esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 1 Introduction
Few studies, however, have been conducted on the calculation of carbon emissions during the transport phase of prefabricated buildings in China. In these studies, the calculation boundaries were generally vague. For example, studies which relate to the carbon emission levels of transportation methods, such as [PERSON]'s comparison of the carbon emission levels of two types of forklifts (electric and LPG) [7] and [PERSON], [PERSON] et al. 's calculation/comparison of the energy consumption/carbon emissions of truck transportation and belt conveyors through a carbon emission calculation model [8], have laid the foundation for the calculation and optimization of the inherent carbon emissions in the transportation of prefabricated buildings. Simultaneously, they have provided research ideas and methods. This study, therefore, addresses the shortcomings of the existing literature through evaluation of the transportation phase of the logistics of assembled buildings, based on the existing building carbon emission formulas. Furthermore, this study establishes a calculation model for the transportation phase of assembled buildings through a clear delineation of the calculation boundary. In addition, considering the particularity of the transportation of prefabricated components, it was necessary to consider the factor of the full-load rate in the transportation of prefabricated components. This ensured that the carbon emission calculation was more consistent with the actual situation. Subsequently, the proposed calculation model was used to determine the carbon emission levels. The effect of the full-load rate on carbon emissions during the transportation of prefabricated and assembled buildings was also examined. The remainder of this paper is organized as follows. In Section 2, the research boundaries and key parameters are identified. A calculation model for the carbon emissions of prefabricated buildings in the logistics and transportation stage (considering the influence of the full-load rate) is also proposed. The study results are provided in Section 3. The influence of different full-load ratios on the transportation stage of prefabricated components, through case analysis, is discussed in Section 4. Finally, conclusions are drawn in Section 5.
###### Abstract  
During the flood discharge in large-scale hydraulic engineering projects, intense flow-induced vibrations may occur in hydraulic gates, gate piers, spillway guide walls, etc. Furthermore, the vibration mechanism is complicated. For the spillway guide wall, existing studies on the vibration mechanism usually focus on the vibrations caused by flow excitations, without considering the influence of dam vibration. According to prototype tests, the vibrations of the spillway guide wall and the dam show synchronization. Thus, this paper presents a new vibration mechanism of associated-forced coupled vibration (AFCV) for the spillway guide wall to investigate the dynamic responses and reveal coupled vibrational properties and vibrational correlations. Different from conventional flow-induced vibration theory, this paper considers the spillway guide wall as a lightweight accessory structure connected to a large-scale primary structure. A corresponding simplified theoretical model for the AFCV system is established, with theoretical derivations given. Then, several vibrational signals measured in different structures in prototype tests are handled by the cross-wavelet transform (XWS) to reveal the vibrational correlation between the spillway guide wall and the dam. Afterwards, mutual analyses of numeral simulation, theoretical derivation, and prototype data are employed to clarify the vibration mechanism of a spillway guide wall. The proposed mechanism can give more reasonable and accurate results regarding the dynamic response and amplitude coefficient of the guide wall. Moreover, by changing the parameters in the theoretical model through practical measures, the proposed vibration mechanism can provide benefits to vibration control and structural design.  
flow-induced vibration mechanism; spillway guide wall; associated-forced coupled vibration; theoretical model; prototype test; cross-wavelet transform +
Footnote †: journal: Applied Sciences
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
###### Abstract  
The development of a battery-type loader is an important research direction in the field of industrial mining equipment. In the energy system, the battery will inevitably encounter the problem of heat dissipation when using high-power electricity. In this study, we took the power battery pack of a 3 m\({}^{3}\) battery-type underground loader as the research object. The influence of single factors, such as the position of the air outlet of the battery pack, the size of the air outlet, the width of the separator, and the reverse plate, on the heat dissipation characteristics of the battery pack were studied. Then, a prediction model between the structural parameters and temperature was established using a radial basis function (RBF) neural network. This prediction model was then used as an adaptation evaluation model for global optimization through the multiobjective particle swarm optimization (PSO) algorithm, using which the optimal combination of structural parameters was obtained. The maximum temperature of the battery pack after optimization was reduced by 22%, compared to that before optimization, and the average temperature was reduced by 12.5%. Overall, the heat dissipation effect significantly improved. The optimization results indicate that the method proposed in this paper is feasible for use in optimizing battery heat dissipation systems in electric vehicles, thus providing a reference for research related to battery pack heat dissipation.  
forced air cooling; lithium-ion battery pack; structural optimization; underground loader; uniform experimental design; particle swarm optimization +
Footnote †: journal: Applied Sciences
## 5 Case Study
### Application of the Assessment Method for Vulnerability
Consequently, based on the predefined criteria, ECA is assessed as Attacked. To determine the probability of the vulnerability being exploited and resulting in an actual attack, this study uses the EPSS score, which, as explained earlier, calculates the probability of the vulnerability being exploited within 30 days. For CVE-2023-36844, the current probability of exploitation within 30 days is approximately 38%. Therefore, the predefined metric EUP in this paper has a value of 0.38. Based on this, EoE, which signifies the ease with which an attacker can exploit the vulnerability, is calculated. Consequently, the final exploitation risk score (ERS) is calculated, yielding a value of 12.385, as follows:  
\[EoE(V) =ECA(V)\times EUP(V) \tag{10}\] \[=2\times 0.38=0.76\]  
\[ERS(V) =ECR(V)+EoE(V) \tag{11}\] \[=11.625+0.76=12.385\]  
Due to its traditional vulnerability scoring system score of 5.3, many organizations overlooked this vulnerability in their managed vulnerabilities. However, this vulnerability was chained with several other vulnerabilities to launch a successful RCE attack, which compromised the integrity of the target system.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 2 Background
### Consortium Blockchain  
In 2008, [PERSON] proposed a peer-to-peer (P2P) digital currency system named Bitcoin for economic transactions based on blockchain technology [4]. Blockchain is a distributed ledger technology that allows participant nodes to share and approve transactions in the network. Then, those transactions that contain data recorded with time-stamp are validated by the particular consensus mechanism before it is stored in an immutable database. Generally, blockchain has three functions: realizing decentralized storing, acting as a distributed ledger, and supporting distributed services relying on smart contracts [23]. Currently, several researchers from different fields are attracted to develop blockchain due to its advantages. Several advantages of blockchain are described as follows:  
* **Anonymity and privacy**: Blockchain allows the user to join the network anonymously. Therefore, the real identity of the user cannot be known by other users. Thus, blockchain is able to provide privacy to the user.
* **Immutability**: This feature is one of the essential blockchain features, where the stored data cannot be deleted or modified from the network as the data was recorded and confirmed in the blockchain. Thus, blockchain makes data difficult to be added or altered arbitrarily [19].
* **Decentralized**: The core value of blockchain is to enable a database to be directly shareable without a central server. The characteristic of a decentralized approachshows the fragmentation of control over the whole system. It allows the system to achieve open participation, immunity from particular attacks, and the elimination of single points of failure [24]. Consequently, the blockchain data are consistent, reliable, have time-stamping for recorded transactions, and widely accessible.
* **Transparency**: Every user can access the transactions transparently once data is stored in the blockchain. Therefore, all the stored data in the blockchain are publicly viewable by the user in the network.
* **Distributed and trustful transaction**: In the blockchain, every node can share and validate the transactions in a distributed manner without the control of the central trusted authority.  
According to the access restriction to the network and node participating in the consensus process, blockchain is distinguished into three types: a public blockchain, private blockchain, and consortium blockchain. A public blockchain is an open blockchain, where anyone can join the network and contribute to a consensus mechanism without permission from a trusted authority. By contrast, private blockchain gives full control to a trusted party for managing participants' access to the network service while selecting a few participants who can conduct the consensus process [25]. The latter, consortium blockchain, provides the same benefits as the private blockchain, but without reliance on a trusted party. A consortium blockchain is a specific blockchain that allows the preselected nodes to execute the consensus process with a particular mechanism [26].
## 4 Performance Evaluation
### Results and Analysis
#### 4.4.2 V2V Channel Propagation Model
Thus, FPBR mitigates the effects of the erroneous selections with fewer packets than those needed by DDOR. Figure 15: Normalized network overhead obtained with FPBR and DDOR when increasing the maximum acceleration, \(\alpha\). Four different vehicle densities, \(\lambda\), were considered for these plots. Each plot shows the network overhead _vs._\(\alpha\) for: (**a**) \(\lambda=133\); (**b**) \(\lambda=100\); (**c**) \(\lambda=66\) and (**d**) \(\lambda=33\).
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification
### Influencing Factors of Balancing Performance
#### 3.3.4 Effect of Balancing Modes  
Compared with the traditional AC2 AC balancing, a variety of different balancing model switching types can be achieved by the MC2 MC balancing in this paper. Because the number of cells connected to the balancing main circuit varies for different balancing modes, different voltage differences in the balancing main circuit and different balancing effects are obtained. Therefore, it is necessary to explore the effect of different voltage differences on the balancing performances. A switching period of \(\mathrm{T_{S}}=40\)\(\mathrm{\SIUnitSymbolMicro s}\) is chosen to simulate the relevant balancing models. The relationship between various voltage differences and balancing performances is shown in Figure 10.  
According to Figure 10, the balancing efficiency of the n-cell-to-n-cell mode is higher than that of the n-cell-to-(n-1)-cell mode, and the balancing time of the n-cell-to-(n-1)-cell mode is shorter than that of the n-cell-to-n-cell mode. This conclusion illustrates that the best effect of the balancing efficiency is ensured by the proposed balancing circuit performing the n-cell-to-n-cell balancing mode, and the best effect of the balancing speed is ensured by the circuit performing the n-cell-to-(n-1)-cell balancing mode.
###### Abstract  
In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth's corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements.  
intelligent vehicle; enhanced berth mapping; trajectory prediction; multimodal localization; underground parking scene +
Footnote †: journal: Applied Sciences  
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# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 3 Stacking and Bending Tests on BFT-UHPC Pedestrian Walkway Thin Slabs  
### Design and Manufacture of Test Slab  
The BFT could perform as well as the CFT while costing only a twentieth of the price, making it a more economical option. Therefore, in this study, the BFT was selected to create the TR-UHPC composite slab specimen (BFT-UHPC slab) based on the results of the mechanical property tests and economic considerations. Figure 7 displays the dimensions and reinforcement layout of the test slab.  
A full-scale two-span continuous BFT-UHPC slab specimen was produced, measuring 2500 \(\times\) 1000 \(\times\) 50 mm overall. Each single span was 1175 mm long, with the upper flange part having a thickness of 20 mm. One layer of BFT was spread over the entire flange part with a 10 mm concrete cover. There were four longitudinal trapezoidal ribs with a rib height of 50 mm and a rib spacing of 250 mm, each reinforced with two \(\Phi\)8 mm HRB400 rebars.  
Figure 7: Dimension and reinforcement layout of the BFT-UHPC slab (mm).  
Three transverse rectangular ribs were made to support the slab. The transverse ribs at the end sections measured 50 \(\times\) 30 mm and were reinforced by one \(\Phi\)8 mm HRB400 rebar. The transverse rib at the mid-support section measured 100 \(\times\) 30 mm and was reinforced by 2\(\Phi\)8 mm HRB400 rebars.  
The manufacturing process of the BFT-UHPC slab specimen is as follows. (1) Fix the reinforcement cage, then cast the UHPC into the designated position for the BFTs. (2) Place the prepared BFT onto the fresh UHPC and gently press to ensure the UHPC surrounds the BFT. (3) Complete the casting of all the UHPC, then after 48 h of curing at room temperature, demold it and place it in steam at 90 \({}^{\circ}\)C for 48 h. The casting process of the BFT-UHPC slab specimen is shown in Figure 8.
## 2 Materials and Methods
### Artificial Neural Networks  
Artificial Neural Networks (ANNs) are built by computing nodes that have several inputs and outputs. Each input has a weight that adjusts its value, and a bias is added to calculate the output. The design of the ANN is adapted to the problem to which it is applied, creating a model of the data. The operation of the Neural Network has several steps: a training phase, a verifying phase and an operational application. A good introduction to ANNs can be found in [PERSON] et al. [18].  
Firstly, the ANN is trained with already classified data as inputs and outputs. With a backpropagation process and using a lost function to evaluate the gains obtained, the weights and the bias of each neuron are adjusted. The training data are presented to the network in a random fashion in batches. The training process is similar to the bootstrap statistical technique [19]. Formula (1) shows how the output of an individual neuron is calculated.  
\[y_{k}=f\left(\sum_{i=0}^{i=n}(w_{i}*x_{i})+B_{k}\right) \tag{1}\]  
ANNs can have several layers, allowing more complex and more accurate calculations to be performed. The Neural Networks described so far can only have a linear behavior. An activation function is used to implement nonlinear arithmetic. As an example, we may see, in Equation (2), how the sigmoid function, applied to the output of the neural cell, generates a nonlinear output.  
\[f(x)=\frac{1}{(1+e^{-x})} \tag{2}\]  
In the training phase, a loss function is used to evaluate the accuracy of the predictions. We used the Mean Squared Error (MSE) function, which is a simple and commonly used technique [20]. The MSE formulation can be seen in Equation (3).  
\[mse=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{3}\]  
To calculate the optimization of the network parameters, optimizer functions are used. Stochastic Gradient Descent (SGD) has been widely adopted in machine learning for many years. A good description can be found in Bottou [21]. We used the Adam optimizer function, introduced by [PERSON] and [PERSON] [22], which has a good performance result in problems that involve a large number or parameters, such as Neural Networks.  
As stated above, when the ANN results are inside the precision criteria previously established, a verification phase is accomplished, where a portion of new data already classified are presented as input and the output is verified. If the tests satisfy the precision criteria, the ANN can start its production life and can be used with real data.  
We chose as a first approach the use of a Neural Network very similar to the original Perceptron. A Perceptron uses the Sequential Model, which is a plain stack of neural layers, where each layer has a tensor as input and as output. Subsequently, a Deep Neural Network (DNN) was used. DNNs have hidden layers, and they have more computing power; a good introduction to and the evolution of DNNs can be found in Samek and Montavon [23]. In further experiments, new Neural Network architectures can be tested.
## 4 Discussion
These differences may be attributed to various factors, such as chitosan purity, deacetylation degree, reaction ratios or even to the centrifugation as executed by [PERSON], [PERSON] [36] in the end of the NP formation process and prior to freeze-drying. This may have removed any remaining non-reacted chitosan that, with the protonated amino groups, may have contributed to the NPs' aggregation during freeze-drying. In an effort to protect NPs during this process, cryoprotectants were employed. The usage of sugars as cryoprotectants is widely described in the literature, as their addition priorto freeze-drying allows them to interact with the solute via hydrogen bonding, which will maintain the solute in a \"pseudo hydrated\" state during the dehydrating process, thus protecting the NPs from damage [23]. Of the tested cryoprotectants, only mannitol was able to prevent NP aggregation. This could be related to peculiar characteristics between sugars [23]. Furthermore, these results are in line with the work of [PERSON], [PERSON] [38], where mannitol was successfully used as a cryoprotectant for chitosan NPs. It formed an envelope around the particles and, thus, inhibited aggregation. In addition, as it is neutral in terms of charge, it did not modify the NPs' ZP. However, and as seen in this work, cryoprotectant concentration also plays a key role in NPs stabilization. [PERSON], [PERSON] [23] showed that at least a 5% (_w/v_) concentration of cryoprotectant was required, whereas [PERSON], [PERSON] [39] stated that high cryoprotectant concentrations may destabilize NPs and lead to their aggregation. Chitosan NPs' biocompatibility is also a contentious topic in the literature. [PERSON], [PERSON] [36] found them non-toxic for MCF-12F cells, and [PERSON], [PERSON] [40] found similar results for the HeLa cell line. [PERSON], [PERSON] [41] reported no toxicity on C26 murine colorectal carcinoma and A375 human melanoma cell lines, [PERSON], [PERSON] [14] showed that there was no NP-based toxicity towards lung epithelial cells and [PERSON], [PERSON] [15] reported no toxicity towards conjunctival epithelial cells, all results in line with those here observed for the HaCat cell line. On the other hand, [PERSON], [PERSON] [33] reported chitosan NPs as being responsible for decreases in viability for two murine cell lines (J774 macrophages and L929 fibroblasts), and [PERSON], [PERSON] [13], despite reporting NPs as being less toxic than chitosan, still found some toxicity towards the Calu-3 cell line. The authors attributed this cytotoxicity to NPs entering the cells, thereby leading to a toxic effect directly affecting cell metabolism. Considering that the methodology employed in this work to measure cell viability was a metabolism-based one, as the assay was based on mitochondrial dehydrogenase capacity to reduce XTT and produce a spectrophotometrically measurable orange formazan product [42], it is possible that this internalisation and consequent toxicity on the cells' metabolism did not occur for the HaCat cells in contact with chitosan NPs.