esa-sceva/satcom-chunk-collection huggingface dataset:
# Sulfate Resistance in Cements Bearing Bottom Ash from Biomass-Fired Electric Power Plants
## 3 Results and Discussion
### Soaking-Induced Mass and Size Changes  
Weight was observed to rise over time in all of the specimens soaked in sulfates (Figure 3), 1.7-fold in OPC, 2.2-fold in OPC + 10 BA, and 2.7-fold in OPC + 20 BA cements.  
The increase in weight was calculated with Equation (3), where: \(\Delta W\) is variation in weight in %, \(m_{0}\) water-cured sample weight at time \(i\); and \(m_{i}\) weight at exposure time \(i\) (ti = 14, 56, 90, or 180 d).  
\[\Delta W=[(m_{i}-m_{0})/m_{0}]\cdot\!100 \tag{3}\]  
Figure 3: Variation in weight with time soaked in Na\({}_{2}\)SO\({}_{4}\).  
Figure 2: Pore system properties in cements soaked in a 0.3 M Na\({}_{2}\)SO\({}_{4}\) solution for 56 or 180 d.  
Weight gain induced by sulfate attack was more accentuated during the early weeks of hydration (t \(<\) 56 days), after which it tapered due to declining cement paste permeability. The latter was a direct result of secondary ettrinigite and gypsum formation prompted by the interaction between cement hydrated phases and external sulfate ions entering the pore system [40]. Weight gain was consistently greater in the BA-bearing than in the BA-free cements at all of the times studied, perhaps due to the higher porosity in the former.  
The variation in length with time of exposure to Na\({}_{2}\)SO\({}_{4}\) in the cements analyzed (plotted in Figure 4) revealed that weight gain (Figure 3) was attendant upon expansive product formation during sulfate penetration [41]. Inasmuch as the products occupied more space than the reagents, such crystallization was followed by expansion, cracking, and surface spalling.  
The curves in Figure 4 followed a pattern observed by other authors, who divided expansion into two periods: initial or induction characterized by \"steady, slow\" or \"progressive\" expansion (up to \(\sim\)56 days) followed by a stage with a \"sharply\" or \"rapidly\" increasing rate of phase expansion that proceeded until the sample disintegrated entirely [42; 43].  
Finally, the graph also indicates the expansion was less intense in the blends containing BA than in the reference OPC, by 7.1% in OPC + 10 BA and 28.5% in OPC + 20 BA. This behaviour might be related to the presence of larger pores and greater connectivity in the blended materials, which would favor first-stage ion transport toward macropores and concomitant crystal formation in their more thermodynamically stable interiors [44], ultimately mitigating expansion. As [PERSON] et al. [38] contended, the greater total porosity in the new pastes may have a beneficial long-term effect by enhancing these materials' capacity to accommodate precipitates generated during exposure.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 2 Preliminary
### Xavier Parameter Initialization Strategy  
In the process of fault diagnosis using a CNN network, the initialization of network weights has a great impact on the fault diagnosis performance. Improper initialization will easily slow down the convergence speed of the network, or even fall into the local optimum prematurely. The random initialization method is a widely used initialization method. This method uses a certain fixed probability distribution to assign initial values to all learnable parameters of the network. However, improperly set random initialization parameters can easily affect the convergence of the network. In addition, when the number of network layers is large, random initialization may also cause the problem of gradient dispersion.  
When the network layer weights are updated through backpropagation, the calculation of the gradient includes the product of the weights of several items from the network output layer to the current layer. If there are many network layers and the weights of most product items are less than 1, it will cause the gradient to decrease continuously. Therefore, the random initialization method may cause large differences in the parameter update effects of different network layers. The network layer parameters close to the output layer can be better optimized, while some network structure parameters close to the input layer are difficult to update. The Xavier initialization strategy [19] can effectively solve this problem. The main idea of this method is to make the variance of each network layer output the same as that of the previous layer output in forward and back propagation process, which can alleviate the problem of gradient disappearance. When using the Xavier initialization method, the parameters of each convolution layer should meet the uniform distribution, as shown in Equation (3).  
\[W\sim U\left[-\frac{\sqrt{6}}{\sqrt{n_{k}+n_{k+1}}},\frac{\sqrt{6}}{\sqrt{n_{ k}+n_{k+1}}}\right], \tag{3}\]  
where \(W\) is the parameters of the convolution layer, and \(n_{k}\) denotes the number of parameters of the \(k\)-th convolution layer.
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
Additional expansion is induced by a phase modulator driven at multiples of the gain-switching frequency, to achieve an expansion factor \(\sim\)3, i.e., producing up to 45 lines and with an OCNR >45 dB (note: unless stated otherwise, the number of comb lines quoted in the article relates to lines within a 5 dB window from the spectral peak). Then, we demonstrate a wide FSR tunability from 390.625 MHz to 6.25 GHz by employing comb densification. Finally, we carry out a comprehensive characterization of the OFC to exemplify its excellent features. The optical linewidth of the comb lines was measured to be \(\sim\)40 kHz, while their relative intensity noise (RIN) was better than \(-\)152 dB/Hz. The comb lines across the expanded OFC also show an excellent phase correlation. It is also important to note that the proposed architecture lends itself to photonic integration, further reducing the cost, size and energy consumption. Such simplicity, flexibility, FSR tuning (GHz to MHz), and potential photonic integration This article is organized as follows: In Section 2, the operational principle and experimental setup of the proposed architecture are presented. Section 3 describes the experimental results: expansion, comb densification, following by detailed comb line characterization. Finally, Section 4 outlines the conclusions.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties
### Intercalation Modification  
Intercalation modification refers to that small molecules reacting with HNTs via the hydroxyl groups in order to improve the performance of HNTs. [PERSON] et al. (2015) used the phenylphosphonic acid (PPA) to unfold and intercalate the HNTs, and mixed the product with epoxy to form the halloysite-epoxy nanocomposites. The modified HNTs achieved better dispersion, large contact area among nanocomposites, and significantly promoted micro-cracks, and plastic deformation took shape at the interface. [PERSON] et al. (2016) treated the HNTs with potassium acetate (PA) and ball mill homogenisation to improve particle dispersion. It was demonstrated that the modified HNTs could enhance the properties of mechanical, interfacial debonding, and provide opportunities for other substances to intercalate.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 4 Fusion Localization Architecture
### Fusion Localization  
In this part, we propose the fusion scheme to achieve better performance. In the proposed scheme, the initial location is set using CHAN estimation. We thus set a threshold \(D_{th}\), where generally \(D_{th}=2*s_{m-1}\), to avoid outliers in localization processing.  
At time \(m-1\), the CHAN estimation is \(U_{m-1}^{c}\left(x_{m-1}^{c},\ y_{m-1}^{c}\right)\), and the location estimation of the proposed method is \(U_{m-1}^{p}\left(x_{m-1}^{p},\ y_{m-1}^{p}\right)\). Two cases can occur:  
Case 1: When the distance between CHAN estimation at time \(m-1\) and the location at \(m-2\) is greater than the preset threshold \(D_{th}\), CHAN estimation is discarded as an outlier. Then, the location at time \(m-1\) is used, where \((x_{m-1},\ y_{m-1})=U_{m-1}\).  
Case 2: When the distance between estimation at time \(m-1\) and the location at \(m-2\) is less than the preset threshold \(D_{th}\), the location \((x_{m-1},\ y_{m-1})\) is achieved by the distance confidence level between CHAN estimation and the proposed method \(U_{m-1}\).  
The distance confidence level is defined as follows:  
\[Conf_{C}=\frac{1}{\|U_{m-2}-U_{m-1}^{c}\|_{2}} \tag{22}\]  
\[Conf_{p}=\frac{1}{\|U_{m-2}-U_{m-1}^{p}\|_{2}} \tag{23}\]  
The normalized distance confidence level for time \(m-1\) can be described by:  
\[Conf_{C}=\frac{Conf_{C}}{Conf_{C}+Conf_{p}} \tag{24}\]  
\[Conf_{p}=\frac{Conf_{p}}{Conf_{C}+Conf_{p}} \tag{25}\]  
with the constraint:  
\[Conf_{p}+Conf_{C}=1 \tag{26}\]  
After determining the distance confidence level, we can obtain the location \(U_{m-1}\). The localization at time \(m\) can be achieved from the following equation:  
\[U_{m}=U_{m-1}+s_{m}*\begin{bmatrix}sin\beta_{m}\\ cos\beta_{m}\end{bmatrix} \tag{27}\]  
Figure 5: Localization of the proposed heading direction method with different devices. (**a**) VivoY85a cell phone. (**b**) Honor60 cell phone.
## 3 Review of Anifrolumab Mechanism of Action and Clinical Trials  
### Development and Mechanism of Action of Anifrolumab  
The pathogenesis of SLE, which is considered a prototypic autoimmune disease, is complex and involves a myriad of immune mechanisms and various cell types [3] (Figure 3). In brief, environmental (e.g., ultraviolet radiation), viral (e.g., Ebstein-Barr virus), and hormonal triggers lead to an increased rate of apoptosis in an (epi)genetically susceptible individual [3, 4]. Autoreactive B and T cells process this increased number of antigens, leading to autoantibody and immune complex formation [4]. As a result, there is an increased production of type 1 interferons (IFNs) by plasmacytoid dendritic cells (pDCs), which is a central pathogenic process [5, 6]. Type 1 interferons maintain an increased autoantibody production through an autocrine loop, further activating B cells, which undergo class switching [4].  
Recently, an increasing number of clinical trials, including the TULIP trials, stratified patients according to their IFN gene expression status (high vs. low) [7]. However, this has not been adopted for routine clinical practice. In view of IFNs as a key mediator in SLE pathogenesis, targeting IFNs by monoclonal antibodies (mAbs) is an appealing approach. Sifalimumab and rontalizumab, two other mAbs targeting IFN alpha, have yielded mixed results in phase II trials [8, 9], and have not been developed and tested in phase III trials.  
Anifrolumab is a fully human, effector-null monoclonal antibody directed against the type I interferon (IFN) receptor subunit 1 (IFNAR1) [10]. It was engineered with mutations inserted in the heavy chain with the aim of reducing Fcy receptor (FcyR)-mediated effector functions, such as antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity [11], ultimately improving efficacy and reducing resistance through internalization by FcR [12].  
Further, it has been shown that ANI promotes IFNAR1 internalization, thus blocking downstream signaling, such as signal transducers and activators of transcription 1 (STAT1) phosphorylation [10]. Finally, ANI reduces the type I IFN autoantibodies loop sustained by pDCs [10, 13].  
The mechanism of action of ANI in the context of a proposed model of SLE pathogenesis is shown in Figure 3.  
Figure 2: Changes in serologic parameters and the disease activity at baseline and after eight weeks. (**A**) Complement factors C3 and C4. (**B**) Anti-double stranded (ds) DNA levels. (**C**) Cutaneous lupus activity and severity index (CLASI).
## 1 Introduction  
### Challenges in Data Protection and Research  
Medical research is increasingly ingesting large, structured data, which is ideal for research due to the proliferation of standardized data models and increasingly powerful processors [1]. This development brings increased attention to dualism at both national and international levels: On one hand, there is a desire to make extensive medical data accessible to researchers and utilize it for research collaborations to improve the quality of medical care. On the other hand, it is crucial to protect sensitive patient data effectively [2]. To ensure that both technically and organizationally everything is performed to safeguard patient data while still enabling research, data protection impact assessments (DPIAs) and related concepts have been established in recent years in Germany. Although data protection has been an important topic in Germany for quite some time now [3], data protection in its technical and organizational implications has gained increased attention in all European countries due to the General Data Protection Regulation (GDPR) which was passed in 2018 and aims to harmonize and standardize European data protection [4].  
In that context, more standardized data protection concepts will evolve to harmonize this heterogeneous field [5]. As European legislation's data protection implications are being integrated across the European Union, the significance of this subject is anticipated to extend to the global stage, particularly regarding the United States [6; 7].
## 1 Introduction
Thanks to the characteristic high surface-to-volume ratio and durable mechanical strength of graphene and its derivatives, such as graphene oxide (GO), they have been extensively studied in terms of their applications in sensors [29; 30]. It is noteworthy that [PERSON] et al. [31] reported the first MIP-functionalized GO hybrid material by reversible addition and fragmentation chain transfer (RAFT) polymerization. Compared with other living/control radical polymerization techniques, RAFT polymerization not only allowed the synthetic tailoring of polymers with narrow molecular weight distribution, controlled molecular weight, and diverse terminal functionality, but has also been performed under mild reaction conditions with a wide range of monomers. Therefore, the specially-designed polymer structure can been used to improve the features of molecular imprinting such as maximum adsorption capacity, selectivity, and sensitivity [32]. Herein, a sensitive electrochemical sensing platform based on GO was proposed for the detection of synthetic cathinones, in which 4-methylethcathinone (4-MEC) was used as the model abused drug. The GO-MIP nanocomposites were firstly fabricated by immobilizing MIPs onto the surface of GO through surface RAFT polymerization (Scheme 1): GO sheets were first prepared using a modified version of [PERSON]'s method [33; 34], and then the RAFT agent was attached onto the surface of the GO through an esterification process. The MIP-modified GO nanocomposites were synthesized by a surface RAFT polymerization strategy using 4-MEC as a template, methacrylamide (MAAM) as a functional monomer, and divinylbenzene (DVB) as a cross-linker. The specific binding sites were achieved by multiple weak interactions (such as hydrogen bonds between amino-containing and amide-containing groups, and \(\pi\)-\(\pi\) bonds between phenyl groups). After removing the template, the selectivity and repeatability as well as the adsorption capacity of the obtained GO-MIP nanocomposites were investigated. In addition, an electrochemical sensor with a high adsorption capacity and excellent selective recognition ability was prepared. The results validated the feasibility of the obtained electrochemical sensor to detect 4-MEC with a low limit of detection (LOD).
# Effects of the Fruit Harvest Date and Shelf-Life Nexus of Apples on Different Quality Perspectives
## 2 Materials and Methods
### Temporal Dominance of Sensations (TDS)  
To perform a temporal dominance of sensation (TDS) analysis, eight sensory attributes were predefined, as follows (Table 2): firmness, juiciness, sweetness, tartness, crispness, flouriness, toughness, and off-flavors. These attributes and their definitions were defined in line with research [42; 43; 44; 45].  
To collect sensory characteristics, each panelist was provided with a sample consisting of two fruits from each variety on the harvest date and on each shelf life testing day. The Lab Scale digital application for TDS was used to capture the choices of sensory characteristics chosen by the participants while chewing. The examination began when panelists placed the sample in their mouths. They identified the sensations they believed to be dominant once the recording began. Upon swallowing, the recording stopped, completing the examination.  
Panelists were instructed to select only one dominant attribute once when they perceived it. In addition, they could choose the same attribute more than once. Each sensation was explained to the panelists in line with recommendations from an ISO standard [46] during a two-hour training session prior to the sensory analysis [47]. The 'Start' and 'End' of the mastication process was followed by an internally developed application [48].  
The name of the dominant attribute, the moment an attribute is chosen as dominant, and the length of the dominance, which can be computed a posteriori, are typically used for the data gathered during TDS [24]. According to [PERSON] et al. and [PERSON] et al. TDS curves display the dominance rates of qualities against time for each sample. The Y-axis of TDS curves, regardless of normalization, consistently represents the dominance rate, or the proportion of attribute selections that are considered dominant at a given time point [22]. The computation involves splitting the total number of citations for an attribute (all replications) by the total number of runs (panelist \(\times\) replication). In addition, the chance level and the significance level were calculated in line with the recommendation from [PERSON] et al.  
Dominance rates could be interpreted as an indicator of the panelists' agreement and, thus, as a gauge of the panel's effectiveness [21]. More specifically, the total of all the dominance rates of all the attributes for every given panelist at any given time is 1, since any judge can only choose one attribute to be dominant at any given time. The agreement is better when the attribute has a higher dominance rate [22]. It is crucial to remember that TDS curves do not provide any information regarding the intensity of the attributes; instead, they only depend on the choice of which attribute is dominant.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 5 Conclusions  
This paper discussed the problem of optimal constrained multicast routing in WMNs. A novel approximation algorithm for the MCOMP problem was proposed, based on the technique of auxiliary graph construction, scaling, rounding, and the MPH algorithm. The proposed algorithm can achieve lower complexity and obtain the approximation optimal multicast routing tree in feasible time. According to the experiments on the special static networks, the proposed NMHA algorithm is capable of finding the optimal (or minimum-cost) multiply constrained optimal multicast routing tree with the approximation factor of \(2(1+\varepsilon)(1-1/q)\) and the time complexity of \(O(qmn^{2}\tau^{K-1})\).  
As for future research work, we plan to study more excellent solutions and faster algorithms for the MCOMP problem according to the time-varying characteristics of wireless networks based on our current research. Investigation on comparisons of the NMHA with other algorithms is also interesting.  
**Acknowledgments: The work described in this paper is supported by the Science and Technology Program of Guangzhou (No. 201510010192), and the Public Welfare Fund and Ability Construction Project of Guangdong Province (No. 2016A010101040), China.**  
**Author Contributions: [PERSON] conceived and designed the experiments, and then performed the experiments; [PERSON] analyzed the data; [PERSON] contributed analysis tools and wrote the paper; and [PERSON] contributed to the advancement of the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
## 4 Blockchain Technologies: Layer 1 and Layer 2
### Optimistic Rollups  
The academic world and industries have already been exploring scaling solutions like Optimistic Rollups (ORs) and Zero-Knowledge Rollups (ZK rollups) to address these issues (e.g., [48; 49; 50; 24]). See Figure 1 for some Layer 2s on top of Ethereum. ZK proofs transfer computationally demanding operations to Layer 2, easing the congestion on the mainchain and stabilizing network fees and number of transactions per second [50; 51]. Optimistic Rollups and ZK rollups have already been implemented in blockchain networks to enhance scalability. These approaches involve conducting transactions off-chain, hence minimizing the need for on-chain data verification. Optimistic Rollups operate on fraud proofs, improving the computational complexity. ZK rollups, on the other hand, employ cryptographic proofs to ensure the correctness of the given transactions, ensuring faster finalization while maintaining security measures. Both approaches have their benefits and compromises, with zkRollups providing a fully trustless architecture while OptimisticRollups offer a more efficient solution (hence, cheaper tx costs) but rely on the assumption that transactions are correct until concerns are raised.  
Optimism replicates the developer experience of the EVM, simplifying the process of constructing and implementing compatible rollup solutions. Furthermore, the protocol facilitates the utilization of pre-existing Solidity smart contracts, integration with off-chain wallets, and user interfaces (UIs) [50].  
Arbitrum fully supports the EVM, which ensures compatibility across all smart contract languages and the Ethereum mainchain. It includes a challenge period to guarantee the integrity of transactions. During this phase, network participants can challenge transactions if they suspect them to be fraudulent. Arbitrum does essential calculations to authenticate the legality of transactions, ensuring rapid processing while safeguarding the network against invalid or hostile operations. Arbitrum stands out due to its emphasis on enhancing the developer experience and ensuring compatibility with Ethereum's current tooling and smart contracts. Developers have the opportunity to implement their Ethereum applications on Arbitrum without making substantial changes, therefore taking advantage of enhanced scalability and efficiency, all while ensuring strong security assurances [50].
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 5 Evaluation of Simplification Effect
### Implementation and Mesh Design  
The algorithm implementation and data analysis was performed through Python programming language. The open source library IfcOpenShell [38] was used to analyze the BIM model and extract the geometry. Using Open CASCADE [39], a computer-aided design (CAD) library, and its wrapper library pythonOCC [40], the simplification method was implemented and the D2 shape distribution was evaluated. The mesh of the target building model was designed using the cfMesh library [41]. cfMesh can automatically generate mesh and boundary layers for each geometric shape, and is basically included for use in OpenFOAM [42]. A hexahedron mesh was generated using cfMesh; in the case of complex geometry where there is difficulty in generating high-quality hexahedron meshes, some cells are generated in the shape of a tetrahedron, prism, and pyramid.