esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Tensor Modeling of Structured Sparse SAR Imaging
### Kronecker Dictionary  
For a vector signal \(\mathbf{y}\), we consider that a \(K\)-sparse representation is existed on a dictionary \(\mathbf{A}\), if the following relationship is satisfied:  
\[\mathbf{y}=\mathbf{A}\mathbf{s},\quad\text{s.t.}\left\|\mathbf{s}\right\|_{0}\leqslant K, \tag{7}\]  
where \(\left\|\mathbf{s}\right\|_{0}\) is the \(l_{0}\)-norm of \(\mathbf{s}\), indicating the number of nonzero elements in \(\mathbf{s}\); \(K\) is defined as sparsity. Usually, \(K\ll M\).  
Because the number of rows is less than the columns, this is a underdetermined system which means there is no unique solution of \(\mathbf{s}\). However, a sparse solution can be obtained if the signal has the feature of sparsity and the following condition is satisfied [30]:  
\[K<\frac{1}{2}\left(1+\frac{1}{\mu\left(\mathbf{A}\right)}\right), \tag{8}\]  
where \(\mu\left(\mathbf{A}\right)=\max\limits_{i\
eq j}\left\|\mathbf{a}_{i}^{T}\mathbf{a}_{j}\right\|\), indicating the coherence coefficient of dictionary \(\mathbf{A}\).  
If the dictionary has Kronecker constraint, i.e., \(\mathbf{A}=\mathbf{A}_{N}\otimes\mathbf{A}_{N-1}\otimes\cdots\otimes\mathbf{A}_{1}\), the coherence coefficient can be defined as  
\[\mu\left(\mathbf{A}\right)=\max\left\{\mu_{1},\mu_{2},\ldots,\mu_{N}\right\}, \tag{9}\]where \(\mu_{n}=\mu\left(\mathbf{A}_{n}\right),\,n=1,2,\ldots,N\). It is shown from (9) that for a Kronecker dictionary, the overall coherence depends merely on the factor matrix with the greatest coherence [34].  
The previous studies [35; 36] indicated that, for the signal recovery problem with the same sparsity level, the reconstruction procedure based on Kronecker dictionary has much less strict requirements for coherence than the classic matching pursuit method. In other words, the former has the higher recovery bound [26]. In addition, by using the structural information of signal, the reconstruction algorithm based on Kronecker dictionary performs robustly under the condition of high coherence.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 4 Requirements of a Collaborative Bin-Picking Robotic Cell
### Software Requirements  
* Object recognition and pose estimation [28] must have a fairly high success rate, thus justifying the automation of manual bin-picking;
* The computer vision system needs to be able to predict if a potential failure may occur. For example, it needs to recognize whether an object cannot be grasped because it is entangled with a neighboring object, whether no objects are recognized but the bin is not empty, or whether a certain pose cannot be reached without collisions. In these situations, the robot will ask for external help;* The system must perform collision avoidance [29]: in particular, collisions with the bin edges and corners must be predicted and, if possible, path planning must be modified accordingly. In addition, collisions with the operators must also be avoided [30];
* For some collaborative bin-picking applications, it might also be necessary for the computer vision system to recognize the position of the bin and, in particular, to recognize if the bin has been moved from its previous position (this can happen when removing an empty bin and replacing it with a new one and the parameters for collision avoidance must be updated);
* The presence of an operator inside the workcell must be detected promptly to ensure his/her safety.
###### Abstract  
The most used global sensitivity analysis (GSA) method is based on variance. This is performed using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS). It requires a large sample to obtain accurate estimates. Density-based methods, such as the GSA PAWN, have been developed to reduce the sample size without compromising the result. PAWN is simpler than other methods because it uses cumulative density functions (CDF) instead of probability density. This method has been widely used in areas such as environmental engineering with very good results, reducing computation time. However, its use in structural engineering is incipient. The PAWN method was used to classify the design variables of the isolation system in relation to their sensitivity, and in relation to the seismic response of industrial storage racks. The above was analyzed in terms of the effectiveness of each variable to reduce the seismic demand using a novel base isolation kinematic device (BIKD). Racks with different combinations of their structural parameters such as the number of storage levels, the height between them, and isolation period, among others, were studied. The dimensions of the racks were chosen to match those that would later be experimentally tested on shaking table. An earthquake whose response spectrum matched the design spectrum of current Chilean regulations, was considered as seismic forcing. The maximum base shear load, the displacement of the top level of storage and the floor drift were considered as target responses to be studied. Fixed base racks (FBR), as reference, and base-isolated racks (BIR) were analyzed. The results showed the effectiveness of using the BIKD system in reducing all three-target responses up to one order of magnitude. Additionally, it was determined that the parameters that have the greatest influence on the response correspond to the number of storage levels and the height between them, both for FBR and BIR.  
kinematic isolation; frictional dissipation; time-history analysis; PAWN global sensitivity analysis 2021 24 November 2021 6 December 2021
# Resource-Efficient Multicast URLLC Service in 5G Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
ACK  Acknowledgment  
ADMM  Alternating Direction Method of Multipliers  
DL  Downlink  
FF-C2  Full Featured Combine-2  
HARQ  Hybrid Automatic Repeat Request  
IU  Iterative Update  
gNB  next generation NodeB  
MCS  Modulation and Coding Scheme  
M-MIMO  Massive Multiple-Input, Multiple-Output  
mMMSE  multicast Minimum Mean Square Error  
mMRT  multicast Maximum Ratio Transmission  
MU  Multiplicative Update  
mURLLC  multicast Ultra-Reliable Low-Latency Communications  
mZF  multicast Zero Forcing  
NACK  Negative Acknowledgment  
PLR  Packet Loss Ratio  
QoS  Quality of Service  
RB  Resource Block  
RC-C2  Reduced Complexity Combine-2  
SBFC  Successive Beamforming-Filter Computation  
SCA  Successive Convex Approximations  
SDR  Semi-Definite Relaxation  
SINR  Signal to Interference plus Noise Ratio  
SRS  Sounding Reference Signal  
TDD  Time Division Duplex  
TX  Transmission attempt  
UE  User Equipment  
UL  Uplink  
URLLC  Ultra-Reliable Low-Latency Communications
## 3 Hardware/Software Description of the Developed Workbench
This experimental practice complements the training in two ways: on the one hand, it provides a basic knowledge about the usefulness of an IDE; and on the other hand, it shows that a Simulink block, despite its graphical appearance, can contain errors. The proposed mockup provides the following advantages:  
Figure 4: Test bench software procedure description: (1) First the model is developed in Simulink. (2) Blocks required in order to manage hardware and I/O features are added. (3) The code is generated automatically. (4) The generated source code remains accessible to be modified or verified. * High-level software capabilities: In alignment with the dual purpose of the HIL mockup, which includes prototyping and setup, the provided software functions cater to a wide range of needs, offering programming capabilities through graphical blocks. These blocks cover a wide range of functions, from basic I/O management to the creation of real-time tasks. Furthermore, advanced low-level functions, such as direct memory access (DMA) and interrupt functions, are seamlessly integrated into the graphical block functionalities. Importantly, users are not required to manage these hardware aspects. * The available functions are directly related to the name of the category. Under the tag \"analog I/O\", you can find the blocks for analog I/O. In the communication category, you can find all available digital channels, such as inter-integrated circuits (I2C), the serial peripheral interface (SPI), and so on. * The device configuration contains the target setup blocks responsible for generating code for the HIL mockup hardware. Under the tag \"digital I/O\", you can find external interrupts, general digital lines, pulse width modulation (PWM) output, and quadrature encoder inputs. In addition, under the miscellaneous heading, you can find the delay and a block for retrieving the elapsed time (in nanoseconds). This block is helpful for measuring the execution times of any part of the model. Under the label \"Multitasking\", you can find the option to create and use additional real-time-based tasks and to use idle time. * The execution of any real-time task can be controlled with an additional block, pausing or resuming it as necessary. The computer page label contains blocks for USB configuration and use of the communication channel to exchange data with the HIL mockup when connected to the computer.
## 3 Methodology
### Implementation
#### 3.5.1 Curtailment  
The (expected) curtailment profile is determined day-ahead and three variations are implemented: option 1, option 2a, and option 2b. Option 1 assumes the DSO will notify a market party that curtailment is needed during every timestep in which a predetermined congestion threshold would be (partially) exceeded. This could for example be used to facilitate larger amounts of PV in a congested region by curtailing the PV during the limited periods of time it runs at peak production. For option 2 (both a and b) it is assumed that the DSO will provide a selected curtailment window. This is for example applicable if, during some periods of time, sufficient load is present, thus curtailment of the PV installation is not necessary for a whole afternoon, but for a window of a few timesteps only, implying the DSO does not need to obtain flexibility for every peak. Option 2 is split in 2a and 2b. For option 2a no flexibility has been activated in the previous week and for option 2b flexibility has been activated in the previous week. This differentiation is of importance for baselining methods using historical data, as this historical data includes  
Figure 2: Overview of the implementation flow of the different baselining methodologies.  
the previously activated flexibility, thus yielding a method vulnerable to the user dilemma, described in Section 2.1.5.
# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
###### Abstract  
Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.  
bearing fault; convolution neural network; fault diagnosis system; induction motor; rotor fault +
Footnote †: journal: Applied Sciences
# The Role of Powered Surgical Instruments in Ear Surgery: An Acoustical Blessing or a Curse?
## 4 History of Otological Surgical Procedures
### Pre-MES Procedures  
Once tools were available for removing bone, humans opened holes in skulls for various purposes. The objective in prehistoric and early historic times could have been to release evil spirits, while other  
Figure 3: (**a**) Sonopet® Omni, (model UST-2001 Ultrasonic Surgical Aspirator, Stryker Corporation, Kalamazoo, Michigan, USA). (**b**) Bone removal view (**c**) Aspirator view.  
procedures may have been to relieve a buildup of pressure within the skull. This pressure buildup could be due to the presence of excess fluids caused by internal hemorrhaging or infection. Prior to the development of antibiotics and the appropriate tools, the main concern of otologists was the treatment of middle ear infections that resulted in pus accumulating within the ear, and having nowhere to go, were potentially life threatening. Such procedures often involved simple incisions of the tympan membrane or abscesses located inside or outside of the ear. The French physician [PERSON] was the first to be credited in the 16 th century with making a surgical incision to drain such an infection [19].  
The first confirmed mastoidectomy is credited to the French surgeon [PERSON] in 1736 [16] and thereafter the mastoidectomy became a standard, but not universally practiced part of ear surgery from around 1860 [19]. A mastoidectomy was sometimes performed to allow draining of the pus and cleaning out of the infected area. However, removing the mastoid bone using the hand-drill-like trepan or by chipping away at the bone using chisels and gouges did not offer the level of control needed. Some patients were reported to have developed post-operative meningitis and subsequently died, an outcome which would have been due to removing too much bone and tissue. The inadequacy of the pre-powered age surgical instruments led to the mastoidectomy falling out of favor.  
It should be noted that since these mastoidectomies were often performed as a life saving measure with little to no regard to the potential damage to internal ear anatomy and hearing caused by the surgical instruments. It is not a stretch, however, to assume that those patients who survived probably did have hearing loss. Eventually, [PERSON] developed improvements to the mastoidectomy with the aim of preserving the middle ear anatomical structures and better results were achieved. [PERSON]'s improvements and the introduction of the electric drill resulted in the mastoidectomy becoming a mainstay of ear surgery. This situation improved even further with the introduction of antibiotics in the 1940s, which dramatically cut down on the severity of middle ear infections [19].
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 3 Equalizers for SDM
### Single-Mode 2 \(\times\) 2 Dynamic Equalization  
The 2 \(\times\) 2 equalization consists of 4 filters compensating for the impulse response of the transmission channel over the input signals (\(\mathbf{h}_{xx}^{(n)}\) and \(\mathbf{h}_{yy}^{(n)}\)) and the respective mixing components (\(\mathbf{h}_{xy}^{(n)}\) and \(\mathbf{h}_{yx}^{(n)}\)):  
\[\overline{x}(n)=\mathbf{x}^{(n)}.\ \mathbf{h}_{xx}^{(n)}+\mathbf{y}^{(n)}.\mathbf{h}_{xy}^{(n)}; \overline{y}(n)=\mathbf{x}^{(n)}.\ \mathbf{h}_{yx}^{(n)}+\mathbf{y}^{(n)}.\mathbf{h}_{yy}^{(n)}, \tag{1}\]  
where the input signals \(\mathbf{x}^{(n)}\) and \(\mathbf{y}^{(n)}\) are vectors accounting for \(N\) samples of the received signals: \(\mathbf{x}^{(n)}=[x(n),x(n-1)\ldots x(n-N+1)]\) and \(\mathbf{y}^{(n)}=[y(n),y(n-1)\ldots y(n-N+1)]\). The '\(\cdot\)' represents the dot product operation, and the filter coefficients are vectors of \(N\) taps, where the number of taps should be, at least, as long as the impulse response of the system for enabling the compensation of the signal impairments:  
\[\begin{split}\mathbf{h}_{xx}^{(n)}&=[h_{xx}^{(n)}(1),h_ {xx}^{(n)}(2)\ldots h_{xx}^{(n)}(N)];\mathbf{h}_{xy}^{(n)}&=[h_{xy}^{( n)}(1),h_{xy}^{(n)}(2)\ldots h_{xy}^{(n)}(N)],\\ \mathbf{h}_{yx}^{(n)}&=[h_{yx}^{(n)}(1),h_{yx}^{(n)}(2) \ldots h_{yx}^{(n)}(N)];\mathbf{h}_{yy}^{(n)}&=[h_{yy}^{(n)}(1),h_{yy}^ {(n)}(2)\ldots h_{yy}^{(n)}(N)].\end{split} \tag{2}\]  
The update algorithms considered in this paper are based on the stochastic gradient update [1], where the equalized signal is compared with the expected transmitted signal by means of the equalizer's error function [6]. For the error functions considered in this paper, the stochastic gradient update can be expressed as:  
\[\begin{array}{l}\mathbf{h}_{lm}^{(n+1)}=\mathbf{h}_{lm}^{(n)}-\mu\
abla_{\mathbf{h}_{lm}^{(n )}}f(\mathbf{h}^{(n)});\;l,m\in\{x,y\},\\ \\ \
abla_{\mathbf{h}_{lm}^{(n)}}f(\mathbf{h}^{(n)})=C_{p}(n)\mathbf{m}^{*\;(\mathbf{n})},\\ \\ p=\left\{\begin{array}{ll}1,\;l=x\\ 2,\;l=y\end{array};\mathbf{m}\in\{\mathbf{x},\mathbf{y}\}.\end{array}\right.\end{array} \tag{3}\]  
The error function determines how filter coefficients are updated, influencing the performance of the system. The other parameter that might impact on the performance is the step size \(\mu\). The step size influences the stability, the convergence speed, and the final performance of the equalizer [20]. The step size should be carefully tuned for each application and equalizer.  
The filter update rule given by Equation (3) includes two error coefficients: \(C_{1}\) and \(C_{2}\), being the error coefficients of the \(x\) and \(y\) polarizations, which are determined by the error function of the equalizer.
## 2 Transcriptional Regulation in Integrons
### The Role of Gene Cassette Position Effect on Transcription  
There is a clear effect of the cassette position in an integon array on transcription since promoterless cassettes closer to Pc are more efficiently transcribed than distal ones. There is considerable information concerning the position effect on gene cassette transcription in class 1 integrons, but such observation was also made for class 2 integrins [49].  
[PERSON] and [PERSON] [2] have examined how the position of resistance gene cassettes inserted in class 1 integrins affected transcription and antibiotic resistance manifestation. They verified that when several cassettes were inserted in the integron, the resistance level increased when the gene was closer to Pc and that such levels reduced in detrimental of the presence of preceding cassettes. Based on these findings, the authors had suggested that not only the relative proximity to Pc influenced gene expression but also that cassettes could decrease transcription of downstream genes placed in distal positions (second, third, and so on) in the array. In that study, Northern blot assays revealed a diversity of transcripts with different lengths, several of them terminating at or near the \(attC\) site placed on the \(3^{\prime}\) end of individual cassettes. Indeed, in spite of their sequence heterogeneity, \(attC\) sites are characterized by imperfect inverted repeats with a strikingly conserved palindromic organization [6], which may potentially produce stem-loop cruciform structures when in a single strand form, as found during transcription. Therefore, they hypothesized that such hairpin structures could act as Rho-independent transcription terminators in polycistronic transcripts, such as those originating from integron-associated gene cassette arrays, accounting for a decrease in transcription levels of downstream cassettes. However, in spite of this evidence, the role of \(attC\) in transcription termination was questionable. First, the Rho-independent transcription terminators present stem-loop structures followed by a T-rich region [58], which was not the case for \(attC\) sites since they are not exactly T-rich sequences. Second, [PERSON] and [PERSON] [2] had observed by Northern Blot that the amounts of mRNA containing only the first cassette or spanning the entire gene cassette array (full-length transcripts) were similar. Third, a more recent study experimentally proved that the transcription of the gene cassettes was not affected by the upstream \(attC\) sites, independent of their size and sequence composition, since destabilization of different hairpin structures had no impact on the total quantity of transcripts [59]. In fact, a previous study had also demonstrated that the formation of \(attC\) secondary structures in the mRNA upstream of the \(cat\) gene had no influence on its transcription [60]. This evidence altogether claims that \(attC\) sites do not act as transcription terminators of the following gene cassettes in an integron array.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 2 Background
### Tag Checking  
The security rules are enforced at the tag checking stage. In hardware-based DIFT designs, the tag checking operation is implemented using dedicated hardware logic. The tag checking rules are configured through either global registers [21] or custom instructions [27]. The software-based methods execute tag checking combined with software context closely. Note that the tag checking uses extra instructions and will cause extra clock cycles. The hardware tag and software context are combined closely in co-design-based DIFT. The security policy in the software part can be expressed as the combination of tag operations in the hardware layer.  
The handling of the exception is another essential part in the tag checking stage. In software-based methods, an exception is raised through a software interrupt in the target program that monitors the program in other threads [2]. The hardware-based DIFT design may add a new hardware interrupt or extend the current hardware exception signal to support the exception handling when executing DIFT operations. However, the normal interrupt and exception handling may engross the whole OS due to the high performance overhead. In order to reduce the overhead caused by exception, the authors in [21] adopted the user-level exception and extra machine mode. In software and hardware co-design-based DIFT, a hardware exception is raised in the hardware layer when the tag checking fails. This exception will be handled at the software level with the intervention from the user or the developer.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 2 Wavelet Neural Network
### Improved WNN  
The WNN still has shortcomings. For example, with the increase of the dimension of the problem, the number of training samples required by the network also increases sharply, which makes the network structure huge, and the learning and training convergence speed decreases; the number of hidden layer nodes of the network is difficult to determine, lacking scientific theoretical guidance; however, whether the selection of the number of hidden layer nodes is appropriate is directly related to the fitting performance and convergence accuracy of the network. The selection of the initial coefficient matrices \(\mathbf{W}(m)\) and \(\mathbf{I}(s)\) has a significant impact on the establishment and performance of the network, as an inappropriate initial coefficient matrix may lead to slow convergence of network errors, low convergence accuracy, or even non-convergence, resulting in an ill-conditioned network.  
As for the problem of the dramatic increase of required training samples, it can be dealt with by sampling. The uniform design method (UDM) is employed to ensure the quality of the sample [40], and the sample experimental design approach is implemented at the expense of the minimal sample size to reduce the required number of samples.  
As for the problem that the number of hidden layer nodes is difficult to determine since there is no conventional selection method for the number of hidden layer nodes, the empirical formula with mature application and universal applicability is employed to determine the number of hidden layer nodes, as in Equation (22) [41]:  
\[\text{s}=\sqrt{m+n}+\gamma \tag{22}\]  
where \(\gamma\) is a constant in the range of 1 to 10.  
To solve the problem of the selection of initial coefficient matrices \(\mathbf{W}(m)\) and \(\mathbf{I}(s)\), the wind-driven optimization (WDO) algorithm with strong robustness and high efficiency is used to select the initial coefficient matrix, thus improving the performance of the network. The combined use of the wind-driven algorithm and WNN is shown in Figure 2.