esa-sceva/satcom-chunk-collection huggingface dataset:
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 5 Case Study
### BN-Based Reasoning  
Based on the description of the case, the following evidence-related information was extracted: when the ambient temperature was 45 \({}^{\circ}\)C, the state of the environmental node in the BN was \(P\) (35-45 \({}^{\circ}\)C) = 1. The transformer panel showed that the internal temperature was 90 \({}^{\circ}\)C; thus, it was assumed that the temperature of the internal line cable was approximately 90 \({}^{\circ}\)C, and the state of the temperature node in the BN was \(P\) (70-90 \({}^{\circ}\)C) = 1.  
These two pieces of information could be obtained directly based on daily inspection, but other information remained unavailable. Because the BN has a strong reasoning function, it can be used for inferring the probability of unknown information based on the known information. Therefore, based on the above two pieces of evidence, the updated state of the BN is shown in Figure 9.  
According to the inference of the BN posterior probability, some additional information was obtained about that dangerous situation.  
1. The event was mostly owing to a high current. The node of the current changed, and the probability of a normal current (under 10 A) was 0, indicating that there must have been an overcurrent in this accident. The likelihood of a serious overload at 20-25 A could reach 44.5%.
2. The temperature rise of the cable was obvious. The cable temperature rise node also changed significantly. The probability of the cable temperature rising to less than 25 \({}^{\circ}\)C was 0. The probability of the cable temperature rising to the 25-40 \({}^{\circ}\)C range was 68.1%, indicating that cable heating was extremely serious, and the risk probability of  
Figure 9: Bayesian network reasoning analysis.  
insulation breakdown and pyrolysis significantly increased when the cable was in the state of a high-temperature heat source for a long time. The main reason for the risk of overheating is the heating of the cable line rather than the high air temperature.
3. The cable exhibited the risk of insulation breakdown, and the main cause was cable overheating. The node of the cable mechanical damage changed only weakly, and the probability of no mechanical damage was 94%, indicating that the main cause of the insulation breakdown risk was overheating rather than mechanical damage.
## 4 Research Methodology
### The Gauss-Markov (GM) Mobility Model  
The Gauss-Markov (GM) Mobility Model was first proposed by [PERSON] and [PERSON] [34]. GM is inspired by the need to have a mobility model that is more realistic in the sense that, for example, a node might gradually accelerate, decelerate, or turn. The present speed and direction of a node are connected to the prior movement using Gaussian equations and tuning parameters, which use the average speed and direction as well as Gaussian random noise [33]. We considered the GM mobility model because it supports the three-dimensional mobility of a UAV in NS-3 and is realistic to adopt in a random search for target victims in destroyed areas [35].  
Figure 5 depicts a UAV trajectory generated using the GM model. At the beginning of the simulation, each UAV is configured with its current speed and direction while following the GM trajectory with a bird curve. Then, after a period, each UAV updates its velocity and direction. The GM model is capable of reducing the abrupt stops and sharp turns observed in random mobility models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & Ns-3.32 \\ Simulation time & 600 secs \\ Size of covered area & 3 km \(\times\) 1.5 km \\ MAC protocol & IEEE 802.11b \\ Antenna & Omni-direction \\ Propagation Model & Free Space Path Loss \\ Radio Frequency & 2.4 GHz \\ Routing protocols & OLSR, D-OLSR, ML-OLSR, P-OLSR \\ Number of UAVs & 20–60 UAVs \\ UAV Altitude & 100 m \\ UAV Transmission range & 300 m \\ UAV mobility model & Gauss Markov (GM) \\ UAV initial energy & 1 \\ Transport protocol & UDP \\ Application Traffic & On/off traffic \\ Application packet size & 512 byte \\ Packet transmission rate & 4 packet/sec \\ \hline \hline \end{tabular}
\end{table}
Table 3: UAV parameter configuration for simulation.  
Figure 5: Example of UAV trajectory in the GM mobility model.
## 2 Materials and Methods
### Impact of the Processing Technologies' Evaluation
#### 2.5.2 Colour and Antioxidant Analyses  
Before and immediately after each treatment, the colour and antioxidant activity of the AP were analysed. For colour parameters, the AP's hue alteration was determined using a colorimeter (Konica Minolta, CR-310, Tokyo, Japan). This apparatus measures the light received and converts it into space coordinates. These values are then translated to a L*a*b* standard, where L* represents lightness, a* indicates the hue from green (\(-\)) to red (\(+\)), and b* the hue from blue (\(-\)) to yellow (\(+\)). To determine the magnitude of colour variation (\(\Delta\)E*), between the colour parameter of the samples processed and the colour parameter of the control sample (unprocessed AP) L* 39.5, a* 5.0 and b* 20.7, Equation (1) was applied.  
\[\Delta\mathrm{E}^{*}=[(\Delta\mathrm{L}^{*})^{2}+(\Delta\mathrm{a}^{*})^{2}+( \Delta\mathrm{b}^{*})^{2})]^{0.5} \tag{1}\]  
The antioxidant activity of the AP was assessed with two different methods: a method based on the free radical 2,2-diphenyl-l-1-picrylhydrazyl (DPPH) and the radical cation of 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS). The DPPH radical scavenging assay was performed as described by [PERSON] and colleagues [19]. Briefly, AP was centrifuged at 4000\(\times\)_g_ for 10 min to separate the pulp from the juice (supernatant). Afterwards, 100 \(\mu\)L of the supernatant was added to 900 \(\mu\)L of a methanolic solution of DPPH (1.5 \(\times\) 10\({}^{-4}\) M) and the mixture was vortexed and left to react in the dark for 30 min. Then, the absorbance of the reaction mixture was measured at 517 nm, using a microplate reader (Multiskan GO microplate Spectrophotometer, ThermoScientific, ThermoFisher Scientific, Waltham, MA, USA). The antioxidant activity was obtained from a standard curve of Trolox and expressed as mmol of Trolox equivalent (TE) per 100 g of fresh weight sample. The ABTS radical scavenging assay was also performed as described by [PERSON] and colleagues [19]. Succinctly, the ABTS* solution was prepared by reacting 7 mM of aqueous ABTS solution with 2.45 mM of potassium persulfate, at a ratio of 1:1 (\(V/V\)). Then, the solution was left for 16 h in the dark at 4 \({}^{\circ}\)C. Afterwards, the solution was diluted with a solution of 80% of ethanol/water (\(V/V\)) to obtain an absorbance of 0.700 \(\pm\) 0.005 at 734 nm. Next, 100 \(\mu\)L of the samples to be tested was added to 3.9 mL of ABTS solution, vortexed and incubated at room temperature for 6 min in the dark. After the incubation, the absorbance was measured at 734 nm using the aforementioned microplate reader. The antioxidant activity was obtained from a standard curve of Trolox and expressed as mmol of TE per 100 g of fresh weight sample.
# Preparation and Application of ZIF-8 Thin Layers
## 3 Results
### Doctor Blade Technique for Composite Layer Containing PVDF Binder and ZIF-8  
The doctor-blade technique is a very common method to create thin homogenous layers of powdered solids with defined thickness. This technique is often used, for example, for coatings of electrode materials for energy storage related applications [39]. A polymer is often added to the powder to increase adhesion and mechanical stability of the coating. By using polyvinylidene fluoride (PVDF), we chose a thermoplastic fluoropolymer that is itself known for its mechanical, chemical and relatively high thermal stability [40] as well as omniphobicity [41]. By combining porous ZIF-8 with PVDF, we created a mechanically stable coating of less than 25 \(\upmu\)m thickness, which also has a high porosity and large surface area. As shown in Figure 5, the resulting coatings consist of small ZIF-8 particles, that are (due to ball milling) slightly smaller than the particles of the commercial ZIF-8 (Basolite(r) Z1200, Sigma Aldrich). The variation of the ZIF-8 to PVDF mass-ratio does not reflect significant differences in coatings morphology. The average ZIF-8 particles size is between 300-400 nm in diameter.  
Further investigations via transmission electron microscopy (TEM) showed that ZIF-8/PVDF composite material obtained as a monolayer contains ZIF-8 particles embedded in a granular polymer matrix (Figure 6). EDS measurements of the selected region (Figure S1) show strong zinc signals consistent with ZIF-8. Due to the high sensitivity of the ZIF-8 material against the electron beam, more detailed investigations of crystallinity were not possible. However, XRD measurements of the monolayer scratched from the Cu substrate convincingly revealed the crystallinity of the ZIF-8 particles in the composite with PVDF, independent from the ZIF-8/PVDF ratio (Figure S1). Note that the two-layer successive coating is mechanically less stable in comparison to the monolayer, even under low mechanical stress. Therefore, the monolayer coating method was favored in the further investigation.  
Figure 5: SEM images showing Basolite(r)Z1200 (**A,D**) and ZIF-8/PVDF composite layers with 10% (**B,E**) and 40% PVDF (**C,F**).  
Figure 6: TEM images showing a ZIF-8/PVDF composite layer with 40% PVDF, at different magnification levels. EDS analysis of the selected area (right image) revealed a strong zinc signal, thus confirming the presence of ZIF-8 in the particle agglomeration (see Figure S2).
## 4 Proposed Framework
### Mathematical Formulation
#### 4.1.1 Functions  
**Create Digital Identity**: CreateIdentity : \(\mathcal{E}\rightarrow\mathcal{K}\times\mathcal{K}\) maps an entity (user, trusted entity, etc.) to a pair of private and public keys:  
**Issue Credential**: The function IssueCred : \(\mathcal{E}\times\mathcal{T}\rightarrow\mathcal{C}\) maps an entity \(e\in\mathcal{E}\) and a credential type \(t\in\mathcal{T}\) to a Verifiable Credential \(c\) in the set of all credentials \(\mathcal{C}\).  
**Sign Credential**: Sign : \(\mathcal{C}\times\mathcal{K}\rightarrow\mathcal{S}\) takes a credential \(c\) and a private key \(k_{\text{priv}}\) from the set of keys \(\mathcal{K}\) and produces a signature \(s\) in the set of signatures \(\mathcal{S}\).  
**Verify Credential**: The function Verify : \(\mathcal{C}\times\mathcal{S}\rightarrow\{\text{True},\text{False}\}\) takes a credential \(c\) and its associated signature \(s\) and determines their authenticity.  
**Register Entity**: Register : \(\mathcal{E}\times\mathcal{K}\rightarrow\mathcal{L}\) maps an entity \(e\) and its public key \(k_{\text{pub}}\) to an entry in the set of distributed ledgers \(\mathcal{L}\).  
**Request Authentication**: RequestAuth : \(\mathcal{E}\times 2^{\mathcal{T}}\to 2^{\mathcal{C}}\) takes an entity \(e\) and a set of desired credential types \(\{t_{1},t_{2},\ldots,t_{n}\}\subseteq\mathcal{T}\) and returns a subset of credentials \(\{c_{1},c_{2},\ldots,c_{m}\}\subseteq\mathcal{C}\).  
**Grant Access**: GrantAccess : \(\{\text{True},\text{False}\}\rightarrow\{\text{Access Granted, Access Denied}\}\) takes a verification result and determines the access decision.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### O-Cloud  
The O-Cloud is a cloud computing platform that will host relevant O-RAN functions [58]. O-Cloud can consist of individual or collection of physical infrastructure nodes of the O-RAN architecture such as Near-RT RIC, O-CU, or O-DU. In order to work properly, O-Cloud should also host the supporting software components and the appropriate management and orchestration functions. Inside the O-Cloud, computing resources, such as general-purpose CPUs and shared task accelerators, are pooled and brokered by an abstraction layer [5].  
The O2 interface will link O-Cloud to the SMO. The O-Cloud organizes the services relating to O2 interface into two groups: Infrastructure management services and Deployment management services. Infrastructure management services are responsible for the management and deployment of the cloud infrastructure. On the other hand, the deployment management services are responsible for life cycle management of the virtualized/containerized deployments. In OSC the development of O-Cloud is taking place within the in INF project.
## 3 Problem Formulation and Model Design
### Computational Complexity Analysis  
In this part, we analyzed the computation complexity of our proposed algorithm. During the training phase, the computational complexity of the single agent algorithm is mainly related to the number of layers of the deep neural network (DNN) used by each agent and the number of neurons used in each layer [27]. In addition, our algorithm is a multiagent algorithm; it is also necessary to consider the impact of the number of agents.  
In the training phase, the computational complexity for a single DNN to both forward propagation and update in a single step is \(\mathcal{O}(S(\sum_{i=1}^{L}n_{i}n_{i+1}))\), where \(S\) is the minibatch size, \(L\) represents the number of layers, and \(n_{i}\) denotes the number of neurons in the \(i\)-th layer. Let \(F_{a}\), \(F_{c}\) represent the computational complexity of the actor and critic of a single agent, respectively. \(F_{a}\), \(F_{c}\) can be calculated as  
\[F_{a}=\mathcal{O}(TMS(\sum_{i=1}^{L_{a}}n_{i}n_{i+1})),\quad F_{c}=\mathcal{O }(TMS(\sum_{i=1}^{L_{c}}n_{i}n_{i+1})) \tag{13}\]  
where \(T\) represents the number of steps in each episode, \(M\) represents the total number of episodes, and \(L_{a}\) and \(L_{c}\) represent the number of layers of actor and critic, respectively. Let \(N\) represent the number of agents. Then the computational complexity of our algorithm in the training phase can be expressed as  
\[C_{train}=\sum_{1}^{N}(F_{a}+F_{c}) \tag{14}\]  
During the distributed execution phase, each agent only needs to use a trained actor network to select action, so the computational complexity of our algorithm is  
\[C_{execute}=\sum_{1}^{N}(\mathcal{O}(\sum_{i=1}^{L_{a}}n_{i}n_{i+1})) \tag{15}\]
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 2 Research Foundation  
The strong multipath resistance of the OCDM system makes it well-suited for underwater acoustic channels, which exhibit complex multipath characteristics. However, like OFDM, OCDM suffers from a high PAPR, which is incompatible with the low SNR typical of deep-sea shadow-zone channels. This section first provides an overview of the characteristics of deep-sea shadow-zone channels, followed by a description of the OCDM modulation scheme and an analysis of the impact of broadband Doppler effects on OCDM systems in underwater acoustic communication.
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
###### Abstract
Although some studies have attempted to solve this problem by using homogeneous ensemble learning methods, when there is an inherent bias in the base model, the ensemble model generated through homogeneous ensemble learning methods will inevitably carry this bias [28]. Consequently, homogeneous ensemble models cannot overcome the inherent biases of individual models, resulting in limited improvements in prediction accuracy. To address these issues, a heterogeneous ensemble learning-based method that considers spatio-temporal heterogeneity was proposed for land subsidence prediction. The main contributions of this study are twofold:  
1. To address the challenge of adequately considering the spatio-temporal heterogeneity of land subsidence in existing prediction models, we propose a two-stage hybrid clustering method based on co-clustering. In the first stage, the Bregman Block Average Co-clustering with I-divergence (BBAC_I) algorithm [29] is used to compress the original spatio-temporal data, dividing the large-scale land subsidence dataset into a series of small, internally homogeneous spatio-temporal clusters. In the second stage, the REgionalization with Dynamically Constrained Agglomerative clustering and Partitioning (REDCAP) method [30] is employed to further cluster the small spatio-temporal clusters, revealing complex cluster structures. Prediction models are then constructed within each spatio-temporal cluster, allowing for accurate modeling of spatio-temporal heterogeneity of land subsidence. 2. To improve prediction performance when data do not meet the assumptions of individual models, this study adopts a heterogeneous ensemble learning method to combine the results of multi-view and multi-type machine learning prediction models. We developed a heterogeneous ensemble method based on the Blending strategy. For time series prediction, we chose the Sequence-to-Sequence model. For spatio-temporal prediction, we chose the GCN-Sequence-to-Sequence model, which captures the complex spatio-temporal dependencies in the graph structure; the Diffusion Convolutional Recurrent Neural Network model, which can capture the dynamic changes in spatio-temporal relationships; and the Graph Multi-Attention Network model, which incorporates the attention mechanism. The Blending strategy is then used to combine the prediction results of these four individual models, improving the accuracy of land subsidence predictions. Experiments on a land subsidence dataset from Cangzhou, one of the most affected areas in China, demonstrate that the proposed method outperforms four state-of-the-art individual prediction models in terms of prediction accuracy. The proposed method considers spatio-temporal heterogeneity and combines multiple individual models, providing a novel approach for spatio-temporal data prediction that may aid in the early monitoring of ground geological hazards.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 2 System Model
### The Threshold Scheme  
The pilot placement of the threshold scheme in [7] is shown in Figure 2. For a given maximum delay index, \(l_{\max}\), and Doppler index, \(k_{\max}\), since each symbol in the DD domain is considered to experience similar fading, only one pilot symbol is needed to complete the channel estimation. The pilot symbol at the transmitter in the DD domain can be represented as  
\[x[k,l]=\begin{cases}x_{p}&k=k_{p},l=l_{p}\\ 0&k\in[k_{p}-2k_{\max},k_{p}+2k_{\max}],\\ &l\in[l_{p}-l_{\max},l_{p}+l_{\max}]\\ x_{d}&\text{otherwise}.\end{cases} \tag{9}\]  
where \(x_{p}\) denotes the pilot symbol, \(x_{d}\) denotes the data symbols, and we have \(N_{n}=(2l_{\max}+1)(4k_{\max}+1)-1\) as guard symbols.  
Figure 2: Pilot placement scheme.  
We can rewrite the input-output relationship of the pilot according to (8) as  
\[y[k,l]=b[k-k_{\max},l-l_{\max}]\hat{h}[k-k_{\max},l-l_{\max}]x_{p}+n[k,l] \tag{10}\]  
At the receiver, for the channel estimation part, \(k\in[k_{p}-k_{\max},k_{p}+k_{\max}],l\in[l_{p},l_{p}+l_{\max}]\), we set a threshold, \(\Gamma=3\sqrt{N_{o}}\), where \(N_{o}\) represents the power spectral density of noise. If \(|y[k,l]|\geq\Gamma\), we can assume that \(b[k-k_{\max},l-l_{\max}]=1\) and \(\hat{h}[k-k_{\max},l-l_{\max}]=y[k,l]/x_{p}\). Otherwise, the result can be written as \(\hat{h}[k-k_{\max},l-l_{\max}]=n[k,l]\). This means that if the path exists, the received signal after this algorithm is a pilot containing Gaussian noise. Otherwise, it is only noise.
## 1 Introduction
Previous research showed that the molar mass of PLA decreased when irradiated at 25 \({}^{\circ}\)C compared to an increase in molar mass at 80 \({}^{\circ}\)C and 170 \({}^{\circ}\)C [34]. [PERSON] states that crosslinking of PLA is optimized at a dose of 40-200 kGy and 3-5 wt% trially! isocyanurate (TAIC) additive [35]. Chain scission is the primary reaction when neat PLA is irradiated from 1-100 kGy [36]. As a result of irradiation, the gel fractions were between 0.5% and 0.9%, the heat of fusion increased, and the glass transition temperature decreased slightly, all indicating the presence of chain scission and a reduction in molar mass [36]. The chain scission occurs predominantly at the C-O-C bonds on the backbone of the PLA chain [37; 38]. Electron beam radiation in the range of 1 to 30 kGy in the presence of oxygen prompts PLA degradation; a decrease was seen in the molar mass, tensile strength, break elongation and elastic modulus, and water vapor permeability of the polymer [39]. Another group found that electron beam irradiation at doses below 25 kGy did not cause any changes in the mechanical properties of PLA [40]. PLA has a resistance to radiation treatment that adds to its potential for applications in food packaging [40]. Another group observed that the degree of crystallinity for poly-L-lactic acid increased with dose up until 50 kGy [33]. Overall, the previous literature shows varied results depending on the irradiation conditions. For the scope of this research, samples were irradiated at doses of 5, 10, 15, and 20 kGy in the absence of oxygen.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Rest Time on Battery Performance
#### 3.3.1 Effects of Rest Time on Battery Capacity  
Tables 2 and 3 demonstrate the effects of 5-min, 15-min, and 30-min rest times on Ni-MH battery capacity. It is seen in the two tables that these three types of rest time made only subtle differences in the maintenance of battery capacity.
## 3 Results and Discussions
### Tensile Splitting Strength  
The tensile splitting strength was determined on 10 samples in accordance with SR EN 12390:6 [16]. The specimens were loaded by a loading rate of 0.05 MPa/s (0.4 kN/s). The obtained results are shown in Figure 13. It can be seen that the tensile splitting strength decreases with the increase in rubber content. The prediction equation presented in ACI 318:14 [28] was used to check whether or not it can be used in the case of rubberized concrete. The data are summarized in Table 5.  
From the obtained results, it can be concluded that the equation can accurately predict the tensile splitting strength of both the reference mix and the rubberized concrete mixes with replacement percentages of sand from 40% to 60%. However, it tends to overestimate the values of the tensile splitting strength of RuC80 by 31.9%.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 3 Results--Radiometric Comparison  
The following section gives the results for the comparison of the SLSTR-A/B tandem phase L1 data, including: overall radiometric differences, uncertainty validation and key sensitivity analyses--in particular, scene temperature dependence and temporal stability of the comparison.
## Methods
### Classification-Based Tree Matching and Co-Registration
#### 3.6.2 Distance-Based Tree Assignment  
To assign trees within a given distance and to combine the two-dimensional neighborhood criterion with the height criterion of e.g., [3] we define the \(\epsilon\)-norm \(\|\cdot\|_{\epsilon}\) (Equation (7)). The key idea of this \(\epsilon\)-norm is that a surveyed tree \(s\in S\) can only be assigned to a detected tree \(d\in D\) if \(\|d-s\|_{\epsilon}\leq 1\). From \(\|d-s\|_{\epsilon}\leq 1\) follows \(\Rightarrow|d_{x}-s_{x}|\leq\epsilon_{x}\), \(|d_{y}-s_{y}|\leq\epsilon_{y}\) and \(|d_{h}-s_{h}|\leq\epsilon_{h}\).  
\[\|v\|_{\epsilon}=\sqrt{\left(\frac{v_{x}}{\epsilon_{x}}\right)^{2}+\left(\frac {v_{y}}{\epsilon_{y}}\right)^{2}+\left(\frac{v_{h}}{\epsilon_{h}}\right)^{2}} \tag{7}\]  
Based on this \(\epsilon\)-norm, we define a tree assignment method which returns a sequence of linked trees \(r^{\omega^{\epsilon}(S,D)}\) using the weights \(\omega^{\epsilon}(S,D)\) (Equation (8)) as presented in Section 3.6.1. This corresponds to a tree assignment in ascending order of tree pair distance.  
\[\omega^{\varepsilon}(A,B)=\left(\omega^{\varepsilon}_{a,b}=\begin{cases}0,&\text{if } \|b-a\|_{\varepsilon}>1\\ \frac{1}{\|b-a\|_{\varepsilon}},&\text{if }a\
eq b\\ \infty,&\text{otherwise}\end{cases}\right)_{a\in A;\ b\in B} \tag{8}\]