esa-sceva/satcom-chunk-collection huggingface dataset:
# A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms
## 3 RLS Algorithm for Trilinear Forms
\begin{table}
\begin{tabular}{l l l} \hline \hline Initialization: & & \\  Set \(\widehat{\mathbf{h}}_{1}(0)\), \(\widehat{\mathbf{h}}_{2}(0)\), and \(\widehat{\mathbf{h}}_{3}(0)\) based on (52)–(54) & \\ \(\mathbf{R}_{32}^{-1}(0)=\frac{1}{\delta_{1}}\mathbf{I}_{L_{1}}\), \(\mathbf{R}_{31}^{-1}(0)=\frac{1}{\delta_{2}}\mathbf{I}_{L_{2}}\), \(\mathbf{R}_{21}^{-1}(0)=\frac{1}{\delta_{3}}\mathbf{I}_{L_{3}}\) & \\ (Regularization parameters: \(\delta_{1}>0\), \(\delta_{2}>0\), \(\delta_{3}>0\)) & \\ \(\lambda_{1}=1-\frac{1}{KL_{1}}\), \(\lambda_{2}=1-\frac{1}{KL_{2}}\), \(\lambda_{3}=1-\frac{1}{KL_{3}}\) & \\ (Tuning constant : \(K\geq 1\)) & \\  For \(n=1,2,\ldots\), number of iterations : & \\  Compute \(\widehat{\mathbf{H}}_{32}(n-1)\), \(\widehat{\mathbf{H}}_{31}(n-1)\), and \(\widehat{\mathbf{H}}_{21}(n-1)\) based on (27)–(29) & \\  Compute \(\mathbf{x}_{32}(n)\), \(\mathbf{x}_{31}(n)\), and \(\mathbf{x}_{21}(n)\) based on (30)–(32) & \\  Evaluate the error signal \(e(n)\) based on (33) & \\  Compute \(\mathbf{k}_{32}(n)\), \(\mathbf{k}_{31}(n)\), and \(\mathbf{k}_{21}(n)\) based on (49)–(51) & \\  Update \(\widehat{\mathbf{h}}_{1}(n)\), \(\widehat{\mathbf{h}}_{2}(n)\), and \(\widehat{\mathbf{h}}_{3}(n)\) based on (43)–(45) & \\  Update \(\mathbf{R}_{32}^{-1}(n)\), \(\mathbf{R}_{31}^{-1}(n)\), and \(\mathbf{R}_{21}^{-1}(n)\) based on (46)–(48) & \\  Evaluate \(\hat{\mathbf{h}}(n)\) based on (22) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RLS algorithm for trilinear forms (RLS-TF). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithms** & \(\times\) & \(+\) & \(\div\) \\ \hline RLS & \(2L^{2}+2L\) & \(2L^{2}+L+1\) & \\ \hline RLS-TF & \(4L\ +\ 3\left(L_{1}^{2}+L_{2}^{2}+L_{3}^{2}\right)\ +\ 3\left(L_{1}+L_{2}+L_{3}\right)\) & \(3L\ +\ 2\left(L_{1}^{2}+L_{2}^{2}+L_{3}^{2}\right)\ +\ L_{1}\ +\ L_{2}\ +\ L_{3}\ +\ \\ \(\min(L_{1},L_{2},L_{3})\) & \(\min(L_{1},L_{2},L_{3})\) & \\ \hline NLMS & \(2L+2\) & \(2L+3\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational complexity of the RLS-TF algorithm, as compared to the conventional RLS and NLMS algorithms.
## 3 Model Construction and Prediction Results
### Deep Learning Model Construction
A batch size that is too small can lead to slow training and unstable convergence, while a batch size that is too large may require more epochs to reach the same accuracy and may cause the model to converge to some bad local optimal points, although it can speed up the training process. The hyperparameters of the model in this study are determined by the Bayesian optimization algorithm [22] except those that have been described and selected. The model is evaluated by five-fold cross-validation when adjusting the parameters. The dataset is randomly divided into a training set and test set according to the ratio of 7:3. The training set is substituted into the model for parameter determination and training. The test set only participates in the verification and evaluation of the accuracy and prediction performance of the final model. The final model hyperparameter values are shown in Table 2.
# Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis
## 4 Experiments
### DS Evidence Theory Fusion  
Given that this experiment was carried out for four-fault states against fault diagnosis, the identification framework obtained was as follow.  
A, B, C, and D correspond to the gear fault status of normal, single tooth single pitting, single tooth double pitting, and single tooth triple pitting, respectively. The three sensors [E1, E2, E3] were selected for data collection from the gearbox. DS evidence theory is mainly the fusion of multiple sensors. Thus, it can constitute four groups of evidence bodies E, respectively: E = {E1, E2}, E = {E1, E3}, E = {E2, E3}, E = {E1, E2, E3}. After evidence bodies were determined, the fusion rules of Equation (8) were used to fuse the local diagnostic results of two sensors to obtain the fused bearing fault diagnosis rate, the fault diagnosis accuracy of each evidence body before and after fusion. Please refer to Figure 17.  
Figure 16: Accuracy of the three algorithms.  
According to Figure 16, before fusion, the accuracy of the three evidence bodies were 99.93%, 99.91% and 99.94%, respectively; when evidence body 1 was fused with evidence body 2, the accuracy after fusion increased by 0.12% and 0.33%, respectively; when evidence body 1 was fused with evidence body 3, the accuracy after fusion rose by 0.61%, and 0.50%, respectively; when evidence body 2 was fused with evidence body 3, the accuracies after fusion elevated by 0.82% and 0.50%, respectively; when the three evidence bodies were fused simultaneously, the accuracy after fusion was 99.99%, then the accuracies went up by 0.68%, 0.89% and 0.56%, respectively. The accuracy after using fusion theory was significantly higher than before fusion and was more stable than usual, compensating for the low accuracy of traditional single fault diagnosis methods to a large extend. However, when two sensors for fusion were used, the accuracy after fusion was not as good as the accuracy of all three sensors fused at the same time. Hence, the multidimensional information of the gears needs to be detected carefully, which can significantly improve the diagnosis accuracy of gears.
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
## 2 IM Torque Response Optimization
### IM Parameter Uncertainty Effects on the Torque Optimization Method (1)  
To discuss the effects of the uncertainty of the IM parameters on torque optimization Method (1) that is based on Equation (13), assume that the factor (\(L\)) is given by:  
\[L=\left.\frac{dT}{dt}\right|_{k+1}-\left.\frac{dT}{dt}\right|_{k} \tag{22}\]  
The factor (\(L\)) is a measure for the optimization between the two adjacent voltage vectors (\(k,k+1\)) of each sector. When the sign of (\(L\)) is positive, then the (\(k+1\))th voltage vector is optimum and vice versa. Now, the sign of (\(L\)) will be checked against the IM parameter uncertainty.  
Substitute Equation (12) into Equation (22) to obtain the following:  
\[L=k^{*}[sin(\theta_{k+1}-\theta_{ro}-\theta_{x})-sin(\theta_{k}-\theta_{ro}- \theta_{x})] \tag{23}\]  
Taken from the space vector relations of the inverter states, we have that (\(\theta_{k}+\theta_{k+1}\)= \(\pi\)/3) and, using trigonometric relations:  
\[L=k^{*}\left[cos(\frac{\theta_{k}+\theta_{k+1}}{2}-\theta_{ro}-\theta_{x})\right] \tag{24}\]  
This means that the sign of (\(L\)) may have an error when the cosine angle becomes near the 90\({}^{\circ}\) angle and its odd multipliers. Hence, we have:  
\[\frac{\theta_{k}+\theta_{k+1}}{2}-\theta_{ro}-\theta_{x}=\frac{2n+1}{2}\pi \tag{25}\]  
where (\(n\)) is an integer.  
Accordingly, the value of the factor (\(L\)) is around zero. Hence, the torque rates of the vectors are nearly the same. Consequently, the optimization process is not valuable at these points. Typically, the rotor angle estimation error is less than \(\pm\)9%. The corresponding uncertainty change in (\(L\)) is less than 30%. However, the optimization is considered valuable when the value of (\(L\)) is larger than 50% [8]. The optimization process of Method (1) is less sensitive to the uncertainty of the IM parameters.
## 2 ITS Architecture and Applications
### ITS Architecture  
The high level architecture of ITS comprises three main communication domains, _i.e._, the in-vehicle domain, the V2X domain and the infrastructure domain, as shown in Figure 1.  
The in-vehicle domain consists of a connected vehicle equipped with electronic control units (ECUs), wireless-enabled on-board units (OBUs), a trusted platform module (TPM) and an application unit (AU). ECUs collect data about the vehicle's dynamics (e.g., location, speed, heading, vehicle size, _etc._), the context of its immediate environment (e.g., the number of neighboring vehicles, local road  
Figure 1: ITS high level architecture. RSU, road side unit; OBU, on-board unit; AU, application unit; ECU, electronic control unit; TPM, trusted platform module.  
traffic conditions, _etc._) and control its functionality. These ECUs collaborate by exchanging messages with the OBU and AU, and form an in-vehicle network (also known as the on-board network). The AU is responsible for running one or multiple applications, which are offered by remote service providers (SPs), and communicates with other nearby ITS entities using the communication capabilities of the OBU. Each connected vehicle is also equipped with a TPM to enable secure and efficient communications and to manage the different keys and certificates. Finally, a Global Navigation Satellite System (GNSS) unit is used to obtain accurate location information.  
The V2X domain (or _ad hoc_ domain) consists of vehicle OBUs and road-side units (RSUs) deployed along the roads. As shown in Figure 2, the information collected at the vehicles' OBUs, are exchanged in real time with nearby ITS entities (e.g., OBUs, RSUs, _etc._) using various vehicular communication technologies (V2X), including: (i) vehicle-to-vehicle (V2V) communications between neighboring vehicles (or OBUs) using a dedicated short-range communications (DSRC) technology; (ii) vehicle-to-infrastructure (V2I) communications between the surrounding OBUs and RSUs, and _vice versa_; and (iii) vehicle-to-pedestrian (V2P) communications between the OBUs/RSUs and the surrounding pedestrian.  
The infrastructure domain includes the trusted third parties (TTP), such as vehicles manufacturers, the service providers (SPs) and the trust authorities (TA). The fixed RSUs are generally not fully trusted and subordinated by the TA and can be considered as a bridge between the V2X and infrastructure domains. The registration and authentication of these RSUs and OBUs are realized by the TA. The SPs provide applications to the vehicles AUs and are responsible for managing software updates, billing and deliver added-value services. Several applications, such as intersection collision warning, wrong way driving warning and remote diagnostic of vehicles, will exploit the integration of the above network technologies to constitute a connected vehicle. We call these applications: intelligent transport system applications.  
Figure 2: ITS V2X communications.
## 2 Materials and Methods
### Coating Preparation  
Generally, three different types of coating system were needed according to the demand of FCP as shown in Figure 1a. Theoretically, a single-layer coating is needed for the incident side of SHG and exit side of THG to enhance the transmission at 1\(\upomega\) and both 3\(\upomega\), respectively. Meanwhile, a dual-layer coating system is required for the exit side of SHG and incident side of THG to enhance the transmission at 1\(\upomega\) and 2\(\upomega\) (named as dual-wavelength coating system) simultaneously. The dual-layer coating system was fabricated by one layer with high refractive index (HRI) and another layer with low refractive index (LRI) to meet the specific spectral demand. In this study, prepolymer sols with PI concentration of 1%, 2%, and 3% by weight were prepared to fabricate the HRI layer, followed by an addition of colloidal silica nanoparticles to adjust its refractive index to proper value. A 180-s UV irradiation from a Hg lamp whose intensity was measured (EIT UVICURE Plus II) to be 200 mW/cm\({}^{2}\) was then applied to cure the HRI coating. This layer is the key part of this design and is discussed particularly in this work. Colloidal silica nanoparticle sol was then spin-coated under the certain velocity and time to fabricate the LRI layer over the HRI layer to form a dual-layer coating system for all sides except the exit side of THG. This special design could not only maintain the original spectral demand from high-power laser system but also induce 3\(\upomega\) absorption at the sides without 3\(\upomega\) transmission requirement. All layers were deposited under the instructions of theoretical design by TFCalc(r) (v.3.5) (Harvest Lane, Portland) [13; 14; 15], in order to satisfy the requirement shown in Figure 1b.
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 4 Discussion
### Biomechanical Analyses  
There has been an increasing number of biomechanical studies in recent decades. This may be accounted for by the development of kinematic and kinetic analyses, the first game-changing technologies that later became the gold standard.  
Biomechanical studies based on state-of-the-art gait analysis are substantial to interpret motion patterns at the knee joints. A number of studies show that excessive load at the patellofemoral joint leads to increased patellofemoral contact force, which can result in altered tibial and femoral kinematics, both in the frontal and transverse planes [30; 31]. Indeed, there is still a debate on these mechanisms, for which biomechanics can provide relevant evidence. To set standardized approaches to the kinematics and kinetics of the major joints of the lower limbs, a number of protocols have been developed and compared [17; 18]. Known limitations include adequate data collection, skin motion artifacts, anatomical landmark identification, and tracking [17]. Appropriate marker positioning is essential to determine the joint kinematics and kinetics, and thus to work out relevant patterns within the gait cycle. The large majority of gait analysis protocols calculate motion of the tiboformal joint in the frontal and sagittal planes, and only a few on the transverse plane. Since skin markers should track motion of the underlying bones [5], this assumption is particularly critical for the patella, because of its small motion, small dimension, and the large gliding of the skin. Metal probes have been implanted into the patella to obtain a rigid connection with the markers and thus a reliable joint motion, but this was possible only in in vitro studies [5; 32; 33; 34; 35; 36; 37]. Thus, adapting the results of these studies to real patellofemoral joint function and PFP is difficult. Nevertheless, there are studies reporting the placement of skin markers on the patella [38; 39; 40; 41; 42; 43; 44; 45]; careful full-text readings, however, revealed that patella movements apparently were not tracked in these articles. The present observation also points out the importance of consistent reports in the literature.
###### Abstract  
Optical packet switching (OPS) networks and its subsystems, like the burst-mode receiver, are an essential technology currently used in passive optical networks (PONs). Moreover, OPS may play a fundamental role on future hybrid optical circuit switching (OCS)/OPS networks and datacenter networks. This paper focuses on two fundamental subsystems of packetized optical networks: the digital coherent burst-mode receiver and the electro-optical switch. We describe and experimentally characterize a novel digital coherent burst-mode receiver that makes uses of the Stokes parametrization to rapidly estimate the state of polarization (SOP) and optimize the equalizer convergence time. This burst-mode receiver is suitable for optical packetized networks that make use of advanced modulation formats such as quadrature amplitude modulation (QAM). We study the suitability of (Pb,La)(Zr,La)O\({}_{3}\) (PLZT) optical switches for amplitude-variable coherent polarization division multiplexing (PDM) 16 QAM modulation format and demonstrate a switching capacity of 10.24 Tb/s/port. We demonstrate a full 2 \(\times\) 2 OPS node with a control plane capable of solving packet contention by means of packet dropping or buffering with a switching capacity of 10.24 Tb/s/port. Finally, we demonstrate the operation of the 2 \(\times\) 2 OPS node with a record capacity of 12.8 Tb/s/port plus 100 km of dispersion-compensated fiber transmission.  
Keywords:optical communications; optical packet-switched networks; coherent burst-mode receivers; PLZT optical switches +
Footnote †: journal: _Photonics_
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Clustering
#### (3) LEACH-EECHS  
[PERSON] in [19] proposed the LEACH Energy Efficient Cluster Head Selection (LEACH-EECHS) protocol that improves the method of CH selection by increasing opportunity for nodes that are geographically close to the center of the cluster. It considers the average energy consumption of the nodes and the network density for the CH selection parameters. Through simulation, it can be shown that this protocol can provide a longer network lifetime than LEACH protocol.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 3 The TMMNN
### Embedding Size Adjustment Layer  
The embedding size adjustment layer saves the current embedding size and user-item frequency. When receiving a user or item, this layer converts the occurrence frequency of the user or item and the current embedding dimension into the embedding vector and one-hot encoding, respectively. Then, the layer concatenates them and feeds them into the multi-layer perceptron. Through the softmax activation function, the adjustment strategy, i.e., whether the embedding vector is expanded or maintained, is confirmed. Finally, the layer sets the dimension size of the embedding vector as the output. The process is described in detail below.  
First, define the state \(s=(f,e)\), which contains the user and item occurrence frequency \(f\) and the current embedding dimension \(e\). The embedding adjustment layer determines how to adjust the embedding vector size of users and items through the two most basic pieces of information: \(f\) and \(e\). Then, it should be noted that the model contains two embedding dimension adjustment layers with the same structure for users and items, respectively. For brevity, in the following part, we take the user's embedding dimension adjustment layer as an example for introduction.  
In order to determine the user's current state, the embedding dimension adjustment layer needs to obtain the user's appearance frequency and the current user's embedding dimension. Considering that the network cannot directly handle integer values, the user frequency \(f\) of integer values needs to be converted into the embedding \(f_{emb}\)  
Figure 2: Overview of the TMMNN.  
and the current user embedding size \(e\) needs to be converted into a one-hot encoding \(e_{one-hot}\) with dimension \(n\), where \(n\) represents the number of candidate embedding size. By concatenating the two elements mentioned above, the embedding \(z\) is obtained from the specific equation, which is shown in Equation (1):  
\[z=[f_{emb}:f_{one-hot}] \tag{1}\]  
In order to determine adjustment policy, the embedding dimension adjustment layer needs to analyze the current user state. Therefore, the spliced embedding \(z\) is sent to the multi-layer perceptron for calculation, and finally, the adjustment policy is obtained with the softmax activation function. There are two adjustment options: enlarge or keep it unchanged. Enlarging means that the user's embedding dimension needs to be expanded from \(d_{k}\) to \(d_{k}+1\), while keeping it unchanged means maintaining the user's current embedding dimension.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Position
#### 3.1.2 Y-Axis  
For the second subset of positions in Figure 6, the ABH was placed in the center of the x-axis, but moved in the y-direction of the panel (P11, P12, P13). The resonance peaks \(f_{1,1}\approx 85\) Hz and \(f_{\rm ABH}\approx 120\) Hz exhibit similar effects to those shown in Figure 5. However, due to a change in direction, the excited even mode shifts from the first peak \(f_{2,1}\approx 150\) Hz to the second peak \(f_{1,2}\approx 190\) Hz. The positioning of the ABH more towards the outer side still results in an increase in the frequency. On the other hand, placing the ABH at the center of the x-axis leads to a decrease in the normalized mean surface velocity \(\overline{v}_{x,y}\) at the third panel resonance frequency \(f_{3,1}\approx 250\) Hz, regardless of the investigated positions on the y-axis (P11, P12, P13).  
Figure 5: Comparison of the normalised mean surface velocity \(\frac{\overline{v}_{xx}}{p}(f)\) for the x-row of different ABH positions P11, P21, P31.  
Figure 6: Comparison of the normalized mean surface velocity \(\frac{\overline{v}_{xx}}{p}(f)\) for the y-row of different ABH positions P11, P12, P13.  
In respect to the previous descriptions, the response of position P12 proves to be the best in Figure 6. It shows a moderate frequency shift for \(f_{1,1}\approx 85\) Hz. For P12, the emphasized even mode \(f_{1,2}\approx 190\) Hz appears at a lower frequency compared to P13.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 2 Routing in the Data Center  
To have adequate routing and forwarding, it is imperative to fully exploit the topological characteristics of fat trees. Some basic requirements should be satisfied: forwarding loops avoidance, rapid failure detection, efficient network utilization (e.g., spanning-tree solutions are not acceptable), routing scalability (in addition to physical scalability). Based on this, a layer two fabric will not scale, and therefore, layer three (e.g., routing) solutions should be adopted.  
Another important requirement is virtual machines and container migration between different servers keeping their IP addresses unchanged. This imposes that a transparent layer-two fabric must connect the data center, implemented as an overlay over the layer-three environment [5].  
In principle, being the data center a single administrative domain, the candidates to fulfill the routing role are popular link-state IGPs. However, as they have been designed for arbitrary topologies, the flood of link-state advertisements may suffer from scalability issues.  
Therefore, the possible solutions should entail reducing the message flooding, exploiting the topology knowledge, or using other routing algorithms. In this regard, the following routing protocols will be considered in this work: BGP with a specific configuration for the data center, link-state algorithms with flooding reduction, and ongoing Internet Engineering Task Force (IETF) efforts, namely Routing in Fat Trees (RIFT) and Link State Vector Routing (LSVR), which are leveraging link-state and distance-vector advantages to design specific routing algorithms for data centers.  
In this paper, we only considered distributed control plane solutions, i.e., routing protocols. Consequently, logically centralized Software-Defined Networking (SDN) solutions will not be analyzed.