esa-sceva/satcom-chunk-collection huggingface dataset:
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme
### Issuing ID and Manufacture Phase
IDI selects a random number \(k_{6}\) and generates a message:  
\[h_{MF_{1}}=H(M_{3}) \tag{29}\]6  
Next, IDI calculates the hash value and executes the function \"Sign\" in Algorithm 2 to generate the signature:  
\[h_{MF_{1}}=H(M_{3}) \tag{29}\]7  
\[h_{MF_{1}}=H(M_{3}) \tag{29}\]8  
A message is encrypted with MF's public key:  
\[h_{MF_{1}}=H(M_{3}) \tag{29}\]9  
Then, IDI sends the message \((ID_{IDI},ID_{MF},C_{ID_{2}},r(t_{ID_{2}},s_{ID_{2}}))\) to MF. 1. MF receives the message from IDI, then decrypts the cipher message by its private key:  
\[(r_{MF_{1}},s_{MF_{1}})=Sign(h_{MF_{1}},k_{3},d_{MF}) \tag{30}\]0  
Then, MF checks for the validation of the timestamp:  
\[(r_{MF_{1}},s_{MF_{1}})=Sign(h_{MF_{1}},k_{3},d_{MF}) \tag{30}\]1  
Next, MF calculates the parameter:  
\[(r_{MF_{1}},s_{MF_{1}})=Sign(h_{MF_{1}},k_{3},d_{MF}) \tag{30}\]2  
MF executes the function \"Verify\" in Algorithm 2 to validate the signature:  
\[(r_{MF_{1}},s_{MF_{1}})=Sign(h_{MF_{1}},k_{3},d_{MF}) \tag{30}\]3  
If the signature is valid, then the MF is able to use the list of TID to manufacture. When the MF produces a tobacco product, a TID should be stuck with the pack, then the MR is used to scan the tag, and a chaincode \"Manufacture\" is triggered to update the information of the tobacco product, the algorithm of which is shown in Algorithm 3. ```
var TP[Tobacco_Product count:= 0 func IDIssue (Info string, string ID_MF, int num, string CA_Signature, IDI_Signature string, MF_Signature string)(list_TID |string)[ for i:= 0; i < num; i++ { count = count + 1 TP = append (TP, new Tobacco_Product{ TID: GenerateTID), Product_Information: Info, Generate_Datetime: time.Now(), Manufacturer_ID: MID, CA_Signature: CA_Signature, IDI_Signature, MF_Signature: MF_Signature } } list_TID = append(list_TID, TP[count].TID) } return list_TID } func Manufacture ( ID_MF string, TID string, GPSLoc string)[ index:= SearchTID(TP, TID) TP[index].Manufacturing_Datetime = time.Now() TP[index].Factory_ID = ID_MF TP[index].GPSLocation = GPSLoc }
```  
**Algorithm 3** Chaincode of issuing ID and manufacture of the proposed scheme.
## 4 Recognition of the Coefficient of the Vibration Error
### The 36-Order EKF
To obtain the first-order approximation of [PERSON]'s expansion, the linear linearization equation of the system can obtained by:  
\[\dot{\mathbf{X}}\approx\left.f(\mathbf{X})\right|_{X=\hat{X}}+\left.\frac{\partial f( \mathbf{X})}{\partial\mathbf{X}}\right|_{X=\hat{X}}(\mathbf{X}-\mathbf{\hat{X}})+\mathbf{\omega}= \left.f(\mathbf{X})\right|_{X=\hat{X}}+\mathbf{F}\cdot(\mathbf{X}-\mathbf{\hat{X}})+\mathbf{\omega} \tag{18}\]  
F is the Jacobian matrix of \(f(\mathbf{X})\), which is expressed as \(\mathbf{F}=\left.\frac{\partial f(\mathbf{X})}{\partial\mathbf{X}}\right|_{X=\hat{X}}\)[32]. Then, the samples of continuous signals are subject to discrete changes, the sampling period \(T=t_{k}-t_{k-1}\) is constant, that its discrete linear error equation is:  
\[\left\{ \begin{array}{l}\dot{\mathbf{X}}_{k}=f(\mathbf{X}_{k-1})+\mathbf{F}_{k,k-1}\cdot \mathbf{X}_{k-1}+\mathbf{W}_{k-1}\\ \mathbf{Z}_{k}=\mathbf{H}_{k}\cdot\mathbf{X}_{k}+\mathbf{V}_{k}\end{array} \right. \tag{19}\]On the basis of the discrete linear equation, the Kalman filter equation of linear Kalman filter can be used [32]:  
\[\hat{\textbf{X}}_{k/k-1}=f(\textbf{X}_{k-1})+\textbf{F}_{k,k-1}\hat{\textbf{X}}_ {k-1} \tag{20}\]  
\[\textbf{P}_{k/k-1}=\textbf{F}_{k,k-1}\textbf{P}_{k-1}\textbf{F}_{k,k-1}^{T}+ \textbf{Q}_{k-1} \tag{21}\]  
\[\hat{\textbf{X}}_{k}=\hat{\textbf{X}}_{k/k-1}+\textbf{K}_{k}(\textbf{Z}_{k}- \textbf{H}_{k}\hat{\textbf{X}}_{k/k-1}) \tag{22}\]  
\[\textbf{P}_{k}=(\textbf{I}-\textbf{K}_{k}\textbf{H}_{k})\textbf{P}_{k/k-1} \tag{23}\]  
\[\textbf{K}_{k}=\textbf{P}_{k/k-1}\textbf{H}_{k}^{T}(\textbf{H}_{k}\textbf{P}_ {k/k-1}\textbf{H}_{k}^{T}+\textbf{R}_{k})^{-1} \tag{24}\]  
\(\textbf{P}\) is the covariance matrix and \(\textbf{K}\) is the Kalman gain [33]. **I** is the identify matrix. With the EKF method above, the error coefficients could be estimated optimally. Once the error coefficients are obtained, the nonlinear error model could be established. With the nonlinear error model, the errors in the FOG-based MWD could be compensated by (25) and (26)  
\[\mathbf{\omega}_{ib}^{b}=(\textbf{I}-\delta\textbf{k}_{g})^{-1}[\tilde{\mathbf{\omega }}_{ib}^{b}-\textbf{B}_{g}-\textbf{d}_{g}\cdot(\tilde{\mathbf{\omega}}_{ib}^{b})^ {2}] \tag{25}\]  
\[\hat{\textbf{f}}_{ib}^{b}=(\textbf{I}-\delta\textbf{k}_{a})^{-1}[\hat{\textbf{ f}}_{ib}^{b}-\textbf{B}_{a}-\textbf{d}_{a}\cdot(\hat{\textbf{f}}_{ib}^{b})^ {2}] \tag{26}\]
## 3 Results
### Field Data
Furthermore, the enlarged window (circled in red) in the original data (Figure 13a) shows reflected waves distorted by noise. However, in the data reconstructed from SMD results (Figure 13b), the enlarged window (circled in red) shows a more clear, denoised version of the reflected waves. Because SMD reduces the noise everywhere in the data, the weak reflected waves can be seen more clearly in the data reconstructed from SMD results (Figure 13b). The examples in Figures 12 and 13 show subsets of the entire data files which are too big to be presented in a single figure. However, the large data size provides a good opportunity for testing the efficiency of SMD. A single data file, which contains 12 s of recorded data, has 1008 traces, each trace containing 3000 pressure samples. Processing this amount of data on a laptop with an i5-6200U CPU and 8 GB of RAM, without parallelization, takes 4.2 s on average. Therefore, SMD is sufficiently fast when processing marine seismic gathers in real time. However, distributed acoustic sensing produces produces greater volumes of data than a long line of receivers during marine gathers. We propose two solutions as we prepare SMD to future application on seismic data obtained by DAS. First, the current SMD algorithm is optimized to provide a best representation of the coherent waves, for a predefined memory requirement. To make SMD more applicable to new developments in the data acquisition (DAS), we could run a new training stage that considers computation time as well as the quality of the results. This could create a new version of SMD that is more applicable to data obtained by DAS. Second is the introduction of signal detection which we will discuss in the following section. The large data file that requires 4.2 s of computation time is heavily populated with signal. This is not the case during microseismic monitoring during which most of the files contain only noise. Running an initial test to check for the presence of coherent waves, prior to fully processing the data with SMD, may significantly reduce the time spent on processing data during seismic monitoring.
## 3 Architecture
### Time and Energy Model  
The objective of the energy-saving algorithm in task migration based on task caching is to reduce the energy consumption of computation migration as much as possible, while satisfying the task completion time requirement through caching some tasks on the edge cloud. Before migration, the algorithm compares the time and energy consumption on mobile devices and the edge cloud, so as to make a task migration decision. Therefore, it is necessary to establish a corresponding task energy model and time model.  
The migration decision variable \(D_{bm}\in[0,1]\) is used to represent the execution location of each subtask of the task \(k\), and the location could be on the mobile device or on the edge cloud. When \(D_{bm}=1\), the subtask \(m\) is migrated to the edge cloud and then executed, while when \(D_{bm}=0\), the subtask \(m\) is executed on the device locally.
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
###### Abstract  
Blade tip-timing is one of the most widely used non-contact measurement methods for rotating blade vibration. The classic blade tip-timing method is to identify the vibration parameters of the blade based on the time of blade tip arrival to the tip-timing sensor installed on the casing. Since the vibration measurement of blade tip-timing is a serious under-sampling method, the blade resonance frequency is usually higher than the sampling frequency of the BIT data acquisition system, resulting in a large identification error of blade vibration parameters. In view of the above shortcomings, a blade vibration parameters identification method is proposed based on the mapping matching between the static waveform of the sensor and the dynamic waveform of the blade tip-timing. The modulation effects of different vibration displacements, velocities and vibration amplitudes on the tip-timing waveform of the sensor are analyzed. The vibration displacements and velocities of the blade tip passing through the sensor are obtained by the waveform mapping method, and then the vibration parameters of the blade are identified. The influence of the number of blade tip-timing sensors on the identification accuracy of the blade vibration parameters identification algorithm is observed based on the waveform mapping theory. The identification accuracy of waveform mapping method is compared with other methods. The results show that compared with the under-sampled time of arrival (TOA) data of classic BIT applications, the modulation of the sensor waveform output by blade vibration can provide additional data, and the waveform mapping method can not only reduce the number of sensors but also improve the identification accuracy of blade vibration parameters. The waveform mapping method has higher identification accuracy of blade vibration parameters compared with the waveform method in the reference.  
vibration parameters identification; rotating blades; blade tip-timing; sensor waveforms 2023  
[PERSON], [PERSON], [PERSON], X  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Faculty of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China; [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (X.L.)  
2
## 1 Introduction
In this context, a simple and inexpensive microprocessor-transmitter arrangement is presented, involving additional easy-to-find electronic parts to form nodes that can host a diverse set of soil sensors and deliver data in satisfactory ranges for a long period in an automated manner. The high-sensitivity characteristics and the power efficiency of the LoRa protocol [19; 20], which was adopted by the proposed implementation, make it ideal for rural agri-field measurement delivery support. Furthermore, the optimized action/sleep mode management arrangements made and the rational use of tiny solar panels are key factors for sustainable operation. Data packets travel properly addressed, numbered and encrypted in order to facilitate the soil monitoring process. The proposed system was tested utilizing various typical soil sensors, and its measurement quality, rigidity, range coverage, and consumption were thoroughly evaluated under different and sometimes extreme installation settings (e.g., with the microcontrollers and antennas placed underground as well). The corresponding results indicate the suitability of the proposed sensor node architecture for supporting efficiently a wide set of monitoring applications apart from soil-related ones. The specific analysis of solutions against several IoT platform deployment problems reduces the steps required for commercial-standard product delivery, achieving a high technical-readiness level. We also hope that the paradigm being presented will serve as a source of guidance and inspiration for other researchers in order to deploy measurement testbeds comprising fast, inexpensive but fluent sensor nodes. Details of the design, implementation, and performance of the proposed system are given in the following sections.
## 7 Route Maintenance
### Dealing with Node Mobility  
Based on our proposed trust model, nodes could identify malicious or normal users along its path. The trust values among neighbor nodes can also be shared with the help of information exchange protocol in our scheme to reduce routing overhead [36]. At start generally, the uncertainty towards each other is normally high, still, when these nodes start to move and interact with each other, this uncertainty gradually decreases by positive or negative interaction, and trust mechanism is there for the entire network. For nodes with lower trust values the network does not send route query packets again, which reduces routing overhead and latency between end to end users. Also, we propose the latest trust interaction among neighbor nodes that comes along the last used route information along the path. Since vehicular network topology changes the time being to update the trust of the connection and time inform about Hand-off process.  
A single node only stores recent interaction history and trust values of neighbor nodes, not all route-cache history as network topology in the vehicular network changes frequently. So a node is free to join or release the network any time. Also, if a particular path remains unused for a specific  
Figure 5: The Hand-off process: (**a**) the original route, from A to F; (**b**) the new route after the Hand-off process.  
period of time, then it will be considered invalid and removed from the source node cache. Meanwhile, a node can learn and cache multiple routes to the target node using the extension of the TW algorithm. It allows that if the route changes rapidly, a node with multiple route cache to the target node can try to find alternatives in case if certain links get failed. This multiple route-cache also reduces overhead as it does not need to perform new route discovery every time the link failure is detected.
###### Abstract  
This study quantitatively analyzed the effects of 3 h of constant sitting on skeletal muscle oxygenation in the lower extremities, using near-infrared time-resolved spectroscopy (NIR\({}_{\text{TRS}}\)). The effects of compression stockings were also evaluated. Eleven healthy men (age, 30.0 \(\pm\) 6.7 years) maintained their knee joints at 90\({}^{\circ}\) flexion during 3 h of constant sitting and wore a compression stocking on either the right or left leg. The side the stocking was worn was chosen randomly. Subsequently, leg circumference and extracellular water were measured. After 3 h of sitting, both factors increased significantly in uncompressed limbs. Furthermore, intracellular water and muscle oxygenation had significantly decreased. In contrast, extracellular water had not increased in the limbs wearing compression stockings. Furthermore, the increased circumference of compressed limbs was significantly smaller than that of uncompressed limbs. Decreases in oxygenated hemoglobin and total hemoglobin were significantly smaller in compressed limbs than in uncompressed limbs (oxy-Hb; \(p\) = 0.021, total-Hb; \(p\) = 0.013). Three hours of sitting resulted in decreased intracellular water and increased extracellular water in the lower extremities, leading to reduced blood volume and oxygenation levels in skeletal muscle. Compression stockings successfully suppressed these negative effects.  
3-hour sitting; near infrared time-resolved spectroscopy; compression stocking; tissue oxygenation; extracellular water; intracellular water; circumference; gastrocnemius +
Footnote †: journal: Applied Sciences
## 4 Results
### Data Preprocessing  
The original logging dataset underwent Z-score outlier detection to eliminate noise caused by instrument drift and drilling vibrations, using a threshold of \(|z|>3\). After preprocessing, 9628 valid samples were retained. According to lithological interpretation, the dataset includes six types of reservoir lithology: clay shale (CS) accounting for 13.48%, silt (SI) 4.67%, fine sand (F) 25.45%, coarse sand (C) 23.84%, fine sandstone (FS) 21.07%, and limestone (LS) 11.49% (Table 1). The statistical results show that the proportions of silt and limestone samples are relatively low, reflecting the limited development of high-energy sedimentary environments in the study area and indicating that these lithologies are not widely distributed in the reservoir formations.  
Well F-14 was selected as the standard well due to its representative lithology and high-quality data. In the Hugin Formation, the baseline GR value for shale was determined to be 82.6 API with a standard deviation of \(\pm\)9.8 API, while the mean RT value for fine sandstone was 12.4 \(\Omega\)-m with a standard deviation of \(\pm\)3.2 \(\Omega\)-m, both reflecting typical regional log response characteristics. Using Equation (3), the data from all eight wells in the study area were standardized and transformed based on these reference values to eliminate instrument scale differences and formation background field offsets (Table 2), ensuring the consistency and comparability of logging responses across wells.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lithology** & **Label** & **Count** & **Proportion (\%)** \\ \hline Clay shale & CS & 1298 & 13.48 \\ Silt & SI & 450 & 4.67 \\ Fine sand & F & 2450 & 25.45 \\ Coarse sand & C & 2295 & 23.84 \\ Fine sandstone & FS & 2029 & 21.07 \\ Limestone & LS & 1106 & 11.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample distribution of each lithology category in the dataset.
# Design of Antibody-Functionalized Polymeric Membranes for the Immunoisolation of Pancreatic Islets
## 3 Results and Discussion
### Site-Directed Functionalization of an Immunorelevant Antibody  
CTLA-4 was selected as immunorelevant antigen, as it is a major negative regulator of T cell responses [46]. In autoimmune diseases and graft rejection, CTLA-4 supports a dynamic but complex process of immune regulation that is prominent in the control of self-reactivity [47]. In a pioneering paper, it was demonstrated that CTLA4-Ig therapy was able to inhibit the rejection of human pancreatic islet in transplanted mice [48]. Following this work, other studies supported the use of CTLA4-Ig as an effective immunomodulatory to prolong allografts survival [26].  
In this sense, an engineered fusion antibody (CTLA-4 Ig) that displays the extracellular domain of CTLA-4 and the Fc portion of a IgG, and that binds with high affinity the two ligands of CD28, respectively B7-1 (CD80) and B7-2 (CD86), was selected [49]. One of its ligands (B7-1 His-tagged) was employed for the immobilization protocol onto the substrate [50], while the CTLA-4-Ig was functionalized with the Acryl-PEG.  
To evaluate the applicability of this functionalization strategy the Acryl-PEG-CTLA-4 Ig complex was detached using the same elution protocol (0.1 M glycine-HCl, pH2.8, at 4 \({}^{\circ}\)C for 10 min); afterwards, WB was performed to detect quantitatively the amount of complex detached (Figure 12). As already observed in the case of the Acryl-PEG-Anti-GFP complex, the analysis did not reveal the complete elution of the Acryl-PEG-CTLA-4 Ig complex from the solid support, likely due to the strength of Ag-Ab binding between B7-1 and CTLA-4 Ig.  
Therefore, the data presented so far show the potential use of this approach to develop a thin and biocompatible immunocoating for pancreatic islets. On the other hand, the methodology requires further optimization to improve the recovery yield of the Ab-functionalized coating and make it applicable to large-scale use.  
Figure 11: Results of glucose-stimulated insulin secretion (GSIS) assay on encapsulated islets compared to control islets on day 1 after encapsulation, for which the glucose stimulation index (SI) is the ratio of stimulated to-basal insulin secretion (n \(\geq\) 5).
## 4 Results
### Photovoltaic Power Influencing Factors and Correlation Analysis  
The Pearson correlation coefficient (PCC) analysis method is employed to calculate the correlation coefficients between each factor and PV power output. The results indicate that global radiation has the highest correlation coefficient, while direct radiation, humidity, and temperature have relatively lower correlation coefficients, and wind speed has the smallest correlation coefficient.  
\[r_{x,y}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y} )^{2}}} \tag{21}\]  
where \(\overline{x}\) and \(\overline{y}\) are the average values of the elements in \(x\) and \(y\), respectively. After computing all the data samples through Equation (21), we can get the variable correlation table, where Rg, Rd, H, T, W, and Wd represent global radiation, direct radiation, humidity, temperature, wind speed, and wind direction.  
Table 1 depicts the PCC values of variables. The larger the absolute value of PCC, the stronger the association. In this paper, meteorological variables with PCC values greater than 0.4 with load are screened as input variables for CNN to reduce the redundancy of inputs and to lay the foundation for improving prediction accuracy.  
Figure 8: Distribution diagram of weather types based on K-Means++ clustering.  
Figure 7: PV power generation data in three dimensions.  
From this table, it can be seen that there are four elements (global irradiation, direct irradiation, humidity, and temperature) that have a strong correlation with power. Therefore, the dimension of the input sequence is 4.  
Because the input sequence contains the information of multiple moments before the prediction point, the computing time and memory consumption will increase dramatically if the length of input sequence is too long. Therefore, the length of the input sequence was of great significance for this experiment. In this paper, we used the autocorrelation coefficient to determine the length of the input sequence. The formula for the autocorrelation coefficient with delay h is as follows:  
\[r_{h}=\sum_{i=1}^{n-h}\frac{\left(x_{i}-\overline{x}\right)\left(x_{i+h}- \overline{x}\right)}{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}} \tag{22}\]  
In the formula, \(x_{i}\) represents the historical power sequence, and \(x_{i+h}\) represents the power sequence with a time lag of \(h\)\({}^{*}\) 5 min.  
According to Table 2, we can see that the correlation gradually decreases with the increase in time delay \(h\). Based on the previous analysis, the input sequence length of 12 is suitable. Each input data point consists of 12 groups of four-dimensional data before the power point to be predicted.