esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Quantitative Phase Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ba Content** & **Main Peak** & **Second Peak** \\  & \(d\) value & \(d\) value \\ \hline
0 & 3.74749 & 2.65358 \\
0.5 & 3.75387 & 2.97368 \\
1.0 & 3.78612 & 3.12588 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(d\) value of main peak and second peak. Figure 2: Fitting effect of CBSA samples sintered at 1400 \({}^{\circ}\)C for 180 min. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase** & **Parameter** & **1200 \({}^{\circ}\)C-180 min** & **1300 \({}^{\circ}\)C-180 min** & **1350 \({}^{\circ}\)C-180 min** & **1400 \({}^{\circ}\)C-180 min** \\ \hline  & a & 13.01032 & 13.0207508 & 13.0270247 & 13.0402404 \\ CBSA-O & b & 12.92488 & 12.9469589 & 12.9600426 & 12.9981782 \\  & c & 9.21658 & 9.22244 & 9.22428 & 9.2330302 \\ \hline CBSA-C & a & 9.21896 & 9.22475 & 9.23109 & 9.23704 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Unit cell parameters of CBSA samples. increase of prolonged time, and the mesophases are rapidly reduced. When the prolonged time is 180 min at 1400 \({}^{\circ}\)C, Ca\({}_{4\to\)Ba\({}_{x}\)Al\({}_{6}\)SO\({}_{16}\) accounts for approximately 88.4%. However, the amounts of Ca\({}_{4\to x}\)Ba\({}_{x}\)Al\({}_{6}\)SO\({}_{16}\) decreased at 1440 \({}^{\circ}\)C due to the decomposition. The content of gypsum is less than 1% when sintered for 180 min at 1350 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C. CaSO\({}_{4}\), free BaO and CaO formed at 1440 \({}^{\circ}\)C due to the decomposition of CBSA. As is shown in Figure 4, the replacement rate of Ba\({}^{2+}\) shows serious dependence on the sintering system. It maintained at less than 20% ignoring the increasing prolonged sintering time when sintered at 1200 \({}^{\circ}\)C and 1300 \({}^{\circ}\)C. At relatively low sintering temperature (1200 \({}^{\circ}\)C and 1300 \({}^{\circ}\)C), not all the Ba\({}^{2+}\) can bound with CSA and the extra Ba\({}^{2+}\) exists as other mesophases, such as BaAl\({}_{2}\)O\({}_{4}\), BaSO\({}_{4}\). The replacement rate of Ba\({}^{2+}\) increases with increasing sintering temperature and prolonged sintering time in the 1350-1400 \({}^{\circ}\)C range. In this sintering range, both the content of Ca\({}_{4\to\)Ba\({}_{x}\)Al\({}_{6}\)SO\({}_{16}\) and the replacement rate of Ba\({}^{2+}\) increase rapidly. The replacement rate is about 80% when the sintered at 1400 \({}^{\circ}\)C for 180 min. The replacement rate also decreases due to the decomposition of Ca\({}_{4\to x}\)Ba\({}_{x}\)Al\({}_{6}\)SO\({}_{16}\). Figure 4: Replacement rate of Ba\({}^{2+}\) at different sintering system. Figure 3: Results of quantitative analysis. From the correspondence between the content of Ca4-\(x\)Ba\(x\)Al6 SO\({}_{16}\) and replacement rate of Ba\({}^{2+}\), the two indexes arrived at the maximum under the same sintering system. Sintering at 1400 \({}^{\circ}\)C for 180 min is considered the optimum and Ca3.2 Ba0.8 Al6 SO16 with a content of 88.4% was synthesized.
## 3 Proposed Method
### Training Details of Neural Network Model  
The structure of P-ResNet is shown in Figure 2. The input layer consists of two parts, the size of the time-frequency images is 32 \(\times\) 32, and the size of the time-domain feature is 2560 \(\times\) 1. In order to avoid the problems of gradient vanishing or explosion, the whole model uses rectified linear units (ReLU) as the activation function. In order to solve the instability of RUL prediction and obtain more accurate results, the RUL percentage is used as the label of bearing data. First, determine the SPT of training data, then the labels corresponding to the training data are set to be linearly decreasing,  
\[y_{i}=R_{i}=1-\frac{T_{i}-T_{1}}{T_{e}-T_{1}} \tag{5}\]  
where \(y_{i}\) is the label of the input at time \(t_{i}\), \(R_{i}\) is the RUL percentage of the training data at time \(t_{i}\). \(T_{e}\) is the total time of the test; \(T_{1}\) is the SPT, \(T_{i}\) is the run time for now. The model first evaluates the level of degradation of bearing performance and then maps the RUL percentage to bearing RUL.  
Figure 3: Flow chart of the proposed method for prognostics.  
Because RUL prediction is a regression problem, mean square error (MSE) [25], which has the advantage of a fast convergence rate, is quite a suitable loss function to adopt for the model,  
\[L(\hat{y},y)=\frac{1}{m}{\sum_{i=1}^{m}(\hat{y}_{i}-y_{i})}^{2} \tag{6}\]  
where \(m\) is the batch size and \(\hat{y}_{i}\) represents the corresponding RUL percentage prediction by the P-ResNet, \(y_{i}\) denotes the actual RUL percentage of the \(i\)th sample.  
In the model training, the model parameters are updated by the back propagation algorithm, and the loss function is iteratively minimized by the Adam optimization algorithm [26]. The weights of all layers are initialized with a mean value of 0 and a standard deviation of 0.01. The biases are initialized with 0. The learning rate is set to 0.01, each batch contains 32 samples, and the number of training epochs is 1000.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 5 Discussion
### Examining the Threat Model  
The introduction of VBA satisfies the concerns listed in the original threat model from Section 3. VBA relies on the principle of LLID uniqueness on the same broadcast domain, and thus threat actors cannot subversively spoof another node's legitimate LLID without introducing obvious network disruptions. Since all the deterministic VBA generations depend upon the node's supposed LLID given during NDAR transactions, two nodes that cannot share the same LLID will never be successfully verified by neighbors for the same IP address. Whether or not the link layer is secure, VBAs are still necessary to validate bindings between IP and link-layer address components during address resolution.  
Likewise, threats from external, off-link actors are mitigated by VBA because generated IP addresses employ the results of hashing algorithms, which generally produce pseudo-random outputs. External actors will not be aware of the voucher details incorporated into final addresses: the stored state required for address generation consists of local-only information, so the threat is abated. Any VBA can also be rotated at a node's discretion to an entirely new, valid address by changing the work factor value (i.e., iteration count) embedded within the address, even if no other parameters or voucherinformation have changed. Thus, even an omniscient adversary capable of observing all traffic from a particular VBA-capable host cannot feasibly correlate activities from the same host based on its past IP addresses alone.
# Mechanical Properties of Cement Concrete with Waste Rubber Powder
## 2 Materials and Methods
### Response Surface Methodology (RSM)  
Response surface methodology (RSM) is an optimization technique that aims to find the optimal experimental design with a minimal number of samples. Despite the limited sample size, RSM effectively captures the complex non-linear relationships between the response and parameters with satisfactory precision [27]. RSM can optimize multiple responses or fulfill specific requirements [28; 29; 30]. The Box-Behnken design (BBD) is a quadratic response surface design that operates independently. The BBD necessitates three levels for each factor and yields fewer experimental runs compared to the central composite design, with an equivalent number of factors [31; 32]. All combinations in the BBD are contained within the region of interest. While less costly than the central composite design, the BBD is unsuitable for sequential experiments [33]. The Box-Behnken design, with its absence of axis points, ensures that all design points remain within their boundaries and prevents the simultaneous elevation of all factors to high levels. This unique characteristic makes it a suitable choice for optimizing the mix ratio of waste rubber powder cement concrete.  
Design-Expert software (version 12) was employed in this study to identify the optimal conditions. Three independent variables--the water-cement ratio (A), sand ratio (B), and 10-mesh waste rubber powder content (C)--were investigated using a three-level, full-factorial Box-Behnken design. A total of 17 experiments were conducted, and the factors and their levels are detailed in Table 3. Using a three-factor, three-level Box-Behnken central composite design of the water-cement ratio, sand ratio, and 10-mesh waste rubber powder dosage, 17 groups of experimental schemes were obtained, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Plasticizer** \\ \hline Water-reducing rate (\%) & 25.6 \\ Shrinking percentage & 72 \\ Chlortion content (\%) & 0.01 \\ Total alkali content (\%) & 0.36 \\ Sodium sulfate content (\%) & 1.232 \\ PH & 7.1 \\ \hline \end{tabular}
\end{table}
Table 1: Technical indexes of superplasticizer.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Content** \\ \hline Water–cement ratio & 0.38 \\ Cement (kg/m\({}^{3}\)) & 375 \\ Water (kg/m\({}^{3}\)) & 150 \\ Sand ratio (\%) & 34 \\ Aggregate quantity (kg/m\({}^{3}\)) & 1925 \\ Superplasticizer (\%) & 1.28 \\ \hline \end{tabular}
\end{table}
Table 2: The concrete mixtures of the tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Factor Levels and Ranges**} \\ \cline{2-4}
**Factors** & \(-\)**1** & **0** & **1** \\ \hline A water–cement ratio & 0.35 & 0.40 & 0.45 \\ B sand ratio (\%) & 32 & 34 & 36 \\ C 10 mesh waste rubber powder content (\%) & 5 & 10 & 15 \\ \hline \end{tabular}
\end{table}
Table 3: The factors of Box–Behnken design.
## 2 Related Works and Motivations
### Path Selection Method (PSM)  
In SEF, the detection power of false reports is affected considerably by the choice of the routing paths. In the worst case, forwarding nodes may not have any of the keys used in report generation so these forwarding nodes cannot verify any false reports.  
In [12], authors proposed a path selection method (PSM) in order to improve the filtering power for false positive attacks. In PSM, routing paths are established by flooding with a control message [13, 14] and can be selected with the consideration of the security level and the transmission distance. The control message contains information about the partition IDs of visited nodes and hop count. This information is used to evaluate the quality of the path.
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.3 Interfacing Implementation of LPC2138 and JN5139  
Since the base station module is connected to the power supply, the JN5139 module is initialized and its working state is set; then it is followed by implementation of the two parallel tasks on the JN5139 (Figure 12).
# Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
###### Abstract
The creation of a cavity was captured, the growth in size of which caused partial separation of the water column in the upper portion of the pipe cross-section. In the experiments, the maximum length of the cavity reached up to 30 cm, approximately three-fourths of the pipe cross-section. Unfortunately, synchronization analyses between transient pressures and the dynamic behavior of a cavity, which are key to exploring the physical processes of the transient events that accompany a column's separation, are limited. The main objective of this paper is to explore the dynamic behaviors of column separation following the closure of an upstream valve in a horizontal pipe. In particular, high-resolution photographs of cavity formation and collapse, captured using a high-speed camera, are synchronized with recorded pressure histories in order to illustrate their interrelations. Moreover, the influence of parameters such as initial flow velocity, reservoir static pressure head, and pipeline length on column separation events is discussed in detail.
## 3 Description of the Maritime Data Transfer Protocol
### Taxonomy of Messages
In any given situation, messages will be either sent by an autonomous maritime vehicle that is present or by the CCS. When they are sent from the vehicles, they are always related to either a mission or any parameter related to it, such as information about the status of mission where the vehicle is currently involved, a report on the data that is requested, any event that may affect the mission or the vehicle behaviour or a query that the vehicle may have to perform in order to better fulfill a mission. On the contrary, if the message is sent by the CCS they will consist of requests that are done to the robots that have been deployed during the mission,a notification that has to be sent to them in order to modify their behaviour, and an answer that has to be sent to them as a result of a requests that they previously sent. In the end, it can be seen how activities that involving requests and answers for those requests (for messages sent by the middleware) and activities related to notifications about the status of the vehicles (for messages sent from the vehicles). Figure 2: Taxonomy for the PDUs in MDTP.
###### Abstract  
Myocardial infarction (MI), caused by the occlusion of the left ventricular coronary artery, leads to the loss of cardiomyocytes and, potentially, heart failure. Cardiomyocytes in adult mammals proliferate at an extremely low rate and thus, a major challenge in MI treatment is supplementing exogenous cells and keeping them viable in MI areas. To address this challenge, injecting hydrogels encapsulating cells into MI areas, to compensate for the loss of cardiomyocytes, shows promise. This study synthesized two types of alginate hydrogels, based on self-crosslinking (SCL) and calcium ion crosslinking (Ca\({}^{2+}\)) in varying formulations. The hydrogels encapsulated living muscle-derived stem cells (MDSCs) and their performance was evaluated in terms of optimizing cell viability during the injection process, as well as the live/dead rate after long-term cultivation. The morphology of the hydrogel-encapsulated cells was characterized by scanning electronic microscopy (SEM) and live/dead cells were examined using an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide staining) assay. The mechanical properties of the hydrogels were also determined via a rheometer, to identify their influence on cell viability during the injection process and with respect to long-term cultivation. The SCL hydrogel with a 0.8% alginate and 20% gelatin formulation resulted in the highest cell viability during the injection process, and the Ca\({}^{2+}\) hydrogel composed of 1.1% alginate and 20% gelatin maintained the highest cell survival rate after two months in culture.  
Keywords:alginate; hydrogels; stem cells; cell survival +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### X-ray Diffraction  
Figure 3 shows an XRD pattern of activated carbon made from \"Olive tree and _Lantana camara_\" with a peak at an angle of 23\({}^{\circ}\) and 43\({}^{\circ}\), indicating the presence of a carbonaceous structure [21]. The small number of stacked layers are present due to the phosphoric acid used as an activator agent. The sample's elemental composition and preparation procedures influence the intensity of the X-ray diffraction line. In this article, the preparation procedures for both activated carbons are the same, but the existing chemical composition is different in each tree leaf. Therefore, the intensity peaks are slightly different in both activated carbons. The chemical activation of H\({}_{3}\)PO\({}_{4}\) is performed in three steps: dehydration, degradation, and ultimately coagulation, which alters the aliphatic structure. Phosphoric  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample ID** & **EDX Elemental Composition (Weight \%)** & **BET Surface Area** \\ \hline
**Composition** & **C (\%)** & **O (\%)** & **P (\%)** & **(m\({}^{2}\)/g)** \\ \hline AC-O-H-24 & 77.51 & 17.85 & 4.64 & 611.21 \\ AC-L-H-24 & 76.16 & 22.54 & 1.30 & 167.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EDX elemental composition and BET surface area of activated carbon.  
Figure 2: SEM micrographs of activated carbon olive trees (**a–c**) and _Lantana camara_ (**d–f**).  
acid (H\({}_{3}\)PO\({}_{4}\)) enhances the lignocellulosic link cleavage, which causes the volatile substance to expand quickly. The phosphate bonds are formed by cross-linking phosphoric acid derivatives and the phosphoric acid itself with the organic species (Equations (1)-(3)). The gases are released from the structure at the end of the process to expand the pore structure of the activated carbon [24]. The following chemical processes (Equations (4)-(6)) were found to be the way that phosphoric acid forms pores with the raw precursor during the carbonization phase [25].  
\[\mathrm{nH_{3}PO_{4}\to H_{n}+2P_{n}O_{3n+1}+(n-1)H_{2}O} \tag{1}\]  
\[2H_{3}PO_{4}\to P_{2}O_{5}+3H_{2}O \tag{2}\]  
\[2H_{3}PO_{4}\to H_{2}P_{2}O_{7}^{-}+H_{2}O+2H^{+} \tag{3}\]  
\[2H_{2}P_{2}O_{7}^{-}\to P_{4}+6O_{2}+2H_{2}O \tag{4}\]  
\[2P_{2}O_{5}+5C\to P_{4}+5 CO_{2} \tag{5}\]  
\[P_{2}O_{5}(liquid)\to P_{2}O_{5}(gas) \tag{6}\]
# Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach
## 2 Literature Review
### Industrial Demand Forecasting Method  
Industrial demand forecasting is mainly based on quantitative methods, including regression models, GM (1,1), SVM, NN, and so on. In terms of the regression model, [PERSON] et al. [8] forecasted industrial energy demand based on the multiple linear regression method. [PERSON] et al. [43] used the multiple regression model to forecast tea demand. [PERSON] et al. [44] used the regression model to predict the demand for films. In terms of theGM (1,1) model, [PERSON] et al. [10] used GM (1,1) to forecast logistics demand. [PERSON] et al. [45] used GM (1,1) to forecast the demand for magnesium products. In terms of the RF model, [PERSON] et al. [9] established an RF model to predict the demand for agricultural products. [PERSON] et al. [46] used RF to effectively forecast bicycle rental demand. In terms of the SVM model, [PERSON] et al. [11] used SVM to forecast freight volume demand. [PERSON] et al. [47] used SVM to forecast the generation of photovoltaic power generation systems. [PERSON] et al. [48] used SVM to forecast the logistics demand. Compared with the above methods, the NN has the abilities of nonlinear mapping and fault tolerance [49]. In terms of the NN model, [PERSON] et al. [12] studied the retail clothing industry and established a NN for sales forecasting. [PERSON] et al. [50] used a NN to predict the urban water-energy demand. [PERSON] et al. [51] used a NN to forecast the movie box office demand.  
In summary, this paper finds that the literature on MIDF is relatively lacking, but the existing research results can provide a wealth of literature for the manufacturing industry demand forecasting model. In other words, existing research can provide a reference for this paper to choose a prediction model. In addition, although a NN more easily fits complex nonlinear relationships, in essence, the accuracy of it will be affected by many factors, such as nonlinear characteristic factors and gradient disappearances. Therefore, the Long Short-Term Memory (LSTM) network, proposed by [PERSON] [52], is suitable for manufacturing industry demand forecasting. Meanwhile, to verify the accuracy of the LSTM network, a variety of prediction models are used as the comparison model.
## 2 Message-Centric vs. Data-Centric Communication Approaches  
The message-centric communication approach is used by traditional communication middleware, where a set of messages and data formats are predefined in participants' nodes. In this approach, the communication middleware only delivers the message through the network layer and does not know the message contents and data types. Messages are being appraised within each node of the system (at the application layer) to check the correctness, integrity, data type, and filter data. It means that each node should locally track the state of data, which significantly increases the processing tasks on the application layer. Furthermore, the system expansion is very complicated because major changes are required on the application layer due to any change in message formats or data types [17; 18].  
Nowadays, modern communication middleware uses the data-centric communication approach. In this data approach, the communication middleware builds the message and updates the system state, therefore, it is aware of the message content and data model [19]. Moreover, instead of processing the messages locally in application, as is done in a message-centric approach; in a data-centric approach the messages are being processed within the middleware layer to assess the correctness of data types delivered to all nodes. It simplifies the application development and system expansion, enhances the system reliability, optimizes the use of network bandwidth, and gives us more opportunities to assign different Quality of Service (QoS) profiles, priorities, and security measures to the data types as comparing with a message-centric approach [18]. In this paper, the DDS is used as a data-centric communication middleware to address the challenges of routing GOOSE message over the WAN. In the following section, DDS is described, and its unique features are mentioned in detail.
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
###### Abstract  
This study presents a comparison between the effectiveness of adding low binder amounts of industrial by-product Petrit T as well as cement to modify and improve fine-grained soil. Binder amount was added by soil dry weight; cement at 1%, 2%, 4% and 7% and Petrit T at 2%, 4% and 7%. The unconfined compressive strength (UCS) was used as an indicator of soil strength. In addition, the consistency limits, laser particle size analysis, and pH tests were also conducted on the treated soil. The samples were cured at 20 \({}^{\circ}\)C for different periods from 7 to 90 days before testing. Results indicate that cement is more effective at improving the physical and engineering properties of the treated soil. Soil plasticity index decreases after treatment and with time. Liquidity index and the water content to plastic limit ratio are introduced as new indices to define the improvement in the workability of treated soil. Soil particle size distribution is changed by reducing the clay size fraction and increasing the silt size fraction after treatment. The findings confirm that adding small binder contents improve soil properties, which subsequently reduce the environmental threats and costs that are associated with using a high amount of binder.  
Petric T; cement; secant modulus; workability; soil strength; solidification; pH +
Footnote †: journal: Applied Sciences