esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Table 2 displays the results of the literature search, presenting a total of 21 articles. The columns in the table compile the extracted information from each paper, facilitating the analysis of their relevance in addressing the research questions posed in this literature review. Figure 1: PRISMA flow diagram for the selection process. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Agility Construct** & **Agility Instrument** & **Paper Findings** & **Quality Score** \\ \hline [PERSON] et al. [34] & Agility of hospital supply chain and hospital agility & Developed based on [7] for organizational agility; for supply chain agility, reference not reported & The related supply chain agility dimensions were positively significant to the hospital agility constraint. Sensitivity and responsiveness showed high correlations to the agility construct as observable variables. & \\ [PERSON] et al. [35] & Agile leadership & Developed by [PERSON] (in Turkish) [36] & Agile leadership has a positive effect on career success, and Job embeddedness plays a mediator role in this relationship for hospital organizations in Turkey. & \\ [PERSON] and [PERSON] [37] & Organizational agility & Developed based on [7] & Appetitive capabilities have a positive effect on organizational performance, while Organizational Agility plays a mediator role in this relationship for healthcare organizations in Turkey. & \\ \hline [PERSON] et al. [38] & Agile practices in supply chain & Developed based on [39] & The agile practices construct has a positive effect on lead-time reduction in supply chains. & \\ \hline [PERSON] et al. [8] & Organizational agility & Adopted from [7] & The organizational intelligence construct has a positive effect on organizational agility, where the organizational learning construct plays a mediating role for the teaching hospitals of Yazd city, Iran. & \\ \hline [PERSON] et al. [40] & Enterprise agility & Developed based on [41; 42; 43] & Enterprise architecture maturity (EAM) has a positive effect on enterprise agility (EA), where Information Technologies effectiveness and alignment (ITE and ITA) play mediating roles. & \\ \hline [PERSON] and [PERSON] [44] & Care delivery agility & Newly developed, reference not reported & Converged device implementation and wireless portable adoption have positive effects on the care delivery agility construct. Care delivery agility is a prominent precursor of clinical productivity. & \\ \hline [PERSON] et al. [45] & Healthcare agility & Developed based on [46; 47] & Healthcare flexibility and the implementation of the Internet of Things have a positive effect on the healthcare agility of the organization for emergency clinics in India. & \\ \hline [PERSON] et al. [48] & Treatment agility & Developed based on [44] & Enterprise resource planning implementation and Internet-of-Things wearable usage have a positive effect on Process integration. Then, process integration has a positive effect on treatment agility. & \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of literature review results.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 2 System Model
### The Co-channel Interference Model
If there are two packets colliding in time, and one of them is from the desired signal link, and the other one is from the interfering link, then the PSR can be related to the Bit Error Rate (BER) as follows:  
\[PSR(C/I)=(1-r\times BER(C/I))^{((r\times p))} \tag{3}\]  
where the term \(p\) represents the number of bits in the packet, and the term \(r,\ 0\leq r\leq 1\), represents the overlap ratio of these two packets/links over the course of the desired packet (ref. Figure 4). The BER is applicable to the portion of the packet under interference. This is derived from the fact that all the bits under interference need to be correctly received in order for the packet to be successfully received [19]. This PSR derivation is made for an uncoded wireless link, which is assumed in the PHY layer. If error-correcting coding is used, its coding gain can be introduced to the C/I value based on measurements. In this derivation, it is assumed that the only impairment in the link is interference as captured in the C/I value. Packets with a larger number of payload bits require a stronger C/I value in order to achieve the same PSR. The PSR depends on the number of payload bits under interference. Smaller collision time overlaps, represented by the ratio \(r\), result in favorable conditions for surviving co-channel interference. The worst case is when \(r=1\) which represents the case of a full overlap across the desired packet. In such a case, the entire packet will be under interference. The numerical results with this model will be presented in Section 6. Figure 4: Occurrence of co-channel packet collision within a data packet.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 3 Construction of Prediction Model
### Attention Mechanism  
The essence of the attention mechanism is to filter out the important information from a large amount of information and focus on this information while ignoring the less important information [26; 27; 28]. The attention module gives a certain score by calculating the importance of different input features to the output result. For features with higher scores that are more critical, larger weight values will be assigned. By highlighting the influence of key features, thus improving the accuracy of the prediction model, the schematic diagram is shown in Figure 5.  
Figure 4: The structure of the TCN residual model.  
Figure 5: Schematic of the attention mechanism.  
As shown in Figure 5, \(x_{t}(t\in[0,T])\) represents the input sequence of the model, \(h_{t}(t\in[0,T])\) corresponds to the output of the hidden layer, \(a_{t}(t\in[0,T])\) is the attention weight of the current input to the historical input hidden layer state, and \(F_{t}(t\in[0,T])\) is the output value of the attention layer. The output of the attention layer is calculated as follows:  
\[P_{t}=u\tan\text{h}\Big{(}w_{t}\;*\;h_{t}^{\text{T}}+b_{t}\Big{)} \tag{11}\]  
\[a_{t}=\text{ softmax}(P_{t})=\frac{\exp(P_{t})}{\sum_{t=0}^{T}\exp(P_{t})} \tag{12}\]  
\[F_{t}=\sum_{t=0}^{T}a_{t}h_{t} \tag{13}\]  
where \(P_{t}\) is the similarity between the hidden layer state and different states, \(u\) and \(w_{t}\) are the weight coefficients, \(\tanh\) is the hyperbolic tangent function, and \(b_{t}\) is the bias coefficient.
## 1 Introduction
Therefore, there is a need for an effective yet less expensive, environmentally safe method to minimize heavy metals from toxic to safe limits in the environment [17]. Recently, biological methods, including prokaryotic as well as eukaryotic microbial cells and their by-products like bacterial EPS, are used as emerging candidates for the biosorption and remediation of heavy metals at contaminated sites in an eco-friendly fashion [18; 19]. Thus, utilizing bacterial EPS in environmental management is an emerging area of research. Previous studies report an L-asparagine monohydrate dependent EPS producing _Bacillus licheniformis_ from the gut of the earthworm, _Metaphire posthuma_, and the modulating factors for microbial synthesis of EPS [4]. Since such bacterium harbors in the gut of the earthwormwhich is highly acclaimed as a soil fertility enhancing and bioremediating organism and, in concert with other bacterial assemblage, it aids the earthworm gut acting as a bioreactor. The study on the role of the EPS synthesized by that bacterium in environmental management deserves special attention. The particular gut isolate _Bacillus licheniformis_ KX657843 has been tested for certain properties such as metal removal, phosphate solubilization, indole acetic acid (IAA) production, and its effect on seed germination has also been reported [20]. The present study focuses on studying the metal sorption and biofocculating properties of the EPS produced by _Bacillus licheniformis_ KX657843. Two main objectives of the present study are the following: (1) characterization of the EPS by Nuclear Magnetic Resonance (NMR) and Mass Spectroscopy (MS) while the Fourier transform infrared spectroscopy (FTIR) study of the EPS was done earlier [4], (2) examining the effects of some fundamental parameters on the sorption of heavy metal Cu(II) and Zn(II) and flocculation by the EPS obtained from _Bacillus licheniformis_.
# Shear Behaviour of Aeolian Sand with Different Density and Confining Pressure
## 3 Results and Discussion
### Strength Characteristic  
The effective internal friction angle of aeolian sand can be obtained by the Mohr stress circle envelope. Since the stress-strain relationship of aeolian sand has no obvious peak when the relative density \(D_{\rm r}=50\%\), the deviatoric stress at 15% axial strain is taken as the failure point. Through this method, the effective internal friction angle of aeolian sand with different relative densities can be obtained as shown in Figure 13. It can be seen from Figure 13 that the effective internal friction angle of aeolian sand increases approximately linearly with the increase in relative density of specimens, which can be fitted by a straight line as:  
\[\varphi^{\prime}=\alpha_{1}D_{\rm r}+\beta_{1} \tag{3}\]  
In Equation (3), \(\alpha_{1}\) and \(\beta_{1}\) are fitting parameters, which are 8.59 and 28.84, respectively. It can be seen that for aeolian sand, the effective internal friction angle increases by 0.859\({}^{\circ}\) for every 10% increase in \(D_{\rm r}\).  
Figure 12: The critical state lines in the \(e\)-ln\(p^{\prime}\) plane of aeolian sand.  
Figure 13: Variation of effective internal friction angle with relative density in aeolian sand under drained condition.  
Figure 14 shows the relationship between effective internal friction angle and confining pressure of aeolian sand with relative density \(D_{\rm r}=50\%\) under undrained conditions. It can be seen from Figure 14 that the effective internal friction angle decreases with the increase in confining pressure. A plot of the observed values of peak \(q\) and \(D_{\rm r}\) are shown in Figure 15. Similarly, the peak \(q\) with \(D_{\rm r}\) can be fitted by a straight line:  
\[q=\alpha_{2}D_{\rm r}+\beta_{2}\sigma_{3}^{\prime} \tag{4}\]  
In Equation (4), \(\alpha_{2}\) and \(\beta_{2}\) are the fitting parameters.
# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Experimental Setup  
#### 5.1.1 Dataset  
In this paper, a subset of TUM GAID dataset [1] containing audio, RGB, and depth recordings suitable for studying gait recognition was used. TUM GAID dataset is freely available for research purposes, contains both RGB and depth recordings of a large number of subjects, and is therefore suitable for the stated research objectives.  
It contains recordings obtained with a Kinect sensor of 305 people in three variations: normal (N, _normal_), carrying a backpack (B, _backpack_), and with silenced shoes (S, _shoes_). The backpack variation is suitable for studying the impact of the degradation of silhouettes on the performance of gait recognition. The variation where the shoes are coated is suitable for studying the performance of recognition methods based on audio stream modality (these methods are not considered in this work). For each person, there are six recordings in _normal_, two in _backpack_, and two in _shoes_ variation. To further investigate challenges of time variation where the recording conditions such as lighting, clothing, etc. may change when recorded in another moment (e.g., days or months later), a subset of 32 people was recorded a second time for six additional recordings in a normal variation (TN, _time normal_), and two additional recordings for backpack (TB, _time backpack_) and with silenced shoes (TS, _time shoes_) variations, for a total of 12 recordings in _normal_, 4 in _backpack_ and 4 in _shoes_ variation (Figure 8). The images were recorded in 640 \(\times\) 480 resolution at a frame rate of 30 fps from a fixed camera placed at approximately 4 m distance perpendicularly to walking direction, i.e., the dataset is suitable for testing side view gait recognition.
# Special Issue on the Recent Advances in Safe Maritime Operations under Extreme Conditions
###### Abstract  
The proposed method for efficient and efficient  
to track the trajectory movements of an oil film with medium-density ice floes and simulate the flow field of moving ice of large displacement in six degrees of freedom (6 DOF). The probability of oil spill area extensions were estimated by a response surface method (RSM). The results show a reduced risk of pollution in icy water conditions and greater drift action of oil film. Accordingly, the spraying location and quantity of oil dispersant could be rapidly specified.  
[PERSON] et al. [4] present a simplified numerical model to predict ice impact forces acting on a ship's hull in level ice conditions. The model is based on ice-hull collision mechanisms and the essential ice-breaking characteristics. The two critical ice failure modes, localized crushing and bending breaking, are addressed. An energy method is used to estimate the crushing force and the indentation displacement for different geometry schemes of ice-ship interaction. The ice bending breaking scenario is taken as a semi-infinite plate under a distributed load resting on an elastic foundation. An integrated complete ice-hull impact event is introduced with ice failure modes and breaking patterns. Impact location randomness and number of broken ice wedges are considered in order to establish a stochastic model. This model can be used to predict ice impact loads, and to establish a link between design parameters (ice properties and ship geometry) and structural loads.  
[PERSON] et al. [5] analyze the effect of the assumptions behind the Popov method by comparing ice load predictions, calculated by the method, with corresponding full-scale ice load measurements. The findings indicate that assumptions concerning the modelling of the ship-ice collision scenario, the ship-ice contact geometry, and the ice conditions, among others, significantly affect how well the ice load prediction agrees with the measurements.  
[PERSON] et al. [6] illustrate the most likely combinations of the statistical properties of the parameters that characterize ice ridges and icebers, using the so-called environmental contour method. Probabilistic models of the key parameters that govern the ship-ice interaction process are introduced. Subsequently, the procedure referred to as the inverse reliability method (IFORM) is applied for identification of the environmental contour. Different forms (i.e., dimensions) of environmental contours are generated to reflect the characteristics of the interaction process. Furthermore, the effect of an increasing correlation between the basic parameters is studied. In addition, the increase in the design parameter values for increasing encounter frequencies is illustrated.
## 4 The \"Call Me Maybe\" Framework
### Principle I: TCP and UDP Capability in a Network  
The core principle of the 'Call Me Maybe' framework is the use of a'shadow' TCP network that exists with the UDP network, providing an alternative protocol and port through which communication can be carried out if the network is under attack. This TCP network can exist through a combination of software changes--i.e., updating the configuration of the VoIP phones and router to use the new protocol--and hardware changes, where entirely new network devices are used. The protocol change has several benefits, for example, by using TCP, network security features incompatible with UDP such as a UDP-restricted stateless firewall (URSF) can be enabled [17]. The URSF will mitigate the DoS attack, while the TCP VoIP network will allow voice traffic to continue for the duration the network is under attack. Ideally, the VoIP phones will be set up with the capability of transferring quickly to the TCP network when required. The speed of this is essential as any delay can result in lost voice packets.
## 2 The Preliminary Background of the Proposed Control Method
### Stability of the Fractional-Order System  
Stability assurance is a fundamental and crucial criterion for constructing a control system. The linear-time-invariant (LTI) system is stable because the roots of the characteristic equation are on the left side of the S plane.  
**Lemma 1**[41; 42].: _Let us consider the following autonomous system:_  
\[\frac{d^{\kappa}x}{dt^{\kappa}}=Ax,\;x(0)=x_{0} \tag{22}\]  
_where \(0<\alpha<1\), \(A\in\mathbf{R}^{n\times n}\) and \(x\in\mathbf{R}^{n}\), it is asymptotically stable if and only if \(\left|arg(\lambda)\right|>\frac{\alpha\pi}{2}\) is fulfilled for each of the matrix \(A\)'s eigenvalues (\(\lambda\)). Also, this system is stable if and only if \(\left|arg(\lambda)\right|\geq\frac{\alpha\pi}{2}\) is fulfilled for each of the matrix \(A\)'s eigenvalues (\(\lambda\)) with those critical eigenvalues satisfying \(\left|arg(\lambda)\right|=\frac{\alpha\pi}{2}\) possessing a geometric multiplicity of one. A matrix \(A\)'s geometric multiplicity of an eigenvalue \(\lambda\) is its dimension within the subspace of vectors \(v\) that satisfy_  
\[Av=\lambda v. \tag{23}\]Figure 3 depicts the stable and unstable regions for \(0<\alpha<1\).
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Radio Stripes
#### 2.3.1 Radio Stripes Are Inexpensive  
The radio stripe system enables flexible and low-cost cell-free massive MIMO implementation. Radio stripes are inexpensive owing to its use in a variety of ways: deployment does not require the use of highly qualified personnel. In theory, an RS requires only one (plug and play) connection to the fronthaul network or directly to the CPU; a standard distributed mMIMO deployment necessitates a star topology, i.e., a unique cable between each AP and the CPU, which may be financially nonviable. The amount of antenna elements has less impact on the difficulty of installing an RS due to its compute-and-forward nature. As a result, cabling is significantly less expensive. Although the star topology may provide a better performance, the cost of installing the front-haul network may be too high. To arrange radio stripes, antenna elements are embedded into long front-haul cables. As a result, a star topology with many RS is formed, and coverage is enhanced. Note that the maintenance costs are reduced because an RS system is more reliable and resilient. This results in highly distributed functionality, meaning that a few broken stripes have less impact on the network as a whole. It is worth noting that the low heat dissipation makes cooling systems easier and less expensive [39].
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme
### Asymptotic Favorable Propagation  
Because the BS has knowledge of \(\mathbf{H}_{1,l}\forall l\), the beamforming vector for a scheduled ISN \(l\) must direct the energy of the beam to concentrate on the location of IRS \(l\). If the BS has a large enough number of antennas \(M\gg L\), it can transmit a beam in the null space of all other ISNs while beaming towards the scheduled ISN. Before defining the beam, we make use of the following lemma.  
**Lemma 1**.: _A set of vector channels \(\mathbf{a}_{l}\), defined in (2) with distinct AoDs \(\phi_{l}\)s, satisfies asymptotic favorable propagation if \((kd(cos(\phi_{l})-cos(\phi_{j}))\pmod{2\pi}\
eq 0)\) for all \(l,j\in\{1,\cdots,L\},l\
eq j\), i.e.,_  
\[\frac{1}{M}\mathbf{a}_{l}^{H}\mathbf{a}_{j}\to 0\text{ as }\quad M \rightarrow\infty,\quad\forall l,j\in\{1,\cdots,L\},l\
eq j. \tag{8}\]  
Proof.: The proof is similar to [16], and is hence omitted.  
We exploit this property in our scheme as follows: the BS beamforms its transmitted signal such that the received signal power at a desired ISN is large, whereas the received signal power at all other ISNs is small. This is explained below through an example. Consider a case \(L=2\) in which \(\mathbf{H}_{1,1}=\beta_{1}\mathbf{a}_{1}\mathbf{b}_{1}^{H}\) and \(\mathbf{H}_{1,2}=\beta_{2}\mathbf{a}_{2}\mathbf{b}_{2}^{H}\). If the BS sends \(\mathbf{x}_{1}\in\mathbb{C}^{M}\), both ISNs receive  
\[\mathbf{y}_{1}=\mathbf{x}_{1}^{H}\mathbf{H}_{1,1}=\beta_{1} \mathbf{x}_{1}^{H}\mathbf{a}_{1}\mathbf{b}_{1}^{H}, \tag{9}\] \[\mathbf{y}_{2}=\mathbf{x}_{1}^{H}\mathbf{H}_{1,2}=\beta_{1} \mathbf{x}_{1}^{H}\mathbf{a}_{2}\mathbf{b}_{2}^{H}. \tag{10}\]  
We aim to precode the transmitted reference signal \(\mathbf{x}_{1}\) defined in (7) for ISN 1 such that the effective channel to ISN 1 is maximized and the effective channel to ISN 2 is negligible. To this end, we choose the precoding vector as \(\mathbf{f}_{1}=\frac{\mathbf{a}_{1}}{\|\mathbf{a}_{1}\|}\) (maximum ratio transmission towards ISN 1), which maximizes the channel to ISN 1. In order to ensure that the channel to ISN 2 is minimal, we need \(\mathbf{y}_{2}=\beta_{1}\mathbf{x}_{1}^{H}\mathbf{a}_{2}\mathbf{b}_{2}^{H}=0\). Thus, we need \(\frac{\mathbf{a}_{1}^{H}\mathbf{a}_{2}}{\|\mathbf{a}_{1}\|}=0\), or equivalently, \(\frac{\mathbf{a}_{1}^{H}\mathbf{a}_{2}}{M}=0\), as \(\|\mathbf{a}_{1}\|=M\) (cf. (2)). Here, asymptotic favorable propagation (see Lemma 1) enters the picture. If the channels \(\mathbf{a}_{1}\) and \(\mathbf{a}_{2}\) satisfy asymptotic favorable propagation, then this condition is satisfied when \(M\) is large, and the effective channel from the BS to ISN 2 becomes negligible.