esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 1 Introduction
NOMA, a current multiple-access method which offers enhanced spectral efficiency, lower outrage possibility and increased capacity, has attracted much attention from scientists and scholars. In NOMA, the transmitter consists of superposition coding (SC) to multiplex the input signals from multiple users by allocating different power levels on the basis of their channel state information (CSI). With respect to the decoding, successive interface cancellation (SCI), a multiple customers detection approach is utilized at the receiver [6;7]. UOWCs performance is presently limited to short distances. Thus, even if sea communication links will become inexpensively available, extended work needs to be carried out on optical systems as well as methodologies for broadband transmission of light signals over long-haul distances. Furthermore, for distinct environmental conditions in land-to-underwater communication, multi-robot links could be employed. In addition to this, the UOWC channel incorporates limitations in restricted bandwidth as well as distance. Hence, considerable efforts have been carried out to enhance the data throughput as well as speed for integrated fiber-wireless links. Here, routing data as well as controlling relay on the ground surface employing optical fiber are necessary. Additionally, information transmission is carried out through diverse underwater devices incorporating VLC links to speed up communication and improve mobility [8]. Moreover, to enhance the transmission range as well as the data rate for long-haul UOWChigh-speed communication, hybrid passive optical network VLC (PON-VLC) can be used for land-to-underwater scenarios. Thus far, PON has been employed to fulfill the enormous bandwidth demands of users globally. Next generation PON stage 2 (NG-PON2) supported time and wavelength divisions. Multiplexing PON (TWDM-PON) has been well-liked as well as known as the foremost technique by scientists, researchers, industries, governments, etc. because of its beneficial features. Minimum energy consumption, cost-effectiveness and 'pay-as-you-grow' are its main features. Again, land-to-underwater communication requires adequate security from threats that appear because of the rise in number of wireless-connecting devices or objects (such as drivers, ships, submarines, sensors etc.) in water. As the TWDM-PON channels' spectral range is not sufficient to control the ever-increasing traffic in the near future, hybrid wavelength division multiplexing and optical code division multiple access (WDM-OCDMA) offer flexibility, security as well as full asynchronous data communication in UOWC [9; 10]. In short, a satellite-to-undersea-based hybrid PON-FSO system utilizing VLC allows high-speed, long-reach and secure communication for many connected devices in satellite-to-undersea scenarios [11; 12; 13]. In this paper, TWDM-PON using a hybrid WDM-OCDMA multiplexing technique with FSO and NOMA-VLC links is recommended and investigated. Here, a modified new zero cross-correlation (MNZCC) OCDMA code is employed in the system.
## 4 Evaluation
### The Delay Trade-Off  
[PERSON] and [PERSON] [41] do not consider delay an important performance metric, as long as messages are delivered before TTL exhaustion. Therefore, the TTL, with respect to relevant application scenarios, becomes an important aspect in the design of OppNet routing solutions. In order to fully utilize the (24-h) diurnal cycle of human mobility [64], a TTL of 1 day is chosen for this evaluation. Experiments with HUMANETS, a routing approach that exploits human movement patterns, show that 75% of messages can be delivered within this period [75]. Hence, FLARoute is able to deliver more messages and achieve more fairness within the bounds of tolerable delay--for instance, Smart City applications such as garbage collection and green zone management may tolerate periodic notifications of up to 24 h [1]. By allowing a wider range of relay nodes with the PoiUtility, buffer occupancy is reduced and nodes are able to retain and deliver messages traversing multiple geographic regions--messages that are more likely to be lost with PRoPHET or CCAF. Hence, on top of PRoPHET's 5.5 h average delivery delay, FLARoute trades an extra 2.7 h for increased fairness and throughput. Provided messages can be delivered within their lifetime and throughput is improved, we consider this delay insignificant unless the application in concern is delay sensitive, in which case opportunistic routing solutions would not be suitable.
# Distributed Uniform Streaming Framework:
## 3 DUST Core for Event Streaming
### Middleware  
The middleware allows the DUST Core to send the event messages between the application components. A variety of message protocols are available which can be used to stream these events. Every middleware has its own advantages and disadvantages. To support all these  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Type** & **Description** \\ \hline ID & uint64 & A field that is incremented with one to create a unique number for every \\  & & message. \\ \hline Type & uint64 & An optional field in which the application can define the type of message \\  & that is send in the payload. \\ \hline Timestamp & uint64 + uint32 & The UTC-time of sending an event message. \\ \hline Key & bytes & An optional field to indicate the key which can be used by the application. \\ \hline Payload & bytes & The payload of the message. The application can define the structure of the payload. \\ \hline Source ID & string & The unique string that defines the source application component of the message. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The DUST Message data structure.  
Figure 2: The common DUST-Core architecture.  
message protocols, the DUST Core makes abstraction of these protocols with a middleware interface. The conversion between the protocol implementation and the interface is handled by a middleware specific implementation. Every middleware specific implementation will also include a configuration parsing module to configure the middleware with the middleware specific functions. This makes that the DUST Core and by extension the application has no middleware specific code. This abstraction allows us to make the middleware transparent to the application and extend the amount of available message protocols.  
The interface requires that the middleware can deliver the serialized message to other application components based on the publish subscribe paradigm. In this research, we used the messaging protocols MQTT [13], ZeroMQ [14] and DDS [15]. This abstraction allows the DUST Coordinator (see Section 4) to change the execution location of the application component without the need to change the software of the application component. The middleware abstraction will also allow us, in the future, to change to middleware based on the context (e.g., when application components run on the same device, we can change to a middleware that allows for shared memory communication). In the future, we can add additional middleware frameworks such as Kafka [16] to extend the amount of use cases that can be developed using DUST.
## 3 Results and Discussions
### Cluster Analysis of AE Signals
It is possible that CL-1 corresponds to matrix cracking and is accompanied by delamination. Although delamination does not occur in tensile loading [47], the matrix cracks can accelerate interfacial failure in the composite material which can lead to delamination in the composites. After that, it is noted that the compositions of S\({}_{3}\) and BU suggest that the composite specimen restores, in part, the ability to store strain energy. A reasonable explanation for this phenomenon may be attributed to the realignment of fibers at a macroscopic and microcosmic scale. In the case of damage, the strain energy storing capability exceeds the amount of energy that is released and the sentry function is characterized by an increasing function. However, due to the fact that the damage in the material is growing, the slope of this function progressively decreases. The last class, CL-3, appears with a higher peak frequency from 350 to 600 kHz which is usually attributed to fiber breakage, accompanied drop in the SF curve. All the failure mechanisms corresponding to the frequency range are summarized in Figure 13. The distribution of the three frequency clusters can well reflect the damage condition of the composites. In addition, combined with Figure 12a, AE signals are mainly concentrated in the range of 150-300 kHz; therefore, the fiber/matrix debonding corresponds to this frequency band and plays a leading role in the failure process of composite specimens. Figure 12: The mechanism-based analysis of AE signals. (**a**) The distribution of frequency regarding the three clusters and (**b**) the SF diagram associated with the applied tensile load. When the energy storing capability decreases, the material damage is increased. At the same time, the tendency of the material to release the internal energy will decrease as well. Thus, when the material damage increases, the ratio between the energy storing capability and the cumulated released energy (\(E_{s}/E_{a}\)) that form the sentry function will decrease. The sentry function can be used to describe the history of the material damage. In particular, the integral of the sentry function is related to the material damage and can be used to predict the residual strength of the composite laminate after an accident and identify the damage evolution process.
## 5 Results and Discussion
### Impact of Augmentation on Class-Specific Level: AP50
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Techniques**} & \multicolumn{6}{c}{**ViT**} \\ \cline{2-7}  & **Non-Helmet** & **Non-Mask** & **Non-Glove** & **Non-Vest** & **Non-Shoes** & **Average** \\ \hline Original & 65.32 & 69.39 & 68.75 & 69.12 & 69.52 & 68.42 \\ Brightness & 68.54 & 71.85 & 71.53 & 71.69 & 72.54 & 71.23 \\ Contrast & 66.54 & 70.31 & 69.96 & 70.47 & 70.97 & 69.65 \\ Perspective & 65.84 & 68.89 & 68.55 & 69.04 & 69.93 & 68.45 \\ Rotation & 65.93 & 69.06 & 68.77 & 69.30 & 69.80 & 68.57 \\ Scale & 67.32 & 70.81 & 70.49 & 71.02 & 71.96 & 70.32 \\ Shearing & 66.58 & 70.32 & 69.93 & 70.51 & 72.01 & 69.87 \\ Translation & 65.76 & 68.67 & 68.42 & 68.95 & 70.10 & 68.38 \\ A sum of techniques & 68.07 & 71.35 & 70.93 & 71.49 & 73.06 & 70.98 \\ Average & 66.66 & 70.07 & 69.70 & 70.18 & 71.10 & 69.54 \\ \hline \multicolumn{7}{c}{Swin Transformer} \\ \cline{2-7}  & Non-Helmet & Non-Mask & Non-Glove & Non-Vest & Non-Shoes & Average \\ \hline Original & 66.05 & 69.71 & 68.94 & 70.11 & 71.94 & 69.35 \\ Brightness & 68.12 & 72.46 & 71.88 & 72.89 & 75.70 & 72.21 \\ Contrast & 67.25 & 70.84 & 70.08 & 71.35 & 73.13 & 70.53 \\ Perspective & 66.84 & 69.14 & 68.56 & 69.87 & 72.79 & 69.44 \\ Rotation & 66.92 & 69.34 & 68.89 & 70.11 & 72.29 & 69.51 \\ Scale & 67.37 & 71.19 & 70.83 & 72.02 & 73.94 & 71.07 \\ Shearing & 67.04 & 70.66 & 70.25 & 71.28 & 74.17 & 70.68 \\ Translation & 66.56 & 68.99 & 68.52 & 69.72 & 73.36 & 69.43 \\ A sum of techniques & 68.02 & 72.71 & 72.04 & 73.16 & 73.97 & 71.98 \\ Average & 67.13 & 70.56 & 70.00 & 71.17 & 73.48 & 70.47 \\ \hline \multicolumn{7}{c}{PVT} \\ \cline{2-7}  & Non-Helmet & Non-Mask & Non-Glove & Non-Vest & Non-Shoes & Average \\ \hline Original & 66.35 & 70.88 & 70.42 & 71.27 & 71.33 & 70.05 \\ Brightness & 68.66 & 73.04 & 72.65 & 74.11 & 77.14 & 73.12 \\ Contrast & 67.27 & 71.62 & 71.08 & 72.15 & 75.03 & 71.43 \\ Perspective & 66.87 & 70.38 & 69.77 & 70.89 & 72.64 & 70.11 \\ Rotation & 67.02 & 70.63 & 69.96 & 70.98 & 72.76 & 70.27 \\ Scale & 68.10 & 72.46 & 71.94 & 73.16 & 74.54 & 72.04 \\ Shearing & 67.45 & 71.82 & 71.24 & 72.31 & 75.33 & 71.63 \\ Translation & 66.69 & 69.84 & 69.71 & 70.47 & 73.74 & 70.09 \\ A sum of techniques & 68.56 & 73.13 & 72.59 & 73.98 & 74.49 & 72.55 \\ Average & 67.44 & 71.53 & 71.04 & 72.15 & 74.11 & 71.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class-wise performance of each augmentation technique with different architectures (AP50).
## 2 Materials and Methods
### Phenolic Compound Analysis by UHPLC-Q-ToF  
All wines were analyzed for monomeric phenolics using an UHPLC-ESI-Q-ToF (Agilent 1290 II-6546, Santa Clara, CA, USA) equipped with a dual Jet Stream ESI source operated in negative ion mode. A C18 UHPLC column (2.1 \(\times\) 100 mm, 1.8 \(\upmu\)m, Agilent Zorbax eclipse plus, Santa Clara, CA, USA) was used for separation, as described previously [25], with minor modifications.  
UHPLC conditions: The mobile phases were water (A) and methanol (B), both containing 0.1% formic acid. The flow rate was 0.4 mL/min, and the column temperature was 35 \({}^{\circ}\)C. The initial mobile phase was 10% B; then, the percentage of B was ramped linearly to 20% B at 4 min, 25% B at 8 min, 36% B at 12 min, 50% B at 14 min, 95% B at 14.6 min, held until 17.6 min, back to 10% B at 18.2 min, and held until 20 min.  
ESI-Q-ToF-MS analysis conditions: MS analysis was performed with an extended dynamic range of 2 GHz (\(m/z\) 3200 Th). The nebulizer pressure and flow rates were 25 psi and 9 L/min; drying gas temperature 300 \({}^{\circ}\)C; sheath gas flow and temperature 11 L/min and 350 \({}^{\circ}\)C; fragmentation, skimmer, OCT, and capillary voltages 150, 65, 750, and 4000 V,respectively. All analyses were performed in negative mode. The collision energies for MS/MS analysis were 5, 10, or 20 V for different compounds. Mass calibration was achieved by infusion of a TOF ESI Tune Mix solution (standard mix G1969-85,000, Supelco Inc., Bellefonte, PA, USA) containing compounds with negative ionization \(m/z\) values of 112.985587, 301.998139, 601.978977, 1033.988109, 1333.968947, 1633.949786, 1933.930624, 2233.911463, 2533.892301, and 2833.873139, and had a residual error for the expected masses of \(\pm 0.3\) ppm. The data analysis was performed on MassHunter Qualitative Analysis software (Agilent, Santa Clara, CA, USA).  
Identification and quantification of phenolic compounds: Overall, 20 phenolic compounds were identified in wines and identified by matching their retention times with those of authentic standards (Table S3 and Figure S2). Standard concentrations were selected to bracket the concentrations of each individual compound as closely as possible, and all standards were analyzed in duplicate. Except for gallocatechin, 19 compounds quantified by standard concentrations were used for data analysis. Phenolic compounds standard mixtures were prepared in methanol aqueous solution that 1:1 (\(v/v\)).
## 3 Results
### Results and Evaluation of Trajectory Estimation
#### 3.4.2 Trajectory Estimation in Environments with Significant Geomagnetic Disturbances  
To evaluate whether the influence of geomagnetic fields from other devices is suppressed, an estimation was performed when other devices were located near the walking trajectory. Figure 13 below shows the actual walking trajectory and the positions of the other devices. Note that the other devices were installed at the same height as the measurement device and that the distance was varied from the walking trajectory at 0.5 m, 0.3 m, and 0.2 m. Walking started from the origin, passing beside Other Device 1 on the right and Other Device 2 on the left. To avoid collisions with the other devices, the minimum distance was set to 0.2 m. Other Device 1 was an iPhone 12 and Other Device 2 was an Apple Watch (Series 5). The estimation results shown in Figure 14 were obtained by simultaneously estimating with (proposed method) and without (conventional method) dynamically changing \(\beta\) while using exactly the same sensor values.  
Figure 11: Position estimation results for hand-held walking (circular trajectory).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Rectangular Trajectory**} & \multicolumn{2}{c}{**Circular Trajectory**} \\  & **First Time (L)** & **Second Time (R)** & **First Time (L)** & **Second Time (R)** \\ \hline Absolute error of hand-held [cm] & 70.1 & 110.5 & 101.7 & 115.3 \\ Absolute error of hand-swinging [cm] & 46.0 & 138.7 & 135.0 & 57.9 \\ \hline \end{tabular}
\end{table}
Table 10: Absolute error between the starting point and endpoint during position estimation.  
Figure 12: Position estimation results for hand-swinging walking (circular trajectory).  
From Figure 14, it can be seen that there is no significant error when the distance is about 0.5 m. However, when the distance is reduced to below 0.3 m, the estimated trajectory deviates. In the case of 0.2 m, when \(\beta\) is fixed, there is a deviation of 0.258 m in the \(x\) -direction; when \(\beta\) is dynamically adjusted, the deviation is reduced to 0.083 m. Although there were only two other devices within a 4 m range in this experiment, in a real-world indoor environment there are likely to be even more devices passing by. Thus, the effectiveness of the proposed method in improving the estimation results is expected to be significant.
## 1 Introduction  
This article will review our current understanding of transcription elongation and termination in _E. coli_. Because of the large scope of the article, we have largely confined ourselves to recent manuscripts. Older references have, in general, been omitted except when necessary or when they consist of our own work.  
RNA synthesis by bacterial RNA polymerases, although processive, does not proceed at a uniform rate. Template sequences can induce pausing or backtracking--movement of the transcription elongation complex (TEC) towards the promoter. Forward or backward movement of TEC does not entail loss of energy. The RNA:DNA hybrid, 9 nt in the post-translocated state and 10 nt in the pre-translocated state, is maintained independently of the direction of RNAP movement.  
Translocation speed and direction is also influenced by accessory transcription factors. NusG and NusA suppress or enhance pausing, respectively. UvrD can push TEC backwards to reveal damagedDNA, and Mfd can push it forward as part of the transcription-coupled repair process. The GreA/B proteins can rescue backtracked TEC by removing extruded RNA and restoring the 3'- end of the nascent transcript to the TEC active center. Backtracking is also suppressed when TEC is coupled to translating ribosomes, which present a barrier to retrograde translocation. Phage functions affect transcription elongation. \(\lambda\) N protein accelerates transcription by inhibiting pausing, whereas HK022 arrests transcription by preventing TEC translocation.  
Transcription terminates at the ends of operons or under certain conditions, within genes. Termination can be template-encoded and factor-independent (intrinsic termination), or require accessory factors, such as Rho, Mfd and DksA.  
There has been considerable progress in understanding these aspects of transcription elongation, although areas of controversy remain. We will summarize the new findings and try to smooth out the contradictions in the following pages.
## 2 Methods
### Attitude Update Algorithm for Strapdown Inertial Navigation
\(\omega_{ib}^{b}\) is the angular velocity of the carrier measured by gyroscope. \(h\) is the altitude. \(v^{n}= \begin{bmatrix}v_{E}^{n}&v_{N}^{n}&v_{Ul}^{n}\end{bmatrix} ^{T}\) is the strapdown inertial navigation velocity. \((V)_{q}\) is the vector \(V\) expressed as a quaternion, as shown in the following formula: \(( \begin{bmatrix}x&y&z\end{bmatrix} ^{T})_{q}= \begin{bmatrix}0&x&y&z\end{bmatrix}  ^{T}\). \(L\) is the latitude value of the vehicle position. \(R_{M}\) and \(R_{N}\) are curvature radius of the earth's meridian circle and the curvature radius of the unitary circle at the location of the carrier, respectively, and their formula is approximately as follows:  
\[R_{M}\approx R_{e}(1-2f+3f\sin^{2}L) \tag{6}\]  
\[R_{N}\approx R_{e}(1+f\sin^{2}L) \tag{7}\]  
\(R_{e}\) is the major half-axis of the earth reference ellipsoid, \(f\) is the flattening of the reference ellipsoid,  
\[R_{e}=6378137(m),f=\frac{1}{298.257}\]  
The attitude update quaternion differential equation is transformed into the following digital recursive form:  
\[q_{b_{n}(m)}^{n}=q_{b_{n}(m-1)}^{n}q_{b_{n}(m-1)}^{b_{n}(m-1)} \tag{8}\]  
In the following formula:  
\(q_{b_{n}(m)}^{n}\) is the attitude quaternion at \(t_{m}\) time, shorthand for \(q_{b(m)}^{n}\);  
\(q_{b_{n}(m-1)}^{n}\) is the attitude quaternion at \(t_{m-1}\) time, shorthand for \(q_{b(m-1)}^{n}\);  
\(q_{b_{n}(m-1)}^{n}\) is based on \(n\) frame of reference when \(b\) frame is from \(t_{m-1}\) moment to \(t_{m}\) moment transformation of quaternions, it is going to use \(\omega_{nb}^{b}(t)\). Let \(\eta_{m}\) be the equivalent rotation vector of \(b\) frame relative to \(n\) frame from \(t_{m-1}\) time to \(t_{m}\) time,  
\[\eta_{m}=\int_{m-1}^{m}\omega_{nb}^{b}(t)dt \tag{9}\]  
\[\eta_{m}=\int_{m-1}^{m}\omega_{ib}^{b}(t)-(C_{b}^{n}(t))^{T}\omega_{in}^{n}(t )dt \tag{10}\]  
Because \(\omega_{in}^{n}(t)\) changes very slowly compared with \(\omega_{ib}^{n}(t)\), if we consider \(\left(C_{b}^{n}(t)\right)^{T}\) and \(\omega_{in}^{n}(t)\) basically unchanged at \(t_{m-1}\leq\ t\leq t_{m}\), and let them be the values of \(t_{m-1}\), then we have  
\[\eta_{m}\approx\int_{m-1}^{m}\omega_{ib}^{b}(t)dt-(C_{b(m-1)}^{n})^{T}\omega_ {in(m-1)}^{n}T_{m} \tag{11}\]  
In the formula, \(T_{m}\)\(=\)\(t_{m}\)\(-\)\(t_{m-1}\), \(C_{b(m-1)}^{n}\) is attitude matrix at \(t_{m-1}\) moment, \(\omega_{in(m-1)}^{n}\) is the rotational angular velocity of the navigation system relative to the inertial system at time \(t_{m-1}\), they are all as the known quantity,  
\[\Phi_{m}=\int_{m-1}^{m}\omega_{ib}^{b}(t)dt \tag{12}\]Lane change process includes turning stage, holding stage, and returning stage (as shown in Figure 2). In the turning stage and returning stage, we can consider the car steering angular speed to be constant. Therefore, monad sample algorithm can be adopted for the solution of \(\mathbf{\Phi}_{m}\), \(\mathbf{\Phi}_{m}=\Delta\theta\), \(\Delta\theta\) is the angular increment from \(t_{m-1}\) time to \(t_{m}\) time. After the \(\mathbf{\Phi}_{m}\) is obtained, the attitude is updated by using Equation (12).
###### Abstract  
Self-compacting concrete (SCC) is a type of concrete that is placed in the formwork under its own weight. Although there are many studies showing the behavior of SCC beams, most relate to the behavior of simple supported beams. Unlike those, this is a study of continuous beams made of SCC aimed to analyze their flexural performance as well as to confirm the possibility of using nonlinear finite element analysis (FEA) in the design of such structural elements. An experimental study of three two-span continuous beams of a total length of 3400 mm, with the span between supports of 1600 mm, with 150/200 mm cross section made of SCC exposed to short-term loading, was carried out. The parameter that varied is the percentage of tensile reinforcement, with values of 0.65, 0.86 and 0.94 being selected. As all analyzed beams have shown a satisfactory load-bearing capacity and stiffness, the research confirmed the possibility of using SCC in continuous beams in civil engineering practice. Using Abaqus/Standard software, a nonlinear numerical model is proposed, which is validated and verified against experimental research, as there is only a 5% difference in the numerically calculated ultimate load compared to the experimentally measured values.  
self-compacting concrete; continuous beam; tensile reinforcement; experimental analysis; Abaqus/Standard