esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications
## 6 The Proposed BDCA
### Data Updating  
In this phase, when \(DO_{S}\) wants to update the data stored in \(SC\) at will, \(DO_{S}\) generates a data updating request \(req\) and sends it to \(SC\). \(SC\) updates the data as the request \(req\) and sends a data update request transaction \(Tx_{2}\) to \(TC\). \(TC\) updates the data as the transaction \(Tx_{2}\). In general, data update operations include \(Insertion(I)\), \(Modification(M)\), and \(Deletion(D)\). It is imperative to highlight that the process of updating data in this paper is instigated by the user node that initially generated the data. Each data update request is accompanied by a public key signature from the data update user node and the execution of the data update operation is contingent upon its successful verification by a majority of nodes in the blockchain. Specifically, the data update operation must be approved by more than 50% of the user nodes in blockchain. The robust approach of this paper provides a high level of security against potential security threats, such as the malicious random or chosen node or link to a blockchain node deletion and the integrity and security of the blockchain are safeguarded. The specific process is shown as follows:  
_Modification_: Assume that \(DO_{S}\) wants to modify the \(i\)-th leaf node into \(m_{i}^{*^{\prime}}\) at \(ts_{2}\) (e.g., modify \(m_{4}^{*}\) into \(m_{4}^{*}\) in Figure 2(b)). * \(DO_{S}\) calculates \(H_{1}(m_{i}^{*^{\prime}})=h_{i}^{*}\) and \(\zeta_{i}^{*}=(g^{h_{i}^{*}}\cdot g^{m_{i}^{\prime}})^{d}\). * \(DO_{S}\) invokes Algorithm 1 to generate an auxiliary verification information form AVF\({}_{1\times t}\). * \(DO_{S}\) modifies the original node into \(v_{i}^{*}=\{l_{v_{i}^{*}},r_{v_{i}^{*}},h_{i}^{*}\}\) and updates the BV-MHT by recalculating all the nodes on the path from the node \(v_{i}^{*}\) to the root, generating a new hash root \(h_{root}^{*}\). * \(DO_{S}\) generates a data update request \(req=\{ts_{2},Modify,i,h_{i}^{*},AVF_{1\times t},h_{root}^{*},m_{i}^{*},\)\(pk_{DO_{S}}\}\) and sends it to \(SC\). * \(SC\) verifies the request \(req\) with Algorithm 3 and updates the data as the request \(req\), generating a new transaction linked to the original blockchain network. Then, \(SC\) generates a data update transaction \(Tx_{2}\) and encrypts \(Tx_{2}\) with AGMS in a similar process in Section 6.3 and uploads \(Tx_{2}\) to \(TC\). \[Tx_{2}=[``Update\",req]\]* After receiving the transaction \(Tx_{2}\), \(TC\) first verifies the correctness and validity of the received transaction in a similar way in Section 6.3. If the verification is valid, \(TC\) updates the BV-MHT as requested; otherwise, \(TC\) rejects to update the transaction. **Input:** Index sets \(\{d_{1},d_{2},...,d_{r}\}(d_{1}<d_{2}<...<d_{r})\) of the verified leaf nodes, the number of all blocks \(k\), the corresponding hash root of BV-MHT based on the batch verified nodes \(h_{root}\), and AVF\({}_{r\times l}\). **Output:** 0/1.
## 4 Conclusions  
_Staphylococcus epidermidis_ CECT 4183 showed a good potential for the removal of Cu(II) from synthetic solutions with a qm for the Langmuir model of 48.14 mg/g when operated under the optimal conditions provided by the experimental design, a value comparable to that of other microorganisms and in many cases higher. FT-IR and FE-SEM-EDX analyses showed that physicochemical mechanisms were mainly involved in the biosorption process, although the possibility that some of the metal was incorporated into the cytoplasm was not excluded. _Staphylococcus epidermidis_ CECT 4183 showed a good capacity to release the cell extract under stress conditions. The extract was effective in catalyzing the synthesis of CuO-NPs of high purity and crystallinity under the conditions developed and optimized in this work, as shown by XRD analysis. The CuO-NPs presented an average size of 14 nm, smaller than most of those reported by other authors, while their application as biocidal agents gave good results that improved significantly when the colloidal suspensions were mixed with PVA. In the case of _B. cereus_, the MIC was reduced up to 16-fold. In general, the MICs were between 62.5 and 500 \(\mu\)g/mL for the bacteria and between 1000 and 2000 \(\mu\)g/mL for the yeast tested; these values indicate that the synthesized nanoparticles have a good potential for their use in biomedicine. The good results obtained in this work, based on the principles of green chemistry, add to the current efforts to transfer the potential benefits of CuO-NPs to the field of biomedicine. This type of nanoparticle presents enormous advantages that must be thoroughly explored: biocidal capacity, antiviral capacity, anticancer capacity, etc., all of them in the current context of microbial resistance to antibiotics and an increase in oncological diseases. Future works should address the mechanisms of action of CuO-NPs and their interactions with prokaryotic and eukaryotic cells in order to establish a detailed theoretical-practical framework that allows their general clinical and agri-food use. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14177623/s1](https://www.mdpi.com/article/10.3390/app14177623/s1)]([https://www.mdpi.com/article/10.3390/app14177623/s1](https://www.mdpi.com/article/10.3390/app14177623/s1)), Table S1: Concentration of elements represented in the EDX spectrum of Figure 3, before and after the Cu(II) adsorption stage. Values are expressed in approximate percentage and arbitrary units, respectively; Figure S1: (a): SEM image obtained after the Cu(II) biosorption stage. (b) to (e): Images obtained by elemental mapping on image (a). (f): Image mixing the previous elemental maps. Figure S2. Biocidal assays for the five microorganisms tested. Growth curves at 24 h and 27 \({}^{\circ}\)C are shown with OD (630 nm) readings every 30 min. Curves for the positive controls (100 \(\mu\)L MH/100 \(\mu\)L NaCl 0.1 M/100 \(\mu\)L UPW-PVA 10%) and the negative controls (100 mL MH/100 mL cell suspension 0.5 McFarland/100 mL UPW-PVA 10%) are also shown.
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 2 Results  
### Analysis of the Volatile Components of Taiwan-Cultivated Vanilla Pods Using HS-SPME  
The results tentatively identified 31 volatile components (Table 1), including one monoterpene, two ketones, three esters, four phenols, five alcohols, five sesquiterpenes, nine aldehydes, and two others, and the aldehydes were primarily responsible for the volatiles of the vanilla pods.  
The primary component of vanilla cultivated in Taiwan is vanillin (73.85%), and this compound is an aldehyde. This compound has been previously identified in the literature as exhibiting a sweet smell that is reminiscent of vanilla [12]. The second is guaicool (2.01%) that was identified by GC-O as a smoky vanilla pod possessing a phenolic smell [13]. The relative percentage of the remaining volatile components did not exceed 1%.  
[PERSON] et al. [14] analyzed Madagascar vanilla pods that were ground into powder after cold storage using HS-SPME. A total of 77 volatile components were identified, and of these, 18 were also tentatively identified in this study. The most important component was vanillin (48.02%). Additionally, the five sesquiterpenes mentioned in the study as being identified in _V. planifolia_ for the first time were also tentatively identified in our study. They contributed to the vanilla aroma of citrus, lemon, wood, or freshness. [PERSON] et al. [15] also analyzed vanilla pod powder using HS-SPME, and they identified 28 volatile components. The major and minor components tentatively identified in their study were the same as those identified in our experiment (vanillin and guaicool). Other similarly tentatively identified components included furfural, benzaldehyde, benzyl alcohol, and methyl salicate.  
Comparison of Volatile Components under Different Conditions during the Processing of Taiwan-Cultivated Vanilla
## 2 Overview of Cyber Threats in EM
### Communication Media  
EM organisations are becoming increasingly aware that a terrorist attack may target key communication infrastructure to maximise the impact of a simultaneous physical attack. The potential impact of malicious communication disruptions has been demonstrated over many recent high-profile disasters where emergency response was hampered by a failure of the communication infrastructure, because of network overload, as in the 9/11 attacks, or because of physical damage, as in Hurricane Katrina and the Haiti earthquake. Lack of reliable communications may result in greater loss of life, as rescuers cannot coordinate effectively and the public is not warned in a timely manner. In [71], [PERSON] has suggested that the EM community would most likely be incapable of an effective response to a terrorist attack on a major metropolitan area if a cyber attack had previously crippled the communication and data networks on which EM relies. However, it is not only the availability of communications that needs to be maintained. Attacks targeting the integrity or the confidentiality of information transmitted could also affect the effectiveness of EM. As a result, it is rather worrying when new protocols currently developed for emergency service communication, such as 802.23, include no special measures for security. Here we discuss what we perceive as the main security threats to currently used  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technology** & **Security threat** & **Impact** & **Countermeasures** \\ \hline \multirow{6}{*}{EMIS} & Attack on satellite & \multirow{2}{*}{Inaccurate mapping} & Satellite sensing countermeasures \\  & sensing & & (Table 2) \\ \cline{1-1} \cline{2-4}  & SQL injection and & & \\  & other database & & \\  & attacks [64, 65] & & \\ \cline{1-1} \cline{2-4}  & VoIP attacks [66, 67] & \multirow{2}{*}{\begin{tabular}{l} Eavesdropping, spoofing, \\ masquerading _etc._ \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{l} VoIP security literature [66, 68] \\ \end{tabular} } \\  & & & \\ \cline{1-1} \cline{2-4}  & Weak authentication & Unauthorised use, breach of privacy &
\begin{tabular}{l} Role based Access Control [69], \\ document encryption [70] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: EMIS security threats and proposed countermeasures.  
EM communication technologies. We discuss both defence mechanisms that are already in use or have been proposed and open issues for security research that, to date and to the best of our knowledge, have not been addressed yet.
## 4 Experimental Results and Analysis
### Oil Leakage Stability Test Results  
The oil leakage stability test results are shown in Tables 7 and 8, Figures 9 and 10, and Table A1 in Appendix A.  
The oil leakage stability test results confirmed that relatively little oil leakage occurred in the samples with smaller particle sizes of 1.4 \(\upmu\)m and 1.0 \(\upmu\)m compared to the CA average particle sizes of 16.0 \(\upmu\)m, 5.0 \(\upmu\)m and 2.0 \(\upmu\)m, and the amount of oil leakage was also relatively small. This result suggests that the specimen with a small CA average particle size is more advantageous in achieving oil leakage stability. In addition, it was confirmed that in the samples with particle sizes of 2.0 \(\upmu\)m, and 1.4 \(\upmu\)m and 1.0 \(\upmu\)m, oil leakage stability was further improved as the ASE content increased. This is the same result as in the viscosity measurement result, which indicates that the effects of precipitation and segregation prevention of inorganic raw materials in BEM is improved as the ASE content increases. However, given that the effect of oil leakage stability according to an increase in the ASE content arises only from the CA average particle size of 2.0 \(\upmu\)m or less, there is a need to increase the ASE content and reduce the CA average particle size at the same time in order to ensure the oil leakage stability of BEM.
## 3 Results
### Environmental Factors and GPP Spatial Characteristics
#### 3.2.2 Spatial Characteristics of GPP  
The GPP attribute values inverted based on the MLR model in the research area exhibit seasonal variation characteristics (left column of Figure 7), with the order of summer > spring > autumn > winter. The peak of GPP is mainly highlighted in the areas where different types of vegetation widely exist, such as the western TM, the southern and northern slopes of TM, and the Altai Mountains. However, the GPP value of the area similar to the underlying surface of Gurbantunggut Desert in the Junggar Basin (short-lived vegetation) is obviously low. But the overall attribute value is reasonable, which conforms to the basic characteristics of GPP in arid regions. Among them, the annual average GPP in grassland areas is approximately 0.19 gC/m\({}^{2}\)/y, in farmland areas it is about 0.23 gC/m\({}^{2}\)/y, and in desert vegetation areas it is about 0.04 gC/m\({}^{2}\)/y. Overall, the GPP attribute characteristics in arid regions depend on the overall vegetation coverage of the underlying surface and the degree of sunlight. The more complete the vegetation coverage and the stronger the sun radiation, the more pronounced the attribute characteristics; conversely, the less complete the vegetation coverage and the weaker the sun radiation, the less pronounced the attribute characteristics.  
Figure 7: Comparison of seasonal average annual spatial variation characteristics of MODIS GPP and GPP (based on improved SIF and MLR model inversion). The applicability of the MLR model for GPP inversion is evaluated in Figure 3, and the applicability of MODIS GPP is evaluated in Figure 2.  
In addition, to further analyze the rationality of inverting GPP using the MLR model, we conducted a comprehensive comparison between the inverted GPP and MODIS GPP (right column of Figure 7). The results further confirm the reasonable attributes of the inverted GPP. Specifically, the rationality of attributes in spring, summer, and winter is particularly significant, with consistent spatial distribution patterns of high and low values. Across the study region, the overall attribute error ranges from 0.01 to 2.68.
## 3 Route Choice Modeling
### Explanatory Variables
The conception of delay specific variable derives from the question that once the time shortest route is delayed by the metro emergencies, will the metro passengers choose to wait for the line to resume operation, or take detour routes, or just abandon metro and switch to other traffic modes? Moreover, under what conditions will the passengers choose to abandon their daily travel routes and which route will they pick to continue their travel? In order to quantify the influences of the emergency-caused delays, an indicator called route-switching cost (RSC) is designed. As can be seen in Equation (3), RSC is a penalty variable depicting the relative time cost if the passengers choose to switch their original route to other routes. It is assumed that all passengers tend to choose the time shortest route (i.e., route \(l\)) as their original route when there are no metro emergencies in the metro network, and once the emergency occurs, waiting or switching to other routes will lead to changes in the time cost. \[RSC_{k}=\left\{ \begin{array}{l}\exp\left(\frac{T_{k}-(T_{l}+DT_{l})}{T_{l}} \right),\,k\
eq l\\ 1,\,k=l\end{array} \right. \tag{3}\]  
where, \(RSC_{k}\) is the route switching cost of route \(k\) between a certain origin \(o\) and destination \(d\); \(T_{k}\) and \(T_{l}\) is the travel time of route \(k\) and route \(l\); \(DT_{l}\) is the delay time of route \(l\), the time shortest route; \(k\), \(l\in R_{od}\), \(R_{od}\) is the set of available routes from \(o\) to \(d\). When the metro emergency occurs, if the travel time of route \(k\) is larger than the summation of travel time and delay time of route \(l\), the time cost of route switching will be larger than 1, passengers may be unwilling to switch to route \(k\). On the contrary, if the travel time of route \(k\) is lesser than the summation of travel time and delay time of the original shortest route \(l\), then the relative time cost of switching route will be smaller than 1, and under this condition, switching route will be a wiser idea. Moreover, if passengers choose to wait for their original route to resume operation, the value of \(RSC\) will be equal to 1, which means that no penalty is attached to alternative \(l\).
## 3 Results and Discussion
### Anti-Inflammatory Capacity of Lentzea sp. JNUCC 0626  
The RAW264.7 cell line, a murine macrophage model, is frequently employed to assess the safety and efficacy of anti-inflammatory drugs due to its strong inflammatory response to stimuli such as LPS, alongside its accessibility, ease of culture, and scalability for large-scale screening. Upon activation by various stimuli or cytokines secreted by immune cells, macrophages produce proinflammatory mediators such as nitric oxide (NO) and prostaglandin E\({}_{2}\) (PGE\({}_{2}\)), which drive inflammation characterized by pain, swelling, redness, dysfunction, and fever [108; 109]. These mediators also facilitate the migration of immune cells to inflamed areas. NO is synthesized from L-arginine by nitric oxide synthase (NOS), particularly the inducible form (iNOS), which is highly expressed in cells such as hepatocytes, smooth muscle cells, bone marrow cells, monocytes, and macrophages when stimulated by inflammatory cytokines or external factors [110]. Thus, we employed the RAW264.7 cell model to evaluate the inhibitory effects of JNUCC 0626 extract on NO production, which plays a key role in regulating proinflammatory mediators. To begin, cytotoxicity was assessed using the MTT assay, where JNUCC 0626 extract concentrations ranging from 6.3 to 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL were tested. After 24 h of treatment with 1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL LPS, cell viability remained above 90% across all concentrations, indicating minimal cytotoxicity even at the highest dose of 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Based on these results, further experiments were conducted using extract concentrations up to 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The impact of JNUCC 0626 extract on NO production in LPS-induced RAW 264.7 cells was then measured using the Griess reagent assay. As shown in Figure 11, LPS treatment caused a significant increase in NO levels, nearly four times the baseline. However, JNUCC 0626 extract effectively inhibited NO production in a concentration-dependent manner in LPS-stimulated cells. These findings suggest that the EtOAc extract of JNUCC 0626 exhibits anti-inflammatory effects, and that various secondary metabolites inferred through the genome mining of JNUCC 0626 may be associated with this activity. Furthermore, these metabolites hold potential for future applications in the treatment of related diseases.  
Figure 11: The effect of _Lentzea_ sp. JNUCC 0626 on cell viability (**a**) and of nitric oxide production (**b**) in LPS-induced RAW264.7 cells. Cells were plated in 24-well plates (1.5 \(\times\) 10\({}^{5}\) cells/well) and incubated for 24 h and then treated with samples and LPS stimulation for 24 h. The cytotoxicity of the  
samples was evaluated using MTT assays. The amount of nitric oxide in the medium was measured using the Griess reagent. The results are presented as the mean \(\pm\) SD from three independent experiments. \({}^{***}\)\(p<0.001\) vs. LPS-induced control group.
# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 3 Earthquake Detection Methodologies
### Traditional Approaches
The STA/LTA ratio (Figure 1, bottom) points out the location of the seismic event's start point, which is one of the most important features required by any detection methodology. The STA/LTA algorithm has shown to be very effective due to its simplicity [5; 27; 28], but it requires the optimization of user-defined parameters to obtain a high rate of positive-event detection. Figure 1: Short-time average through the long-time average ratio (STA/LTA) algorithm. Parameters such as the sampling rate, detection threshold, or even, pre-event and post-event parameters are required to achieve a desired positive detection with the algorithm.
## 3 Results and Discussion
### Influence of Two-Sided Surcharge/Unloading on the Tunnel Below Using Wet Sand
#### 3.2.2 Effect on Ground Settlement  
The variation in ground shift under different surcharges for Case 4 is shown in Figure 19, and the variation in the ground shift of each measurement point under different surcharges for Case 4 is shown in Figure 20, where the positive values of ground shift represent augmentation and the negative values represent settlement.  
Figure 19: Variation in ground shift under different surcharges for Case 4.  
Figure 20: Variation in ground shift at each measurement point under different surcharges for Case 4.  
Combining Figures 11, 19 and 20, the following can be seen:  
1. When the soil is wet sand, the peak of ground settlement on the left and right sides occurs when the surcharge on the right side is 172 kg and the unloading on the left side is completed, while the effect of unloading on the mitigation of ground settlement is not obvious and is the same as when the soil is dry sand.
2. Under two-sided surcharge in wet sand, the peak of ground settlement on the two sides is not significant; the ground settlement caused by surcharge in wet sand is significantly smaller than that caused by surcharge in dry sand under the same conditions, and the peak ground settlement on the left and right sides in wet sand is 0.4232 mm. The peak settlement in wet sand is 0.4232 mm and 0.4133 mm, which is only 51.1% and 38.2% of the peak settlement on the left and right sides with dry sand soil, respectively.
## 2 Materials and Methods
### Samples' Preparation and Extraction  
_P. pinaster_ and _P. pinea_ bark samples were collected from standing trees cultivated in the Aveiro region, Portugal, in January 2017, and were air-dried for 2 weeks. Thereafter, the samples were ground and sieved to a particle size lower than 1 mm. Milled bark samples (7-10 g dry weight (dw)) were Soxhlet-extracted with dichloromethane (175 mL) for 8 h. The extraction yield was determined by weighting the extract's mass and expressed as a percentage of dry biomass (% \(w/w\)). The extracts were prepared in duplicate.