esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quantile Regression Neural Network
### Quantile Regression Based on Neural Network  
A quantile regression neural network is employed to predict the quantile of traffic demand in the future en route sector. Subsequently, the Sigmoid function is utilized as the hidden layer function of the neural network, and the predicted quantile of traffic demand in the en route sector is used as the input variable for kernel density estimation to achieve the probability density prediction of traffic demand in the en route sector. The expression of the quantile regression model of the neural network is shown in Formula (5).  
\[Q(\theta|X)=f[x,\mathbf{u}(\theta),\mathbf{v}(\theta)]=\sum_{j=1}^{J}\left\{ \frac{2v_{j}(\theta)}{1+e^{-2\frac{n}{\mu_{1}}v_{j}(\theta)X_{i}}-v_{j}( \theta)}\right\} \tag{5}\]  
In Formula (5), \(\theta\) is the quantile, and \(\mathbf{u}(\theta)=\left\{u_{ij}(\theta)\right\}_{i=1,\ 2,\ \ldots,\ n;\ j=1,\ 2,\ \ldots,\ j}\) is the weight matrix to be estimated between the input layer and the hidden layer; \(\mathbf{v}(\theta)=\left\{v_{j}(\theta)\right\}_{j=1,2,\ldots,J}\) is the weight vector between the hidden layer and the output layer. In order to achieve the final parameter estimation of Formula (5), the objective function can be optimized using Formula (6):  
\[\widetilde{E}_{\theta}=\frac{1}{N}\sum_{i=1}^{N}\rho_{\theta}\{Y_{i}-f[x, \mathbf{u}(\theta),\mathbf{v}(\theta)]\} \tag{6}\]  
However, in order to keep the trained neural network from over-fitting, a penalty parameter term is added to the objective function, and a new objective function is obtained as shown in Formula (7).  
\[E_{\theta}(\mathbf{u}(\theta),\mathbf{v}(\theta))=\widetilde{E}_{\theta}+ \lambda_{1}\underset{i,j}{\sum}u_{ij}(\theta)+\lambda_{2}\underset{i,j}{\sum} v_{j}(\theta) \tag{7}\]  
In Formula (7), \(\lambda_{1}\) and \(\lambda_{2}\) are penalty parameters. By determining the optimal penalty parameters, the model can be effectively prevented from over-fitting empirical data, thereby reducing prediction errors and improving accuracy. Formula (7) can be optimized to obtain the optimal estimates of \(\widetilde{\mathbf{u}(\theta)}\) and \(\widetilde{\mathbf{v}(\theta)}\) for \(\mathbf{u}(\theta)\) and \(\mathbf{v}(\theta)\). Then, \(\widetilde{\mathbf{u}(\theta)}\) and \(\widetilde{\mathbf{v}(\theta)}\) are substituted into Formula (5) to obtain the conditional quantile estimation function of the response variable.
# Glass Beads for Road Markings: Surface Damage and Retroreflection Decay Study
## 4 Results  
### Abrasive Study Outcome  
The outcomes of the laboratory testing of \(\mathrm{R_{L}}\) are provided in Table 4, along with the service life prediction parameters calculated according to Equation (1). The results are presented visually in Figure 2 (the initial data point was discarded for clarity; note that it was not used for calculations of the service life and \(R_{a}\) was substituted instead). The measured maximum \(\mathrm{R_{L}}\) were lower than those typically achieved, which was expected due to the purposeful inadequate GB embedment; this was particularly evident in sample _HI_, but sample _SP_ also yielded \(\mathrm{R_{L}}\) lower than typically reported from the field [32; 33; 63].  
While looking at the results, one should pay particular attention to much more rapid \(\mathrm{R_{L}}\) deterioration in the case of _high index_ GB (RI 1.9, sample _HI_) as compared to other GB types. Despite the high initial \(\mathrm{R_{L}}\), the service life was only within the range furnished by GB with RI 1.5 (sample _ML_). This was also reflected in the high absolute value of the calculated coefficient of \(\mathrm{R_{L}}\) decay \(C\) and can be seen in Figure 2, which shows that a drop of \(\mathrm{R_{L}}\) to \(150\,\mathrm{mcd/m^{2}/lx}\) occurred after similar number of drum rotations as in the case of GB with much lower RI (_large_ GB, sample _ML_). As can be seen in the images provided in the section below, this was the result of surface damage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Coefficient of Retroreflected Luminance (mcd/m2/lx)**} \\ \hline
**Glass Bead Type** & **Standard** & **Large** & **Premium** & **High Index** \\ \hline Sample Code & _SF_ & _ML_ & _SP_ & _HI_ \\ \hline Initial & 278 & 413 & 892 & 715 \\
168 rotations & 255 & 377 & 511 & 681 \\
840 rotations & 249 & 377 & 548 & 391 \\
1680 rotations & 224 & 321 & 401 & 426 \\
3360 rotations & 191 & 202 & 314 & 326 \\
6720 rotations & 136 & 160 & 208 & 193 \\
16,800 rotations & 116 & 127 & 166 & 119 \\ \(R_{m}\) (maximum measured \(\mathrm{R_{L}}\)) (\(\mathrm{mcd/m^{2}/lx}\)) & 278 & 413 & 892 & 715 \\ \(R_{a}\) (calculated maximum \(\mathrm{R_{L}}\)) (\(\mathrm{mcd/m^{2}/lx}\)) & 246 & 348 & 534 & 528 \\ \hline \(C\) (coefficient of exponential \(\mathrm{R_{L}}\) decay per 1000 rotations) & \(-0.051\) & \(-0.070\) & \(-0.083\) & \(-0.099\) \\ \(\mathrm{R^{2}}\) (exponential line fit) & \(0.85\) & \(0.80\) & \(0.73\) & \(0.85\) \\ Service life estimate (rotations \(\times\) 1000) 1 & 9.6 & 12.0 & 15.4 & 12.7 \\ \hline \hline \end{tabular}  
* Service life is assumed to end when \(\mathrm{R_{L}}<150\,\mathrm{mcd/m^{2}/lx}\), calculated according to Equation (1).  
\end{table}
Table 4: Abrasion test: loss of \(\mathrm{R_{L}}\) and service life parameters calculated per Equation (1).
###### Abstract
In addition, according to some researchers, within the ultra-dense heterogeneous networks, the incorporation of the power domain non-orthogonal multiple access (PD-NOMA) technique, orthogonal multiple access (OFDMA) techniques has been identified to provide an improved solution to achieve proficient resource allocation with enhanced capacity and load balancing in ultra-dense heterogeneous networks [5]. The PD-NOMA scheme superposes coding at the transmitter, and the base stations transmit superposed signals to multiple users in the power domain. In addition, PD-NOMA does not require significant alterations to the existing 3 GPP LTE architecture, which helps in achieving the implementation of low-complexity solutions. PD-NOMA implements successive interference cancellation (SIC) [6] to decode the signal one by one and concurrently yield a high SE while allowing some degree of multiple access interference at the receivers. The downlink PD-NOMA with SIC enhances the network performance, but the complexity associated with both the capacity and user throughput still continues with the increased network densities, as each user has to decode the neighboring user signal, which increases the computational complexity and leads to longer delays with increased channel state information feedback overhead at the BS. Thus, the PD-NOMA scheme also shows several challenges requiring mitigation through efficient solutions [7].
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.3 Scalability  
Scalability refers to the ability of an HIE system to accommodate more healthcare facilities. Indeed, the emergence of COVID-19 required an exponential increase in the number of health organizations and centers along with the need of patient informationexchange among them. Unfortunately, some health entities have failed due to the weakness of their systems in integrating and expanding with others [44]. We distinguish between two types of scalability challenges when integrating AI and blockchain into HIE systems:  
* Horizontal scalability: also known as scaling out, it involves adding more health entities to the consortium. This allows for the handling of more transactions and effectively increases the sharing system capacity in distributing the workload across multiple networks. However, horizontal scalability will increase, on one hand, the communication overhead in the blockchain network due to the large number of integrated nodes and its need to ensure that all nodes agree on the state of the blockchain [45,46]. On the other hand, the huge number of transactions will increase the computational complexities of the AI models [47].
* Vertical scalability: also known as scaling up, it involves increasing the capacity of each health entity by upgrading their hardware (e.g., more powerful processors, increased memory). On one hand, vertical scalability can enhance the computational power, storage, and network bandwidth of existing nodes to handle more transactions and data. On the other hand, such hardware upgrades can be expensive and may not be feasible for all entities in a decentralized health network [48]. Furthermore, vertical scalability is subject to a single point of failure, and when a powerful node fails, it can significantly impact the entire health network [49,50].  
To overcome both types of scalability challenges, researchers have proposed a significant number of strategies such as mini blockchain, short-term data sharing, cashing systems, the Chord algorithm, tokenization, and sharding. In [51], the authors gave an overview of different solutions for scalability issues while classifying them into two main categories: storage optimization and blockchain redesign.
## 1 Introduction  
Recently, the number of Internet accesses through smartphones has surpassed the number of accesses through desktop PCs [2]. This implies that a smartphone is no longer an office assistance tool but an essential device in everyday life. It is important to note that, as of 2012, the market share of Android platform has reached about 60\(\%\) of the global smartphone operating systems [2]. This fact asserts that improving the performance of Android based smartphones is not trivial but a meaningful effort.  
[PERSON] _et al_. recently have shown that the storage I/O is one of the key factors that govern the overall system performance in smartphones [3]. There is much value in generating accurate workload to properly measure and analyze the I/O performance of Android based smartphones.  
Android I/O stack consists of DBMS, file system, I/O daemon, and I/O scheduler. The Android platform uses SQLite [4], EXT4 [5], mmcqd, and CFQ scheduler [6] as DBMS, file system, I/O daemon, and I/O scheduler, respectively. Android applications use SQLite to maintain information in persistent manner. The default DBMS of Android, SQLite, generates 90\(\%\) of the entire write operations in the Android platform [7,8]. [PERSON] _et al_. revealed that Android I/O behavior suffers from excessive inefficiency, which is caused by uncoordinated interaction between EXT4 and SQLite, known as Journaling of Journal [8]. They achieved 300\(\%\) improvement in SQLite DB performance through proper optimization. Some of representative characteristics of I/O workload in Android are as follows: (i) 4 KB random write followed by fsync() and (ii) frequent creation and deletion of small (less than 12 KB) short-lived files.  
The applications, often referred as \"apps\", in Android based smartphones, generate unique I/O requests. Existing I/O workload generators and trace capturing tools are not designed to generate nor to capture this unique I/O workload of Android apps. This paper introduces the Android Storage Performance Analysis Tool, _AndroStep_, which is specifically tailored for characterizing and analyzing the behavior of the I/O subsystem in Android based devices. AndroStep consists of workload generator (Mobibench), workload analyzer (MOST: Mobile Storage Analyzer), and workload replayer (Mobigen).  
The remainder of the paper is organized as follows: Section 2 explains the existing workload generation tools and presents analysis of Android I/O workload. We introduce AndroStep, Android Storage Performance Analysis Tool, in Section 3. Section 4 presents the results of experiments with AndroStep. Section 5 concludes the paper.
## 2 Methods
### Design and Fabrication of the Combiner
#### 2.3.2 Fabrication and Measurement  
The fabricated four-way combiner PCB (FR4 substrate and 35 um copper layer) measured 300 \(\times\) 200 \(\times\) 10 mm. Performance characterization was performed using an E5071 network analyzer (Keysight Technologies, Santa Rosa, CA, USA). The test bench like as Figure 6, and performance evaluation of the P5 port is achievable by interchanging the connections between P4 and P5 ports. Final, the test results summarized in Table 6.  
The results in Table 6 show an insertion loss of 6.30-6.40 dB, a port VSWR of \(<\)20 dB, and isolation \(\succ\)-25 dB, with the loss significantly reduced compared to conventional designs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **S11/S22/S33/S44** & **S21/S31/S41/S51** & **S23/S24/S25** & **S34/S35 (dB)** \\  & **(dB)** & **(dB)** & **(dB)** & **S34/S35 (dB)** \\ \hline Simulation & \(<\)\(-\)25 & 6.30–6.32 & \(<\)\(-\)25 & \(<\)\(-\)25 \\ results & \(-\)26.5 & 6.38–6.40 & \(-\)27 & \(-\)28.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Four-way combiner test results @127–129 MHz.  
Figure 5: The PCB of Hierarchical four-way combiner. (**a**) Top view; (**b**) Cross-section.  
Figure 6: Four-way combiner and testing bench.
## 2 Basic Characteristics of Novel SCPC Connection  
### Construction of Connection  
The novel SCPC hybrid joint is a new ductile connection, the deformations of precast beams and columns are within the elastic range and the inelastic damage of beams and column is minimal. The inelastic damage is usually concentrated in an easily replaceable energy dissipation device. Figure 1a illustrates the detailed construction of the novel joint. One duct is arranged at the central axis of the precast beam section and unbonded tendons are arranged along the central axis of the beam horizontally. PT tendons not only assemble the precast beam and column in the construction stage but also resist the bending moment in the use stage under the seismic actions. If the moment is large beyond the critical opening moment, the gap-opening of the hybrid joint has occurred. The length and the stresses of PT tendons increase subsequently. The residual deformation is eliminated after the earthquake due to the characteristic of the self-recovering of prestressed tendons provided. Shear force at the beam end is undertaken by the friction at the beam-column interface caused by tendons and the friction bolts. The steel plate is shop fabricated in the column through the embedded part to reduce the local concrete damage. As shown in Figure 1a, the hysteretic dampers are installed at the upper and lower part of the beam end and welded between the steel connectors and the steel plates. The yield point of steel used in hysteretic dampers is low and the yield point is only 235 N/mm\({}^{2}\). In addition, the slot area of long strip on the steel plate will firstly produce in-plane plastic deformation. Hence, the hysteretic damper can enter the yielding state faster under the seismic loadings and then dissipate seismic energy by generating large plastic deformation after yielding. Compared with the out-of-plane buckling hysteretic damper, the in-plane buckling hysteretic damper has the advantage of large initial stiffness, high bearing capacity and good energy dissipation capability. The four steel connectors are bolted on the two sides of the beam and every steel connector uses two high strength friction bolts. The other side of the steel connector is welded with the hysteretic damper, in order to fix the damper and limit the damper's displacement. Therefore, the mechanical principle of the novel connection is--shear force at the beam end is taken by friction and the moment is undertaken by unbonded prestressed tendons and hysteretic dampers, PT tendons provide the self-recovering and hysteretic dampers improve energy consumption characteristics.
###### Abstract  
Cutting beech plywood using abrasive water jet (AWJ) technology represents a significant area of research due to increasing demands for precision, quality, and environmental sustainability in manufacturing processes within the woodworking industry. AWJ technology enables non-contact cutting of materials without causing thermal deformation or mechanical damage, which is crucial for preserving the structural integrity and mechanical properties of the plywood. This article investigates cutting beech plywood using technical methods using an abrasive water jet (AWJ) at 400 MPa pressure, with Australian garnet (80 MESH) as the abrasive material. It examines how abrasive mass flow rate, traverse speed, and material thickness affect AWJ lag, which in turn influences both cutting quality and accuracy. Measurements were conducted with power abrasive mass flow rates of 250, 350, and 450 g/min and traverse speeds of 0.2, 0.4, and 0.6 m/min. Results show that increasing the abrasive mass flow rate from 250 g/min to 350 g/min slightly decreased the AWJ cut width by 0.05 mm, while further increasing to 450 g/min caused a slight increase of 0.1 mm. Changes in traverse speed significantly influenced cut width; increasing the traverse speed from 0.2 m/min to 0.4 m/min widened the AWJ by 0.21 mm, while increasing it to 0.6 m/min caused a slight increase of 0.18 mm. For practical applications, it is recommended to use an abrasive mass flow rate of around 350 g/min combined with a traverse speed between 0.2 and 0.4 m/min when cutting beech plywood with AWJ. This balance minimizes jet lag and maintains high surface quality comparable to conventional milling. For thicker plywood, reducing the traverse speed closer to 0.2 m/min and slightly increasing the abrasive flow should ensure clean cuts without compromising surface integrity.  
abrasive water jet (AWJ); abrasive water jet lagging; traverse speed; abrasive mass flow rate +
Footnote †: journal: Applied sciences
###### Abstract  
Recently, multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.  
MIMO; OFDM; Riemannian manifold; optimization Article
###### Abstract  
The light scattering properties of seawater play important roles in radiative transfer in the ocean and optically-based methods for characterizing marine suspended particles from in situ and remote sensing measurements. The recently commercialized LISST-VSF instrument is capable of providing in situ or laboratory measurements of the volume scattering function, \(\beta_{p}(\psi)\), and the degree of linear polarization, \(DoLP_{p}(\psi)\), associated with particle scattering. These optical quantities of natural particle assemblages have not been measured routinely in past studies. To fully realize the potential of LISST-VSF measurements, we evaluated instrument performance, and developed calibration correction functions from laboratory measurements and Mie scattering calculations for standard polystyrene beads suspended in water. The correction functions were validated with independent measurements. The improved LISST-VSF protocol was applied to measurements of \(\beta_{p}(\psi)\) and \(DoLP_{p}(\psi)\) taken on 17 natural seawater samples from coastal and offshore marine environments characterized by contrasting assemblages of suspended particles. Both \(\beta_{p}(\psi)\) and \(DoLP_{p}(\psi)\) exhibited significant variations related to a broad range of composition and size distribution of particulate assemblages. For example, negative relational trends were observed between the particulate backscattering ratio derived from \(\beta_{p}(\psi)\) and increasing proportions of organic particles or phytoplankton in the particulate assemblage. Our results also suggest a potential trend between the maximum values of \(DoLP_{p}(\psi)\) and particle size metrics, such that a decrease in the maximum \(DoLP_{p}(\psi)\) tends to be associated with particulate assemblages exhibiting a higher proportion of large-sized particles. Such results have the potential to advance optically-based applications that rely on an understanding of relationships between light scattering and particle properties of natural particulate assemblages.  
marine optics; inherent optical properties; volume scattering function; degree of linear polarization; marine particles; light scattering measurements; LISST-VSF instrument +
Footnote †: journal: Applied Physics Letters