esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance and Measurement Devices for Membrane Buildings in Civil Engineering: A Review
## 5 Conclusions and Perspectives  
### Conclusions  
This paper reviewed the performance of membrane buildings and measurement devices, which can be summarized as follows:  
1. The loading response of membrane structures is related to the specific structural form. Damage to the joints between the membrane and the rigid boundary and seams between the membranes are the leading causes of membrane structure failure under external loads. The presence of wrinkles significantly reduces the performance of the structure. Appropriate building configuration design is the preferred solution to enhance the mechanical performance of the structure. Furthermore, achieving this goal by attaching reinforcing elements to the inflatable structure is one of the recommended methods.
2. The proper placement of the inflatable membrane structure in its uninflated state reduces the difficulty of self-deployment, and assisted lifting during the inflation process greatly augments the deployment efficiency. Variation in the internal pressure during the deflation process begins with a sharp drop and then reaches a steady state. The magnitude and location of the external load affect the fluctuation of internal pressure and the value of residual pressure and determine the form of structural collapse.  
Figure 24: SHM system framework for the air-supported membrane structure of Shanghai Jiao Tong University [19].  
3. Passive methods such as structural design and natural light utilization and active methods such as ventilation systems and retractable devices both substantially change the thermal environment inside the building and reduce energy consumption.
4. Non-contact sensors with no interference on membranes make them more suitable, and equipment such as cameras allow for multi-point measurements at a low cost. The measurement points for monitoring large-scale membrane structures are mainly arranged on steel cables or rigid members to provide indirect measurements of the response of the membrane surface.
## 3 Results and Discussion
### Resilient Modulus (M\({}_{\text{R}}\))  
Figure 3 shows the average values of M\({}_{\text{R}}\) for R0 and RAP mixes. It can be seen that the M\({}_{\text{R}}\) increased with the increment in RAP content. Mixes with higher M\({}_{\text{R}}\) were assumed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{5}{c}{**Mix Type**} \\ \cline{2-6}  & **R0** & **R30** & **R50** & **R70** & **R100** \\ \hline OBC (\%) & 5.9 & 5.8 & 5.6 & 5.5 & 5.5 \\ Modified binder used (\%) & 100.0 & 78.2 & 62.0 & 45.9 & 22.2 \\ Binder amount saving (\%) & 0.0 & 23.2 & 41.7 & 57.4 & 79.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of OBC and the amount of binder.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mix Types**} & \multicolumn{5}{c}{**Marshall Parameters**} \\ \cline{2-5}  & **Stability (kN)** & **Flow (mm)** & **VIM (\%)** & **VMA (\%)** & **VFB (\%)** \\ \hline R0 & 10.2 & 3.0 & 4.3 & 17.4 & 75.0 \\ R30 & 11.9 & 3.1 & 3.6 & 16.5 & 78.5 \\ R50 & 11.1 & 3.4 & 3.6 & 16.1 & 78.0 \\ R70 & 10.4 & 3.4 & 3.6 & 16.0 & 77.0 \\ R100 & 10.2 & 4.0 & 4.6 & 16.7 & 72.9 \\ PWD limit & \(\geq\)8.0 & 2–4 & 3–5 & \(\geq\)14.0 1 & 70–80 \\ \hline \hline \end{tabular} \({}^{\dagger}\)Asphalt Institute standard [90].  
\end{table}
Table 5: Marshall test results in OBC.  
to be more resistant against deformation. The main reason was the existence of an aged binder (the higher the RAP content, the more the aged binder), indicating that the aged binder was able to increase the elastic component of the viscoelastic HMA mix. Moreover, the incorporation of CRM, which has exceptional viscoelastic properties, increased the elastic recovery value. Nonetheless, the use of WEO reduced the \(\mathrm{M_{R}}\) as demonstrated by the slow growth rate of \(\mathrm{M_{R}}\) value by some 25% with R100 mix compared with the R0. This occurrence may be associated with the change in the binder's rheological properties. Thus, it can be concluded that the combined use of WEO and CRM was effective to obtain a balanced \(\mathrm{M_{R}}\) value.
## 3 Network and Energy Model  
This paper adopted the radio energy model in HEED [10] for free space and multipath channel models. As the nodes are equipped with quasi-stationary distribution and scattered autonomously in a network field, the HEED's energy model is considered to be the most appropriate choice. In addition to the built-in functionality of sensing nodes, the polar coordinates of each sensing node are known to that node, and sensing devices are GPS enabled.  
The sensing nodes are generally deployed independently and the distance is measured to connect the scattered nodes. After the outer structure is defined, the sensing field is divided into virtual circles. The diameter of the inner layer should be greater than that of the outer layers to avoid the hotspot issue. The network field is divided by using polar coordinates defined as follows:  
* The radius of the circle (\(r\))
* The angle (\(\theta\))
* The center position or midpoint of the area (\(x^{\prime}\),\(y^{\prime}\))The sensing field is divided into circular-shaped clusters using polar coordinates of the circle, which uses the \(r\), \(\theta\), and (\(x^{\prime}\),\(y^{\prime}\)) based on the following formula:  
\[\theta=\frac{angle}{2}*i \tag{1}\]  
\[x=r*\cos(\theta)+x^{\prime} \tag{2}\]  
\[y=r*\sin(\theta)+y^{\prime} \tag{3}\]  
The deployment structure design is shown in Figure 1. The diameter of the first layer should be higher than those of the remaining outer layers. Each segment is further divided into equal-sized groups. Equal-sized groups contain randomly distributed nodes to achieve load balancing. The layers in the area are partitioned into \((2*i-1)*n\) groups with n quadrants where \(i=(1,2,3,4\ldots,m)\) is the layer from inner to outer structure and \(n=(2,4,6,8,\ldots)\) represents the number of quadrants the area gets partitioned.  
Since the network field is portioned into concentric rings in fan shaped clustering by [PERSON] et al. (2018), the same layering and quadrants approach is adopted in this work. The quadrants are applied from 2 to 8 and dynamically changing the quadrant is beyond the scope of this paper. The Euclidean distance is used in HEED and applied to calculate energy requirements within the cluster region, and also the energy requirements for intercluster communication.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## Appendix A The Structure of the Model  
Total radiation at the top of the atmosphere (\(AT\)) is the ratio of total downward shortwave radiation (\(S_{t}\)) to extraterrestrial radiation (\(S_{0}\)).  
\[AT=S_{t}/S_{0} \tag{1}\] \[S_{0}=S_{c}\times cos(\theta) \tag{2}\]  
where \(\theta\) is the solar zenith angle and \(S_{c}\) is a solar constant (1367 W/m\({}^{2}\)).  
We estimated the cloudiness fraction (\(C_{f}\)) based on the total incoming shortwave radiation using the following empirical relationship [45]:  
\[C_{f}=\left\{\begin{array}{ll}0&,clearsky\\ 1-\frac{AT-a_{s}}{b_{s}}&,cloudiessfraction\\ 1&,fullcloudsky\\ \end{array}\right. \tag{3}\]  
where \(a_{s}\) is the fraction of extraterrestrial radiation on overcast days and \(a_{s}+b_{s}\) is the fraction of extraterrestrial radiation on clear days (in this paper, \(a_{s}=0.25\) and \(b_{s}=0.5\)).  
[PERSON] and [PERSON] estimated the proportion of total radiant energy from the top of the atmosphere reaching the canopy using the diurnal temperature difference method [106]. Through this proportion, the direct radiation (\(AT_{b}\)) and scattered radiation (\(AT_{d}\)) reaching the canopy can be calculated as follows:  
\[AT_{b}=\lambda AT_{c}(1-C_{f}) \tag{4}\] \[AT_{d}=AT-AT_{b}\] (5) \[AT_{c}=\max(AT,a_{s}+b_{s}) \tag{6}\]  
where \(\lambda\) is the ratio of direct to total radiation for a clear sky (in this paper, \(\lambda=6/7\)) and \(AT_{c}\) is the clear-sky transmission factor.  
The total downward shortwave radiation (\(S_{t}\)) that reaches the top of the atmosphere is partitioned into incoming direct radiation (\(S_{b}\)) and diffuse radiation (\(S_{d}\)) [44] as follows:  
\[S_{b}=\frac{AT_{b}}{AT}S_{t} \tag{7}\] \[S_{d}=\frac{AT_{d}}{AT}S_{t} \tag{8}\]
# Key Issues in Using the Freezing Method in Power Tunnel Rehabilitation Projects
## 1 Introduction
[20] introduced the use of horizontal freezing technology to repair tunnels, using vertical sealing water retaining soil and horizontal freezing of the concealed excavation in a project involving destruction of a tunnel and construction of an intact tunnel connection section, with the repair project being successful. [PERSON] et al. [21], in conjunction with the Sichuan gas east pipeline project Wuhan Yangtze River shield crossing project, tracked and monitored aspects such as freezing brine temperature, permafrost temperature, and groundwater level at Wenkong. Through analyzing and researching the monitoring results, they obtained dynamic data regarding frozen brine temperature, frozen soil temperature, and hydrological borehole water level, and put forward a proposal for tunnel repair and freezing construction on this basis. [PERSON] et al. [22] detailed the use of a four-row pipe local freezing method to repair subway tunnels, and monitored and analyzed the site freezing temperature, freezing force, and so on. The analyzed results showed that the quality of the freezing method is affected by temperature. By reasonably arranging temperature monitoring points, adopting informationized construction, and mastering changes in temperature, the safety of freezing construction as a part of complex projects can be ensured. Upon reviewing the relevant literature, it is evident that the artificial ground freezing method has been widely applied in tunnel rehabilitation projects. Experts and scholars have delved into the mechanism of reinforcement through numerical simulations and on-site measurements, examining the temperature field, freezing expansion, and thawing sinking rules, thereby establishing a comprehensive freezing theory. However, there is a dearth of research on the vertical freezing method. Consequently, it is imperative to investigate the temperature and freezing degree fields of the vertical freezing method in tunnel rehabilitation projects. This study aimed to explore the development and change laws of the temperature field of the vertical freezing method, offering a foundation for future tunnel repair projects. Utilizing an underground collapsed tunnel freezing reinforcement repair project as a case study, in this study we constructed a three-dimensional water-heat coupling model to examine the frozen permafrost curtain and temperature field development and distribution under different seepage conditions. Through dynamic simulations of the evolution of the permafrost curtain, the findings from this project can serve as a theoretical basis for the design of similar projects in the future.
## Appendix B Analysis Using Only One Counterweight
### Numerical Results  
Three solutions are selected form the Pareto front using only Counterweigh-3, they are chosen as follows:  
1. If the interest is to optimize the index related to the ShF (\(\beta_{ShF}\)), without giving importance to the index related to the ShM (\(\beta_{ShM}\)), the selected solution is: \[x_{\alpha 3}=-0.11950597007297\quad y_{\alpha 3}=0.0984494486824036\quad t_{ \alpha 3}=0.04\] By using this solution, the ShF can be reduced by 78.74%, while the ShM by 0.42%.
2. If the interest is to optimize the index related to the ShM (\(\beta_{ShM}\)) without giving importance to the index related to the ShF (\(\beta_{ShF}\)), the selected solution is: \[x_{\alpha 3}=-0.240986266107467\quad y_{\alpha 3}=0.0012908547612233\quad t_{ \alpha 3}=0.00715115109817927\]By using this solution, the ShM can be reduced by 73.61% while the ShF by 3.22%.
3. If the interest is to optimize both indexes (\(\beta_{ShF}\) and \(\beta_{ShM}\)) the selected solution is: \[x_{c3}=-0.130165416712609\quad y_{c3}=0.0500126887169449\quad t_{c3}=0.04\] This solution reduces the ShF by 51.19% and the ShM by 53.31%.  
In Table 2 the comparison between the optimization results when using three, two and only one counterweight can be observed. It is evident that the best results are obtained when using three counterweights. Still, it is interesting to notice that when using only Counterweight-3, it is possible to improve the balance of the whole mechanism and reduce the total cost of the implementation.
# A Review and Future Directions on the Deterioration Evaluation of Concrete Utility Poles
## 6 Summary  
This paper examines technological trends and challenges in utility pole maintenance by summarizing existing regulations and research findings related to utility poles. The results are as follows:  
* Regarding deterioration evaluation methods for concrete utility poles, there are few case studies due to the global prevalence of wooden utility poles. However, some findings from wooden utility poles can be applied.
* In existing studies, it is important to understand the mechanisms of deterioration in concrete utility poles in service and identify the threshold of accumulated mechanical damage requiring replacement. However, existing research on concrete utility poles has not addressed these aspects. Future studies on the deterioration evaluation of concrete utility poles should focus on analyzing the mechanisms of mechanical damage and failure progression in service poles and defining the ultimate state and damage conditions that necessitate replacement.
* Non-destructive inspection methods for concrete utility poles, such as vibration-based evaluation methods and advanced visual inspection using drones, which have also been explored for wooden utility poles, are deemed appropriate. While the application of failure probability analysis, which requires the construction of analytical models, may take time, it is worth considering due to its compatibility with economic impact assessments in existing studies. An important challenge for the future is the need to establish the correlation between damage states and data obtained through non-destructive testing, as current methods only allow for identifying the presence or absence of damage. This step is crucial for efficiently maintaining both concrete and wooden utility poles.  
Conceptualization, T.U. and H.T.; methodology, T.U.; data curation, T.U. and K.Y.; writing--original draft preparation, T.U.; writing--review and editing, T.U. and H.T.; supervision, H.T. and M.H.; project administration, M.H. and H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 12: Method of rational deterioration evaluation.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] were employed by the company Kyushu Electric Power Co. Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Denoising Diffusion Models on Model-Based Latent Space
## 2 Related Works
### Evaluation of Generative Models  
Assessing the performance of generative image models is notoriously complicated, and different strategies have been proposed thought the years. Inception Distance (ID) [7] measures the dissimilarity between the distribution of generated images and a reference dataset using an InceptionV3 network pretrained on ImageNet [8]. ID is computed by extracting a feature representation from both the real and the generated images and then computing the Kullback-Leibler (KL) divergence between the related distributions. The Frechet Inception Distance (FID) [9] takes this concept by instead computing the Frechet distance between the features distributions. From a practical standpoint, computing the FID score requires a large set \(R\) of images from the real distribution and a comparably sized set of generated images \(G\). First, the pretrained InceptionV3, up to its final pooling layer, is used to extract 2048-dimensional feature vectors from each image in both sets. Afterwards, the FID is computed by measuring the Frechet distance between the two sets of feature vectors, as defined in Equation (1). This computation is carried out after determining the mean vectors (\(\mu_{R}\), \(\mu_{G}\)) and covariance matrices (\(\Sigma_{R}\), \(\Sigma_{G}\)) for both sets of feature vectors:  
\[\text{FID}(G,R)=||\mu_{G}-\mu_{R}||_{2}^{2}+\text{Tr}\Big{(}\Sigma_{G}+\Sigma_{R }-2(\Sigma_{R}^{1/2}\Sigma_{G}\Sigma_{R}^{1/2})^{1/2}\Big{)} \tag{1}\]  
where \(\text{Tr}(\cdot)\) defines the trace operator. The metric defined with this formulation ideally should quantify the perceivable difference in appearance between the generated and real images; in fact, as the FID score approaches zero, it should indicate that the two sets of images are indistinguishable. However, how the FID and similar metrics might fail at capturing semantic aspects of image content, such as object composition or scene coherence, is still being investigated. Moreover, subtle details of the implementation have been shown to invalidate the reliability of these metrics [10]. In addition, a disadvantage of these types of metrics is that they require the generation of an image set in the tens of thousands, which, depending on the model under consideration, can take quite an onerous amount of computation time. Nonetheless, for the purpose of this work, the assessment by means of FID score was opted for.
# Tracking and Registration Technology Based on Panoramic Cameras
## 3 Method
### Panoramic View Reconstruction
The interpolated pixel value is computed as (Equation (2)):  
\[I^{\prime}_{t+1}(p_{t})=I_{t+1}(p_{t+1})=\sum_{i\in\{t,b\},j\in\{lr\}}\omega^{ ij}I_{t+1}\Big{(}p^{ij}_{t+1}\Big{)} \tag{2}\]  
where \(p^{ij}_{t+1}\) denotes one of the four neighboring pixels around \(p_{t+1}\), and \(I_{t+1}\Big{(}p^{ij}_{t+1}\Big{)}\) is the corresponding pixel value in image \(I_{t+1}\). Let \(\Delta u\) and \(\Delta v\) represent the horizontal and vertical distances from \(p_{t+1}\) to its top-left neighbor \(p^{ll}_{t+1}\), respectively. Then, the interpolation weights \(\omega^{ij}\) for \(i\in\{t,b\}\) and \(j\in\{l,r\}\) are given by \(\omega^{ll}=(1-\Delta u)(1-\Delta v)\), \(\omega^{br}=\Delta u\Delta v\), \(\omega^{tr}=\Delta u(1-\Delta v)\), and \(\omega^{bl}=(1-\Delta u)\Delta v\), which meet the condition \(\sum_{i,j}\omega^{ij}=1\). According to Equation (2), the final pixel value is obtained by bilinear interpolation of the top left, top right, bottom left, and bottom right neighbors of \(p_{t+1}\), and the result is used to fill the corresponding pixel \(p_{t}\) in the synthesized view \(I^{\prime}_{t+1}\).
## 5 Discussion
### Use of ML/DL Methods in Cybersecurity Research by Attackers and Defenders  
Both cyber defence entities and attackers depend increasingly on ML/DL models to enhance their automatic approaches in their strategies. The organisations utilise real-time intrusion detection systems (IDSs), adaptive malware analysis, and large-scale network anomaly detection as their main protective functions. Manufacturers leverage SVM and decision trees alongside CNNs to analyse vast traffic or log data because these classification tools exceed traditional anomaly detection rules. The ability of ML-driven models to learn from recent data acquisitions allows them to effectively detect new threats in zero-day exploit scenarios.  
Support vector machines (SVMs) and convolutional neural networks (CNNs) have considerably impacted organisations' cybersecurity systems. SVMs are comprehensively applied in cybersecurity for their efficiency in structured and high-dimensional feature classification tasks such as IDS for detecting distributed denial of service attacks, malware classification using features like network behaviour, and network anomaly detection to detect abnormal network traffic patterns. In contrast, CNNs are effective for unstructured and multidimensional data in malware detection by image-based techniques and intrusion detection by analysing network traffic data.  
Cybercriminals utilise ML/DL to create attacks, which have become more sophisticated than before. The [PERSON], [PERSON] [60], and [PERSON] [53] academic works demon strate that hackers employ ML techniques to disclose private user data by studying side-channel frequency interactions. Growing research shows how electromagnetic side-channel analysis of in-display fingerprint sensors combined with GenAI models and sensor analysis techniques enables adversaries to recover fingerprints, as presented in \"Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel\" [61] and \"Accear: Accelerometer acoustic eavesdropping with unconstrained vocabulary\" [62]. Through these abilities, attackers develop better ways to evade detection, producing polytopic malware and launching massive human-plausible phishing operations. Security researchers now invest their work in developing adversarial training systems, blockchain validation, and strong data sanitisation techniques to confront modern threats [63; 64; 65].  
Adversarial learning techniques, such as the fast gradient sign method and projected gradient descent, facilitate intrusion detection, malware classification, and facial recognition. The key purpose of adversarial training is to enhance model resistance against attacks. On the other hand, federated learning helps minimise risks, provides resistance from poisoning attacks, and learns from evolving cyber threats. Security efforts evolve through an escalating competition between offensive and defensive ML/DL applications, continually impacting the cybersecurity environment.