity from MS Excel(r) is desired, it is possible to make the change and note it in the comments so that the intensity of the damage is modified in the model.
* The second limitation lies in the bidirectionality of the project, as the reciprocity of the components is only total in the pile divisions, that is, in the support lintels, shafts, and foundations, while for the beams and arches of the span and the superstructure of the non-structural elements, it is not possible to export the damage intensity parameters because these components were not parameterized. However, the comments and ID of the elements do have the desired bidirectionality, and the damage severity level can be imported for all bridge components, which explains why all elements of the model have a value in the \"NR (1-4)\" parameter.
* Although the automated spreadsheet is designed to be generic and applicable to any type of bridge, it is necessary to adjust the names of the elements, parameters, and sheets according to the specific case, which requires additional configuration effort.  
These limitations open up opportunities for future research and improvements in this BIM/data automation to achieve a digital twin for the monitoring and maintenance of railway bridges.
## 3 Experiments for Jason-3 Attitude Modes and Receiver PCVs Calibration
### Effect of Jason-3 Attitude Modes on Receiver PCO and POD
#### 3.2.3 SLR Validation  
Satellite Laser Ranging (SLR) validation is an important means to validate the orbital accuracy [32; 36]. We have selected the SLR normal point data, corresponding with the periods of the Jason-3 POD ([[ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)]([ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov))). The SLR validation results denote the distance accuracy between the validated satellite and SLR station, which cannot validate the accuracies of the orbital position and components in each direction. The SLR validation are more exhibited as the radial accuracy when cut-off angle is set higher. Thus, the cut-off angles are respectively set to \(0^{\circ}\) and \(60^{\circ}\), in which \(0^{\circ}\) validate errors of equivalent distance and \(60^{\circ}\) validate the orbital radial accuracy.  
Table 4 lists the orbital SLR residuals statistics for Jason-3 satellite. When the cut-off angle is set to \(60^{\circ}\) and \(0^{\circ}\), the residuals with PCVs are approximately \(-3.7\pm 13.2\) mm and \(-0.8\pm 17.2\) mm. The validated results with the cut-off angle of \(60^{\circ}\) and \(0^{\circ}\) show that the equivalent distance error by introducing PCVs is improved by 0.7 mm and 4.1 mm, respectively, compared with neglecting PCVs. The SLR validation results do not have the large systematic bias. The total results indicate that the orbital radial accuracies are approximately 1.4 cm, nearly 1 cm, and the equivalent distance errors are within 2 cm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Our Orbits-CNES** & **R-RMS** & **T-RMS** & **N-RMS** & **3D-RMS** \\ \hline No PCVs & 11.3 & 27.7 & 14.5 & 33.2 \\ PCVs & 11.3 & 21.7 & 12.7 & 27.5 \\ \hline \end{tabular}
\end{table}
Table 3: Mean RMS of differences between Jason-3 orbits and CNES orbits [unit: mm].  
Figure 10: Residuals RMS statistics for Jason-3 POD.  
The above comparisons show that the orbital accuracies by introducing receiver PCVs map have the millimeter-level improvement. These results indicate that receiver PCVs should be considered for high-accuracy orbits, especially when the orbital accuracy is close to centimeter level.
## 2 Implementation of GPU-MOC Method
### Implementation of MOC on a GPU
#### Implementation Procedures on CPU  
GPUs and CPUs maintain their own dynamic random access memory (DRAM). Data transmission between GPU memory and CPU memory is the bridge between GPU and CPU. In order to simplify the codes on CPU, the unified memory is adopted. The codes of data transmission between CPU memory and GPU memory are omitted, because unified memory can access both CPU memory and GPU memory. Though codes for data transmission process are omitted, it is still needed to be conducted. Thus, the computing efficiency is not improved.  
Data transmission process usually includes three steps: first, copy input data from the CPU memory to the GPU memory; second, load the GPU codes and execute them; and, thirdly, copy output results from the GPU memory back to CPU memory. It is worth mentioning that data transmission efficiency is the determining factor that limits the computational efficiency of the GPU, and many optimizing strategies are aimed at accelerating these processes or reducing data transfer loads.  
Two sets of 1D arrays are used to store discharge and head variables for MOC sections, one stores input data and the other stores output data. Initially, the input data are stored in arrays q[tx] and h[tx], and the output data will be stored in arrays fq[tx] and fh[tx]. After the calculation of one time step, the output data are used as the input data for the next time step. Then the input data are stored in fq[tx] and fh[tx], and the output data have to be stored in q[tx] and h[tx] instead. As a result, the MOC kernel has to be invoked alternately by parity counts to switch the arrays that store input and output data. A synchronous function cudaDeviceSynchronize() is used to make sure all computation of this time step are completed before starting the next time step calculation.  
In general, the implementation procedures of the CPU codes are listed as follows.  
1. Allocate unified memory and initialize two set of arrays, and copy input data from the CPU memory to the GPU memory.
2. Launch MOC kernel for parallel computing.
3. Synchronize threads.
4. Write the output data back from the GPU memory to the CPU memory, and set them as input data for the next time step.
5. Repeat steps (2)-(4) until the calculation for the last time step finishes.
6. Output the results. In Appendix B, the details of the CPU codes for direct water hammer are given.
###### Abstract  
This paper presents ReproRun, a flexible and extensible run-time framework for the reconfiguration of functions in field programmable gate array (FPGA) devices used in popular software-defined radio (SDR) platforms. The FPGA devices embed a hardwired or soft processing system (PS) which communicates with the programmable logic (PL) using a standard embedded bus interface. In order to apply a seamless run-time partial reconfiguration, we made use of all the related building blocks, design guidelines, and tools offered by AMD-Xilinx. In ReproRun, each partial bitstream targeting a reconfigurable region (RR) of the PL area comes with its respective firmware (i.e., software functions) that runs on the PS side. Our work guarantees run-time updates of the firmware without interrupting the functionality of other software processes running in the PS or PL, by employing a specialized controller, denoted as Run-timE firmWare reconfiguration contRollEr (REWIRE). The latter leverages the open asymmetric multiprocessing framework (OpenAMP). The partial bitstreams and respective firmware are fetched from a remote location using the trivial file transfer protocol (TFTF). ReproRun can be applied in different FPGA accelerators residing in disaggregated open radio access network (RAN) equipment, adaptive radio access technologies, and Edge servers hosting virtualized functions.  
FPGA; 5G; B5G; SDR; RAN; reconfigurable computing; adaptive computing; function accelerators +
Footnote †: journal: Electronics Electronics  
Article
## 3 Algorithm for TSAFP
### Congruence of Periodic Tasks  
Consider two tasks \(t_{1}\) and \(t_{2}\) represented purely as binary sequences. This is shown in Figure 4 for the case where \(p_{1}=5\) and \(p_{2}=3\), with offsets of \(o_{1}=1\) and \(o_{2}=2\). The coincident task releases (logical AND) are also indicated, where a '1' indicates that the two tasks are released (phased) together.  
Given the thrift scheduling algorithm, each task can be represented as a linear congruence of the form:  
\[x\equiv r_{i}\bmod(p_{i}) \tag{11}\]  
where \(x\) is one of the (multiple) possible solutions to the congruence. If the two tasks are phased together (denoted by _(\(t_{1}\), \(t_{2}\))_) then \(x\) must be a valid integer solution to both congruences simultaneously. According to the _linear congruence theorem_ this occurs iff:  
Figure 4: Congruence relationship of two periodic tasks.  
\[\varphi(t_{1},t_{2})\mapsto(o_{2}-o_{1})\,|\,\gcd(p_{1},p_{2}) \tag{12}\]  
That is, if the relative offsets of the tasks divides the _gcd_ of the periods. Suppose now that we wish to consider the phasing of a set of \(m\) tasks. Clearly, \(x\) must now provide a valid solution to each of the \(m\) simultaneous congruences for the tasks to all be in phase. According to the _Chinese remainder theorem_[12, 18], (12) can be extended such that a valid integer solution to such a set of \(m\) congruences exists iff:  
\[\varphi(t_{1},\ldots,t_{m})\mapsto\forall i,\forall j;i\
eq j;(o_{j}-o_{i})\,| \,\gcd(p_{i},p_{j}) \tag{13}\]  
That is, if each pair wise combination of tasks in the set \(m\) satisfies (12) simultaneously. In such a case that each pair wise combination of periods in \(m\) are coprime then (13) is guaranteed to be true. Equations (12) and (13) form the basis for the LCS algorithm. A _congruent subset_ is defined as a set \(T\)\(\subseteq\)\(\tau\) s.t. \(\forall i,j\in T\), equation (13) holds. Let \(|T|\) be the cardinality of the subset, _i.e._, the number of tasks it contains, and let (\(T\)) refer to the magnitude of the subset:  
\[(T)=\sum_{i\in\tau}c_{i} \tag{14}\]  
The purpose of the LCS algorithm is to search for a congruent subset of tasks with the largest magnitude, given an asynchronous task schedule. Central to the proposed algorithm is the notion of a _phase matrix_\(\phi\). This is an \(n\)-by-\(n\) matrix that contains all pairwise task phasing information, encoded in binary format. For all elements \(i,j,j>i\), a '1' is placed in the \(i^{th}\) element of the matrix indicating a phasing of tasks \(i\) and \(j\), and a '0' indicating otherwise. The matrix is '0' both on and below the diagonal, and can be generated by repeated application of the _gcd_ algorithm and Equation (12) to each pair wise combination of tasks in the set, and effectively defines the powerset of all possible task phasings. Each row of this matrix is referred to as a 'phase code', and represented as an \(n\)-bit long binary string; in this paper, each phase code is represented as an unsigned integer. The phase code \(P^{j}\) corresponds to the \(j^{\text{th}}\) row of the phase matrix \(\phi\).