esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismic Risk Assessment and Retrofit Strategy
### Retrofit Strategy for Risk Mitigation  
Due to the constraints of a limited budget, only some of the bridges in a transportation network can in practice be retrofitted. The mitigation problem is to select the optimal set of bridges to minimize the seismic risk of the network. To simplify the problem, the budget constraint is specified as the number of bridges that can be optionally retrofitted. Suppose \(m\) bridges can be retrofitted from the total \(n\) bridges in a transportation network, and \(\mathbf{B}_{m}=\{B_{1},B_{2},\ldots,B_{m}\}\) denotes a candidate bridge set. Since retrofitting a bridge means improving its seismic capacity and reducing its damage probability in the context of earthquakes, \(p\big{(}\lambda_{i}=DS_{i}\big{|}IM_{i}\big{)}\) for \(B_{i}\
i\mathbf{B}_{m}\) needs to be updated in the retrofitted network, and the PDF for network damage vector becomes:  
\[p_{m}(\mathbf{\lambda})=p(\mathbf{\lambda}_{m})p(\mathbf{\lambda}_{\overline{m}})=\prod_{i \in\mathbf{B}_{m}}p(\lambda_{i})\prod_{i^{\prime}\
otin\mathbf{B}_{m}}p(\lambda_{i^{ \prime}}) \tag{3}\]  
where \(\mathbf{\lambda}_{m}=\big{\{}\lambda_{B_{1}},\lambda_{B_{2}},\ldots,\lambda_{B_{m }}\big{\}}\) represents the damage states of retrofitted bridges, and \(\mathbf{\lambda}_{\overline{m}}\) represents those of the remaining bridges. Then the seismic risk of the retrofitted network associated with \(\mathbf{B}_{m}\) can be calculated as:  
\[P_{F}(\mathbf{B}_{m})=\int\varphi(\mathbf{\lambda})p_{m}(\mathbf{\lambda})d\mathbf{\lambda}= \!\!\int\varphi(\mathbf{\lambda})p_{m}(\mathbf{\lambda}\big{|}\mathbf{IM})p(\mathbf{IM}|EQ \,)p(EQ)dEQ \tag{4}\]  
Subsequently, if each damage-state vector \(\mathbf{\lambda}\) of the network is enumerated, and the corresponding network functionality \(Q(\mathbf{\lambda})\) is computed, the failure probability for any retrofit strategy can be obtained according to Equations (3) and (4). The optimal solution of the problem can be written as:  
\[\mathbf{B}_{m}^{*}=\underset{m<n}{\text{argmin}}P_{F}(\mathbf{B}_{m}) \tag{5}\]  
However, computing the network functionality of each damage-state vector is often extremely time-consuming. As mentioned previously, researchers tried to reduce the computational cost by MCS and heuristic algorithms [16; 23; 24]. These approaches will somehow sacrifice the accuracy of the results, and still face considerable computation pressure for multidimensional problems. Instead, in the next section, we will introduce a novel importance-based alternative algorithm for the optimization model, which can significantly reduce the computational cost and still consider the impact of multiple uncertainties and the interactive effect between components.
## 3 Controller Design
### Proposed Controller Design
#### 3.2.2 Time Delay Estimator (TDE)  
The time delay estimator (TDE) is used to estimate the magnitudes of the other (i.e., non-payload-related) disturbances and uncertainties, which is a type of disturbance estimator. TDE is a simple, highly capable estimation method for the estimation of uncertainty terms using dynamic model equations in accordance with known state variables and their derivatives. In other words, TDE modifies the control signal directly using previous observations of system responses and control inputs rather than by adjusting the controller gain. It follows, therefore, that estimation accuracy is inversely proportional to time delay \(\Delta t\).  
For the dual-axis manipulator system dealt with in this paper, Equation (27) stipulates that the target term \(\Delta\big{(}q,\hat{q},U,t\big{)}\) can be expressed as follows:  
\[\Delta\big{(}q,\hat{q},U,t\big{)}=\ddot{q}-M_{0}^{-1}U+F+\mathcal{N} \tag{38}\]  
In estimating this term, we assume that its value at the current time (i.e., \(t_{0}\)) is very close to the value at \(t_{0}-\Delta t\), if time delay \(\Delta t\) is sufficiently small, such that  
\[\Delta\big{(}q,\dot{q},U,t\big{)}|_{t=t_{0}}\cong\Delta\big{(}q,\dot{q},U,t \big{)}\big{|}_{t=t_{0}-\Delta t} \tag{39}\]Thus, we define the output value of the TDE as  
\[\hat{\Delta}\big{(}q,\dot{q},U,t\big{)}|_{t=t_{0}}\triangleq\Delta\big{(}q,\dot{q },U,t\big{)}\Big{|}_{t=t_{0}-\Delta t}=\Big{(}\tilde{q}-\Big{(}M_{0}^{-1}U \Big{)}-F-\mathcal{N}\Big{)}\big{|}_{t=t_{0}-\Delta t} \tag{40}\]  
The estimation process generates estimation error \(\varepsilon\) satisfying the following equation:  
\[\Delta\big{(}q,\dot{q},U,t\big{)}|_{t=t_{0}}=\hat{\Delta}\big{(}q,\dot{q},U,t \big{)}|_{t=t_{0}}+\varepsilon \tag{41}\]  
The output value of the time delay estimator is  
\[U_{TDE}=\hat{\Delta}\big{(}q,\dot{q},U,t\big{)}|_{t=t_{0}}=[u_{TDE1}\ \ u_{TDE2}]^{T} \tag{42}\]  
Accordingly, we can use ARBNN (\(U_{NN}\)) to estimate the payload portion without a known upper bound in conjunction with TDE (\(U_{TDE}\)) to estimate factors other than payload. The equivalent control (\(U_{eq}\)) and switching control (\(U_{r}\)) of NFTSMC can then be combined to output the final control signal as long as the estimation error of ARBNN (\(\Delta N\)) and estimation error of TDE (\(\varepsilon\)) satisfy the following inequalities:  
\[|\Delta\mathcal{N}|\leq\varphi\in R^{n\times 1} \tag{43}\]  
\[|\varepsilon|\leq\varrho\in R^{n\times 1} \tag{44}\]  
where \(\varphi\) and \(\varrho\) are both positive vectors. Accordingly, the control input of the proposed controller can be redesigned as  
\[U_{DM2}=M_{0}U_{eq}-M_{0}U_{NN}-M_{0}U_{TDE}-M_{0}U_{r} \tag{45}\]  
where \(U_{eq}=O_{2}^{-1}\otimes\big{(}-\dot{e}-O_{1}\otimes\dot{e}\big{)}-F-\varpi +\tilde{q}_{d}\), \(\varpi\in R^{n\times n}\) is a small positive definite diagonal matrix, and \(U_{r}=(\delta+\varphi+\varrho)\otimes\text{sign}(s)\).
## 2 Materials and Methods
### Synthetic CT Evaluation  
The evaluation of the performance of the best neural network obtained was investigated on the test set, composed of patients clinically treated using Volumetric Arc therapy (VMAT) and Intensity Modulated (IMRT) planning. The clinical and dosimetric characteristics of the patients included in the test set are reported in the Section 3.  
For each of the test cases, a sCT was generated from the CBCT image of the first treatment fraction, and the clinical treatment plan originally calculated on the simulation CT was recalculated on the sCT. Dose calculation was performed using the Monte Carlo algorithm (Acuros XB version 16.1.0), adopting a grid resolution of 1.25 mm\({}^{3}\).  
For each test case, the sCT was evaluated in terms of image and dose accuracy.  
To avoid any possible bias in algorithm performance evaluation, a preliminary visual consistency check was carried out by a radiation oncologist in the test set, with the aim of removing patients showing large differences in positioning and anatomical conditions between the CBCT acquired at first fraction and CT simulation.  
Image accuracy was quantified by calculating the mean absolute error (MAE) and mean error (ME) in terms of Hounsfield Units (HU) between the synthetic and the original CT within a field of view of 28 cm.  
Dose accuracy was estimated by calculating the difference in the estimation of various Dose Volume Histogram (DVH) indicators and performing gamma analysis between the dose distribution calculated on the sCT and those calculated on the original CT [22].  
As regards the DVH comparison, the following five parameters were considered for Planning Target Volume (PTV):  
- Percentage volume covered by 95% of the prescription dose (V95% [%]).  
- Percentage volume covered by 105% of the prescription dose (V105% [%]).  
- Near-maximum dose in Gy, which is the dose covering 2% of the PTV volume (D2%[Gy]).  
- Near-minimum dose in Gy, which is the dose covering 98% of the PTV volume (D98%[Gy]).  
- Median dose in Gy, which is the dose covering 50% of the PTV (D50%[Gy]).  
As for the organs at risk (OARs), the last three parameters (D2%[Gy], D98%[Gy] and D50%[Gy]) were considered DVH indicators for the rectum and bladder. The difference among the DVH indicators was evaluated using a box plot analysis [16; 23].  
As for gamma analysis, the mean 3D gamma passing rate, indicating the percentage of points reporting a gamma value inferior to one, was reported as the global gamma index. A low dose threshold level equal to 10% of the maximum dose and three different tolerance criteria (1%/1 mm, 2%/2 mm, and 3%/3 mm) were used, as also reported in similar studies in this field [16; 17]. The Verisoft software (version 3.5, PTW, Freiburg, Germany) was used to calculate the gamma analysis.
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 4 Methodology
### EVN Embedding  
EVN embedding is the combination of VNE and NFV resource allocation and it can be separated in two stages. The first stage is to embed all of the nodes and simple VLis, and the second stage is to embed the VLis with a VNF request. The node mapping first considers the ID demand to map devices (specific VNo on a specific SNo). Subsequently, the type, subtype, location, and domain are considered to map the other nodes. For the nodes of type \"application\" and subtype \"computeInstance\", the CPU demand/resource is considered.  
The link mapping uses [PERSON]'s algorithm [21] for finding k shortest paths between the source and destination VNos of the VLi. For the simple link mapping (without VNF demand), the candidate paths are verified for the bandwidth demand. For mapping the VLis with VNF demand (VNF request), the candidate paths are given to the chain embedding heuristic. In both cases, the redundant VLi is mapped on another candidate path from the k shortest paths. This path is chosen as the path with the least number of common SNos with the path used for the original VLi. This method provides high reliability, but with accepted resource utilization by finding the most disjoint short path from the shortest path.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### Ground-Based Observation
#### 2.1.2 COCCON (COllaborative Carbon Column Observing Network)  
KIT began constructing the COCCON (Collaborative Carbon Column Observing Network) using the portable FTIR spectrometer EM27/SUN in 2016 [15]. The EM27/SUN has a spectral resolution of 0.5 cm\({}^{-1}\) (corresponding to a maximum optical path difference of 1.8 cm) and an acquisition time of approximately 58 s (the average time for 10 scans at a scanning speed of 10 kHz). It employs a DC-coupled InGaAs (indium gallium arsenide) detector and an extended InGaAs detector [17] to simultaneously observe various gas molecules, including CO\({}_{2}\), CH\({}_{4}\), CO, H\({}_{2}\)O, O\({}_{2}\), N\({}_{2}\)O, and HDO, within the frequency range of 4000-11,000 cm\({}^{-1}\). The dual-channel observation mode covers the same spectral range used by TCCON and TROPOMI for short-wavelength inversion of CO and CH\({}_{4}\).  
The COCCON network is designed to enhance greenhouse gas monitoring by addressing spatial and temporal gaps left by TCCON (Total Carbon Column Observing Network). By utilizing portable FTIR spectrometers like the EM27/SUN, COCCON accurately measures column concentrations of gases such as CO\({}_{2}\) and CH\({}_{4}\). The portability of COCCON's instruments allows for flexible deployment in various environments, including remote and challenging areas, thereby extending the spatial coverage of greenhouse gas monitoring. The network provides high-resolution data, improves overall atmospheric observations, and collaborates with existing networks like TCCON to integrate data and enhance monitoring quality. Additionally, COCCON data supports the validation and improvement of satellite-based measurements and aids in climate change research and policy-making. Currently, nearly 100 EM27/SUN units are used by research teams worldwide for greenhouse gas research, including fixed-site concentration observation and field experiment emission estimation.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Host Discovery  
In the process of communicating and exchanging packets (in and out) in OpenFlow protocol, and when an OpenFlow SDN-Switch receives a packet that does not match any flow rule in its flow table. In this case, one possibility is that a new host is connecting to the network. The new host starts sending the packets to SDN-Switch, then the OpenFlow SDN-Switch will encapsulate that package with a Packet_In message and send it to the SDN-Controller. The SDN-Controller will in turn use this Packet-In message to discover the hosts on the network, then the SDN-Controller will extract the host's location (i.e., to which SDN-Switch port it is connected to), host's IP address, and its MAC address from Packet_In messages [32].  
Figure 4: Flowchart showing details of packet flow via OpenFlow SDN-Switch [58].
## 2 State of the Art  
### Systems Engineering  
Systems engineering (SE) aims to manage increased complexity and achieve a cross-disciplinary optimum within the financial and time constraints of a project. A strong emphasis is placed on user-centered development, enabled by focused requirement management and requirements engineering (RE). This focus on stakeholder requirements increases the efficiency of product development as the process is not slowed down by addressing irrelevant issues [3; 4]. The early phase identification of requirements and design constraints and concepts speeds up and reduces the costs of development, as shown, for example, for hydrogen refueling stations in [5].
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 6 Performance Evaluation
### Proposed Approach Properties and Operation
#### 6.1.2 Power Allocation  
In this subsection, the second step of our approach i.e. the power allocation Algorithm 2 is evaluated. After the application of Algorithm 1, a RB selection for all users of the network is obtained.  
Figure 5: Interference before and after the application of Algorithm 1.  
Figure 6: Rate per Hertz before and after the application of Algorithm 1.  
Then as explained before, we apply Algorithm 2 for every co-sharing RB. The simulation setup for this section of our experimentation is the same as presented in Table 1. It is noted that in principle users may have different SINR requirements depending on the type of service they request. For demonstration only purposes in the set of results presented here, we have assumed a common SINR requirement for all users of the network equal to 10. Algorithm 2 instructs users who share a common RB to act in a parallel fashion. The convergence of Algorithm 2 takes place after a quite low number of iterations. All users start from an initial power allocation which is their maximum transmission power. This is considered to be 2 watts for the cellular users and 0.1 watts for the D2D users. During the convergence process of Algorithm 2, each user tries to find the minimum necessary transmission power in order to satisfy his SINR requirement. In case this is not found, it transmits at his maximum power.  
In Figure 7, the transmit power convergence is shown for a randomly selected RB, that is RB 6. It is shown that all users of the network who share RB 6 converge to their desired transmission power values. All users of this RB either meet or exceed their SINR requirement. The transmit power radically reduces for all users, and as a result the total power consumption decreases by 57% with respect to the initial power values. Moreover the average number of users, throughout the whole network, who satisfy their SINR requirement increases from 29.15 to 55.35 after the application of Algorithm 1 corresponding to an increase of approximately 90%, and finally to 61.19 users after the combined application of Algorithms 1 and 2, raising this improvement to 110% in total.
## 4 Aperture Design: Parametric Analyses and Performance Evaluation
### Quadridge Radiating Elements
The scan loss is an important figure-of-merit that quantifies the reduction in the array beam's maximum radiated power (with respect to broadside \(\theta=0\)) as a function of the scan angle/plane. The circular polarization data have been computed according to Equations (4) and (6) as a function of the radiated field for linear polarization. \[E_{co,LH} =\frac{1}{2}\big{[}(E_{\theta,1}+jE_{\theta,2})-j\big{(}E_{\phi,1 }+jE_{\phi,2})\big{]} \tag{4}\] \[E_{cross,LH} =\frac{1}{2}\big{[}(E_{\theta,1}+jE_{\theta,2})+j\big{(}E_{\phi,1 }+jE_{\phi,2})\big{]}\] (5) \[E_{co,RH} =\frac{1}{2}\big{[}(E_{\theta,1}-jE_{\theta,2})+j\big{(}E_{\phi,1 }-jE_{\phi,2})\big{]}\] (6) \[E_{cross,RH} =\frac{1}{2}\big{[}(E_{\theta,1}-jE_{\theta,2})-j\big{(}E_{\phi,1 }-jE_{\phi,2})\big{]} \tag{7}\]  
From Figure 13a, a maximum difference of 2 dB is observed between planes at 17.7 GHz (blue lines) and 19 GHz (red lines). At 20.2 GHz (yellow lines) the scan loss drops more than 6 dB due to the scan blindness discussed above. The same consideration applies for circular polarization (Figure 13c). When the evanescent section is added (Figure 13b),  
\[S_{11}^{Circ} =\frac{1}{2}[S_{11}-S_{22}-j(S_{12}+S_{21})] \tag{2}\]0 Scan loss for both quadridge designs. the drop due to scan blindness at the upper frequency of the band disappears, obtaining values between \(-2\) and \(-4\) dB for all the planes and frequencies. Similar values are visible for circular polarization. The XPD performance in D-plane (Ludwig 3) is shown in \[S_{11}^{Circ} =\frac{1}{2}[S_{11}-S_{22}-j(S_{12}+S_{21})] \tag{2}\]1 This parameter quantifies the polarization purity of the array beam at the corresponding scan angle. The E- and H-planes are not reported due to the two-fold symmetry (the simulated values are above 40 dB). Circular polarization data have been computed as the ratio between the Equations (4) and (5), and Equations (6) and (7). The quadridge for linear polarization (Figure 14a) provides values around 10 dB at 17.7 and 19 GHz, and a value of about 7 dB at the upper frequency. The presence of the blindness is clearly visible for circular polarization (Figure 14c), where XPD approaches 0 dB. This happens because one of the two linear components is completely reflected and hence the radiated field is linearly polarized. The values of the scan limits improve for the evanescent quadridge, being higher than 14 dB for the three frequencies for linear polarization (Figure 14b). The values of XPD are related to both the intrinsic cross-polarization levels of the fundamental modes presented in Section 3.1 (that are non-zero in the D-plane) and the contribution of the evanescent higher-order modes excited at the aperture (discontinuity between waveguide and free-space). In terms of circular polarization (Figure 14d), there is a decrease at 20.2 GHz in the planes \(\phi=0^{\circ}/90^{\circ}\). This is due to the different radiation patterns for the two principal polarizations at high scan angles, as can be seen when comparing solid and dotted lines in Figure 13b (see Equations (4)-(7)).
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 1 Introduction
In contrast, the scheme DSCSD in this paper can route flows from S\({}_{1}\) and S\({}_{2}\) to D to select different paths according to their types and the real-time link information (Figure 1b), and the congestion can also be well controlled. The main contributions of this paper are as follows. First, we proposed a new approach for traffic scheduling that could route a newly arrived flow based on the real-time link information, and also dynamically schedule flows on the link. Better than traditional approaches and the SDN-based scheme with threshold value, we could improve the efficiency of the link utilizing the shortest paths. Second, we innovatively adopted a multilevel feedback queue mechanism of congestion control suitable for different types of flows and could realize anomaly detection by preventing malicious flows from occupying the bandwidth for a long time. The remainder of the paper is organized as follows: In Section 2, we review the traditional network and SDN used in data centers. Then, we elaborate on the design and implementation of our proposed scheme, DSCSD (Dynamic Scheduling and Congestion control across data centers based on SDN) in  
Figure 1: Flows that preempt the shortest path (**a**) can be shunted into two different paths (**b**) with DSCSD. Section 3. The performance evaluation of DSCSD is presented in Section 4. Finally, Section 5 concludes the paper.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 4 Experiments
### Qualitative Evaluation  
To further assess NuCap's ability to generate count-aware captions, we conduct a qualitative analysis using a subset of images from the CountBench dataset, each containing 3 to 10 instances of a specific object. Figure 5 shows the results of captions generated by different captioning models, including NuCap and traditional baseline tools.  
Figure 4: Confusion matrix of different models evaluating quantitative counting performance. The horizontal axis denotes the predicted object count, and the vertical axis shows the ground-truth count. Diagonal concentration indicates higher accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of Parameters** & \multirow{2}{*}{**Inference Time (s)**} \\  & **(M)** & \\ \hline mPLUG-owl3 (2B) [21] & \(\approx\)2000 & 349 \\ mPLUG-owl3 (7B) [21] & \(\approx\)7000 & 465 \\ BLIP (base) [20] & 247 & 151 \\ BLIP (large) [20] & 470 & 164 \\ GIT (base) [37] & 129 & 826 \\ GIT (large) [37] & 347 & 2638 \\ \(MeCap_{TF}\) [12] & 829 & 2143 \\ ExpansionNet v2 [11] & 38 & 144 \\ GRIT [26] & 180 & 130 \\ NuCap & 180 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model size and inference time across different image captioning and VLMs. Inference time is measured on the CountBench dataset.  
The examples reveal clear differences in numerical reasoning ability. Conventional captioning models such as GRIT and GIT frequently omit explicit count information or rely on vague quantifiers such as \"a group of\" or \"a set of\", particularly in images with higher object counts (6-10). While VLMs like mPLUG-Owl3 exhibit relatively better numeric awareness, they still struggle to consistently generate accurate object counts.  
In contrast, NuCap consistently produces captions that explicitly and correctly reflect the number of objects in the scene. Its performance is especially strong in mid-range object counts (4-8), where baseline models tend to falter. These qualitative findings align with the quantitative results reported in Table 4 and Figure 4, reinforcing NuCap's ability to integrate numeric reasoning into fluent caption generation. These findings suggest that NuCap can serve as a practical solution in vision-language tasks requiring precise numerical grounding.
## 3 Results and Discussion
### Effects on the AE and Failure Behavior  
The deterioration caused by chemical erosion makes the fracture processes of CGB very complex. Therefore, it is crucial to understand the fracture behavior of CGB during loading. The acoustic emission data can be used to study the spatial evolution of deformation and fracture in CGBs during loading, making it an effective method for damage assessment [24]. The raw acoustic emission waveforms can be analyzed for various parameters, such as the peak counts, energy, and entropy, to determine the fracture initiation point and identify the damage stage [25]. [PERSON] et al. [26] found a specific relationship between the acoustic emission signal and the stress-strain behavior of the material during loading. Therefore, in this study, the changes in the stress threshold of CGB during their damage process after chemical erosion were explored with acoustic emission technology, and the changes in the acoustic emission signals of the CGB samples with different erosion times were analyzed and evaluated. Ultimately, these results are related to the damages caused by chemical erosion on the CGB.