esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Regional Scale Changes in NDVI and NPP  
Based on an analysis of time-series data from 2000 to 2016, the regional NDVI and NPP conditions of the Sanjiangyuan National Park exhibited a slightly increasing trend, with an annual increase by an average of 0.0009/a and 0.7523 gC/m\({}^{2}\)/a, respectively. According to an intuitive analysis of the interannual statistical data, there was a decline in NDVI and NPP after 2014 [36], which may be due to climate change or the ignorance of spatial heterogeneity [37; 38]. Therefore, to reduce the impact of the value of possible outliers, we analyzed the trends of NDVI and NPP from 2000 to 2014. From 2000 to 2014, the NDVI exhibited a significant increasing trend in all vegetation types (Table 1). Shrub exhibited the highest rate of 0.0028/a (\(p<0.05\)) by ranging from 0.5714 to 0.6444, followed by the meadow with a rate of 0.0026/a (\(p<0.05\)), ranging from 0.4081 to 0.4691. From 2000 to 2014, the NPP also exhibited a significant increasing trend in all vegetation types. Shrub had the highest rate, 2.90 gC/m\({}^{2}\)/a (\(p<0.05\)), by ranging from 224.60 to 301.92 gC/m\({}^{2}\)/a, followed by the meadow with a rate of 2.03 gC/m\({}^{2}\)/a (\(p<0.05\)), ranging from 121.40 to 184.25 gC/m\({}^{2}\)/a.  
From 2000 to 2014, the NDVI exhibited a significant increasing trend in the study area, with a rate of increase of 0.0022/a, ranging from 0.32 to 0.36 (\(p<0.05\)) (Figure 3(a)). There were significant differences between the three headwater regions; the annual maximum NDVI at the Yellow River headwater region ranged from 0.39 to 0.47, with a significant upward trend at a rate of 0.0035/a (\(p<0.05\)) (Figure 3(b)), which was faster than the trend of the entire study area. The annual maximum NDVI at the Yangtze River headwater region ranged from 0.26 to 0.30, with a significant upward trend at a rate of 0.0020/a (\(p<0.05\)) (Figure 3(c)). However, the annual maximum NDVI at the Lancang River headwater region ranged from 0.50 to 0.56 with no significant change (Figure 3(d)).  
The annual NPP exhibited a significant upward trend at a rate of 1.3824 gC/m\({}^{2}\)/a (\(p<0.05\)), ranging from 81.49 to 123.46 gC/m\({}^{2}\)/a between 2000-2014 (Figure 4(a)). The annual NPP at the Yellow River headwater region ranged from 107.22 to 180.37 gC/m\({}^{2}\)/a, with a significant upward trend at a rate of 2.661/a (\(p<0.05\)) (Figure 4(b)), which was faster than the trend for the entire study area. The annual NPP at the Yangtze River headwater region ranged from 57.38 to 88.50 gC/m\({}^{2}\)/a (Figure 4(c)), with a significant upward trend at a rate of 1.0207 gC/m\({}^{2}\)/a (\(p<0.05\)), which was slower than the trend for the entire study area. However, the annual NPP at the Lancang River headwater region ranged from 183.73 to 245.58 gC/m\({}^{2}\)/a (Figure 4(d)) with no significant change.
## 4 Problem Definition and Modeling
### Cost Function  
In our study, we define the cost of a charging station plan, denoted as \(p\), in terms of the overall time expended during the charging process for drivers. This comprises three key components: travel time (\(t_{1}\)), charging time (\(t_{2}\)), and waiting time (\(t_{3}\)). Travel time encapsulates the cumulative duration required for all electric vehicles within the road network to reach a charging station. Charging time, on the other hand, refers to the aggregate time spent by all electric vehicles during the charging process itself. Finally, waiting time represents the collective duration that electric vehicles are queued for charging. We employ the sum total of these times as a proxy for drivers' comfort in our analysis. The implication here is that a reduction in total time correlates with an enhancement in driver comfort.  
Let \(demand(v)\) represent the charging demand of the junction \(v\); then, which charging station the vehicles at the junction \(v\) are heading to need to be determined. Thus, the function of attraction for CS \(s\) and junction \(v\)\(f_{att}(v,s)\) is defined as follows:  
\[f_{att}(v,s)=\frac{w_{1}}{dis(v,s)}+w_{2}C(s) \tag{16}\]  
where \(w_{1}\) and \(w_{2}\) are the weight coefficients.  
For a given junction \(v\) and all the possible CS \(s\), the larger \(f_{att}(v,s)\) is, the more likely the EVs in the junction \(v\) going to \(s\). Furthermore, we use the softmax function to calculate the possibility of going one CS for junction \(v\):  
\[att(v,s_{k})=\frac{e^{f_{att}(v,s_{k})}}{\sum_{s\in S}e^{f_{att}(v,s^{\prime})}} \tag{17}\]  
\(\sigma(v,s)\) is used to record whether EVs at junction \(v\) are heading to \(s\):  
\[\sigma(v,s)=\begin{cases}1&ifd(v,s)\leq r(s)\;and\;s=\arg_{s^{\prime}\in S} \max att(v,s^{\prime})\\ 0,&otherwise\end{cases} \tag{18}\]  
Then, for the junction \(v\), the total travel time of all EVs \(t_{1}\) induced by a plan \(p\) on the road network is:  
\[t_{1}=\sum_{v\in V}\sum_{s\in P}\frac{\sigma(v,s)demand(v)dis(v,s)}{\bar{V}} \tag{19}\]  
where \(\bar{V}\) is a constant, representing the average speed the EV drive on the road.  
Then, the charging time \(t_{2}\) is modeled and calculated. The total capacity of CSs is \(C(S)\), and the expected charging time of all CSs is \(\frac{1}{C(S)}\). Thus, the charging time for all EVs is:  
\[t_{2}=\sum_{s\in P}\sum_{v\in V}\frac{\sigma(v,s)demand(v)}{C(s)} \tag{20}\]  
For waiting time \(t_{3}\), it is first modeled as an \(M/D/1\) queue [34], where \(M\) denotes the coming event of EV follows a Poisson process, \(D\) denotes the service time is a deterministic function, and \"1\" means only one queue for a station. Then, the expected waiting time \(t_{3}\) can be calculated using the Pollaczek-Khintchine formula [34] as follows:  
\[t_{3}=\sum_{s\in p}W(s)D(s) \tag{21}\]  
where \(W(s)=\frac{\rho(s)}{2\mu(s)(1-\rho(s))}\)\(\rho(s)=\frac{D(s)}{\mu(s)}<1\), and \(D(s)=\sigma(v,s)*demand(v)\).  
Finally, the total time cost is calculated as follows:  
\[cost(p)=t_{1}+t_{2}+t_{3} \tag{22}\]
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 2 Motivation and Background
### Background
#### 2.2.1 Message-Oriented Middleware (MOM)  
The term Message-Oriented Middleware (MOM) is used to describe a software and/or hardware infrastructure used to send and receive messages between distributed systems and to provide storage (persistence) for those messages. The MOM decouples application components, so that they can use different operating systems and network protocols [17; 18]. An important feature of modern MOM implementations, such as Apache Kafka, is that they provide persistent storage and mechanisms to organize messages, guarantee message queue properties (such as immutability, authenticity or ordering), balance the load between MOM servers, etc.  
Figure 1 below shows a simplified architecture of an MOM. In particular, the figure does not show the distributed aspects of the MOM, but the logical structure. It is normal that an MOM is distributed among several servers (usually called \"brokers\"). The figure shows that messages are organized into \"topics\" and \"partitions\". A topic is a logical way to separate messages for processing, and each topic is maintained in at least one partition (sometimes more). According to the Kafka documentation _\"Each partition is an ordered, immutable sequence of records that is continually appended to--a structured commit log. The records in the partitions are each assigned a sequential id number called the offset that uniquely identifies each record within the partition \"_. To improve scalability and throughput, partitions may be managed in different replicas.  
Message-sourcing (i.e., Event-sourcing) is an effective way to maintain the state of an application, while at the same time greatly increasing trust in the current state of the system and opening the system to new and enhanced functionalities. Although they share some similarities, an MOM used in this way is not the same as blockchain. If used correctly, this solution can be made tamper-resistant, just like blockchain. Also, like blockchain, data can only be appended. But those are the main similarities. The main difference with blockchain is that this solution can scale to several millions of Transactions Per Second (TPS), while the fastest blockchains reach a maximum of 65,000 TPS.
# The Efficiency of Large Hole Boring (MSP) Method in the Reduction of Blast-Induced Vibration
## 4 Real-Scale Test Blasting  
### Overview of Test Blasting  
Real-scale test blasting was conducted to analyze the effects of vibration reduction depending on several blasting patterns, including the MSP method. The test blasting was carried out at a subway construction site in Seoul, concerned with damage to existing underground infrastructures due to blast-induced vibration and minimizing civil complaints in the urban area. A total of five patterns, including general blasting (B) and controlled blasting (CB), were applied in the full-face excavation method, and the order of test blasting was planned, as shown in Figure 6. The test blasting was performed at one site to compare the results under the most similar conditions as possible. The area comprised granite gneiss with approximately 80 MPa, and rough discontinuities under slightly weathered conditions were identified in the rock mass at intervals of 0.2-0.4 m. Minimate plus (Instantel Inc., Ottawa, ON, Canada), which is a vibration monitoring system, was installed at the same distance with a total of three points just above the tunnel face for measuring blast-induced vibration, and the detailed information of the measuring instruments are shown in Table 2.  
The patterns of test blasting for each method are shown in Table 3. The base case, Pattern B, represents a cylinder-cut utilizing smooth blasting, a conventional uncontrolled blasting pattern currently used in the field. In comparison, pattern CB-1, which also used cylinder-cut, instead utilizes a combined Controlled Blasting (CB) method. Furthermore, the other three controlled blasting patterns, CB-2, CB-3, and CB-4, are constructed with the MSP method. In particular, the CB-4 pattern was used to maximize the blast-induced vibration through the multi-MSP method using low-vibration explosives of 32 mm. Through comparison of vibration data collected from the each of the five patterns, the effect of cut  
Figure 5: Comparison of advance rates according to the length and diameter of empty holes. and controlled blasting methods on the reduction in blast-induced vibration have been investigated and identified. High-performance emulsion with 32 mm loaded in the cut, stoping, and lifter holes were mainly used for breaking rocks, and a finex which is low-vibration explosives of 17 mm was used in the contour hole to reduce overbreak, and the specification of the explosives are shown in Table 4. Sequential blasting was performed using millisecond (MS) delay detonators with a delay time of 20 milliseconds incremen \begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Description** \\ \hline Name & Minimate Plus (Instantel Inc.) \\ Range & Up to 254 mm/s \\ Resolution & 0.127 or 0.0159 mm/s with built-in preamp \\ Accuracy & \(\pm\)5\% or 0.5 mm/s \\ Frequency range & 2 to 250 Hz \\ Sampling rate & 1024 to 16,000 S/s per channel \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specification of instruments for measuring vibration.
# Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera
## 4 Conclusions
In general, the internal and external compensation model proposed in this paper had a good compensation effect on the target image point positioning error, which provides a certain reference for its on-orbit application. External parameter compensation under different temperature conditions was carried out, and the installation angle and time were established. The change model was consistent with the phase angle of the added installation error, which could also prove the validity and correctness of the internal and external parameter compensation model proposed in this paper. The compensation of the target image point positioning error caused by the existence of various errors in the imaging process was successfully carried out. The results of this paper can provide accurate trajectory reconstruction for the subsequent motion parameter estimation of high-value point targets. This paper focuses on the error compensation method of internal and external parameters of an area array camera, and the main error components were internal geometric distortion and installation angle error. Therefore, the error compensation method of internal and external parameters in the imaging model proposed in this paper can be used for all orbit altitudes and types of satellites. The authors declare no conflict of interest.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 2 Related Work
### Class-Imbalance Learning  
In the machine learning field, class-imbalance learning methods can be divided into two categories [68]: data-level methods and algorithm-level methods. Data-level methods are characterized by easy implementation and being independent of the model training process. Algorithm-level methods need to modify the classifiers.
## 2 Materials and Methods  
### Study Area Description  
Situated in central Anhui Province, the study area lies in the midsection of the Huai River. It is geographically positioned between 116\({}^{\circ}\)52\({}^{\prime}\)02.1\({}^{\prime\prime}\) E and 116\({}^{\circ}\)56\({}^{\prime}\)19.0\({}^{\prime\prime}\) E longitude, and 32\({}^{\circ}\)34\({}^{\prime}\)45.5\({}^{\prime\prime}\) N and 32\({}^{\circ}\)36\({}^{\prime}\)38.9\({}^{\prime\prime}\) N latitude. It is located in the transition zone between the subtropical zone and the warm temperate zone. The climate characteristics are obvious, the four seasons are distinct, the annual average temperature is 15.3 \({}^{\circ}\)C, the annual average frost-free period is 224 days, and the precipitation in the four seasons is relatively average. The annual precipitation is 937.2 mm, and the average annual sunshine is 2609.9 h. Due to the superior light conditions, abundant heat, moderate precipitation, and long frost-free period, it is suitable for planting food crops and economic crops such as soybeans, wheat, rice, and rapeseed. The study area is the Jianghuai Plain area, and the terrain is a low hilly area with little ups and downs. According to the soil classification of WRB, the soil types of sampling sites in the study area include Gleyic Fluvisols, Haplic Fluvisols, Ferric Luvisol, and Anthraqui-Stagnic Luvisol. The physical properties of the typical soil in the study area are shown in Table 1, and the soil texture of sampling points is shown in Figure 1. Most of the sampling points are A-B-C and A-B-C-D. The color of the whole section is yellowish brown to brown, the clay plate appears above 20 cm, and the texture is heavy soil to clay. From massive to prismatic structure, there are new bodies such as iron-manganese binding and dark film, and their number is increasing from top to bottom.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Level** & **Depth** & **Sand** & **Silt** & **Clay** & **Bulk Density** & **Porosity** \\  & **(cm)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline A & 0–15 & 16.58 \(\pm\) 0.53 a & 33.61 \(\pm\) 0.66 a & 49.82 \(\pm\) 1.53 d & 1.32 \(\pm\) 0.012 b & 41.46 \(\pm\) 0.08 c \\ B\({}_{1}\) & 15–30 & 19.03 \(\pm\) 0.51 b & 34.65 \(\pm\) 0.71 b & 46.32 \(\pm\) 1.52 a & 1.34 \(\pm\) 0.009 bc & 39.25 \(\pm\) 0.07 a \\ B\({}_{2}\) & 30–53 & 16.90 \(\pm\) 0.55 a & 35.58 \(\pm\) 0.63 c & 47.52 \(\pm\) 1.47 b & 1.31 \(\pm\) 0.011 a & 41.17 \(\pm\) 0.08 bc \\ C & 53–100 & 16.59 \(\pm\) 0.41 a & 34.87 \(\pm\) 1.07 b & 48.54 \(\pm\) 1.43 c & 1.35 \(\pm\) 0.010 c & 40.62 \(\pm\) 0.09 b \\ \hline \hline \end{tabular} Note: Layer A is the surface layer of soil, layer B is the middle layer of soil, and layer C is the deep layer of soil. The data is the average value. The different lowercase letters a–d after the same column value indicate that there is a significant difference between different treatment groups (\(p\) < 0.05 ), the same below.  
\end{table}
Table 1: Physical properties of soils in the study area.
## 3 Node Clustering for WMSNs
### Cooperative Monitoring in Clusters  
Let us see the potential of cooperative node monitoring in clusters in terms of sensor area coverage. We define the Maximum Cluster Coverage Domain (MCCD) parameter for a cluster as the maximum monitoring area which is covered by that cluster. Since each cluster is established considering an overlapping scale equal to \(\gamma\), the MCCD can be computed as follows (C\({}_{\rm size}\) is the size of the cluster):  
\[\rm{MCCD}=\gamma\cdot\rm{A}_{{}_{FoV}}+(1-\gamma)\cdot\rm{A}_{{}_{FoV}}\cdot \rm{C}_{{}_{size}}=(\rm{C}_{{}_{size}}-\gamma\cdot(\rm{C}_{{}_{size}}-1))\cdot \rm{A}_{{}_{FoV}}=\beta\cdot\rm{A}_{{}_{FoV}} \tag{10}\]  
where:  
\[1\leq\beta=\rm{C}_{{}_{size}}-\gamma\cdot(\rm{C}_{{}_{size}}-1) \tag{11}\]  
The effective cluster covering domain can be inferior to the MCCD calculated by (10) since some nodes can overlap more than the region determined by \(\gamma\). Since MCCD gives us an upper bound on the area covered by the cluster, using MCCD will allow us worst-case dimensioning. Factor \(\beta\) represents the increment of area that the cluster senses with respect to an individual sensor. When each node of a cluster obtains an image from its FoV, a part of the related MCCD with a ratio at least equal to 1/\(\beta\) respect to the MCCD is captured whereas this part includes overlapped areas of other nodes in the cluster. Sensing the environment by each member delivers information not only from the FoV of the active node but also from some overlapped parts of other nodes in the same cluster: at least \(\gamma\)\(\rm{A}_{FoV}\) of the area is common to the first-member and more than 1/\(\beta\) of the MCCD is monitored. For example, in a cluster consisting of just 2 members, assuming an overlapping scale of \(\gamma\) = 0.5, the MCCD is 1.5\(\rm{A}_{FoV}\). Thus, when each of the two members of the cluster is activated and monitors the environment, an area of one FoV is captured that is at least 2/3 of the whole MCCD of the cluster.  
Consequently, scheduling and coordination among members in order to sense the field in a collaborative manner may yield a gain in energy saving and performance efficiency even with a low number of members in the cluster.
## 3 Pathogenic Mechanisms of _P. gingivalis_  
### Microbiome Formation and Microbial Synergy  
Biofilm formation is a critical step in the pathogenesis of periodontal disease. _P. gingivalis_ expresses several virulence factors that facilitate its survival, modulate interactions with other species within the microbiome, and influence the inflammatory response of colonized host tissue. _P. gingivalis_ expresses several virulence factors, including fimbiae, LPS, and its cysteine proteases, namely, gingipains. These include the arginine-specific proteinases RgpA and RgpB and the lysine-specific proteinase Kgp (20). Gingipains can utilize free amino acids as a carbon and nitrogen source, which allows them to evade host defenses by degrading antibacterial peptides, such as neutrophil-derived \(\alpha\)-defensins, complement factors, such as C3 and C4, and T-cell receptors, such as CD4 and CD8. Unlike other bacteria, _P. gingivalis_ does not produce siderophores to sequester and transport iron, but its gingipains mediate iron uptake from hemoglobin, heme proteins, and ferritin (21).  
This may disrupt the crosstalk between C5a receptor and Toll-like receptor signaling to prevent bacterial clearance and cause dysbiosis, resulting in periodontal bone loss (22). Despite the importance of gingipains in the pathogenicity of _P. gingivalis_, limited research has been conducted on their role in biofilm formation. However, it has been shown that _P. gingivalis_ initially attaches to the substrate as a primary colonizer, subsequently co-aggregating with _Treponema denticola_ to form a mixed microbiome. These biological interactions are essential to supporting the virulence of the microbial community (23).
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Total Polyphenol Content and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Treatment Time [s]**} & \multicolumn{2}{c}{**Antioxidant Activity [\%]**} \\ \cline{3-4}  & & **ABTS** & **DPPH** \\ \hline \multirow{4}{*}{Black pepper seeds} & 0 & 47.03 \(\pm\) 1.71 a & 45.91 \(\pm\) 1.75 a \\ \cline{2-4}  & 15 & 40.51 \(\pm\) 0.45 b & 42.19 \(\pm\) 1.86 a b \\ \cline{2-4}  & 30 & 51.15 \(\pm\) 0.87 c & 42.77 \(\pm\) 0.41 a b \\ \cline{2-4}  & 45 & 48.14 \(\pm\) 0.62 a & 41.53 \(\pm\) 1.14 b \\ \cline{2-4}  & 60 & 57.60 \(\pm\) 1.50 e & 48.61 \(\pm\) 2.27 a \\ \hline \multirow{4}{*}{Juniper berries} & 0 & 30.00 \(\pm\) 2.61 A & 32.91 \(\pm\) 0.80 A \\ \cline{2-4}  & 15 & 28.08 \(\pm\) 2.28 A & 26.16 \(\pm\) 1.39 B \\ \cline{2-4}  & 30 & 29.23 \(\pm\) 0.44 A & 28.76 \(\pm\) 0.50 B \\ \cline{2-4}  & 45 & 33.08 \(\pm\) 1.31 B & 39.03 \(\pm\) 1.69 C \\ \cline{2-4}  & 60 & 35.77 \(\pm\) 0.33 B & 42.69 \(\pm\) 1.09 C \\ \hline \multirow{4}{*}{Allspecies berries} & 0 & 30.57 \(\pm\) 1.85 [A] & 32.47 \(\pm\) 1.29 [A] \\ \cline{2-4}  & 15 & 40.83 \(\pm\) 0.72 [B] & 47.97 \(\pm\) 0.20 [B] \\ \cline{2-4}  & 30 & 24.02 \(\pm\) 0.62 [C] & 23.42 \(\pm\) 3.17 [C] \\ \cline{2-4}  & 45 & 24.75 \(\pm\) 0.62 [C] & 24.47 \(\pm\) 0.30 [C] \\ \cline{2-4}  & 60 & 36.17 \(\pm\) 1.96 [D] & 40.53 \(\pm\) 2.58 [D] \\ \hline \hline \end{tabular}  
Significant differences between investigated samples, based on the Tukey test results, are indicated by different small letters (a, b, c...), capital letters (A, B, C...) or capital letters in brackets ([A], [B], [C]...) for different spices species (black pepper seeds, juniper berries and allspices berries, respectively). \(n=3\). ABTS - 2.2’-azino-bis(3-ethylbenzothiazalone-6-sulfonic acid; DPPH - 2.2-diphenyl-1-picryhydraz.)  
\end{table} 
Table 2: Antioxidant activity of investigated spices measured by ABTS and DPPH assays. Figure 7: Total polyphenic content of plasma treated spices.