esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Study Outcomes  
Another important issue was the heterogeneity in the approaches used to evaluate in vivo bone regeneration, which, in many cases, enabled a direct comparison of outcomes between different treatment groups. Standard diagnostic approaches to the evaluation of bone regeneration in vivo comprise radiological, histological, and mechanical testing [2]. The majority of studies used a histological analysis of the explanted mandibular defect to measure the quantity and quality of bone regeneration. Micro-CT and plain radiography were commonly used to quantitatively assess new bone formation. Micro-CT offers certain advantages for new bone formation, and several objective variables could be extracted: bone mineral density, trabecular thickness, and the percentage of newly formed bone [2]. Cone-beam computed tomography (CBCT) may be an interesting alternative because it is available, economical, and less expensive than CT or micro-CT [36]. CBCT can calculate the amount of new bone from grey-scale changes; however, the results are unpredictable, and more objective means of bone characterization are still not possible [36]. Immunohistochemistry-tochemistry was used along with histology and radiologic examinations to evaluate the maturity and formation rate of new bone [2]. Noting that the mandible, during the function of mastication, has to withstand significant muscle forces, the biomechanical characteristics of newly formed bone are extremely substantial. Still, only 12.5% of studies performed a biomechanical testing of mandibular bone defects.  
This research has several limitations. The significant variations among the included studies, which made any reasonable comparison complex, were as follows: differences in animal models, various scaffold types, various means of producing scaffolds, differences in cell source, and inconsistent evaluation methods. In addition, several studies did not have control groups. Due to variations in animal models, there were significant differences in the concentrations of growth factors used, and only a few studies determined the dose-response curve. This review included studies with various animal models with substantial differences regarding the follow-up duration and the critical defect size, even within a single animal model. Due to a lack of consistency in the defect size, it is difficult to compare the outcomes between the studies. Similarly, there is no standard animal model for mandibular bone regeneration. There was considerable heterogeneity in the evaluation of in vivo bone regeneration, which enables the direct comparison of outcomes between treatment groups. Future studies should provide standardized measurements of bone regeneration, including histological analysis, imaging results (micro-CT), and biomechanical testing of the newly formed bone for a reliable comparison of the results.
# Earthquake Shake Detecting by Data Mining from Social Network Platforms
###### Abstract
For example, the content of the Gossiping board, the most popular board, includes news, social affairs, politics, and disastrous events for discussion. This discussion board can be used for asking, revealing, and reposting issues by discussing and sharing with others. In addition, PTT has many discussion boards that enable the user to post issues and comments. Therefore, the platform records a large number of data daily, which can be collected and analyzed for investigations. Furthermore, analyzers should abide by the data protection regulations, even PTT registers use an alias to post the tweet. It is necessary to think of abiding by the analysis of dos and don'ts as insurance against charges of incompetence or sloppiness. In this study, the author analyzes the content of the earthquake recorded on the PTT Gossiping board from 2013-2016. The author attempted to derive a possible seismic intensity scale by using the data mining method in posts comprising traditional Chinese semantic words. For example, Figure 1 shows that the short message talks about body-feelings or concrete damage during the earthquake, such as \"Chungli, felt\" (tags 1-2), \"Pingliang, felt shaking and like the car impacted to the house\"(tags 3-5), \"TaoYuan, felt shaking\" (tags 6-7), or \"The chair was shaking\" (tag 13). In the past, posts concerning an earthquake were always considered meaningless messages and did not receive attention. However, in this study, the author used the data collected from the social media network PTT to derive the seismic shaking scale in different regions in Taiwan. Furthermore, the study identifies the semantic word and seismic intensity scale by using the data mining method and the decision tree model, respectively. The derived intensity scale distribution is compared with the report of seismic intensity scale from the Central Weather Bureau (CWB) in Taiwan.
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae  
According to the findings from the preceding sections, both duckweed and _Chlorella_ and _Scenedesmus_ have the capacity to remediate diluted RW at proportions below 40%. However, if a microalgae compound-targeted approach is sought for RW bioremediation, it is essential to consider that the biomass yield of microalgae in the flask cultures decreased with higher proportions of raw whey (RW%) in the culture medium. Employing a sequential batch photobioreactor approach, where RW% is pretreated with duckweed before its use by microalgae, may offer a potential solution to mitigate this inhibitory effect and enhance microalgal biomass production. To assess long-term growth reproducibility in RW-based medium during scale-up from flasks to PBRs, the described photobioreactor from Section 2.4 was used, labeled as PBR-1 for duckweed and PBR-2 for microalgae.
# Economic Feasibility of Underground Pumped Storage Hydropower Plants Providing Ancillary Services
## 2 Methodology
### Technical Data of UPSH Plants  
Figure 2 shows two different schemes of UPSH plants in a closed mine. Figure 2a shows a UPSH scheme with an upper surface reservoir and an underground lower reservoir. Conversely, Figure 2b depicts a shallow upper reservoir and an underground lower reservoir. Note that the gross head is reduced when a shallow upper reservoir is considered. This reduction in gross head implies a decrease in storable amount of energy.  
The technical data of the three hydropower stations (HPSs) are shown in Figure 3, considering DTCs between 4-10 h day\({}^{-1}\). The water flow rate is lower in pumping mode and therefore the pumping cycle time at full load increases to 5.40 and 13.30 h day\({}^{-1}\). The storage capacities are 0.46, 0.8 and 1.6 Mm\({}^{3}\) for HPS 1, HPS 2 and HPS 3, respectively, and remain constant in each DTC (4-10 h). Although the amount of storable energy does not vary, the output powers decrease from 125, 219 and 440 MW to 50, 87 and 176 MW for HPS 1, HPS 2 and HPS 3, respectively, when the turbine cycle time increases from 4 to 10 h day\({}^{-1}\). A maximum gross pressure of 4.41 MPa has been considered. The water flow rates in turbine mode are 12.78, 22.23 and 44.45 m\({}^{3}\) s\({}^{-1}\) for HPS 1, HPS 2 and HPS 3, respectively, considering a DTC of 10 h, while the efficiency values are 0.91 and 0.90 in turbine and pump mode, respectively. The water flow rates in pumping mode are 9.61, 16.71 and 33.42 m\({}^{3}\) s\({}^{-1}\) for HPS 1, HPS 2 and HPS 3, respectively, when the pumping cycle time at full load is 13.30 h day\({}^{-1}\). In addition, the round trip energy efficiency is assumed to be 0.77 [6].  
Figure 2: UPSH scheme in a closed mine. (**a**) Surface upper reservoir and underground lower reservoir; (**b**) Shallow upper reservoir and underground lower reservoir.
# RACHEM: Reinforced Reliable Computing in Cloud by Ensuring Restricted Access Control
## 2 Literature Survey
### Identified Research Gaps and Objectives
Coherency with REG-1: Traditional cryptosystems lack privacy from CSP; REO-1 aims to protect data confidentiality even from the service provider. The key implementation aspects are as follows:  
* Integration of partial homomorphic encryption to perform operations on encrypted data without revealing plaintext;
* Use of access control mechanisms to restrict unauthorized data access, even from internal CSP actors;
* Encryption key management strategies that prevent CSP-level decryption, ensuring end-to-end confidentiality. REO-2 (addressing REG-2): To develop a dynamic, identity-aware role-based access control mechanism that enables secure policy updates, effective revocation, and credential validation through ACL and role secrets, to prevent unauthorized access by revoked or malicious users. The key implementation aspects are as follows:  
* Dynamic updates to ACL and revocation list;
* Role hierarchy and identity validation;
* Use of role secret values and ACL for validation;
* Invalidating unauthorized users. Coherency with REG-2: Traditional RBAC models lack robust identity verification and revocation handling; this objective introduces dynamic, identity-driven enforcement. Proposed Model--Reinforced Restricted Access to Handle Vulnerable CSP Using Homomorphic Encryption and Ensure Identity Management (RACHEIM)  
The proposed model enhances access control policies and enforces identity management in cloud computing systems by securely channeling the original data through a cipher cloud gateway (CCG), ensuring that the data is transmitted in encrypted form from the data owner to the CSP.
## 4 Model Evaluation Parameters  
The efficiency of the aforesaid tools was evaluated using statistical variables, namely, the MSE, MRE, \(R^{2}\), the standard deviation (STD), and the root of MSE (RMSE) between the empirical and predicted values. These analyses are presented in Equations (22)-(26), respectively:  
\[\text{MSE}=\ \frac{1}{N}\sum_{i=1}^{N}\left(X_{i}^{actual}-X_{i}^{predicted} \right)^{2}, \tag{21}\]  
\[\text{MRE}=\frac{100}{N}\sum_{i=1}^{N}\left(\frac{X_{i}^{actual}-X_{i}^{predicted} }{X_{i}^{actual}}\right), \tag{22}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(X_{1}^{actual}-X_{i}^{predicted}\right)^{2}} {\sum_{i=1}^{N}\left(X_{1}^{actual}-\overline{X^{actual}}\right)^{2}}, \tag{23}\]  
\[\text{STD}=\left(\frac{1}{N-1}\sum_{i=1}^{N}\left(error-\overline{error} \right)\right)^{0.5}, \tag{24}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\left(X_{i}^{actual}-X_{i}^{ predicted}\right)^{2}\right)}. \tag{25}\]  
In these relationships, \(X_{i}^{actual}\), \(N\), and \(X_{i}^{actual}\) show the actual variable, the number of data points, and the output of the network, respectively. Furthermore, \(\overline{X^{actual}}\) is the average of the actual points.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Texture Analysis  
Texture is very important for food products, as it is one of the factors that influence the consumer's decision when buying the product. The values of texture parameters are to be seen in Table 4. For all the samples, a continuous decrease in firmness, cohesiveness, springiness and chewiness was noticed up to day 21 of the fermentation process. After that moment, the parameters did not register significant changes. These results could be explained by the structural changes that occur within the vegetal tissue during fermentation. The main changes are the degradation of macromolecular compounds from the cellular walls under the activity of the enzymes produced by the microorganisms involved in the ferentation process. NaCl is well-known as a firmness protector due to its inhibiting effect on microorganism development. On the other hand, magnesium has the potential to protect the structure of vegetal tissue, bonding the free carboxyl groups left after pectin degradation [45]. Thus, the textural parameters of NaCl samples are similar to those of the MgCl\({}_{2}\) samples. Moreover, tissue with softer texture, as in the case of the KCl samples, is easier to destroy during the mechanical stress induced by the texture analyzer, resulting in lower values of cohesiveness and chewiness. At the same time, once the tissue was destroyed, the deformation is difficult to recover, leading to lower values of springiness. Similar changes were reported for pickled jalapeno [46] or carrots [47]. Slight differences in values could be noticed when compared to data reported previously for pickled apples [17], maybe due to the different analysis method: in this study, only the mesocarp was taken into consideration, while in the previous study, the whole pericarp (including the peel) was analyzed.
## 2 Experimental Methods and Materials
### Characterisation of Hybrid Tensile Bars  
The subsequent tests and characterization work were performed on the samples in the three sets to study the microstructure and mechanical properties of hybrid tensile bars:  
* _Metallographic analysis_ was performed on hybrid samples to assess their microstructural integrity. Samples were mechanically polished, using standard silicon carbide grinding paper, diamond suspension, and a final step employing a 50 nm colloidal silica solution. The polished samples were etched using a reagent of alcoholic FeCl solution to reveal the microstructure. Microstructural characterisation of the hybrid components and their defects analysis were performed using scanning electron microscopy (SEM), JOEL 7000 with a backscatter detector (BSE), and 5 V and 13 nA. The optical microscopy equipment was employed to detect defects and examine the weld successions without performing any destructive tests. Moreover, EDS was employed to evaluate the chemical composition of the specimens and thus to investigate the behaviour of the post treated samples. The grain size of the MIM and PBF sections was measured using the open-source images analysis software, ImageJ [51].
* _Tensile tests_ were employed to assess the structural integrity of the hybrid components. A Zwickkoell generic testing machine was used for measurements. A pre-load of 3 MPa with a constant velocity of 6 mm/min at an ambient temperature were set for all the tests, tested in accordance to BS EN ISO 6892-1-2016.
* _Micro-Hardness_ tests were carried out with two sets of 10 indentations on the hybrid samples with a load of 100 g and an indent time of 10 s, tested in accordance to ASTM E384. The measurements were recorded and subsequently used to assess the impact of the post treatments.
* _Corrosion resistance_ standard test method ASTM B895, for stainless steel powder metallurgy parts, by immersion in a sodium chloride solution consisting of 5% NaCl at 21 \(\pm\) 1 \({}^{\circ}\)C, was employed to evaluate the corrosion resistance of the hybrid parts.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods  
### DNN Prediction  
A DNN model comprises three layers: Input, hidden, and output. Figure 4a depicts the DNN-structure model. Each layer comprises multiple nodes connected hierarchically to all nodes in the subsequent layer. The input and output layers are, in general, single layers, whereas the hidden layer may comprise two or more layers. Data features are fed to the input layer, and prediction values are derived from the output layer after processing them in the hidden layers. Figure 4b describes the prediction principle of a DNN model. The weighted sum of nodes is calculated and predicted values are derived using an activation function that exists within each hidden-layer node that receives the weighted sum of nodes as input and converts them into valid values. The most commonly used activation function in regression analysis is the rectified linear unit (ReLU) [66], which produces a value equal to the input if the weighted sum of nodes exceeds or equals zero; otherwise, it yields a value of zero.  
ReLU predicts values by repeatedly modifying the weight during each DNN-model training [67]. This weight modification is performed in reverse from the output layer to the input layer via backpropagation [68] until the cost function is minimized. The cost function can be expressed as the sum of the squares of differences between the observed and predicted values. Equation (1) defines the relation to evaluate the cost function (E). Here, n refers to the number of output-layer nodes, and \(y_{k}\) and \(\hat{y}_{k}\) denote the observed and predicted values of the kth output node, respectively. Equation (2) adjusts the weight in such a way that the difference between the previous weight and partial derivative of the error function can be assumed as the next weight. Here, \(i\) denotes the node number in the previous layer, \(j\) denotes the node number in the next layer, \(w_{ij}^{t}\) denotes the weight at time \(t\), and \(\eta\) denotes the learning rate.  
\[E=\ \sum_{k=1}^{n}(y_{k}-\hat{y}_{k})^{2} \tag{1}\]  
\[w_{ij}^{t}=\ w_{ij}^{t}-\eta\frac{\partial E}{\partial w_{ij}^{t}} \tag{2}\]
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 3 Energy-Efficient Reliable Multipath Routing Protocol Using Opportunistic Routing
### Exception Handling
21:\(p^{\prime}.\)SetNumberofPaths(\(n-h\))\(\triangleright\) set the number of paths, \(n-h\), to construct \(n\) paths
22: Send (\(p^{\prime}\))\(\triangleright\) modified packet is retransmitted
23:endif
```  
**Algorithm 3** Pseudocode for an exceptional case handling process at an intermediate node \(i\)  
In addition to the transmission failure problem, the path merger problem occurs because a proposed scheme cannot guarantee a node-disjoint transmission. That is, an arbitrary node can receive two or more packets from different parent nodes. To deal with this problem, when the node receives two or more packets, the node finds the \(P_{req}\) of each packet. Then, the node combines the \(P_{req}\) of each packet into one \(P_{req}\). The node calculates the required number of paths with a combined \(P_{req}\) when it transmits the packet to the destination. Figure 4 shows an example of the path merger problem. Suppose that the intermediate node 1 performs a branch to nodes 2 and 3 with 30% of \(P_{req}\). Suppose also that node 4 is selected as the opportunistic routing results of nodes 2 and 3. In this case, node 4 finds the \(P_{req}\) of each packet form from nodes 2 and 3. In the case of Figure 4, the \(P_{req}\) of nodes 2 and 3 is 30%. Thus, node 4 merges \(P_{req}\), i.e., \(1-(1-0.3)(1-0.3)\). As a result, node 4 can obtain approximately 50% of \(P_{req}\). A comparison of \(P_{req}\) of the intermediate node 1 and calculated \(P_{req}\) of node 4 confirms that node 4 can obtain the same value through the merger process. That is, even if the path merging problem occurs in the bifurcation process, the proposed scheme can prevent the degradation of \(P_{req}\). Figure 4: An example of the path merger problem.
## 3 New Adaptable Strategy for RT and NRT troposphere Monitoring
### Combining RT and NRT Processing Supported by Observations from Files or Streams  
In E-GVAP, the NRT products are updated on hourly basis and the requirement for a delivery to the E-GVAP server at UK Met Office is 45 min after the last observation used in the analysis. The NRT LSQ batch processing, initiated every hour when obtaining a majority of data files, is thus a relevant solution for this purpose when using hourly data files. Although the backward smoothing approach is the most beneficial for the post-processing solutions, it can be effectively used in NRT applications too. We have thus used it for the new adaptable strategy combining a continuously running forward filter with a regularly triggered backward smoothing filter. The former is aimed for the estimating epoch-wise tropospheric parameters in real time indicated by white points in Figure 8. In addition, the filter solutions are stored from all epochs relevant for a possible improvement in the second step. The backward smoothing is started periodically at a pre-defined time stamp as indicated at HR:00 in the figure. It uses the initial state vector from the same epoch provided by the real-time filter for recalculating the past parameters from the Kalman filter. Obviously, such recalculation is able to significantly refine older parameters, but cannot improve parameters at the initial epochs of the backward smoothing. The length of the smoothing period can be set flexibly to reflect an actual user preference for a higher accuracy or a shorter latency of the product. In such a way, the standard E-GVAP NRT tropospheric product can also be provided on hourly basis as shown by blue points in the figure.  
Initially, the new adaptable strategy was designed to use the precise orbit and clock corrections disseminated through RT streams, it can however exploit observations coming from both real time (streams) and near-real time (hourly or sub-hourly files). While the former is necessarily used in a simultaneous RT and NRT product generation, the latter is applicable for NRT only. In any case, both data flows can be mixed and analyzed for each station independently. Additional advantage of the NRT analysis utilizing the backward smoothing and RT observations may profit from the 45-min requirement in E-GVAP for the product delivery in NRT. By starting the backward smoothing shortly  
Figure 8: Stochastic modeling of the troposphere represented by real-time Kalman filter (white points), hourly backward smoothing (blue points), and 45 min postponed hourly backward smoothing (red points).  
before the delivery request, indicated in Figure 8 by the red arrow starting at 12:40, new observations can further improve the accuracy of NRT product, especially last parameters of the NRT product and still reduce systematic errors typical for data interval boundaries.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 3 Results  
### Prediction of Wave  
The NARX neural network model was used to forecast the wave height and period based on a random wind force. Because the gradient of the performance function tended to be zero, the ANN network training was terminated after 436 training epochs. The performance of the NARX neural network model was evaluated by comparing the predicted results with the observed data for the wave height and wave period (Figures 2 and 3, respectively).  
Figure 4a,b compares the observed data and the results predicted by the ANN model of wave period and wave height for the training results. Here, the ANN model's predicted results and observed values are plotted with corresponding trend lines. In the ANN model, the overall prediction accuracies were discovered to be good.  
The predictions of wave period and wave height, using a randomly chosen wind force as the input, along with the observed data for testing, are shown in Figure 5a,b, respectively. Despite modest changes in the observed and anticipated wave height and length due to the new randomly picked wind force, the ANN model's overall performance was good.  
Figure 3: Comparison of ANN results and observed data of wave period for the training results.  
Figure 2: Comparison of ANN results and observed data of wave height for the training results.  
Figure 4: Comparison between the observed and predicted results for the training results of (**a**) wave height and (**b**) wave period.  
Figure 5: Variation in observed and predicted (**a**) wave height and (**b**) wave period from randomly chosen wind force.