esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Searches for Physics Beyond the Standard Model
### Exotic Contribution to Photon-Photon Scattering  
As stated before, the VMB measurement provides an efficient strategy for observing the vacuum process, and this is also true for the ALP search for a mass region around 1 meV [10]. Figure 8 shows a diagram where ALPs contribute to VMB. However, it inclusively searches for a wide mass region since the mass information of ALPs is somewhat lost by the virtual off-shell photons conveying magnetic fields. By contrast, the \(s\)-channel scattering of real photons provides mass information with a precision of the monochromaticity of the light (Figure 9a). Once the ALP mass is roughly determined, the \(s\)-channel search can precisely sweep the relevant mass region by continuously changing the incident photon energy. This is advantageous for real photon-photon processes [19; 20], and thus the two methods provide complementary approaches.  
Figure 8: Interaction of a photon with an external magnetic field caused by an axion-like particle, contributing to vacuum magnetic birefringence.  
Figure 7: Repetitive operation of the four pulsed magnets. (**Left**) Typical waveform of the capacitor voltage (to left, dashed) and magnetic field (to right, solid). The pulse interval is adjustable and set to 1/30 s here. (**Right**) Peak-field distribution at the magnet center for the first and second pulses, obtained by 1-h operation with 0.2-Hz pulse repetition. Adapted from Reference [37].  
Each kind of light source probes a distinct mass region corresponding to its photon energy. As shown in Figure 4(right), we are currently capable of the combinations of XFEL-XFEL, XFEL-laser, and laser-laser, for which the center-of-mass energies are about 10 keV, 100 eV, and 1 eV, respectively. Various combinations of these different light sources widens the experimental opportunities, since the new mass scale is still not clear.
## 1 Introduction  
The carbon imprint of ICT technologies is estimated to be over 2% of the world total, similar to that of air travel [1]. Until recently, energy saving in networks focussed more on longer battery life of mobile devices and smart design of the wireless infrastructure [2; 3; 4], and more attention has been devoted to energy consumption of Cloud Computing [5; 6]. Traditionally, the energy cost of the much more energy demanding backbone networks that support Internet communications, both fixed and mobile, has not been taken into account, but researchers have recently turned their attention to the energy cost and the carbon emissions of the backbone network [7; 8].  
In this paper we investigate the effect of deep sleep based power savings in the backbone network on the quality of service (QoS), and more specifically, the tradeoff between power consumption and network delay and packet loss. We propose a novel queuing mechanism for routers to store outgoing packets that would otherwise be lost if the next node on their route were sleeping.  
The remainder of this paper is organised as follows: we start with a review of the various approaches in the use of deep sleep and related mechanisms for energy saving. We continue with a detailed description of our proposed neighbour-aware packet queuing mechanism, then we describe the network configuration for our experiments and their results on a real large scale testbed. We conclude with the summary of our findings and suggestions of future work.
# Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples
## 3 GFormer
### Encoder  
Our encoders are built like VIT encoders, but there are two differences, as shown in Figure 3. (1) We do not use learnable positional embedding, rather we use sinusoidal positional embedding [23]. Compared with learnable positional embedding, sinusoidal positional coding does not need to be learned, which is suitable for the characteristics of small training data in few-shot semantic segmentation. Note that our position embedding is not directly added to the image embedding but is added to the query and key during the self-attention process, because the support image and the query image will go through the same encoder. If the position embedding is directly added, then the prototype feature will have position information in the process of prototype extraction, which will produce wrong guidance for the final segmentation. (2) At present, the mainstream relative position embedding adopts the contextual product method [24]. This kind of learnable relative position embedding is difficult to achieve the desired effect when the training sample is small. We propose a non-parameter relative position embedding \(rpe\), which is directly obtained according to the relative distance of image patches, calculated as:  
\[d(i)=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{1}\]  
\[rpe=softmax\{\left[\max(d)-d\right]\times\lambda\} \tag{2}\]  
where \(d\) is the relative distance of image patches, \(d(i)\) is the relative distance between patch \(i\) to image, and \(\lambda\) is the scale factor.  
Figure 2: The overview of the proposed GFormer. Each image goes through an encoder to obtain image embedding. The prototype extractor extracts the positive and negative prototypes from positive and negative image embeddings. The adversarial prototype generator generates the adversarial prototype. The decoder guides the segmentation of the query embedding through the joint action of multiple prototypes to obtain the final segmentation.  
The scale factor is used to control how much attention is paid to the surrounding patches.
# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 3 Proposed Method
### Architecture of SSD  
This section firstly gives a brief review about the most widely used single-stage detector Single Shot MultiBox Detector (SSD) [10], which is the basis of the proposed method FFESSD.  
As shown in Figure 1a, the Single Shot MultiBox Detector (SSD) is based on the reduce VGG16 [14] and an auxiliary structure is added to the end of the base network. It is noteworthy that the SSD takes advantage of multiple convolution layers for target detection, specifically, the Conv4_3 layer is used to detect smaller targets and the deeper layers are adopted for detecting targets of bigger size. However, the shallower layers lack the semantic information and each layer in the feature pyramid is used independently as an input to the classifier network, which results in poor detection accuracy of small targets and causes the problem that a single target is detected by two overlapping boxes. Hence, in this work, we attempt to enhance the relationship between each layer of the feature pyramid and enhance the feature for detecting small targets.
## 5 Limitations and Challenges  
### Limitations in Scalability  
Although the simulations yielded promising results, the current approach still has several limitations that warrant further investigation:  
\(\bullet\)**Dependency on specific parameter settings:** The power transfer characteristics of the noise-suppression structure vary with changes in the pump light or fiber parameters, consequently affecting output performance. This necessitates prior knowledge of the power range for both high and low signal levels during design, potentially limiting the structure's generalizability. Under our parameter settings, input signals below 15 mW are consistently compressed to lower power levels, while those between 20 mW and 30 mW stabilize at around 54 mW.  
\(\bullet\)**Sensitivity to device variations:** The four-wave mixing effect demands stringent phase-matching conditions, particularly for high-power pump lights, requiring exceptional device stability. Since the noise suppression relies on four-wave mixing, optimal performance depends on highly stable laser output. Therefore, when designing the system, it is necessary to pay attention to the design of the temperature control circuit to ensure the stable operation of the lasers. In addition, the method for generating phase-locked pump light in [14] can also be used to improve the stability of the pump light in the noise-suppression structure.  
\(\bullet\)**Scalability for higher-order modulation:** The QPSK system requires two second-order noise-suppression structures. For all-optical matching systems handling higher-order modulation formats, additional noise-suppression structures would be needed, increasing both system complexity and power consumption. To adapt this solution for higher-order systems, careful monitoring of nodal signals throughout the all-optical matching system becomes essential to identify the optimal insertion points for noise-suppression structures, thereby minimizing overall complexity and energy consumption.
## 3 Bit Error Rate of the OAM Link and Channel Capacity
### Channel Capacity  
Consider a line-of-sight communication link using OAM eigenstates in the range of topological charge number \(l_{0}=0,1,\ldots,M-1\) that means \(M\) dimension Hilbert space for different signal values. Further, by classical Shannon information theory of a stationary discrete memory-less system [3] and Equation (17), the channel capacity of a communication link with coaxial double-position power Gaussian vortex in weakly absorbent seawater can be given by [3]  
\[C=\log_{2}M+\frac{1}{M}\sum\limits_{l=-\infty}^{\infty}\sum\limits_{l_{0}=0}^{ M-1}P_{l|l_{0}}\bigg{[}\log_{2}P_{l|l_{0}}-\log_{2}\sum\limits_{l_{0}}P_{l|l_{0}} \bigg{]}. \tag{20}\]with the entropy of the signal vortex mode \(l_{0}\)  
\[\underset{\{P_{\{0\}}\}}{\max}H_{l_{0}}=\log_{2}M, \tag{21}\]  
and conditional entropy of the \(l\) mode for given signal mode \(l_{0}\)  
\[\underset{\{P_{\{0\}}\}}{\max}H_{l_{0}|l}=-\frac{1}{M}\underset{l=-\infty}{ \sum}\sum_{l_{0}=0}^{M-1}P_{l|l_{0}}\left[\log_{2}P_{l|l_{0}}-\log_{2}\sum_{l_ {0}}P_{l|l_{0}}\right]. \tag{22}\]
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 5 Emerging Technologies for Photonics Packaging
The principle disadvantages of both of these sub-wavelength coupler schemes is that they can normally only be fabricated by e-beam lithography, because they require feature-sizes on the order of 100 nm, which are not readily accessible using the 193 nm deep-UV lithography typically used in the Si-Photonic foundries. Until the performance of these sub-wavelength couplers can be transferred to a scalable UV lithographic process, they cannot be developed into standardized building blocks for commercial photonic devices. In addition to Fiber-to-PIC coupling, grating-couplers can be used for optical proximity coupling or interlayer coupling [62; 63; 64], which allows for efficient vertical chip-to-chip and even board-to-chip connections (in conjunction with optical-PCBs mentioned above). Combined with low-loss waveguides for horizontal distribution, this vertical chip-to-chip optical connection allows for the possibility of true 3D integrated routing of light across optical-motherboards to multiple Si-PICs. Even though the bandwidth of Fiber-to-PIC grating-couplers is typically rather narrow (normally, the 1 dB bandwidth is 30-40 nm), the same gratings in an optical proximity configuration will offer broadband coupling, because a near-field interaction between the gratings mediates the coupling. For the same reason, the insertion-loss between two adjacent grating-couplers is not simply the sum of two equivalent Fiber-to-PIC interfaces; it can be much lower, on the order of a single Fiber-to-PIC interface. These optical proximity couplers are expected to have a large impact on wafer-level packaging, because they allow for the flip-chip alignment and/or wafer-bonding of different photonic systems for advanced hybrid photonic devices.
## 5 Discussion and Use Cases
### List of Call Servers and Blocking  
Through experiments, many trace files were captured and saved during call activity. As discussed earlier, the IP address of the call receiver can be captured during call activity, unlike messages activity. It is worth noting that at least three servers were involved. The initial server authenticates the client and the DNS query response, where two more IP addresses of the servers were sent to the client for further communication. The client connected with the first server (i.e., Google's) which helps to find the receiver's IP address. The second server helps to relay the communication between the caller and receiver. After collecting the above, the client was connected to the desired receiver as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Step** & **Protocol** & **Source Port** & **Destination Port** & **Action** & **Observation** \\ \hline
1 & TCP & Any & Any & Default allow LAN to any rule. & All services work smoothly \\ \hline
2 & TCP & Any & Any & Block the entire list of IP addresses of chat servers. & Message sending failed \\ \hline \hline \end{tabular}
\end{table}
Table 6: Firewall rules for blocking the chat servers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trace Files** & **Client’s IP** & **Initial Server** & **Google’s Stun Server** & **Signal’s TURN Server** & **Receiver’s Private IP** & **Receiver’s Public IP** \\ \hline
1 & 192.168.137.34 & 13.248.212.111 & 64.233.161.127 & 52.47.185.9 & 192.168.0.5 & \\
2 & 192.168.137.206 & 13.248.212.111 & 64.233.161.127 & 52.47.185.9 & 192.168.10.10 & \\
3 & 192.168.137.206 & 13.248.212.111 & 64.233.161.127 & 52.47.185.9 & 192.168.10.10 & \\
4 & 192.168.137.25 & 76.233.92.165 & 64.233.161.127 & 52.47.185.9 & 192.168.0.6 & 182.182.252.29 \\ \cline{2-6}
5 & 192.168.137.25 & 13.248.212.111 & 64.233.161.127 & 52.47.185.9 & 192.168.0.6 & \\ \cline{2-6}
6 & 192.168.137.25 & 76.233.92.165 & 64.233.161.127 & 52.47.185.9 & 192.168.0.3 & \\ \cline{2-6}
7 & 192.168.137.25 & 76.233.92.165 & 64.233.161.127 & 52.47.185.9 & 192.168.0.3 & \\ \cline{2-6}
7 & 192.168.137.43 & 13.248.212.111 & 64.233.161.127 & 52.47.185.9 & 192.168.0.6 & 182.182.252.29 \\ \hline \hline \end{tabular}
\end{table}
Table 7: IP addresses found for call servers and receivers.  
Figure 21: Traffic pattern responses from blocked servers.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 3 Proposed Model
### Enhancement and Fine-Tuning
#### 3.2.2 Exploitation Phase  
Once the exploration phase has identified promising areas within the search space, the SCA component of the algorithm takes over to refine these solutions. The exploitation process is governed by Equation (4), where the SCA uses trigonometric functions to adjust the positions of individuals towards the best solution found. This phase is characterized by more focused and deliberate movements, utilizing the sine and cosine functions to make precise adjustments based on the relative distances to the best solution.  
The SCA's ability to alternate between sine and cosine adjustments allows for a dynamic balance between intensification and diversification within the local search area.  
This ensures that the solutions are not only honed towards the current best but also periodically re-evaluated to escape potential traps set by misleading local optima.
## 2 Calculation Theory and Model
### Finite Element Analysis Model
#### 2.1.1 Correction of Track Longitudinal Resistance  
The space geometric position of the primary beam will change under Phase II dead load due to the low vertical stiffness of the suspension bridge. This will cause extra force in CWR. In practical engineering, the rail is in a zero-stress state, theoretically, when it is just laid. Therefore, it is necessary to modify the model.  
Specific correction methods are as follows. Suppose the longitudinal displacement of the bridge deck is \(a\). The longitudinal resistance curve corresponding to the bridge node should be revised. The modified resistance curve of the nonlinear longitudinal spring is shown as the solid line in Figure 1. The longitudinal resistance can be expressed as:  
\[F=\left\{\begin{array}{ll}F_{\text{max}}&D\geq a+u\\ F_{\text{max}}(D-a)/u&a-u<D<a+u\\ -F_{\text{max}}&D\leq a-u\end{array}\right. \tag{1}\]  
where \(D\) refers to rail-beam relative displacement, \(F_{\text{max}}\) and \(u\) are respectively the limit force and slip displacement corresponding to the longitudinal resistance of track.
## 3 Methodology
### Light Use Efficiency Model  
#### 3.2.1 Structure of the LUE Model  
The general structure of a light use efficiency model to estimate GPP can be formulated as follows:  
\[\mathrm{GPP}=\varepsilon_{\mathrm{g}}\times\mathrm{PAR}\times\mathrm{FAPAR} \tag{9}\]  
where \(\varepsilon_{\mathrm{g}}\) is the actual maximum light use efficiency (\(\mathrm{\SIUnitSymbolMicro mol}\)\(\mathrm{CO_{2}}\)/\(\mathrm{\SIUnitSymbolMicro mol}\) photosynthetic photon flux density, PPFD), PAR is the incident photosynthetically active radiation (\(\mathrm{\SIUnitSymbolMicro mol}\) PPFD), and FAPAR is the fraction of PAR absorbed by vegetation. The \(\varepsilon_{\mathrm{g}}\) is affected by temperature and water stress and can be expressed as:  
\[\varepsilon_{\mathrm{g}}=\varepsilon_{0}\times\mathrm{T_{scalar}}\times \mathrm{W_{scalar}} \tag{10}\]  
where \(\varepsilon_{0}\) is the apparent quantum yield or maximum LUE (\(\mathrm{\SIUnitSymbolMicro mol}\)\(\mathrm{CO_{2}}\)/\(\mathrm{\SIUnitSymbolMicro mol}\) PPFD or \(\mathrm{g}\)\(\mathrm{C}\)/\(\mathrm{\SIUnitSymbolMicro mol}\) PPFD), and \(\mathrm{T_{scalar}}\) and \(\mathrm{W_{scalar}}\) are the scalars for the effects of temperature and water on the light use efficiency of vegetation, respectively. Using the equation developed for the terrestrial ecosystem model [52], \(\mathrm{T_{scalar}}\) from the vegetation photosynthesis model (VPM) was used in this study [53]:  
\[\mathrm{T_{scalar}}=\frac{(\mathrm{T-T_{min}})(\mathrm{T-T_{max}})}{\left[( \mathrm{T-T_{min}})(\mathrm{T-T_{max}})\right]-\left(\mathrm{T-T_{opt}}\right) ^{2}} \tag{11}\]  
where \(\mathrm{T_{min}}\), \(\mathrm{T_{max}}\), and \(\mathrm{T_{opt}}\) denote the minimum, maximum, and optimal temperature for photosynthetic activity, respectively. Their values were determined based on the previous research on GPP estimation in specific ecosystems, along with the sites' real circumstances [3]. \(\mathrm{W_{scalar}}\) was estimated from a satellite-derived, water-sensitive vegetation index:  
\[\mathrm{W_{scalar}}=\frac{1+\mathrm{LSWI}}{1+\mathrm{LSWI_{max}}} \tag{12}\]  
where \(\mathrm{LSWI_{max}}\) is the maximum land surface water index (LSWI) during the vegetation growing seasons and was calculated separately for each year.
## 5 Discussion  
### Effects of Different \(\overline{k}\) Assignment Methods on the Macro-Properties  
In previous studies, the assignment of model \(\overline{k}\) is mostly implemented by using the term _contact method pb_deformability_. Using this method, the model parallel-bond stiffness is calculated as follows [37]:  
\[\overline{k}_{n}=\overline{E}_{c}/L_{R} \tag{1}\]  
\[\overline{k}_{s}=\overline{k}_{n}/\overline{k}^{*} \tag{2}\]  
where \(\overline{k}_{n}\) is bond normal stiffness, \(\overline{k}_{s}\) is bond shear stiffness, and \(L_{R}\) is the contact length. If the contact is particle-particle, then \(L_{R}=R^{1}+R^{2}\), where \(R^{1}\) and \(R^{2}\) are the radii of the two particles, respectively; if the contact is particle-wall, then \(L_{R}=R^{1}\).  
According to the above formula, this assignment method actually achieves the target \(\overline{k}^{*}\) by maintaining constant \(\overline{k}_{n}\) and adjusting \(\overline{k}_{s}\). Increasing \(\overline{k}^{*}\) essentially leads to a continuous decrease of the overall bond stiffness of the specimens.  
In this study, the term _contact property_ was, therefore, used to assign values to \(\overline{k}_{n}\) and \(\overline{k}_{s}\). The target \(\overline{k}^{*}\) was set by maintaining constant while varying \(\overline{k}_{n}\) to evaluate the effects of different \(\overline{k}^{*}\) assignment methods on the model macro-properties.  
The effects of different \(\overline{k}^{*}\) assignment methods on the macro-properties for \(\overline{\sigma}_{n}/\overline{\tau}_{n}=0.8\) and \(4\) are shown in Figure 12. In case I, the assignment was implemented using the _contact method pb_deformability_; in case II, the assignment was implemented using _contact property_. In these two schemes, the UCS and [PERSON]'s ratio generally exhibited a consistent relationship with \(\overline{k}^{*}\), whereas [PERSON]'s modulus showed distinct relationships with \(\overline{k}^{*}\). In case I, [PERSON]'s modulus was inversely proportional to \(\overline{k}^{*}\) and decreased continuously with increasing \(\overline{k}^{*}\). In case II, [PERSON]'s modulus was positively proportional to \(\overline{k}^{*}\) and increased approximately linearly with increasing \(\overline{k}^{*}\).  
The stress-strain relationships with different \(\overline{k}^{*}\) for \(\overline{\sigma}_{n}/\overline{\tau}_{n}=0.8\) and \(4\) in case II are shown in Figure 13. In the two schemes, the UCS changed with \(\overline{k}^{*}\) following consistent trends; however, the trends of strain change were distinct between schemes. In case II, owing to the constant \(\overline{k}_{s}\) and varying \(\overline{k}_{n}\), the overall stiffness of specimens increased with increasing \(\overline{k}^{*}\), while the deformability decreased continuously. Thus, the strain decreased constantly with increasing \(\overline{k}^{*}\).
## 5 Study on Single-Parameter Control of Synchronization in Incremental Launching Construction
### Stress Changes Due to Lateral Offset  
When no lateral offset occurs, the pads are positioned on both sides of the beam axis, symmetrical about the axis. When lateral offset occurs, the contact position will be shifted from side to side. The lateral offset of the beam in incremental launching is simulated by changing the relative position of the pads and the base plate of the steel box girders, and the changes in the local stresses of the main components at lateral offsets of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm are analyzed, respectively, and the results are shown in Figure 11.  
From the figure, it can be seen that, with an increase in the lateral offset distance, the pads on one side are gradually deflected away from the web on that side and close to the outermost longitudinal stiff rib of the base plate. At this time, the base plate and the longitudinal stiff rib of the base plate are involved in more stress, and the maximum equivalent stress and deformation gradually increase. Due to the coordinated deformation of the structure as a whole, the maximum equivalent stresses of the web and the diaphragm are gradually increased. In contrast, the maximum equivalent stresses in the roof plate and longitudinal stiff ribs of the web, which are farther away from the contact region, do not change significantly. When the lateral offset exceeds 25 mm, the pads on one side  
Figure 11: Effect of lateral offset on stress.  
Figure 10: Effect of unsynchronized vertical jacking on stresses.  
leave the web. At this time, only the base plate side is located in the direct support area of the pads and the base plate stress increases steeply. When the lateral offset distance is 20 mm, the maximum equivalent stress of the base plate is 229.85 MPa, and when the lateral offset reaches 25 mm, the maximum equivalent stress of the base plate reaches 237.37 MPa, which is more than the permissible stress of steel. As the lateral offset distance continues to increase, the stress level in the base plate may even reach the yield stress of the steel, resulting in the yielding of the plate. Therefore, 20 mm is initially considered as the lateral offset control threshold when considering only a single parameter.