esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Decoder
#### 2.2.4 Feature Aggregation Head  
In the encoding stage, the original input features retain abundant spatial elements, but lack semantic details. In the decoding stage, the output global-local features have fine semantic information, but its spatial resolution is insufficient. Therefore, adding the two features directly may reduce the classification accuracy [38]. In order to deal with this problem, we used a feature aggregation head (FGH) to fuse the two features rationally for more efficient image segmentation.  
First, a weighted addition operation was conducted to fuse the two features, and the weights can be updated with model training to take full advantage of the abundant spatial elements and precise semantic information. As shown in Figure 6, the input of FGH is the fusion feature after weighted addition, which is fed into the spatial branch and channel branch, respectively, after processing by a 3 \(\times\) 3 convolution. Second, the carefully designed paths in the module help to strengthen the channel-based and space-based feature representation. For the spatial path, a depth-wise separable convolution was used to produce the first path feature, P\(\in\)R\({}^{\text{h}\times\text{w}\times 1}\), where h and w are the height and width of the feature map. After that, we used a matrix multiplication operation and sigmoid function to obtain the output feature. For the channel path, we used a pooling operation to obtain the second attention feature, C\(\in\)R\({}^{1\times 1\times\text{c}}\), where c represents the channel number. Furthermore, the rescale operation utilizes convolution layers (1 \(\times\) 1) to reduce the channel numbers by a fixed ratio and then restores the channel numbers. Finally, a 1 \(\times\) 1 convolution was used to classify the refined features of the FGH.
## 3 System Model and Problem Formulation
### Offloading Cost
#### 3.3.2 Offloaded to TC  
In this subsection, we formulate the cost of real tasks when tasks are offloaded to urban TC for execution. In this case, apart from the transmission cost of STs, the forwarding cost of the access satellite and computing cost of TC should be included. We denote \(\alpha_{N+n}\) as the proportion of the bandwidth allocated for forwarding the task of the \(n\)-th ST, then the 2 nd-hop transmission rate for \(n\)-th ST is given by \(C_{2,n}=\alpha_{N+n}B\log_{2}(1+p_{SAT}h_{TC}/N_{0})\), where \(p_{SAT}\) denotes the transmission power of the access satellite, and \(h_{TC}\) denotes the channel gain between the access satellite and TC. Therefore, the forwarding latency and energy consumption for \(n\)-th ST are respectively given by \(T_{2,n}=L/C_{2,n}\) and \(E_{2,n}=p_{SAT}T_{2,n}\).  
The computing latency at TC is given by \(T_{2,n}^{c}=kL/f_{0}\), where \(f_{0}\) denotes the CPU frequency (in cycles/s) of TC. Thanks to the continuous electrical power supply for TC, we simply ignore the computing energy consumption of TC. Therefore, the total latency that the \(n\)-th ST perceives and the energy consumed for \(n\)-th ST are respectively given by  
\[T_{n}^{TC}=T_{1,n}+T_{2,n}+T_{2,n}^{c} \tag{3}\]  
\[E_{n}^{TC}=E_{1,n}+E_{2,n} \tag{4}\]
## 4 System and Problem Definition for Network Aware Server Consolidation
### Complexity Analysis  
Optimizing the performance of the scheduler in the data center has been a major research focus area for the last few years [46]. The calculation for the consolidation algorithm operations take place on the scheduler. The complexity of server consolidation over the entire data center to provide the optimal solution using exhaustive search method is \(O(N^{N})\) where \(N\) is the total number of servers in the entire data center. This is because \(N\) set of VMs can be potential candidates for migration to \(N\) servers in \(N\) ways. Therefore, each of \(N\) set of VMs has \(N\) options for potential migrations and for each of the \(N\) such scenarios the other sets of VMs also have all \(N\) options to create each possible migration scenario. However, this complexity is too high even for moderately large data centers. Therefore, we compare our proposed BCC consolidation algorithm with the Clustered Exhaustive Search (CES) algorithm, which finds the optimal migration within each cluster using the exhaustive search. We have adopted the Kernighan-Lin algorithm to do the clustering in the beginning of the BCC consolidation. If all the servers are equiprobable to have links between themselves, the computational complexity becomes \(O(N^{2}\log N)\)[44]. If the average number of servers in a cluster is \(n\) and if the number of the clusters formed is \(m\), the computational complexity of the CES algorithm after clustering is \(O(mn^{n})\) and is an np-hard problem. With the clustering, the complexity of the CES algorithm is \(O(N^{2}\log N+n^{n}m)\). On the contrary, for the BCC algorithm, inside each cluster, the servers are sorted according to their utilization having a complexity of \(O(n\log n)\) using merge sort [47]. Next, the two loops for finding the source and destination of the migration has the complexity of \(O(n^{2})\) in the worst case. So the overall complexity of BCC for all \(m\) clusters becomes \(O(N^{2}\log N+m^{n}\log n+mn^{2})\). Therefore, BCC has a much lower complexity compared to the overall exhaustive search algorithm. As the clustering is similar in both CES and BCC, the difference in their complexity comes from the mechanism of determining candidates for migration. The complexity of BCC after clustering is \(O(mn\log n+mn^{2})\approx O(mn^{2})\) which, is lower than that of the CES after clustering.
## 3 Results and Discussion
### Modeling Inactivation Kinetics
Previous studies have shown other CAP systems' capabilities, such as DBD, to inactivate bacterial biofilms [38; 44]. Further research is needed to assess the effectiveness of the piezoelectric CAP on biofilms. The predicted log reductions in both models were similar to the experimental values obtained after CAP treatment. A different study using a surface barrier discharge (SBD) configuration of a DBD plasma cold plasma device achieved a \(>\)3.55 and 2.06 log CFU/mL reduction of _Listeria monocytogenes_ and _Salmonella_ Typhimurium, respectively, at 5 mm distance within 3 min [3]. In addition, [PERSON] et al. reported a reduction of 3.65 log for \(S\). Typhimurium inoculated on glass slides after 300 s exposure time using a high-voltage CDPJ device that used air as inducer gas at 51% relative humidity [43]. These results are similar to those achieved with the studied low-voltage CAP device; however, since a \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Bacteria**} & \multirow{3}{*}{\begin{tabular}{c} **Distance** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{**Cleanliness**} & \multirow{3}{*}{\
\begin{tabular}{c} **Weibull** \\ \end{tabular} } & \multicolumn{3}{c}{**Weibull + Tail**} & \multirow{3}{*}{
\begin{tabular}{c} **Predicted** \\ **Log** \\ **Reduction** \\ **(300 s)** \\ \end{tabular} } \\ \cline{3-10}  & & & & & & **Weibull** + tail model parameters and predicted log reduction achieved during \\ \hline \multirow{3}{*}{_L. monocytogenes_} & 10 & Clean & 23.14 \(\pm\) 5.47 & 0.52 \(\pm\) 0.05 & 3.77 & 33.29 \(\pm\) 6.61 & 0.67 \(\pm\) 0.08 & 2.38 \(\pm\) 0.15 & 3.61 \\  & 10 & Protein-soiled & 70.31 \(\pm\) 0.06 & 2.82 & 75.57 \(\pm\) 9.1 & 0.82 \(\pm\) 0.1 & 3.32 \(\pm\) 0.26 & 2.74 \\  & 20 & Clean & 69.4 \(\pm\) 10.38 & 0.85 \(\pm\) 0.08 & 3.45 \(\pm\) 78.78 \(\pm\) 11.16 & 0.99 \(\pm\) 0.13 & 2.44 \(\pm\) 0.29 & 3.34 \\  & 20 & Protein-soiled & 122.47 \(\pm\) 16.44 & 1.06 \(\pm\) 0.14 & 2.59 & 127.93 \(\pm\) 15.97 & 1.23 \(\pm\) 0.25 & 3.39 \(\pm\) 0.45 & 2.52 \\ \hline \multirow{3}{*}{_Salmonella_} & 10 & Clean & 20.49 \(\pm\) 5.34 & 0.49 \(\pm\) 0.05 & 3.73 & 32.48 \(\pm\) 6.45 & 0.68 \(\pm\) 0.05 & 2.22 \(\pm\) 0.12 & 3.55 \\  & 10 & Protein-soiled & 73.42 \(\pm\) 11.14 & 0.73 \(\pm\) 0.07 & 2.81 & 82.88 \(\pm\) 10.63 & 0.92 \(\pm\) 0.13 & 3.16 \(\pm\) 0.2 & 2.69 \\ \cline{1-1}  & 20 & Clean & 55.69 \(\pm\) 8.82 & 0.77 \(\pm\) 0.07 & 3.63 & 61.2 \(\pm\) 10.02 & 0.84 \(\pm\) 0.11 & 2.04 \(\pm\) 0.47 & 3.57 \\ \cline{1-1}  & 20 & Protein-soiled & 104.25 \(\pm\) 15.68 & 0.83 \(\pm\) 0.1 & 2.41 & 109.41 \(\pm\) 15.15 & 0.95 \(\pm\) 0.19 & 3.36 \(\pm\) 0.54 & 2.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between Weibull and Weibull + tail model parameters and predicted log reduction achieved during the study period (300 s). high-voltage power supply is not required, it could facilitate scaling up and adoption by the food industry.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 2 Background
### 5g-V2x  
5G-V2X uses Single Carrier Frequency Division Multiple Access (SCFDMA) at the Medium Access Control (MAC) layer and OFDM at the physical layer, whereas 5G-V2X sidelink communications employ Cyclic Prefix Orthogonal Frequency-Division Multiplexing (CPOFDM), which excels in latency as well as high spectral and throughput perfor  
Figure 1: Cellular V2X (C-V2X) communications transmission modes [11].  
mance. It is definitely more resistant to frequency Doppler shift and oscillator phase noise as opposed to different multi-carrier waveforms. With the adoption of various modulation formats in 5G-V2X, such as QPSK, 16-QAM, 64-QAM, and 256-QAM using Gray mapping with a binary reflection, high-order modulations can now achieve higher throughput and spectral efficiency at the cost of sidelink coverage [15].  
The 5G-V2X Radio Resources (RRs) at the physical layer (PHY) include the frequency and time domain [4]. RRs are organized in frames and subframes with fixed durations of 10 ms and 1 ms, respectively, in the time domain. When the conventional cyclic prefix is used, each subframe is separated into a number of slots, each of which is made up of 14 OFDM symbols. Physical resource block (PRB)s, subchannels, and RRs are arranged in the frequency domain. K consecutive PRBs make up a subchannel, with K being any number of 10, 12, 15, 20, 25, 50, 75, or 100. A total of 12 consecutive subcarriers with subcarrier spacing make up a PRB (SCS). In Figure 2, we illustrate time and frequency domains, the relationship between the Resource Blocks (RB)s, subcarrier, PRB, and subchannel.
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 2 Materials and Methods  
### Tri-State Pulse Position Modulation (Eurofix)  
Eurofix was first proposed by researchers at the Delft University of Technology. In the Eurofix system, the LORAN-C signal additional data channel does not affect the normal LORAN-C operation. The modulation method adopted by Eurofix is 3S-PPM, with a modulation amount of \(\pm 1\)\(\upmu\)s [21, 22, 23, 24].  
3S-PPM modulates pulses 3-8 in each pulse group. The modulated signal consists of a \(1\)\(\upmu\)s pulse transmission phase shift relative to the unmodulated pulse. The three possible states of modulation are given in Table 1 and Figure 1.  
In the 3S-PPM pulse group, the number of advanced and delayed pulses of a channel is equal. The modulation of six pulses in a pulse group will produce 141 possible modulation modes, of which 128 modes represent valid data, one mode represents no data transmission, and 12 models are not used. The 3S-PPM mode is shown in Figure 2. Each of the 128 effective modulation modes uniquely represents a 7-bit binary data block [13, 14].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Pulse State** & **Transmission Time Minus Time of Reference Pulse (\(\upmu\)s)** & **Indication** \\ \hline Advanced pulse & \(-1\) & \(-\) \\ Prompt pulse & \(0\) & \(0\) \\ Delayed pulse & \(+1\) & \(+\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: States of modulation.  
Figure 1: 3S-PPM modulation.  
According to the different group repetition interval (GRI), the data rate is 70-175 bits/s, and uses forward error correction (Reed-Solomon encoding). The length of the Eurofix message is fixed at 210 bits and consists of 30 seven-bit words (GRI). Seventy bits are used to represent the application data, while the remaining 140 bits are used for forward error correction. The 70 bits of valid data include 4 bits for the message type, 52 bits for application data, and 14 bits for cyclic redundancy check (CRC) bits [25,26].
## 2 Materials and Methods  
### Pulse Forming Circuit  
The pulse forming circuit is based on a standard H-bridge topology, operated in a synchronized double-crowbar mode. The simplified scheme is shown in Figure 1.  
It can be seen that the circuit has 8 MOSFET switches T\({}_{1}\)-T\({}_{8}\) (C2M0080120D, Cree Inc., Durham, NC, USA), with two switches in each wing. For pulse forming, 4 switches are driven in synchronization (i.e., T\({}_{1}\)-T\({}_{4}\) or T\({}_{5}\)-T\({}_{8}\)), which enables a bipolar pulse shape. However, the dynamic characteristics of a MOSFET are load-dependent, therefore, a synchronized double-crowbar driving circuit is introduced. It implies that after each pulse, an additional wing is opened to shunt the circuit (e.g., when T\({}_{1}\)-T\({}_{4}\) are going to be closed to end the pulse, the T\({}_{7}\), T\({}_{8}\) are opened at the same time). Therefore, a second route for the current is formed, which is limited only by R\({}_{\text{LIM}}\) = 10 \(\Omega\), thus a minimum fall time of the pulse is ensured. The capability to form MHz range and sub-microsecond pulses is the most important factor to ensure the novelty of the electroporator, therefore, it is counter-productive to use lots of series switches due to the respective increases in the rise and fall times. However, the use of series switches is required to increase the handling voltage. As a result, the circuit had to be designed to feature minimum parasitic inductance and still have a reserve in terms of breakdown voltage to account for reverse voltage during maximum loads (\(\sim\)40 A). The breakdown voltage of a single switch is 1.2 kV, thus when switches are used in an array of 4, the total breakdown voltage is 4.8 kV. However, during pulse forming (a stage is opened), for the whole duration of the pulse, the charging voltage is distributed only between two switches (either the pair of T\({}_{7}\), T\({}_{8}\) or T\({}_{3}\),  
This implies that when the device is charged to 3 kV, the voltage is already exceeding the datasheet ratings. Nevertheless, it was empirically determined that such an overvoltage (3 kV versus 2.4 kV) is still within the handling range of the used SiC MOSFETs, but the pulses must be limited to 1 ms. Additionally, when the crowbar is opened, up to 300 A currents are induced. However, we have limited the operation of the crowbar to 65-100 ns, which again is not sufficient to thermally destroy the pn junction. All the solutions combined to make a trade-off between the number of switches, the handling voltage, and the dynamic characteristics of the circuit. T4).  
As high voltage sources, two UM4P30 (Spellman High Voltage Electronics Corp., Hauppauge, NY, USA) voltage supplies were used. The R\({}_{\rm DIV}\) = 1 M\(\Omega\) were used as bleeder resistors to equally distribute the DC voltage. The capacitor array included two capacitors in each stage (32 uF, KEMET MKP C4 AQ, Fort Lauderdale, FL, USA).
## 2 Materials and Methods
### RNA Extraction and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)  
Total RNA was extracted using the RNAprep Pure Tissue Kit (CAT:DP431, Tiangen Biotech Co., Ltd., Beijing, China). The RNA concentration and quality were assessed using a NanoDrop2000. RNA (1.0 \(\mu\)g) was reverse-transcribed into cDNA using the Evo M-MLV RT Mix Kit (CAT:No. AG11728; Accurate Biotechnology Co., Ltd., Hunan, China). A real-time quantitative polymerase chain reaction (RT-qPCR) was performed using the SYBR Green Premix Pro Taq HS kit (CAT:No. AG11701; Accurate Biotechnology Co., Ltd., Wuhan, China) in a StepOnePlus Real-Time Fluorescence Quantitative PCR System (Thermo Fisher Scientific Co., Ltd., Shanghai, China). The relative expression of each target gene was normalized to the housekeeping gene actin beta (_Actb_) using the 2\({}^{-\Delta\Delta\text{CT}}\) method. The target genes measured include alcohol dehydrogenase 1 (_Adh1_), aldehyde dehydrogenase 1 family, member A1 (_Aldh2_), cytochrome P450, family 2, subfamily e,polypeptide 1 (_Cyp2e1_), catalase (_Cat_), nuclear factor, erythroid-derived 2-like 2 (_Nrf2_), heme oxygenase 1 (_Ho-1_), interleukin 1 beta (_IL-1_), tumor necrosis factor alpha (_TNF-_a_), interleukin 10 (_IL-10_), fatty acid binding protein 2 (_Fabp2_), acetyl-CoA carboxylase alpha (Acac), carnitine palmitosyltransferase 1A (_Cpt1a_), collagen type I alpha 1 chain (_Colla1_), and alpha-2 smooth muscle actin (_Acta2_). The primers used in RT-qPCR are shown in the Supplemental Table S1.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 1 Introduction
### Application of the SDN Model to Optical Networks  
All the above considerations demonstrate the considerable changes that the SDN concept is introducing in the technical and economic outlook of telecommunications networks. Nevertheless, while the SDN concept has been adopted inside data centers in the past five years, the SDN implementations in the context of wide area networks (WANs) are still rare: the most notable ones are the two intercontinental backbone networks interconnecting Google's data centers, even if it can be argued that Google's backbone does not have the complexity of a public Internet network operator [8]. Anyway, a great research effort is being invested to develop solutions for optical networks based on the SDN paradigm and to standardize its application. The ONF has a working group dedicated to SDN applications for optical transport networks (OTNs): the fact that such networks transmit data over lightwave-based channels lends itself naturally to the SDN concept of a flow [2]. According to the ONF vision, transport SDN (T-SDN) extends OpenFlow to support Layer 0 and Layer 1 networks, allowing logically centralized control for some critical operations, administration, and maintenance (OAM) capabilities, i.e., provisioning, recovery, performance monitoring, and network inventory. Some of the T-SDN use cases considered by the ONF group include BoD services and photonic enterprise networks [9].  
The above-described context depicts a really dynamic scenario where the SDN technology promises to dramatically simplify network management and enable innovation and evolution, also in the field of optical WANs. However, the SDN concept is still in an early stage of penetrating metro and access segments. In the next paragraphs, the possibility of extending SDN to optical metro and access networks is explored. Firstly, the main technical driving forces and the main recent proposals are analyzed and the evolution required for photonic components and devices is then examined. Finally, the architectural solutions for reconfigurable and programmable metro and access networks are reviewed.
## 3 Component Design and Modeling  
### Apodized Bidirectional Grating Coupler  
For the proposed silicon photonic transmitter, the apodized bidirectional grating coupler design is very important as it largely affects the WDM optical transmitter performance. Ideally, the perfectly vertical grating coupler should feature high efficiency, low parasitic reflection, large optical bandwidth and ease of fabrication. However, it is hard to fulfill all the requirements simultaneously. Here we present a grating design which fits well with the design requirement. The structural view and working principle of the grating coupler is depicted in Figure 1b,c. According to the grating coupling theory [34], the grating structure design should fit the Bragg condition for perfectly vertical coupling:  
\[\beta_{Fz}+q\cdot K=\beta_{z}(q) \tag{1}\]  
where \(\beta_{Fz}\) represents the \(z\)-axis (the waveguide transmission direction) component of the propagation constant of the single-mode fiber fundamental mode, q indicates the diffraction order, \(K\) is the absolute value of the grating vector \(K\) which equals to \(2\pi/\Lambda\), \(\beta_{z}(q)\) represents the \(z\)-axis component of the propagation constant of the \(q^{th}\) diffraction order mode. For bidirectional grating coupling, \(\beta_{Fz}\) equals to zero. Designed as a second-order grating, the bidirectional GC supports diffraction orders from \(-1\) to \(1\); diffraction order modes of \(-1\) and \(1\) correspond to the fundamental waveguide modes of two opposite directions;  
Figure 1: (**a**) Schematic of the proposed WDM silicon photonic transmitter. (**b**) Schematic of the grating coupler interfacing with an external input single mode fiber. (**c**) Schematic of the grating coupler interfacing with an external output single mode fiber. diffraction order modes of 0 corresponds to the substrate leakage power (\(T\)) and the upward reflection power (\(R\)). Thus, the fiber-to-chip coupling efficiency can be expressed as:  
\[CE=(1-R-T)\cdot\gamma(TE_{0}) \tag{2}\]  
where \(\gamma(TE_{0})\) represents the fundamental \(TE\) mode coupling ratio (the optical power coupled into the \(TE\) waveguide fundamental mode divided by the total optical power of waveguide coupling). According to our simulations, the bidirectional grating couplers with waveguide thickness of 220 nm corresponds to \(\gamma(TE_{0})\) of about 0.99, which means almost all the waveguide coupling are in \(TE\) fundamental mode. From the view of out-of-plane coupling (as depicted in Figure 1c, the working principle can be expressed as follows. Firstly, two grating diffraction modes can be excited by the two incident waveguide modes in opposite directions, and the two diffracted electric fields (\(E_{d1}(x)\) and \(E_{d2}(x)\)) can be given by the following equations:  
\[\left\{ \begin{array}{l}E_{d1}(x)\propto\sqrt{2\alpha\Gamma}e^{-\alpha(x+ \frac{L}{2})}\\ E_{d2}(x)\propto\sqrt{2\alpha\Gamma}e^{\alpha(x-\frac{L}{2})}\end{array} \right. \tag{3}\]  
where \(\alpha\) is the coupling strength of the grating coupler, \(\Gamma\) stands for the directionality of the grating coupler, \(L\) is the lateral length of the grating.
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 4 Results
### Reduction Rate in PAM  
Table 2 illustrates the impact of different reduction rates on PAM. The experiment incorporates PAM into the BottleNeck. When \(r=8\), the network pays more attention to vascular information, with the highest Se index reaching 82.69%. When \(r=32\), the network achieves optimal performance in terms of Acc, Sp, and F1, reaching 97.14%, 98.53%, and 82.93%, respectively. To further demonstrate the effectiveness of PAM, Figure 7 presents segmentation results of the network integrating PAM with different reduction rates. The red boxes indicate some parts where the segmentation results of PAM-UNet are better. The red box in the bottom right corner of the figure indicates the enlarged segmentation detail. TP pixels are marked in white, FP pixels in green, and FN pixels in red. Both FP and FN pixels represent misclassified pixels. Observing the first row of segmentation results, it is evident that when \(r=32\), the network can better handle complex vascular trends. The second row indicates that the introduction of PAM alleviates vascular discontinuity to varying degrees, with the best results achieved when \(r=32\). The third row demonstrates that when \(r=32\), the network can more accurately segment fine blood vessels. In this paper, PAM selects \(r=32\) as the reduction ratio.  
Figure 7: Segmentation results of PAM with different reduction rate (r).