esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 2 The Icelandic Power System & the Storm Events
### Icelandic Power System  
The Icelandic power system is an islanded network. The peak load of the system in 2019 was 2345 MW [14], the majority of which is consumed by heavy industry and generated almost exclusively by hydro and geothermal power plants. Generation and load are primarily located in the south-western and eastern 220 kV regions, shown in Figure 1, which is connected by a 132 kV ring of transmission lines around the perimeter of the country.  
Significant operational challenges are caused by the ring connection, primarily through congestion that restricts the flow of energy from one 220 kV region to the other, and through dynamic instabilities that arise due to the relatively large electrical distance separating the two 220 kV regions [15]. Consequently, operational security is dominated by the management of inter-region congestion and reliant upon synchrophasor-measurement unit based wide-area monitoring systems and fast acting system integrity protection schemes. These protection schemes induce topological islanding between regions, or fast ramping of generation or load, as a response to the regional rate of change of frequency measurements. Fast responding protection allows for disturbances to be quickly contained, such that they do not propagate further disturbances across the grid.
## Materials and Methods
### Risk Decision Based on RUL Prediction
#### 2.2.2 Reconstruction of Degradation Track Phase Space  
According to [PERSON]'s theorem, the potential dynamics laws of a system can be studied by constructing the phase space that preserves the topological properties of the original system. Coupled with the nonlinear characteristics of degradation, the time delay embedding theorem is often applied to construct high-dimensional phase space.  
Assume that the time series is \(X=(x_{1},\,x_{2},\,\cdots,\,x_{N})\), then the points in the phase space can be expressed as the following form of row vector.  
\[X_{i}=(x_{i-(d-1)\tau},\,x_{i-(d-2)\tau},\,\cdots,\,x_{i-\tau},\,x_{i}),\,i=1+ (d-1)\tau,\,2+(d-1)\tau,\,\cdots,\,N, \tag{7}\]  
where \(d\) is the embedded dimension of phase space and \(\tau\) is the time delay.  
Embedded dimension \(d\) is the key parameter in phase space construction, whose value will be determined by the non-subjective algorithm. \(X_{i}(d)\) is a point in the \(d\) dimension phase space. If the point \(X_{n(i,d)}\) nearest to \(X_{i}(d)\) exists, the following relation can be deduced.  
\[\|X_{i}(d)-X_{n(i,d)}(d)\|=\min_{j=1+(d-1)\tau,-\,N,j\
eq i}\|X_{i}(d)-X_{j}(d) \|_{\infty}, \tag{8}\]  
For all points in the \(d\) and \(d+1\) dimensional phase spaces, the definitions for \(a(i,d),E(d)\)\(E^{*}(d),E_{1}(d)\), and \(E_{2}(d)\) are given respectively.  
\[\begin{array}{c}a(i,\,d)=\frac{\|X_{i}(d+1)-X_{n(i,d+1)}(d+1)\|}{\|X_{i}(d)- X_{n(i,d)}(d)\|}\\ E(d)=\frac{1}{N-d}\sum\limits_{i=1+(d-1)\tau}^{N-\tau}a(i,d)\\ E_{1}(d)=\frac{E(d+1)}{E(d)}\\ E^{*}(d)=\frac{1}{N-d\tau}\sum\limits_{i=1}^{N-\tau}\left|x_{i+d\tau}-x_{n(i,d) +d\tau}\right|\\ E_{2}(d)=\frac{E^{*}(d+1)}{E^{*}(d)}\end{array}, \tag{9}\]  
Then the value of the embedded dimension \(d\) can be determined by finding the smallest spatial dimension that makes the \(d\) and \(d+1\) dimensional phase spaces topologically equivalent. The topological equivalence here refers to that the nearest neighbors in the \(d\) dimensional space still remain the closest in the \(d+1\) dimensional space, which means that the \(E_{1}(d)\) tends to be a stable value when the \(d\) and \(d+1\) dimensional phase spaces are topologically equivalent. However, in reality, it is difficult to find the smallest \(d\) that makes \(E_{1}(d)\) a stable value. That's why the \(E^{*}(d)\) and \(E_{2}(d)\) are defined. Ifis a random time series, then \(E_{2}(d)\) will vary with \(d\), or \(E_{2}(d)\) will approach 1. Then by combining \(E_{1}(d)\) and \(E_{1}(d)\), the embedded dimension of the degradation track phase space can be determined. After the construction of the phase space, the time series can be transformed into the form of special track. By referring to the degradation data transformed from historical data and comparing the similarity of the new track with the reference track in phase space, the RUL can be predicted.
## 4 Conclusions  
In this study, an ET partitioning approach based on the concept of underlying water use efficiency is used to calculate T/ET ratio and then compare the vegetation's impact on T/ET ratio at five sites in Northern China with different vegetation types. Half-hourly flux eddy covariance data are applied to derive the \(uW\Pi\)\({}_{p}\), \(uW\Pi\)\({}_{a}\), and T/ET ratio. The effect of vegetation on the T/ET ratio is examined using NDVI data by MODIS. The results reveal that the \(uW\Pi\)\({}_{p}\) and T/ET ratio vary among different sites with different vegetation types and irrigation schemes, and the vegetation effect over ET partitioning shows different type of patterns.  
In an alpine meadow site, the T/ET ratio increases gradually when NDVI is low, whereas it increases rapidly as NDVI becomes high.  
In arid shrubland, the T/ET ratio grows rapidly when NDVI is low and the T/ET ratio is constrained to a certain value when NDVI reaches a relatively high value.  
In deciduous forest, the T/ET ratio becomes insensitive to NDVI variation beyond the threshold value.  
For the two crop sites, it is observed that the correlation between T/ET ratio and NDVI is determined by irrigation schemes. The T/ET ratio shows a positive correlation with NDVI variation during flood irrigation, whereas it proves irrelevant to NDVI variation during mulch drip irrigation.  
In addition, the correlation of T/ET ratio and NDVI becomes more complex when we consider the impact of anthropogenic factors such as irrigation. This study can be extended by taking into account ET partitioning at different sites and under different human activities. This work will help policy makers to better understand the relationship between vegetation and climate change or human activities, and also provide significant information for policy-making on water management.  
Conceptualization, H.H., F.T. and L.C., methodology, H.H., L.C. and H.L.; writing--original draft preparation, H.H. and L.C.; writing--review and editing, all authors.  
This research was funded by the National Science Foundation of China, grant number \"51879136\", \"91647205\" and the National Key Research and Development Program of China, grant number \"2016 YFC0402701\", \"2017 YFD050540503\".  
[PERSON] (201704910065) would like to acknowledge the fellowship from the China Scholarship Council (CSC).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Effect of Building Orientation on Energy Saving  
Figure 11 illustrates the effect of building orientation on energy saving of fibre-reinforced LECA-LWAC, NWC, clay and sand-cement bricks.  
In Putrajaya, changing the building orientation from 90\({}^{\circ}\) to 0\({}^{\circ}\) (base model) for NWC reduced energy consumption by 2.43% (EUI reduction of 3.6 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)). HST0.75 + PP0.1 hybrid fibre achieved a higher energy reduction of 3.19% (EUI reduction of 3.1 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)). [PERSON] et al. investigated two-storey residential houses in Brunei using clay brick wall with insulation and found that it could reduce the annual EUI by 2.3 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)[59].  
Figure 11: Energy-savings because of building orientations to the base model.  
Figure 10: Percentage of energy saving using fibre-reinforced LECA-LWAC as compared with NWC.  
In Makkah, changing the building orientation from 90\({}^{\circ}\) to 0\({}^{\circ}\) (base model) for NWC reduced energy consumption by 3.36% (EUI reduction of 5.4 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)). HST0.75 + PP0.3 hybrid fibre achieved a higher energy reduction of 3.47% (EUI reduction of 5.3 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)).  
In Istanbul, changing the building orientation from 90\({}^{\circ}\) to 0\({}^{\circ}\) (base model) for NWC reduced energy consumption by 1.30% (EUI reduction of 2.0 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)). HST0.75 + PP0.3 hybrid fibre achieved a higher energy reduction of 1.36% (EUI reduction of 2.0 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)).  
In Lillhardal, changing the building orientation from 90\({}^{\circ}\) to 0\({}^{\circ}\) (base model) for NWC reduced energy consumption by 0.37% (EUI reduction of 0.6 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)). PP0.3 hybrid fibre achieved a higher energy reduction of 0.22% (EUI reduction of 0.4 kW\(\cdot\)h\(\cdot\)m\({}^{-2}\)).  
In a cold sub-arctic climate like Lillhardal, building orientation does not play a significant role in energy saving. The energy savings amounted to only 0.22% as compared with the energy savings of up to 12.8% for changing the building envelope from NWC to fibre-reinforced LWAC.  
The effect of building orientation on energy saving was insignificant for Istanbul and Lillhardal and marginally significant for Putrajaya and Makkah. The building orientation has less of an effect on energy savings than optimising the building envelope material.
## 2 Test Setup
### Sensors and Data Acquisition System  
The objective of this study is to examine the influence of SCDI on the motion of the caisson foundation. To this end, an instrumentation system comprising accelerometers and tensile-wire displacement meters was employed to measure the acceleration response of the soil and the caisson, and the vertical displacement of the top of the caisson, respectively. The range of the tensile-wire displacement meters is 0-1100 mm, with an accuracy of 0.03 mm. All sensors were connected to the multi-channel data acquisition system of the shaking table to ensure signal synchronization, with a sampling frequency of 256 Hz.
## 2 Materials and Methods
### Extreme Learning Machine
According to Theorem 1, when \(L\) is less than \(N\), SLFNs will approach the training samples with very little training error, and the matrix \(\mathbf{H}\) is a non-square matrix, there exists \(\hat{\mathbf{w}}_{i},\hat{b}_{i},\hat{\beta}\), so that Equation (7) can be established. \[\left\|\mathbf{H}(\hat{\mathbf{w}}_{1},\ldots,\hat{\mathbf{w}}_{L},\hat{b}_{1 },\ldots\hat{b}_{L})\hat{\beta}-\mathbf{T}\right\|=\min_{\mathbf{w}_{i},b_{i}, \hat{\beta}}\left\|\mathbf{H}(\mathbf{w}_{1},\ldots,\mathbf{w}_{L},b_{1}, \ldots,b_{L})\beta-\mathbf{T}\right\| \tag{7}\]  
Unlike the traditional function approximation theories, the input weights \(\mathbf{w}_{i}\) and the hidden layer biases \(b_{i}\) are in fact not necessarily tuned and the hidden layer output matrix \(\mathbf{H}\) can actually remain unchanged once random values have been assigned to these parameters in the beginning of learning, and this makes Equation (7) is considerate as a linear system. The training for SLFNs is simply equivalent to finding the least squares solution \(\hat{\beta}\) of the linear equations \(\mathbf{H}\beta=\mathbf{T}\), that is  
\[\left\|\mathbf{H}(\mathbf{w}_{1},\ldots,\mathbf{w}_{L},b_{1},\ldots b_{L}) \hat{\beta}-\mathbf{T}\right\|=\min_{\beta}\left\|\mathbf{H}(\mathbf{w}_{1}, \ldots,\mathbf{w}_{L},b_{1},\ldots,b_{L})\beta-\mathbf{T}\right\| \tag{8}\]The smallest norm least squares solution of the weights of the above linear system is unique, which is  
\[\hat{\beta}=\mathbf{H}^{+}\mathbf{T} \tag{9}\]  
where \(\mathbf{H}^{+}\) is the Moore-Penrose generalized inverse of the matrix \(\mathbf{H}\)[20; 21]. Thus, the main steps of ELM's learning algorithm can be summarized as follows:  
With the given training set \((x_{i},t_{i})\in R^{n}\times R^{m}\), \(i=1,\,\ldots,\,N\), the activation function \(g(x)\) and the number of the hidden nodes \(L\),  
**Step 1:** Assign input weight, \(\mathbf{w}_{i}\) and bias of hidden layer, \(b_{i}\), randomly (where \(i=1,\,\ldots,\,L\));  
**Step 2:** Calculate the hidden layer output matrix, \(\mathbf{H}\);  
**Step 3:** Calculate the output weight, \(\hat{\beta}\).
## 6 Improving Projection Stability
### Mesh Simplification Method  
Mesh simplification involves a decrease in the number of triangle meshes using an edge reduction algorithm that decomposes and merges the triangles (Figure 7). Meshes can be either manually or automatically simplified. Manual simplification uses 3D computer graphics software, such as Autodesk's 3 DS Max and Robert McNeel & Associates' Rhinoceros 3D, to select portions of multiple triangle meshes and combine them into a single mesh. This is a precise method, but it is intensive in terms of time and labor.  
By contrast, automatic simplification uniformly reduces all triangle meshes of a 3D model and thus is consistent and less time-consuming. However, it has the disadvantage of not being able to simplify a specific part within a given 3D object.  
Civil structures are large and consist of multiple subparts, so using a manual method can be inefficient. Moreover, considering that civil structures are perpendicular and planar in nature, which reduces the risk of losing relevant details during simplification, the automatic approach was deemed appropriate. Unity's Pixyz software was employed, which provides a 'decimate' function for mesh simplification and allows direct conversion to AR file formats.
# Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
## 3 Results
### Robustness Exploration Against Perturbations  
Figure 14 shows the performance of different network depths in the face of external interference (such as image blur and Gaussian noise). The blue line shows the result without  
Figure 11: Performance of different network depths in Ms. Pac-Man.  
Figure 12: Performance of different network depths in Bowling.  
Figure 13: Performance of different network depths in River Raid.  
image blur, the yellow line shows the result with image blur, and the green line shows the result with Gaussian noise. The experimental results show that the seven-layer network shows the best robustness in the face of external interference such as image blur. This advantage comes from its good balance between depth and feature expression ability, which can effectively extract and utilize environmental features without over-relying on subtle pixel-level information so that it can still maintain a high policy quality when subjected to external interference.  
The reason why the seven-layer network shows better robustness may be that it has achieved an appropriate balance in network depth. This balance allows the network to fully extract environmental features while avoiding the overfitting problem that may be caused by too deep networks. Overfitting problems usually occur when the network depth is too large, and the network begins to learn the noise in the data rather than the underlying true pattern, causing the model to perform poorly when facing new and unseen data.  
Figure 14: (**a**) shows the results of a 6-layer network across the 10 game environments mentioned above. (**b**) depicts the results for a 7-layer network, while (**c**) presents the results for an 8-layer network. The blue line shows the result without image blur, the yellow line shows the result with image blur, and the green line shows the result with Gaussian noise.  
In contrast, although the eight-layer network has better adaptability to blur effects in some cases, this may be because blurring changes the features in the image in an unintuitive way. At certain stages of model training, this feature change may help the model better identify or distinguish certain key features. Overall, the seven-layer network is more robust. This may be because the eight-layer network is too deep, making it more susceptible to overfitting when processing blurred images, or it is difficult to stabilize during network training.
## 3 Results and Discussion
### Cerebral Hemodynamics Simulation  
The computational meshes can be used for computational fluid dynamics simulations. Detailed hemodynamic simulation can be achieved by using commercial software solving the 3D Navier-Stokes equation as shown in Figure 6. The meshes are loaded into the software and assigned with boundary conditions. Our 3D simulation demonstrates velocity field, pressure gradient, and dye convection with a pulsatile boundary condition. Figure 6 also shows preliminary hemodynamic simulations for blood flow rate and blood pressure using the cylinder mesh and simplified flow principle equations. The computational time for these simulations are less than one minute. Additionally, we also performed dye convection simulation with time step = 0.1 s for 10 s duration to produce artificial dynamic angiography in Figure 6. The computational time for dye convection is dependent on the step size and in this case it took less than five minutes. The simulation results demonstrate the use of our image processing pipeline for rapid simulation to aid clinical diagnosis. In addition to cerebral hemodynamics, we also acquired images for full body CSF reconstruction along with phase contrast MRI for CSF flow measurements as shown in Figure 7. The CSF model allows us to study full body CSF dynamics [14; 17]. Combined with the whole brain model, we can investigate the relationship between cerebral hemodynamics and CSF dynamics.  
Figure 6: Parametric meshing of the cerebral vasculature for 3D hemodynamic simulations. (**A**) Cross section of the parametric meshing of the segmented cerebral vasculature; (**B**) Parametric mesh at the bifurcations; (**C**) 3D hemodynamic simulation of the cerebral vasculature with pulsatile boundary conditions showing velocity fields, pressure fields, and dye convection at three time frames, 0.25 s, 0.45 s, 0.65 s; (**D**) Volumetric blood flow rate for each individual segment; (**E**) Blood pressure estimation for the entire tree; (**F**) Dynamic contrast agent distribution for three time frames 0.25 s, 0.45 s, 0.65 s after injection of a contrast agent. Inlet boundary conditions are provided in C for velocity and outlet boundary conditions are static pressure.
## 4 Discussion
### Site-Specific Responses to Storm Intensity  
In this study, fourteen days after the storm surge, the HK site exhibited a net ecosystem exchange (NEE) that reached 1.4 times the pre-storm baseline, indicating enhanced CO2 uptake; however, methane (CH4) emissions also increased to 1.2 times the antecedent level. In contrast, at the PYR site, NEE recovered to 1.2 times the baseline level prior to the storm surge, while CH4 emissions remained comparable to pre-disturbance conditions. Consistent with our findings, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [38] demonstrated that storm- or hurricane-induced inundation can lead to rapid and substantial increases in CO2 efflux, with emissions intensifying by approximately 2 to 10 times within short timeframes.  
The HK site exhibited greater storm-induced disruptions in carbon flux correlations, reflecting its stronger exposure to storm impacts (Figure 4 and Table 2). The weakening of RH, VPD, and WS correlations with NEE during the storm, combined with the strengthening of the negative TA-NEE relationship, suggests that temperature fluctuations became a dominant factor influencing net CO2 exchange under high turbulence and moisture stress [19]. By comparison, the PYR site, representing a restored mangrove system, exhibited relatively modest variations in carbon flux correlations, which may be attributed to the attenuated storm intensity and its farther proximity to the storm's core (Table 2). The overall faster post-storm recovery of carbon flux correlations at HK, despite experiencing a more intense storm, suggests that natural mangroves exhibit stronger functional resilience compared to restored mangroves. These findings support the idea that mature mangrove forests play a crucial role in buffering carbon flux disruptions following extreme weather events.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 4 Implementation and Methodology Overview
### Libraries  
The implementation of TLS was performed via OpenSSL 3.0.16, which is the default cryptographic backend for both the Mosquito broker and Python-based MQTT clients such as Paho MQTT utilized in our experiments. To ensure modern cipher suite support, the protocol was limited to TLS 1.3, which allowed for the consistent benchmarking of all phases. Cryptographic operations were conducted in the transport layer without applying application-layer encryption. In Phase 1, ECDHE using the elliptic curve 25519 was selected to establish forward secrecy between the clients and the broker. In Phases 2 and 3, authentication was achieved by presenting Certificate Authority (CA)-signed certificates. In Phase 4, AES_256_GCM or Chacha20_Poly1305 were selected from the TLS algorithm options, to secure the payload.  
OpenSSL generated TLS certificates and provided them to all clients, as well as to the server. All entities shared a common root CA certificate in order to verify the identities of the peers. Depending on the MQTT configuration, data transmission is possible with no authentication, server-only authentication, or mutual authentication. These settings allowed us to quantify the computational and network overhead that each phase introduces.
## 2 GidA/MnmE-Associated Virulence of Gram-Negative Bacteria
syringae_, MnmE is hypothesized to be important for growth at low temperatures [48]. In their study, transposon screening identified a _tmrE_ mutant with inhibited growth at 4 \({}^{\circ}\)C, but not at 22 and 28 \({}^{\circ}\)C when compared to the growth of the wild-type _P. syringae_ Lz4W cells indicating the importance of MnmE for rapid growth at unfavorable conditions [48]. To our knowledge, the only other study involving either GidA or MnmE in _Pseudomonas_ is in _Pseudomonas aeruginosa_, where GidA was found to regulate _rhl_ quorum sensing using a post-transcriptional mechanism [68]. In their study, _gidA_ was identified by transposon screening for mutants displaying deficient quorum sensing phenotypes. Further analysis of a _gidA_ mutant showed a deficiency in _rhl_-dependent quorum sensing, growth, LasA activity, and pyocyanin production [68]. Other GidA studies in Gram-negative bacteria include _Aeromonas hydrophila_, an emerging human pathogen that causes gastroenteritis and septicemia, where GidA was found to regulate the cytotoxic entertoxin (Act) [14]. A _gidA_ deletion mutant was avirulent in mice, with minimal to mild pathology, due to a significant reduction in hemolytic and cytotoxic activity [14]. In a follow up study, the same group found that _act_ was under the control of both GidA and DNA adenine methyltransferase (Dam), and Dam regulates Act production via GidA [49]. In _Myxococcus xanthus_, GidA was found to be a FAD-binding protein involved in _M. xanthus_ development where a _gidA_ deletion mutant was unable to form fruiting bodies after several generations [10]. In _Helicobacter pylori_, [PERSON] _et al._ found the _gidA_ gene was in close proximity to the _dapE_ gene (which encodes succinyl-diaminopimellate desuccinylase) and that the _gidA_ gene was essential for cell viability [50]. These findings, however, do not provide evidence of GidA-associated viurlence, but does suggest GidA is essential for _H. pylori_ viability [50]. To our knowledge, the only other study involving GidA or MnmE in Gram-negative bacteria is a recent study by [PERSON] _et al._ where deletion of _mmmE_ is associated with _F. tulareis_ pathogenicity island gene expression [43]. In their study, a \(mmmE\) deletion mutant displayed reduced levels of the small alarmone ppGpp, as well as an altered growth rate in mammalian cells suggesting MnmE is an important part of a complex regulatory system responsible for coordinating virulence gene expression in different host cell environments [43].