esa-sceva/satcom-chunk-collection huggingface dataset:
# Ballast Flow Characteristics of Discharging Pipeline in Shield Slurry System
## 2 Simulation Model of Ballast Motion in Slurry Discharging Pipeline
### Slurry Flow Model  
The conservation of mass and the conservation of momentum laws are followed during the slurry flow process. The time-mean Navier-Stokes equation is used to solve the slurry velocity and pressure distribution in the slurry system. The expression of the conservation of mass equation is expressed, as follows:  
\[\frac{\partial}{\partial t}\alpha_{f}\rho_{f}+\
abla\Big{(}\alpha_{f}\rho_{f} \mathbf{u}_{f}\Big{)}=0 \tag{1}\]  
\[\frac{\partial}{\partial t}\alpha_{f}\rho_{f}\mathbf{u}_{f}+\
abla\Big{(} \alpha_{f}\rho_{f}\mathbf{u}_{f}\Big{)}=-\alpha_{f}\
abla p+\alpha_{f}\
abla \tau_{f}+\alpha_{f}\rho_{f}\boldsymbol{g}+\mathbf{F}_{j}^{f} \tag{2}\]  
where \(\alpha_{f}\) is the slurry volume fraction, \(\rho_{f}\) is the slurry density, \(\mathbf{u}_{f}\) is the slurry flow velocity, \(\tau_{f}\) is slurry shear stress, \(p\) is slurry pressure, \(\boldsymbol{g}\) is the acceleration of gravity, and \(\mathbf{F}_{j}^{f}\) is the is force between the \(j\) th ballast and slurry in a single grid volume.  
The slurry that is used in the system is composed of bentonite, pulping agent, and water, which is different from the water in terms of density and viscosity. The rheological property of the slurry will have a stronger impact on the force of the ballast and the velocity distribution of slurry. The slurry density that was extracted from the site was 1100 kg/m\({}^{3}\), and Figure 2 shows the slurry rheological characteristic curve.  
Figure 2: The rheological curve of slurry.  
The slurry is pseudoplastic fluid based on the rheological curve. The shear rate and shear stress meet the power law rheological property model, shown as follows:  
\[\tau_{f}=k\dot{y}^{n} \tag{3}\]  
where \(\tau_{f}\) is shear stress, \(\dot{\gamma}\) is shear rate, \(k\) is slurry viscosity coefficient, and \(n\) is the power law index, the values of which are listed in Table 1.
# Linking Off-Road Points to Routing Networks
## 2 Preliminaries
### Lower-Bounding Metrics  
The cost function \(c\) of a routing network can express various measures: distance length, travel time, fuel consumption, etc. The definiteness of \(c\) does not allow us to add new vertices to networks with edges of zero cost. In fact, we want to give the new reference location a penalty for the expense to travel from (to) the current location to (from) the routing network. Most scenarios inherit a lower bound for this expense, e.g., the beeline when \(c\) encodes travel distances. We discuss some further scenarios and show possible lower bounds, which we formalize in the following definition:  
**Definition 6**.: _We call a metric \(d:N\cup M\times N\cup M\rightarrow\mathbb{R}\) such that \(d(i(a),i(b))\leq\ell(a,b)\) for all vertices \(a,b\in V\), where \(\ell\) denotes the quasimetric induced by \(c\), a lower-bounding metric of \(\ell\). For a set \(U\subseteq N\) and a point \(n\in N\), we use the common shortcut \(d(U,n):=\inf_{u\in U}d(u,n)\) with \(d(\emptyset,n)=\infty\)._  
**Example 1**.: _Let \(G=(V,E,c,i,N)\) be a routing network in which \(i(e)\) shall represent a street segment for an arbitrary \(e\in E\). For each street segment \(e\), the network can query with \(c(e)\) the estimated time needed to get from the start point of \(e\) to its end point. The induced quasimetric \(\ell\) respects the triangle inequality: For each start and end point \(s,t\in V\), the obtained value of \(\ell(s,t)\) is the length of a shortest path from \(s\) to \(t\), i.e., a path whose accumulated, estimated time is shortest._  
**Example 2**.: _Let \(c(u,v)\) be the Euclidean distance between the mappings of \(i(u)\) and \(i(v)\) to a local plane. Such a mapping exists for each edge due to the containment property. For such a \(c\), the following metrics are valid lower bounds:_  
* _A common local lower-bounding metric is the beeline when the network is mapped to a local plane. It would be tedious to adhere multiple charts by the transition map in order to calculate the beeline between two distant points. Fortunately, it is easy to calculate the geodesic on_ \(M\) _by approximation_ _[_23_]__. For example, [PERSON]'s algorithm_ _[_24_]_ _is a good approximation for calculating geodesics between two different points on the surface of the Earth._
* _The Euclidean metric in_ \(\mathbb{R}^{3}\) _is a lower-bounding metric. In particular, this metric is also a lower bound of the local beeline because it is allowed to neglect the curvature of the manifold_ \(M\)_, cf. Figure_ 1  
**Definition 7**.: _Let \(G=(V,E,c,i,N)\) be a routing network and \(d\) a metric. If \(d\) is a lower-bounding metric of \(\ell\), where \(\ell\) is the quasimetric induced by \(c\), we say that \(G\) respects the metric \(d\)._
## Materials and Methods
### Raman Spectral Measurement  
The Raman spectra of pure and adulterated Grignard reagents were recorded using a portable i-Raman spectrometer (BWTEK Inc., USA) configured with a charge-coupled device (CCD) detector having a pixel size of 14 \(\times\) 900 \(\upmu\)m and a 785-nm laser. All the Raman spectra of the chemical samples were collected in a dark room to avoid light interference during spectral acquisition. The laser light source was operated at 785 nm with laser power of 200 mW, and spectra were collected for each sample in the wavenumber range of 400-1650 cm\({}^{-1}\) under 1 s exposure time and with a spectral resolution of 2 cm\({}^{-1}\). The BAC100 model from BWTEK Inc. (Newark, DE 19713, USA) was utilized as a standard probe for the experiment. During the spectral collection, the cuvette was dried before the measurement. Subsequently, each sample was injected with a syringe into the closed quartz cuvette from the top to prevent the sample from coming in contact with moisture. The sample was then placed at a distance of 2 mm in front of the probe, which had been calibrated in advance to ensure that the laser would penetrate the sample inside the cuvette. After the spectral acquisition, the cuvette was first cleaned with toluene to avoid crystallization inside the cuvette, which was then further cleaned with alcohol before being dried for further measurement. The integration time during the scan was 10,000 ms, four scans were used to generate high-quality Raman spectra, and the averaged spectra of each sample were used for model development.  
\begin{table}
\begin{tabular}{c c c} \hline
**Toluene (\%)** & **Toluene (mL)** & **Grignard Reagent (mL)** \\ \hline
0 & 0.0 & 30.0 \\
1 & 0.3 & 29.7 \\
2 & 0.6 & 29.4 \\
3 & 0.9 & 29.1 \\
4 & 1.2 & 28.8 \\
5 & 1.5 & 28.5 \\
10 & 3.0 & 27.0 \\
15 & 4.5 & 25.5 \\
20 & 6.0 & 24.0 \\ \hline \multicolumn{3}{c}{Total amount used = 18 mL} & Total amount used = 252 mL \\ \end{tabular}
\end{table}
Table 1: Grignard reagent spiked with different toluene composition for Raman spectral measurement.
## 4 Results and Discussion  
In the experiment, it should be noted that when collecting the data for each experimental condition, it was necessary to ensure that the data collection time for each condition was sufficiently long to facilitate statistics to ensure that the time-averaged pressure and the root mean square value of the vibration amplitude did not change with the increasing time. The pressure fluctuation at P1 was measured twice at a gas-liquid mixing speed of 1 m/s with a gas content of 50%. By using fast Fourier transform, the measured data were transformed from the time domain to the frequency domain, and the results ob  
Figure 6: The mesh of the fluid domain inside the subsea jumper.  
Figure 7: The results of the mesh convergence study: (**a**) the maximum pressure at P1; (**b**) the time-averaged value of the pressure difference between P1 and P2.  
tained twice were compared. The distribution patterns of the two power spectral densities (PSDs) were similar, and their dominant frequencies and amplitudes were generally the same, as shown in Figure 8, demonstrating the consistency and independence of the experimental measurements.  
Before starting the studies involving two-phase flow, modal analysis was performed for the jumper structure using the hammering method to determine the natural frequencies of the jumper under two states--the empty jumper and the jumper filled with water. The hammering method was conducted by utilizing several points of excitation and three-point responses. Fast Fourier transform was performed on the acceleration signal acquired at two points on the jumper to obtain the first six orders of the natural structural frequency of the rigid M-shaped jumper, as displayed in Table 3. It can be seen that with the jumpers filled with water, the natural frequency of each order was reduced.  
To characterize the slug flow, all the conditions used in the experiment are represented in the flow diagram shown in Figure 9. All of the experimental conditions of the present investigation belonged to the slug flow condition.  
Figure 8: Experimental results’ repeatability verification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & \(f_{ie}\) **(Hz)** & \(f_{ff}\) **(Hz)** \\ \hline
1 & 7.44 & 5.89 \\
2 & 10.39 & 7.56 \\
3 & 12.58 & 9.42 \\
4 & 16.68 & 13.67 \\
5 & 16.97 & 14.78 \\
6 & 19.11 & 18.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The first six natural frequencies with empty and water-filled jumpers.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Pixel Correlation  
One of the characteristics of conventional images is that they exhibit high correlation between adjacent pixels. In CGHs, this correlation is very small or practically absent, since, currently, the pixel size is much larger than the wavelength used to calculate the stored phases (8 \(\upmu\)m vs. 0.633 \(\upmu\)m). These phases are calculated with the contribution of millions of points, resulting in completely different values between pixels (see Figure 2b).
## 3 Controller Design
### Reinforcement Learning-Based Control Design
The adaptation laws for \(\hat{W}_{c}\) and \(\hat{W}_{a}\) are designed as:  
\[\dot{\hat{W}}_{c}=-A_{1}\Big{\{}\bar{\theta}\Big{[}\theta^{\top}\hat{W}_{c}+V_{s }+J\big{(}\hat{\mathbf{M}}_{a}^{*}\big{)}\Big{]}+p_{c}\big{(}\hat{\mathbf{W}}_{c}-\hat{ \mathbf{W}}_{a}\big{)}\Big{\}}, \tag{32}\]  
\[\dot{\hat{W}}_{a}=-A_{2}\bigg{[}p_{a}\big{(}\hat{W}_{a}-\hat{W}_{c}\big{)}- \frac{\lambda p_{a}}{p_{c}}\
abla\sigma_{s}B_{1}\mathbf{\tau}\bar{\theta}^{\top} \hat{W}_{c}\bigg{]}, \tag{33}\]  
where \(\mathbf{\theta}=\
abla\sigma_{s}\big{(}\mathbf{\Gamma}+\mathbf{B}_{1}\hat{\mathbf{M}}_{a}^{*} \big{)}\), \(\bar{\theta}=\theta/\big{(}\theta^{\top}\theta+1\big{)}^{2}\); \(\mathbf{A}_{1}\) and \(A_{2}\) are positive diagonal matrices; \(\mathbf{\
abla}=\tanh\big{(}\hat{\mathbf{M}}_{a}^{*}\big{)}-\tanh\big{(}\hat{\mathbf{M}}_ {a}^{*}/\kappa\big{)}\); \(p_{c}\), \(p_{a}\) and \(\kappa\) are positive real constants. The projection operator \(\text{Proj}(\cdot)\) is imposed on (33) to guarantee that \(\hat{\mathbf{W}}_{a}\) is bounded [28]. It is assumed that \(\mathbf{\theta}_{1}=\theta/\big{(}\theta^{\top}\theta+1\big{)}\) is persistently excitation (PE) [29]. The proposed control scheme is illustrated by a block diagram in Figure 1.
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 2 Material and Methods
### Transmitted Power for SPR-Based Fiber-Optical Sensor  
The modal power corresponding to the incident angle \(\theta\) can be expressed as [35; 36]:  
\[p(\theta)=\frac{n_{1}^{2}\sin\theta\cos\theta}{\big{(}1-n_{1}^{2}\text{cos}^{2} \theta\big{)}^{2}} \tag{18}\]  
Based on the reflectance value for a single reflection at the core/metal interface, the normalized transmitted power of unpolarized light can be calculated by Equation (19):  
\[p=\frac{\int_{\theta_{cr}}^{\pi/2}R^{N}p(\theta)d\theta}{\int_{\theta_{cr}}^{ \pi/2}p(\theta)d\theta} \tag{19}\]  
where \(N\) represents the number of reflections that a ray undergoes in the fiber sensing area. \(\theta_{cr}\) is the critical angle. The \(N\) and \(\theta_{cr}\) can be expressed as in Equations (20) and (21):  
\[N=\frac{L}{Dt_{S}\theta} \tag{20}\]  
\[\theta_{cr}=\sin^{-1}\bigg{(}\frac{n_{cl}}{n_{1}}\bigg{)} \tag{21}\]  
where \(L\) and \(D\) are the length and core diameter of the fiber sensing region, as shown in Figure 1. \(n_{cl}\) and \(n_{1}\) are the refractive indices of the cladding and core.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols  
Geographic routing, often known as position-based routing, is a straightforward and scalable solution. It does not necessitate establishing or maintaining entire pathways to the destinations. Furthermore, no routing messages are required to update routing path states. Instead, the route is selected locally. At each hop, a locally optimum next-hop node, the neighbor closest to the destination, is chosen to continue forwarding the packet. This process iterates until the packet arrives at its final destination. Geographic routing can be used with opportunistic routing (OR) to enhance data delivery and minimize energy usage compared to packet re-transmissions. Each packet is broadcasted to a forwarding set of neighbors using the opportunistic routing paradigm. The priority of the nodes \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Hello Packet** & **Sink Position** & **Localization** & **Simulation Tool** & **No. of Sink** & **Residual Energy** & **Security Threat Detection** \\ \hline RER & Yes & Fixed & Not Needed & NS2 & Single & Reduced overhead & - \\ EECOR & Not given & Fixed & Not Needed & NS2 & Single & Increase lifetime & \\ LEER & Yes & Fixed & Not Needed & NS3 & Single & Compares with neighbors & - \\ SEECR & Yes & Fixed & Not Needed & Not given & Multiple & - & Active routing attack \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Cooperative-Reliability-based protocols. determines how the node are arranged. As a result, if a next-hop node in the forwarding set successfully receives the packet, it will only forward it if its highest-priority nodes fail to do so. If a higher-priority node transmits a packet, the next-hop forwarder node will cancel the scheduled transmission of that packet. If all the nodes in the set fail to obtain a packet, opportunistic routing becomes the preferred mode to transmit the packet. The communication void region problem is a significant drawback of geo-opportunistic routing. The communication void region issue arises when the current forwarder node does not have a neighbor node closer to the destination, i.e., when it is the only one close to the destination. A node in the void zone is known as a void node. When a packet gets caught in a void node, the routing protocol should either attempt to route it via a recovery mechanism or reject it. Figure 4 describes the particular categorization of geographical information-based routing methods.
## 4 The IEEE 802.15.5 WPAN Mesh Standard
### Asynchronous Communications: ASES  
Unlike SES, ASES is an energy saving mechanism suitable for applications that do not consider strict time requirements, since each node belonging to the source-destination path must contend with its neighbors to transmit data. This results in different delays for each message sent by a same source. In ASES, every network node triggers its own periods of activity and inactivity (ASES uses the same scheme as SES, comprising the _wakeup interval_ and _active duration_) regardless of the rest of devices in its coverage range. However, ASES furnishes the functionality of coordination between nodes in order to conduct communications successfully. In particular, ASES must guarantee that a receiver remains in its corresponding AD when a sender transmits a message. To this aim, at the beginning of the _active duration_, every network node individually transmits an announcement message (denoted by the standard as _Wakeup Notification_) so as to inform all the neighbors in its coverage range (and listening to the physical medium) that it _can_ receive data. Under this premise, for unicast transmissions, a sender device waits for the reception of the receiver's _wakeup notification_ message, in order to later carry out the data transmission by making use of the unslotted CSMA-CA. As Figure 5(a) shows, the sender node extends its own _active duration_ waiting for the receiver's WN arrival and, when it occurs, the sender accomplishes the data transmission. Furthermore, ASES provides an additional feature that allows a sender to extend the _active duration_ of the receiver by sending an _Extension REQuest_ message (EREQ). This happens when the sender detects that the _active duration_ of the receiver is not long enough to guarantee the data reception. Again, Figure 5(b) illustrates this functionality: the sender receives the receiver's WN, whose information indicates the sender that the receiver's _active duration_ is too short to perform the data transmission. Thus, the sender transmits an EREQ message to the receiver to extend its _active duration_. After the reception of this EREQ message, the receiver acknowledges it by sending a reply message (denoted as _Extension Reply_) and then, the sender node can dispatch data. Finally, broadcast and multicast transmissions can be done in ASES by exploiting the procedure depicted in Figure 5(c). In this Figure, the sender broadcasts several EREQ messages during a time interval longer than one _wakeup interval_. This mechanism must assure that all the receiver nodes (for broadcast transmission, all neighbors in coverage area; for multicast transmission, nodes belonging to the multicast group) are awake when the transmission task is performed (broadcast or multicast). Thus, once the sender stops transmitting EREQ messages, it sends the data message. When the data transmission ends, all nodes (sender and receivers) continue with their normal operation.
## 2 Results
### Analysis of Cis-Acting Elements in the Promoter of EhFPS  
To understand the expression regulation mode of the _FPS_ genes in _E. hirta_, the cis-acting elements of the 2000 bp sequence upstream of the _EhFPS_ gene promoter were analyzed (Figure 8). The promoters of the eight _EhFPS_ genes contained a large number of cis-acting elements related to the light response, plant hormone induction, and stress response. Statistics for all cis-elements revealed that the _EhFPS_ promoter sequence contains 1-27 light-responsive and regulatory elements, which mainly include Box4, G-Box, and GT1-motif elements. Among them, 87.5% of the members had Box4 and G-box elements, and in the hormone response module, they mainly included the jasmonate response elements CGTCA-motif and TGACG-motif and the abscisic acid response element (ABRE). Among them, 30 jasmonic acid (JA) response elements were enriched. Except for the _EhFPS4_ promoter, which did not detect the response element of this hormone, the other promoters contained it to varying degrees. Antioxidant response elements (AREs) were significantly enriched in the stress response module, and this element was not detected only in the promoter of _EhFPS2_ among the members. In summary, _EhFPS_ plays an important role in biological processes such as light induction, hormone induction, and adversity stress response in _E. hirta_.
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 4 Data Link Layer Design
### Adaptive SF Algorithm  
The solution proposed in this paper implements the ASFS algorithm, described in [15], to make it possible for the packet forwarders to correctly detect and decode packets transmitted by its end nodes using unknown spreading factors. Through this algorithm, if a preamble is not detected through CAD, the packet forwarder switches the SF until one is detected. If a preamble is detected, then it uses the same spreading factor and performs CAD again. At the third consecutive successful detection, it either prepares to receive the LoRa packet or performs CAD detection on an adjacent SF until the current SF is not able to find a valid preamble. This step is necessary for some spreading factors, because of the imperfect orthogonality between adjacent spreading factors. In the latter case, the SF is decreased by one, since it is the last valid candidate, and the LoRa transceiver is placed in reception mode. By adopting this algorithm, false SF selection is mitigated, solving the orthogonality problem stated before. The ASFS algorithm described in [15] was converted into the flowchart shown in Figure 3 and implemented in the developed prototype.  
One improvement made to this algorithm is the introduction of a fixed preamble length for each SF, which has to be high enough so that the minimum amount of packets is missed. This can be calculated by analyzing the worst-case scenarios for packet detection with the ASFS algorithm, for the spreading factors that do not suffer from orthogonality (\(SF=7\)) and for those that do (\(SF=10\)). The preamble length needs to correspond to an entire cycle of cumulative CAD detection periods until it successfully detects the right SF. This process still has to take into account the necessary time to process the CAD detection interrupt service routine (ISR), which may include the reading of a register through SPI (serial peripheral interface) or an input GPIO (general purpose input/output) pin, along with the clearing of the necessary flags, for every CAD detection cycle. The maximum values (\(T_{max}\)) for \(SF=7\) and \(SF=12\) are shown in Equations (4) and (5), respectively,  
Figure 3: Flowchart of the version of the ASFS algorithm implemented in the developed prototype.  
where \(T_{CADsf}\) (obtained from the transceiver datasheet [20]) is the time taken by the CAD detection period for the given SF and \(T_{ISR}\) is the period taken to process the interrupts to control the ASFS algorithm.  
\[T_{max}(7)=3T_{CAD7}+T_{CAD8}+T_{CAD9}+T_{CAD10}+T_{CAD11}+T_{CAD12}+8T_{ISR}\quad (s) \tag{4}\]  
\[T_{max}(12)=T_{CAD7}+T_{CAD8}+3T_{CAD9}+3T_{CAD10}+2T_{CAD11}+T_{CAD12}+11T_{ISR} \quad(s) \tag{5}\]  
The communication parameters of the end nodes are calculated by the packet forwarder, similarly to the ADR mechanism of LoRaWAN, by defining a default SNR margin and relating the maximum SNR of the received packets to the SNR limit for each SF, as shown in Equation (6).  
\[margin=\max\left(SNR_{packet}\right)-SNR_{limit}(SF)-margin_{default}\quad(dB) \tag{6}\]