esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Studies  
Before the adsorption experiments, chloride solution with a series of concentrations of 1-240 mmol/L was achieved using a continuous dilution method. To study the influence of chloride concentration on the Cl\({}^{-}\) uptake of CAL, 1 g of each CAL samples was added into some conical flasks containing 100 mL sodium chloride solution with various Cl\({}^{-}\) concentrations. After sealing these conical flasks, the mixture was magnetically stirred vigorously for 24 h at 25 \({}^{\circ}\)C. The supernatant in the conical flasks was obtained by centrifugation for the determination of Cl\({}^{-}\) concentration. The amount of Cl\({}^{-}\) adsorbed by CAL was determined by potentiometric titration and calculated by the following formula:  
\[\mathrm{Q_{e}}=\frac{\mathrm{V}\times(\mathrm{C_{0}}-\mathrm{C_{e}})\times 35.45}{\mathrm{m}} \tag{1}\]  
where \(\mathrm{Q_{e}}\) (mg\(\cdot\)g\({}^{-1}\)) is the adsorbance of Cl\({}^{-}\) by CAL, V (L) represents the volume of NaCl solution, \(\mathrm{C_{0}}\) and \(\mathrm{C_{e}}\) (mmol\(\cdot\)L\({}^{-1}\)) are the initial and equilibrium concentration of Cl\({}^{-}\), respectively. The number 35.45 is the relative molecular mass of chloride ion and m (g) is the mass of CAL.  
To examine the effect of reaction time on the absorbance of Cl\({}^{-}\) by CAL, i.e., adsorption kinetics, CAL sample was added into the conical flasks containing 100 mL sodium chloride solution and stirred vigorously for different specified times. Then the supernatant in the conical flask was centrifuged at a predetermined time and the concentration of Cl\({}^{-}\) was determined.  
To simulate the pH conditions of carbonated and uncarbonated cement pore solutions, NaOH solution with 0.1 M concentration was chosen to adjust the initial pH values of NaCl solution as 7-13. The initial concentration of Cl\({}^{-}\) solution and reaction time was fixed as 80 mmol/L and 2, respectively. In addition, the other experimental conditions were kept the same as above. The effect of coexistence anions (such as OH\({}^{-}\), SO\({}_{4}\)\({}^{2-}\) and CO\({}_{3}\)\({}^{2-}\)) on Cl\({}^{-}\) uptake of CAL was also studied. The initial concentrations of those interfering anions were set as 40 mmol/L and 80 mmol/L while the other experimental conditions including contact time, temperature and initial Cl\({}^{-}\) concentration were also fixed as described above.
## 3 Results
### Observation Windows and Precision Levels  
The incidence of AKI was greatly influenced by changes in observation windows and precision levels (Table 2). Each version of AKI identified meaningful associations with LOS and mortality and the major difference between versions was the incidence of AKI detected. The incidence of AKI increased from 1.9% to 17.3% as observation windows expanded from 1 calendar day to 172 h. Day-based precision systematically underestimated the incidence of AKI compared to hour-based precision. The addition of a 4-h margin detected a higher incidence of AKI. Compared to patients with no AKI, patients with AKI experienced an increase in hospital length by five or more days (all \(p\) < 0.001) regardless of the observation window or precision level used. Compared to patients with no AKI, patients who developed AKI that was detected by two SCr assessments on the same calendar day experienced a 20.1% absolute risk for in-hospital mortality (\(p\) < 0.001), although this rarely occurred (incidence of 1.9% of all admissions). The risk difference for mortality was 5.1% to 8.8% (all \(p\) < 0.001) for all other observation windows and precision levels.  
Figure 5: Histogram of hours between two serial serum creatinine assessments. The x-axis displays the number of hours since the last serum creatinine (SCr) assessment rounded down to the nearest hour. The bar at ”0” represents a time of 1 to 59 min since the last SCr assessment. For example, if SCr was measured at 04:00 a.m. on hospital day 1 and 04:30 a.m. on hospital day 2, the delta time between those assessments would be 24.5 h, which would be displayed on the bar at “24” on this figure. Of the 546,644 non-index SCr assessments that had a calculated time since last assessment, 87.5% were collected less than 37 h since the previous assessment. Data for 68,274 (12.5%) assessments that were collected more than 37 h since the previous assessment are not shown on this figure.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Observation** & \multicolumn{2}{c}{**AKI Incidence**} & \multicolumn{4}{c}{**Mean Length of Six Days *} & \multicolumn{4}{c}{**In-Hopital Mortality Incidence**} \\ \cline{2-10}
**Window and** & \multicolumn{1}{c}{**Precision Level**} & \multicolumn{1}{c}{**\(n\)**} & **(\%)** & **AKI** & **No AKI** & **Unadjusted Difference** & **95\% CI** & **AKI** & **No AKI** & **Unadjusted Risk** & **95\% CI** \\ \hline
**1 day**
## 4 Discussion
### Limitations  
There are several limitations to this work. The lack of experimental data needed for model validation means that the results are focused on trends rather than individual simulations. However, the sensitivity study to assess the dependency of these trends on unknown factors showed no significant reliance. Future model validation research utilizing full-scale staged tests would be most beneficial; however, the financial cost of such tests would be high. Retrospective injury analysis of similar impact cases to those used in the STS would advance model validation; however, NYC medical examiner records and injury data were not available at the time of this study. While multibody modelling allowed for a large simulation test sample to be evaluated, it prevented the simulation of deformations and amputations, meaning that decapitations and bisections could not be modelled. This may cause an overestimation of third rail contact likelihood as the body can be dragged alongside the train instead of becoming separated from the body region in contact with the wheels. Furthermore, the subway environment includes more sharp or edge contacts compared to automotive collisions. For primary contact, potential sharp interactions include the anticlinber, coupler, and train wheels. For secondary contact, sharp interactions could include the rails, crossties, and trough edges. The contact characteristics used in this study are based on force-penetration relationships and as such, any change in head acceleration due to the object's geometric sharpness is not considered in head injury risk calculations. The initial conditions, track environment, and train front geometry used in this study are based on the NYC subway system. At the time of the study, the NYC subway is one of the largest subways in the world and offers the most up-to-date pedestrian incident information [26]. However, to represent the growing global issue of subway-pedestrian collisions, further studies using different pedestrian characteristics such as sex, height, and weight as well as track and train types are needed, and this will be the focus of future work.  
Notwithstanding these limitations, this paper offers the first model of subway-train interactions and assesses the effect of drainage troughs as a potential means of improving pedestrian safety.
## 4 The Trust Evaluation Process
### Trust Calculation for Two-Hop Neighbor Nodes  
There are no direct interactions between non-neighbor nodes; therefore, the trust relationship between two-hop neighbor nodes needs to be established based on the trust recommendations from the third intermediate nodes. As shown in Figure 5, the trust evaluation process of non-hop neighbor nodes can be described as follows. First, for multi-hop neighbor node, several intermediates nodes are selected to obtain recommendation trust. The principle to select the multi-hop intermediates nodes is the same as the recommender selection in previous sub-section. Then, a trust routing path is established to propagate trust from the sponsor node to the target node. The trust attenuates along with the growing routing path. The longer the path is, the greater attenuation of trust is. Therefore, the shortest path should be selected to route recommendations. Finally, the indirect trust is calculated based on all the recommendations.  
If there are \(k\) anchor nodes on the trust propagation path, we can obtain \(k\) recommendation values as \(\left\{\,T_{ind1}(E_{x1},E_{n1},E_{e1}),T_{ind2}(E_{x2},E_{n2},E_{e2}),\cdot \cdot\cdot,T_{indk}(E_{xk},E_{nk},E_{ek})\right\}.\) Therefore, the indirect trust \(T_{i}nd(E_{x},E_{n},E_{e})\) can be calculated by  
\[T_{ind}=T_{ind1}\otimes T_{ind1}\otimes\cdot\cdot\cdot\otimes T_{indk} \tag{14}\]  
\[T_{ind}=T_{ind1}(E_{x1},E_{n1},E_{e1})\otimes T_{ind2}(E_{x2},E_{n2},E_{e2}) \otimes\cdot\cdot\cdot\otimes T_{indk}(E_{xk},E_{nk},E_{ek}) \tag{15}\]  
\[\left\{\begin{array}{l}E_{x}=\prod\limits_{i=1}^{k}E_{xi}\\ E_{n}=\min(\sum\limits_{i=1}^{k}E_{n1}{}^{2},1)\\ E_{e}=\min(\sum\limits_{i=1}^{k}E_{ei},1)\end{array}\right. \tag{16}\]  
In this paper, only two-hop anchor nodes and reference nodes are used in the secure localization, therefore, \(k=2\). However, the proposed trust model is also suitable for other networks where \(k>2\).
## 2 Advanced Instruments in Remote Sensing of EBC  
Based on the current status of remote sensing instruments, their existing applications in the literature, and future potential contributions to EBC, the aforementioned five types of instruments: high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors, were selected. In order to avoid overlapping between high spatial resolution and hyperspectral sensors, the hyperspectral sensors discussed below will mainly refer to sensors with medium spatial resolution, such as Hyperion with 30 m spatial resolution. Radar sensors are not selected because their applications mostly concentrate on geology, ice and snow, marine surveillance, and agriculture. In addition, some uncertainties in radar remote sensing, such as the saturation issue under high vegetation biomass, hamper its applications on EBC.
## 3 Materials and Dataset
### Performance Evaluation Methods  
When analyzing regression models, it is important to use various evaluation metrics to evaluate their performance. This study uses five metrics: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and the coefficient of determination R\({}^{2}\). These metrics are defined as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{2}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{3}\]  
\[RMSE=\frac{1}{n}\sqrt{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{4}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{\hat{y}_{i}} \right|\times 100 \tag{5}\]  
\[R^{2}=\frac{\left(\sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)\left( \hat{y}_{i}-\overline{\hat{y}}_{i}\right)\right)^{2}}{\sum\limits_{i=1}^{n} \left(y_{i}-\overline{\hat{y}}_{i}\right)^{2}\sum\limits_{i=1}^{n}\left(\hat{ y}_{i}-\overline{\hat{y}}_{i}\right)^{2}} \tag{6}\]  
where \(y_{i}\) is the actual measured compressive strength; \(\hat{y}_{i}\) is predicted value of compressive strength; \(\overline{y}_{i}\) is the average value for \(y_{i}\); \(\overline{\hat{y}}_{i}\) is the mean value for \(\hat{y}_{i}\).
# Distributed Centrality Analysis of Social Network Data Using MapReduce
## 2 Methods
### Distributed Approach for Degree and Closeness Centrality  
The degree is the number of nodes that area the distance of one to node \(i\), that is, nodes at one-hop distance. The driving idea behind degree centrality is that the higher number of nodes that a particular node is linked to, the higher its importance within the network is [32]. Given an edge-list, in which each line means an edge in the network, the degree of each node can be computed by calculating the number of occurrences of that node within the file (see pseudo-code 1).  
Given an edge-list, with each line representing an edge in the network, the degree of each node can be computed by counting the number of occurrences of that node within the file.  
The algorithm for degree centrality is illustrated in pseudo-code in Algorithm 1. Finding the shortest path that connects two nodes is the first step in calculating closeness centrality. This calculation is done in a distributed manner by using the MapReduce paradigm. Cumulative distance is calculated from node \(i\) to all other nodes, and the reciprocal is taken. The reciprocal indicates the closeness of all other nodes to node \(i\). The steps illustrate the distributed computation method of degree and closeness centrality.  
```
0: Twitter datasets in edge list format.
0: Centrality score of each user
1: For every \(i\in V\),  1.1. Calculate the \(R_{i}^{1}\) denoting set of nodes at one-hop distance.
2: For each node \(j\in R_{i}^{1}\) (calculated in step 1)  2.1. Identify set of neighboring nodes at \(t+1\) hop distance using Equation (2).
3: Calculate the number of elements in traveling from node i to node j by using Equation (3): \[\left|R_{i\to j}\right|=\sum_{t=1}^{d_{\text{max}}}\left|R_{j}^{t}\right|,\] (3)
4: Evaluate degree and closeness centrality using Equations (4) and (5): \[D_{i}=R_{i}^{1},\] (4) and \[C_{i}=\frac{1}{\sum\limits_{t=1}^{d_{\text{max}}}\left(R_{i}^{t}\cdot t \right)}.\] (5)

**Algorithm 1** Distributed Calculation for Degree and Closeness Centrality

### Distributed Approach for Eigenvector Centrality

To compute the eigenvector centrality of node \(i\) one has to evaluate the importance of all the other nodes that node \(i\) is connected to. Based on this relative importance, the eigenvector centrality of a node is calculated as follows (see Algorithm 2).
``` Input: Twitter dataset in edge-list format. Output: Eigenvector Centrality for each user in the network.
1: Initialize a vector list \(v=[1,1,1,1,\ldots]\) for each node \(i\in V\).
2: Initialize a weight list \(w=[0,0,0,\ldots]\) for each node \(i\in V\).
3: For each node \(i\) and for each of its neighbors \(j\),  3.1. Calculate relative importance by \(w[i]=w[j]+v[j]\).
4: Update the vector list by setting \(v=w\).
5: Calculate sum of all numbers in vector list.
6: Divide each entry in vector list by the sum for eigenvector centrality. ```
**Algorithm 2** Distributed approach for eigenvector centrality
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 6 Conclusions  
This paper studies the direct parameter estimation algorithms for signal models only using the discrete observed data. By using the gradient search, we need to select a small step-size in order to ensure the convergence, and this will increase the search time and decrease the convergence rate. The proposed NI and MDW-GI algorithms have higher accuracy than the GI estimation algorithm for estimating the unknown parameters. The MWD-GI is used to obtain the parameter estimates at the current moment based on the estimates of previous data obtained at the moment time. Furthermore, the MDW-GI algorithm based on the moving data window technique can estimate the parameters of signal models in real time. The proposed algorithms can be extended to multi-frequency signal models. In the next work, we will consider the estimation of the initial phase of the signal models, that is to say, the signal model is a highly nonlinear function in regard to the frequencies and phases, and estimate all the parameters of dual-frequency signals including the unknown amplitudes, frequencies and initial phases simultaneously.  
This work was supported by theScience Research of Colleges Universities in Jiangsu Province(No. 16 KJB120007, China), and sponsored by Qing Lan Project and the National Natural Science Foundation of China (No. 61492195).  
[PERSON] conceived the idea and supervised his student [PERSON] to write the paper and [PERSON] designed and performed the simulation experiments. [PERSON] was involved in the writing of this research article. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 3 Background: Challenges in Detecting Three Behaviors
### Joint Detection of Falling, Loitering, and Violence  
The difficulties in the combined detection consist in the possibility of the system mistaking one abnormal activity with another due to their characteristic similarity in some ways. In the following, some cases involving such confusions are described.  
1. _Loitering and falling:_ Loitering without intent may involve lifting of arms, touching nearby objects or walls, crouching down, bending down, and lying on the floor. On the other hand, these acts are typical characteristics of the visuals of a person falling.
2. _Violence and falling:_ Crouching down, bending down, destabilized posture, posing with raised arms in an attempt to balance, and arms lifted overhead are often found in scenes containing both behaviors.
3. _Violence and loitering:_ Holding an object, lifting of arms, touching nearby objects or walls, crouching down, bending down, and lying on the floor are common in both cases.  
Nevertheless, it should be noted that the above similarities are not accidental. Indeed, they are inherent to the nature of the problem.
## 4 Conclusions  
The simulation and empirical investigations in this study showed that the capacitance displacement sensor could effectively measure minor vibrations that were generated in the metal diaphragm and accurately assess the nonlinear parameters of a miniature transducer with a metal diaphragm. The exponential function was used in the fitting of nonlinear parameters, which effectively reduced the computational workload for large displacements. With regard to the optimal iteration method, [PERSON]'s method and transducer resistance constraints were improved, which reduced the computational workload and allowed for the determination of the nonlinear parameters most corresponding to the actual situation quickly.  
This study explored the influence of errors generated in the measurement of nonlinear parameters. The results indicated that, when \(\sigma\geq 0.05\), more accurate nonlinear parameter curves were obtained. Conversely, when \(\sigma<0.05\), deviations were observed in the obtained nonlinear curves; however, the numerical value of results remained within the acceptable range. The results indicated that a better search step was achieved through the integration of the two methods; iteration efficiency was accelerated, optimal parameter calculations were reduced, and all nonlinear parameters could be accurately predicted. With regard to empirical verification, the use of different methods was able to better fit the theoretical parameter calculations when there were fewer measurement errors.  
To sum up the advantages that are described above, the capacitance displacement measurement system proposed in this study can be applied in electroacoustic measurements. The method proposed in this study can be widely applied in the development of electroacoustic transducers, as it can help in solving engineering issues and improve transducer designs.  
[PERSON] and [PERSON] defined the research theme included the parts of conceptualization, methodology, validation. [PERSON] developed the code and provided experimental resources. [PERSON] did the laboratory experiments, data curation and original draft preparation in Chinese. [PERSON] interpreted the results. [PERSON] wrote this manuscript, and did the final review and editing with [PERSON]. Everyone interacted with each other and made corresponding modifications in their domain.  
The authors would like to thank the Ministry of Science and Technology of Taiwan for financial support under Contract Nos. MOST 105-2511-S-035-012-MY3, MOST 106-2221-E-035-051-107-2218-E-035-016-and MOST107-2221-E-035-061-.  
The authors declare no conflict of interest.  
Figure 13: Comparison of displacement values obtained using the capacitance sensor and different methods.
# A Non-Fungible Token and Blockchain-Based Cotton Lint Traceability Solution
###### Abstract  
As a critical raw material for the textile industry, cotton lint provides various types of cotton yarns, fabrics and finished products. However, due to the complexity of the supply chain and its many links, information records are often missing, inaccurate or lagging, resulting in low transparency and traceability. In the traditional cotton lint supply chain, the data of each link are stored in isolation; due to the lack of an effective sharing mechanism and the formation of \"information silos\", complete traceability is challenging to achieve. In addition, the completeness and authenticity of documents such as lint quality reports and certificates of origin must be rapidly strengthened. Otherwise, quality problems may arise. To solve the above problems, this study proposes a cotton lint supply chain traceability system based on blockchain and non-fungible tokens (NFTs), covering the whole cotton lint production process from harvesting to selling. We use an NFT as an asset token to digitise seed cotton, cotton lint and quality inspection reports and allow participants to store and manage these assets on the blockchain. The system design includes architecture diagrams, sequence diagrams and Ethernet smart contract development based on the ERC721 standard. In addition, the integration of Interplanetary File System (IPFS) technology solves the problem of storing large files on the chain and ensures that the data are permanently preserved and cannot be tampered with. We provide a diagram of the interactions between the system components and the four core algorithms' design, testing and verification process. We present an in-depth analysis of the solution regarding the transaction costs and smart contract security. We confirm the solution's security, reliability and applicability through a cost evaluation and security analysis.  
blockchain; cotton lint; smart contracts; NFT; supply chain; traceability +
Footnote †: journal: Applied Sciences