esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classification Framework  
By using the proposed RDCA and SCNN, this section presents a new framework of automatic PQD diagnosis and classification. Its framework is illustrated in Figure 4. The detailed parameter settings are as follows: the size of C1, C2, C3, and C4 are \(11\times 11\times 8\), \(9\times 9\times 16\), \(9\times 9\times 24\), and \(7\times 7\times 4\), respectively. The \(P1\), \(P2\), and \(P3\) are all set to \(2\times 2\). The stride s are all set to \(1\times 1\). We set the learning algorithm as the Adam. The learning rate is 0.001 and the dropout rate is 0.5. The batch size is 50.  
The sign \(C_{i}\) and \(P_{i}\) are the sizes of convolutional filter and max-pooling in the \(i\)th layer, respectively. The \(F_{i}\) denotes the number of nodes in the FC layer. For example, when the input size \(m\times n\) is set as \(128\times 256\), it can be inferred from Figure 4 that the output of the first max-pooling layer is \(64\times 128\times 8\) according to Section 2.2.  
It is worth noting that the original shape of the amplitude matrix \(\mathbf{x}\) is 256 \(\times\) 512. However, when there is an output of RDCA model with the shape \(64\times 128\times 8\), the super-parameters of RDCA can exceed millions. Thus, one compromise to reduce the RDCA model parameters is to down-sampling DRST, i.e., the size of amplitude time-frequency matrix decreases from 256 \(\times\) 512 to 128 \(\times\) 256.  
Schematically, the classification of PQDs can be divided into three steps.  
1. Time-frequency analysis: Obtain the amplitude matrix data set \(\mathbf{x}\) based on DRST. Each amplitude matrix is normalized to [0, 1] for better training.
2. Automatic feature extraction: A multilayer RDCA is designed and trained using the amplitude time-frequency matrix. Then obtain the output features \(\mathbf{z}^{\prime}_{pi}\) of the encoder.
3. Classification: Remove the decoder structure of RDCA, and then feed features \(\mathbf{z}^{\prime}_{pi}\) to the well-designed SCNN.  
Figure 4: The structure of automatic PQD diagnosis and classification based on RDCA.  
To reduce over-fitting, a dynamic learning rate is used in the training process of RDCA and SCNN. Specifically, the learning rate is reduced by half when the training loss is no longer reduced by five consecutive iterations.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Poorly Soluble Bioactive Compounds
#### 4.2.1 Curcuminoids (and Curcumin in Particular)
In experiments involving in vitro anti-cancer evaluation against human cancer cell lines (HL-6, A549 and PC3), [PERSON] and colleagues [142] demonstrated that curcumin loaded in SLNs shared the same anti-cancer mechanisms claimed for free curcumin, including induction of cellular apoptosis by activation of caspases, release of nuclear factor kappa B (NF-kB) activation, and upregulation of TNF-R, but they exhibited significantly higher apoptotic actions against these cancer cells. The curcumin-loaded SLNs and NLCs had improved in vitro antitumor activities in cancer cells (MCF-7 cells, lung cancer cells A549, colon adenocarcinoma cells, prostate cancer cells, or human brain cancer cells A172) [131; 132; 133; 139; 143]. The improved in vitro anti-cancer activities of curcumin loaded in SLNs were clearly associated with the enhanced permeability across the cell surface or increased intracellular uptake by the cancer cells. During in vivo animal (mice) experiments with intraperitoneal (i.p.) administration, [PERSON] et al. [137] reported that the incorporation of curcumin in the NLCs enhanced the targeting effect of curcumin to the brain and tumor, and subsequently increased the inhibition efficiency of curcumin from 19.5% to 82.3%, through enhanced apoptosis of cancer cells. They attributed such significant improvements of in vivo anti-cancer activities to enhanced cellular uptake and reactive oxygen species production. [PERSON] and colleagues [144] also showed that curcumin loaded in NLCs exhibited higher cytotoxic activities against gliomas (C6 and U251 MG) than free curcumin, through similar mechanisms, including induction of G2/M arrest and autophagy. They confirmed similar anti-cancer activities in rats bearing C6 gliomas in vivo. In recent years, there has been increasing interest in the neuroprotective effects of curcumin [60]. [PERSON] and [PERSON] [39] evaluated the protective potential of the oral administration of curcumin-loaded SLNs against AlCl\({}_{3}\)-induced neurotoxicity in mice and, interestingly, found that the oral administration of curcumin-SLNs completely reversed the adverse effects of AlCl\({}_{3}\). The loading of curcumin in NLCs was effective at treating amyloid-\(\beta\)-induced cognitive deficiencies in a rat model of Alzheimer's disease (AD), as a result of a significantly increased accumulation rate of curcumin in the rat brain, as well as its serum levels [140]. The protective role of curcumin-loaded NLCs for brain delivery of curcumin was confirmed by reduced oxidative stress parameters (e.g., ROS formation, lipoid peroxidation, and ADP/ATP ratio) in the hippocampal tissue and improvements in spatial memory [140]. In addition, SLNs have been successfully used as delivery nanovheicles for curcumin to treat major depression disorders [145; 146].
## 2 Materials and Methods
### Biofilm Formation  
Experiments to assess biofilm formation were conducted in 12-well microtiter plates (MTPs, VWR International, Carnaxide, Portugal) (Figure 1, top). Two coupons of the materials under investigation were fixed to the wells using double-sided adhesive tape. Prior to the experiments, the coupons underwent UV sterilization for a duration of 1 h. Subsequently, 3 mL of each inoculum at an OD\({}_{610}\) = 0.1 were added to the wells. The MTPs were incubated at 30 \({}^{\circ}\)C, 185 rpm, for 9 days. This shaking frequency was chosen based on our prior findings, which showed that higher flow conditions enhanced recombinant protein production [58], and because 185 rpm is the maximum frequency that could be used without liquid overflow. Sampling and medium changes were performed on days 1, 3, 5, 7, and 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LB Medium (a)** & **TB Medium** & **M9 ZB Medium** \\ \hline
10 g\(\cdot\)L\({}^{-1}\) Tryptone & 12 g\(\cdot\)L\({}^{-1}\) Bacto tryptone (b) & 10 g\(\cdot\)L\({}^{-1}\) N-Zamine A (a) \\
5 g\(\cdot\)L\({}^{-1}\) Yeast extract & 24 g\(\cdot\)L\({}^{-1}\) Yeast extract (a) & 5 g\(\cdot\)L\({}^{-1}\) NaCl (b) \\
5 g\(\cdot\)L\({}^{-1}\) NaCl & 4 mL\(\cdot\)L\({}^{-1}\) Glycerol (b) & 1 mL\(\cdot\)L\({}^{-1}\) 1 M MgSO\({}_{4}\) (c) \\  & 2.31 g\(\cdot\)L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\) (b) & 10 mL\(\cdot\)L\({}^{-1}\) 40\% Glucose (a) \\  & 12.54 g\(\cdot\)L\({}^{-1}\) K\({}_{2}\)HPO\({}_{4}\) (a) & 1 g\(\cdot\)L\({}^{-1}\) NH\({}_{4}\)Cl (d) \\  & & 3 g\(\cdot\)L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\) (b) \\  & & 6 g\(\cdot\)L\({}^{-1}\) Na\({}_{2}\)HPO\({}_{4}\) 7H\({}_{2}\)O (b) \\ \hline \end{tabular}
\end{table}
Table 1: Culture media composition.  
Figure 1: Schematic representation of biofilm formation and analysis procedures.  
Three independent experiments, with two technical replicates for each condition, were performed.
###### Abstract  
Dynamic deflection deformation is a crucial index which can reflect the healthy operation of bridges; due to their limitations, monitoring technologies (e.g., sensors, automatic total stations, and GPS) cannot perform high-frequency and whole-process non-contact dynamic deformation monitoring. Therefore, taking the Jinan Fenghuangshan Road Bridge as an example, this paper developed key low-cost and high-precision close-range photogrammetry technology and combined this with GB-RAR technology to remotely monitor bridge dynamic deformation. The results indicate that the measurement accuracy pertaining to the technology, which is based on an isometric virtual surface, is approximately 0.5 mm when the monitoring distance is 250 m, which can meet the accuracy requirements of bridge deformation monitoring. A vehicle's dynamic load is transmitted in the form of a stress wave inside the bridge, and with regard to span, the wave is most significant in the 1/2 to 1/4 range. This study observes that the maximum deflection deformation of the bridge is 47.2 mm, which is within the allowable deformation range of the General Specifications for Design of Highway Bridges and Culverts. The research results herein provide technical support and data reference for safety monitoring, and for the reinforcement and maintenance of bridges such as the Fenghuangshan Road Bridge.  
close-range photogrammetry; GB-RAR; safety monitoring; wading bridge
# Fine-Grained Boundary Conditions in Field-Based Routing
## 3 Revisit of Field-Based Routing Protocol
### Operation Principle  
Field-based routing operates by selecting the path with the steepest gradient within the routing field, which is formed based on congestion levels and proximity to gateways. Consequently, it favors the most efficient path in terms of traffic congestion levels and proximity to gateways as the next hop for forwarding packets. Initially, potential values are assigned to both inner and outer boundary nodes based on the respective boundary conditions, while initial values are assigned to the other general mesh nodes. Subsequently, each node calculates its potential value using the information received through periodic field-related information exchange, including recent traffic congestion levels and proximity to gateways, from its one-hop neighboring nodes. This process leads to the construction of the routing field. Throughout this process, the boundary conditions act as reference points for the convergence of the routing field, significantly impacting the overall routing performance.
# Quality-Aware Resource Model Discovery
## 3 Quality-Aware Resource Model Discovery
### Model Simplification  
The final phase of the proposed approach is the model simplification based on the integrated context-aware resource model from Section 3.4 and the state properties from Section 3.5. As discussed earlier, the moderate states that belong to the same activity are merged with a specific condition. In this paper, we provide three types of conditions, and they are introduced in Definition 6.  
**Definition 6**.: _(Merging). Let \(TS=(S,E,T)\) be a transition system. Let \(s_{i},s_{j}\in S\) be the target states that belong to the same activity, i.e., \(s_{i}[0]=s_{j}[0]\). Let \(M\subseteq S\) be a moderate set. States \(s_{i},s_{j}\) can be merged if they hold the following conditions._  
* _Type_ 1_: All states_ \(s_{i}\)_,_ \(s_{j}\) _belong to the moderate set_ \(M\)_, i.e.,_ \(s_{i}\)_,_ \(s_{j}\in M\)_._
* _Type_ 2_: All states_ \(s_{i}\)_,_ \(s_{j}\) _belong to the moderate set_ \(M\)_, i.e.,_ \(s_{i}\)_,_ \(s_{j}\in M\)_, and all events that enter into_ \(s_{i}\)_,_ \(s_{j}\) _have the same property (where, the event property includes superior, inferior, and moderate)._
* _Type_ 3_: All states_ \(s_{i}\)_,_ \(s_{j}\) _belong to the moderate set_ \(M\)_, i.e.,_ \(s_{i}\)_,_ \(s_{j}\in M\)_, and all events that come out from_ \(s_{i}\)_,_ \(s_{j}\) _have the same property (where, the event property includes superior, inferior, and moderate)._  
The first type of merging is to combine all moderate states in a simple way. For example, if some of the states that are relevant to the same activity have a moderate property, those are straightforwardly combined into one moderate state. This approach, however, neglects the performance of arcs presented in Section 3.4. The second or the third method considers both moderate states and the corresponding arc performances. In the second approach, only moderate states with the same incoming event property are merged into one. On the other hand, the third approach conducts merging when the outgoing event property is the same.  
Figure 4 gives an example of how to simplify models based on three different merging conditions. The default model IM has 10 states for three activities, i.e., A, B, and C. Here, if the first type is applied, as depicted in Simplified IM1, all states in activity B are merged into one state S4\(-\)7, and some of the states, i.e., S8 and S9 in activity C are combined. If the second type is chosen, we should check whether the property of incoming states is the same. For example, in activity B of Simplified IM2, only S4 and S5 have the neutral incoming event property, while others have different ones. Then, only S4 and S5 can be merged into one state, i.e., S4\(-\)5. If we consider the third option, S4\(-\)5 and S6\(-\)7 can be created as presented in Simplified IM3. This is because the property of outgoing events are the same as each other.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 4 Security Analysis
### Data Integrity  
To ensure the integrity of the transaction data, this study uses elliptic curve cryptography to calculate the session key, \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\), and also to protect the data's integrity. The malicious attacker cannot use the signatures \((K_{AP1},K_{AP2})\), \((K_{PA1},K_{PA2})\), \((K_{BP1},K_{BP2})\), \((K_{PB1},K_{PB2})\), \((K_{AB1},K_{AB2})\), and \((K_{BA1},K_{BA2})\) to calculate the correct session key \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\).  
Only the legal patient or hospital A can calculate the correct session key \(SEK_{AP}\). The legal hospital A calculates the session key as follows:  
\[SEK_{AP}=H_{2}(K_{AP1},K_{AP2})\]  
and the legal patient calculates the session key as follows:  
\[SEK_{AP}=H_{2}(K_{PA1},K_{PA2}).\]  
\[\begin{array}{ll}K_{PA1}&=S_{PAT}T_{HPA}+aPK_{HPA}\\ &=S_{PAT}bP+aS_{HPA}P\\ &=bS_{PAT}P+S_{HPA}aP\\ &=bPK_{PAT}+S_{HPA}T_{PAT}=K_{AP1}\\ &=aT_{HPA}=abP=baP=bT_{PAT}=K_{AP2}\end{array}\]  
Only the legal patient or hospital B can calculate the correct session key \(SEK_{BP}\). The legal hospital B calculates the session key as follows:  
\[SEK_{BP}=H_{2}(K_{BP1},K_{BP2})\]  
and the legal patient calculates the session key as follows:  
\[SEK_{BP}=H_{2}(K_{PB1},K_{PB2}).\]\[\begin{array}{ll}K_{PB1}&=S_{PAT}T_{HPB}+cPK_{HPB}\\ &=S_{PAT}dP+cS_{HPB}P\\ &=dS_{PAT}P+S_{HPB}cP\\ &=dPK_{PAT}+S_{HPB}T_{PAT2}=K_{BP1}\\ K_{PB2}&=cT_{HPB}=cdP=dcP=dT_{PAT2}=K_{BP2}\end{array}\]  
Only legal hospital B or hospital A can calculate the correct session key \(SEK_{AB}\). The legal hospital A calculates the session key as follows:  
\[SEK_{AB}=H_{2}(K_{AB1},K_{AB2})\]  
and the legal hospital B calculates the session key as follows:  
\[\begin{array}{ll}SEK_{AB}=H_{2}(K_{BA1},K_{BA2}).\\ \\ K_{BA1}&=S_{HPB}T_{HPA2}+ePK_{HPA}\\ &=S_{HPB}fP+eS_{HPA}P\\ &=fS_{HPB}P+S_{HPA}eP\\ &=fPK_{HPB}+S_{HPA}T_{HPB2}=K_{AB1}\\ K_{BA2}&=eT_{HPA2}=efP=feP=fT_{HPB2}=K_{AB2}\end{array}\]  
Only the correct session key will make a successful communication. Therefore, malicious attackers cannot modify the transmitted information. Thus, data integrity is achieved by the proposed scheme.  
Scenario: A malicious attacker intercepts the information transmitted from hospital A to the patient and sends a modified information to the patient.  
Analysis: The attacker will not succeed because the legal patient will use  
\[CHK_{PA}\overset{?}{=}H_{3}(SEK_{AP},T_{PAT})\]  
to check the data integrity of the transmitted information. The malicious attacker cannot calculate the correct session key \(SEK_{AP}\). Therefore, the attack will fail when the legal patient authenticates the received information. The malicious attacker cannot achieve his/her purpose by sending modified information to the patient in the proposed scheme. For the same reason, the attack will fail when the legal hospital A uses  
\[CHK_{AP}\overset{?}{=}H_{3}(SEK_{AP},T_{HPA})\]  
to check data integrity. Thus, malicious attackers cannot achieve their purpose by sending a modified message to hospital A.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 6 Experimental Validation
### Assessment of Online Cluster Adaptation  
After detailed analysis of the CHaChA phase sequence for initial clustering, the concepts for dynamic cluster adaptation in long-term operation, described in Section 4.4, were also functionally tested in example scenarios. For this, we kept parameterization P2 of the prototype implementation (see Table 9). By specifying an initial cluster constellation, all nodes immediately jumped to phase 7 at program start, where the cluster adaptation mechanisms took effect.  
In addition to the offline visualization of cluster constellations from the status files of the nodes, it was helpful for the subsequent experiments to obtain and display current cluster information at runtime. A Java application developed for this purpose was run on a separate lab PC and generated an image of the current cluster status based on messages received over the control network. To achieve this, all CHs periodically sent their cluster information (WLAN channel and list of CMs) not only as broadcast in the mesh network, but simultaneously as unicast via Ethernet to the PC. Hence, the freshness of cluster information on the nodes and the PC was given by the broadcast interval of the CH messages (CH_PERIOD). In the experiments, all devices were started simultaneously. As for the offline visualization, the grid positions of the nodes were again predefined. Based on received information about their role and cluster membership, nodes were highlighted accordingly. To diagnose the cluster adaptation mechanisms, videos of the desktop application were recorded during experiment execution. Shown below are snapshots of the times when cluster changes became visible in the application (time resolution of 1 s).
## 5 A Hybrid Scheme Using MuL-ACO for Trajectory Generation and Optimization  
For the robot to effectively find a short and smooth trajectory while avoiding crossing steep areas, a hybrid scheme using mutual learning and adaptive ant colony, namely, MuL-ACO, is presented in this work. Figure 7 shows the flow chart of the scheme.  
1. Establish a 2D-H grid map based on the uneven environment and initialize the parameters. Set the starting point \(S\), the end point \(E\), the number of ants \(k\), the maximum number of iterations \(I_{\text{max}}\), pheromone heuristic factor \(\alpha\), expected heuristic factor \(\beta\), pheromone intensity coefficient \(Q\), and the initial pheromone \(\tau(0)\).
2. Trajectory selection. Calculate the heuristic function by Equation (6). The ant is placed at the starting point and the probability of transferring to the next node is calculated by Equation (2). All trajectory nodes from the starting point to the current point are stored in the Tabu list.
3. Determine whether all the ants have completed the trajectory search in this generation. If it is, go to step 4, otherwise, return to step 2.
4. Record the nodes of the trajectory walked by all ants and find the optimal trajectory in this iteration.
5. Adjust the pheromone volatilization factor \(\rho\) adaptively, according to Equations (10) and (11). Update the pheromone by Equation (4). Re-zero the taboo table.
6. Determine whether the maximum number of iterations \(I_{\text{max}}\) is reached. If it is, go to step 7, otherwise, return to step 2.
7. Initial trajectory generation. Obtain the optimal trajectory node and total height.
# Portable Raman Spectrometer for In Situ Analysis of Asbestos and Fibrous Minerals
## 2 Materials and Methods
### Mineralogical Investigation  
Rock and mineralized vein samples were identified and chemically characterized by polarized light microscopy (PLM) on thin sections, X-ray powder diffraction (XRPD), and scanning electron microscopy associated with energy dispersive X-ray spectroscopy (SEM-EDS).  
The bulk mineralogy was determined by qualitative XRPD analysis using: (i) for Balangero samples, Rigaku Miniflex 600 diffractometer; (ii) for Valmalenco samples, PANalytical X'Pert PRO PW3040/60 diffractometer; and (iii) for Caledonian samples, Bruker-AXS D8 Advance diffractometer equipped with an Si(Li) solid-state detector. Intensity measures were run at 40 kV and 15-40 mA, using CuK\(\alpha\) radiation (\(\uplambda\) = 1.54178 A) over a 20 range from 3 to 80\({}^{\circ}\) 20 in steps of 0.02\({}^{\circ}\), with a counting time of 1 s per step. The limit of detection of XRPD depends on the mineral phase and is generally comprised between 0.1 wt% for highly crystalline phases and 5 wt%; for serpentine minerals, it is usually 1 wt%. Representative diffraction patterns are reported in Supplementary Materials S2.  
Sample morphology and semi-quantitative EDS microanalysis were investigated on natural samples and thin sections using: (i) Zeiss Evo50 SEM for Balangero samples; (ii) Vega TS Tescan 5163 XM in combination with EDAX Genesis 400 analyzer for Valmalenco samples; and (iii) JEOL JSM-IT 300 LV/LA with Low Vacuum Mode equipped with an Oxford-X-Max EDS for Caledonian samples. Secondary Electron Images were acquired at various magnifications and accelerating voltages, commonly 3-10 kV or 20 kV with 90 pA or 200 pA.
## 3 Malicious Document Classification
### Model Construction  
The proposed framework uses classification methods for malicious document detection. As described in Section 3.3, we obtained the document vector of a document in a training corpus. We built a classification model with training documents and used the model to predict whether a given target document is malicious. In this paper, we use three well-known classification models: decision trees, support vector machines (SVMs), and deep neural networks (DNNs).
## 2 Overview
### BIM and Energy Management  
Energy cost or infrastructure energy consumption simulation are concepts being increasingly important currently. Sustainability has become a key factor in construction projects and it is widespread in AEC tools. The same occurs with BIM. BIM profits in already existing buildings are well-known. As-built management documentation [22], maintenance [23; 24; 25], quality control [26; 27], parameter monitoring [22; 23; 24; 28], emergency management [19], or space management [28; 29] are examples of it. In addition to all the referred profits, energy management has also shown to be an attractive alternative. Commonly named as Building Energy Modelling (BEM), BIM-based management techniques have been applied to control, analyse, and manage energy [22; 30; 31; 32; 33].  
Published research has shown the outstanding possibilities of BEM for the energetic simulation of infrastructure [34; 35]. Those BEM techniques permit connecting BIM models with external databases which collect the main material properties, allowing a detailed energetic analysis [34]. Another main advantage attributed to BEM is data visualization. Through use of the BIM model, data visualization environment is provided in a user friendly graphical way. This allows an ease understanding data and information [36; 37]. In this way, [PERSON] and [PERSON] [37] performed a complete energy simulator based on BEM, providing real time data through the infrastructure BIM model.  
Heritage management, as one of the most important BIM research lines for the built environment [22], has also been extrapolated to energy management field. Technical data, consumption projections, historical consumptions or location information are essentials for infrastructure management. [PERSON] et al. [38] proposed a combination of sensors and elements of the BIM model for the electrical system of the infrastructure. Other researchers such as [PERSON] et al. [39] associated energy consumption with parameters such as temperature, humidity or occupation. Another field related with BEM is the one referred to data interoperability. There are multiple information databases hosting various material properties and its synchronization with each element of the BIM model is not a challenge and even more if the great variety software is considered.  
As has been reviewed, a large number of published research applied BEM for infrastructure energy management. However, concepts as BIM and Origami applied in a single way for energy management purposes would be considered a novel topic for future research lines due to the lack of research in this regard.