esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Embodied Energy  
Energy requirements in agricultural production were divided into three groups: direct and indirect (two subgroups) (Figure 1). A direct energy flow included the energy of fuelsand the electricity that are used by machines. An indirect energy flow consisted of two subgroups. The first was the energy used in the production processes of fertilizers, herbicides, and other chemicals. The second subgroup was the energy used in the manufacturing processes of farm machinery.  
The direct energy is determined as:  
\[DE=\sum_{i=1}^{n}(B_{i}\cdot EEf_{i})+EEe\cdot W,\ \mathrm{MJ/ha}, \tag{2}\]  
where \(B_{i}\) is the fuel consumption of the \(i^{\mathrm{th}}\) type, kg/ha; \(EEf_{i}\) is the energy equivalent (embodied energy) of the \(i^{\mathrm{th}}\) fuel, \(\mathrm{MJ/kg}\); \(W\) is the electricity consumption, kWh/ha; \(n\) is the number of fuels; \(EEe\) is the energy equivalent (embodied energy) of electricity, \(\mathrm{MJ/kWh}\).  
The indirect energy of the first subgroup is equal to:  
\[IDE_{1}=\sum_{j=1}^{m}\bigl{(}M_{i}\cdot EE_{j}\bigr{)},\ \mathrm{MJ/ha}, \tag{3}\]  
where \(M_{j}\) is the mass consumption of \(j^{th}\) chemical, kg/ha; \(EE_{j}\) is the energy equivalent of \(j^{th}\) chemical, \(\mathrm{MJ/kg}\); \(m\) is the number of chemicals.  
The manufacturing energy requirements for farm machinery depend on a number of factors such as a certain machinery's mass, use lifetime, etc., and can be computed by a formula:  
\[IDE_{2}=\sum_{k=1}^{l}\biggl{(}\frac{EMM_{k}\cdot MM_{k}}{ULT_{k}\cdot OFC_{k} }\biggr{)},\ \mathrm{MJ/ha}, \tag{4}\]  
where \(EEM_{j}\) is the embodied energy of \(k^{th}\) machinery, \(\mathrm{MJ/kg}\); \(MM_{k}\) is the mass of \(k^{th}\) machinery, kg; \(ULT_{k}\) is the utilization lifetime of \(k^{th}\) machinery, h; \(OFC_{k}\) is the operation field capacity of \(k^{th}\) machinery, \(\mathrm{ha/h}\); \(l\) is the number of machinery.  
The total energy inputs are:  
\[TE_{input}=DE+IDE_{1}+IDE_{2},\ \mathrm{MJ/ha}. \tag{5}\]  
The embodied energy (or energy equivalents) of main fuels, chemicals, and machinery are presented in Table 1. They are used to determine the indirect energy inputs.  
Figure 1: Energy expenditure of agricultural operations.  
The embodied energy of electricity can be presented in different units such as MJ/MJ or MJ/kWh. Consumers usually measure electricity in kWh. However, some sources of information express embodied energy in MJ/MJ. To simplify subsequent calculations, in this study, we converted MJ/MJ to MJ/kWh. For this conversion, we assumed that 1 kWh = 3.6 MJ.
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Security Requirements  
The security requirements are needed to design watermarking protocols able to resist increasingly dangerous attacks. However, the search for security has led to less usable and complex protocols, which, for example, often involve trusted third parties (TTPs) acting as watermark certification authorities (WCAs) [8] to manage the implemented protection schemes. As a consequence, the key challenge in designing modern watermarking protocols consists of finding the right balance between conflicting requirements, such as simplicity and usability on the one hand and security on the other hand.
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 2 Review on Extended Kalman Filter with Maximum Correntropy Criterion
### MCCEKF Algorithm 1: MCCEKF-1  
The Maximum Correntropy Criterion (MCC) forms the foundation for deriving a nonlinear extension of the linear Kalman filter, as detailed in [25]. Alternatively, a new objective function can be constructed to enhance robustness by adopting the Weighted Least Squares (WLS) approach. The goal, therefore, is to improve the objective function [26; 27]  
\[J_{\text{MCC}-1}=G_{\sigma}(||\mathbf{z}_{k}-\mathbf{h}(\overset{\boldsymbol{ \kappa}}{\mathbf{x}}  
Update the state vector \(\mathbf{\kappa}_{k|k}\) and the error covariance matrix \(\textbf{P}_{k|k}\).  
Repeat Steps (2)-(5) for each subsequent time step in the estimation process.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 2 Literature Review
### Analytical Methods Affect the Inverter in the PV Inverter  
The study by [96] discussed the issues affecting the distribution system as a result of PV penetration, such as harmonics, voltage balance, voltage rise, and voltage fluctuation and their consequences on the system. However, this study did not discuss the PV/inverter power sizing ratio.  
Although one study [32] reviewed the sizing optimization issues of PV systems and took into account grid connected systems, hybrid PV/wind/diesel generator systems, hybrid PV/wind systems, hybrid PV/diesel generator systems, as well as standalone PV systems, this study did not discuss the sizing optimization problems for inverter oversizing. In addition, the study has become somewhat outdated on the inverter sizing ratio technology, since it reviewed over 100 articles in the period of 1982-2012.  
Analytical studies such as [97] calculated the optimum inverter size in grid-tie PV systems, but with limited (four) unidentified parameters, one related to the location, and three related to the inverter. In the same context, the optimal inverter size for PV systems placed on two-axis tracking mechanisms in European locations ware estimated analytically in [98]. The duration curve of the power of the PV array's DC terminals was used as the foundation for the analytical formulation of the ideal inverter size. However, inverter undersizing issues and inverter clipping were been taken into consideration and the calculations were constrained by the inverter's maximum output power.  
Overvoltage issues are frequently brought on by the growing use of PV systems in distribution networks. The provision of reactive power (RP) by the PV converters is one approach to solving this problem. As a result, increased power losses on the PV converters could raise operating expenses. This issue was discussed by [99], where the losses were individually computed for the system inverter as the losses affected by the RP. These losses are comprehensively reviewed in Section 2.2 of [33].
## 4 Segmentation Performance Evaluation
### Spatial Distance-Based Metrics  
Area-based metrics are susceptible to differences between the positions of segmented regions and strongly dependent on their own size. To take into account the spatial position of the pixels, it is necessary to quantify the distance between the boundaries computed by the semi-automatic methods and the ground-truth delineated by the expert. Let \(A=\{a_{i}:i=1,2,\ldots,K\}\) be the set of vertices belonging to the semi-automatic mask and \(T=\{t_{j}:i=1,2,\ldots,N\}\) the set of vertices belonging to the ground-truth, the distance between the \(i\)-th pixel in \(A\) and the set \(T\) is defined as:  
\[d(a_{i},T)=\min_{j\in\{1,2,\ldots,N\}}||a_{i}-t_{j}||_{2}, \tag{26}\]  
where \(||a_{i}-t_{j}||_{2}\) denotes the Euclidean distance between two points.  
Many metrics can be defined in order to quantify the similarity/dissimilarity between two segmentations.  
The Mean Absolute Distance (MAD)--defined in Equation (27)--quantifies the average error in the segmentation process. The Maximum Distance (MaxD)--defined in Equation (28)--measures the maximum difference between the two ROI boundaries. The Hausdorff Distance (HD) between the point sets \(A\) and \(T\)--defined in Equation (29)--measures the maximal distance from a point in the first set to a nearest point in the other one.  
\[\text{MAD}=\frac{1}{K}\sum_{i=1}^{K}d(a_{i},T) \tag{27}\]  
\[\text{MaxD}=\max_{i\in 1,2,\ldots,K}\{d(a_{i},T)\} \tag{28}\]  
\[\text{HD}=\text{max}\left\{h(T,A),h(A,T)\right\}, \tag{29}\]  
where \(h(T,A)=\max_{t\in T}\{\min_{a\in A}\{d(t,a)\}\}\) is the so-called 'directed Hausdorff Distance'.  
It is important to point out that all the measured distances are expressed in pixels: in this way, they result will be independent from the spatial resolution among different MRI datasets (i.e., pixel spacing).
## 5 Numerical Results
Figure 6: Total cost (**a**), processing cost (**b**), bandwidth cost (**c**) and number of reconfigured switches (**d**) as a function of the maximum number \(R\) of switch reconfigurations. The offered traffic in the peak SI equals 800 Gbps. The cloud resource cost unbalancing parameter \(\alpha\) is varied from 1 to 1.5. The Deutsche Telekom network topology is considered.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 4 Joint Resource Allocation in Cognitive LEO SatComm System
### Joint Resource Allocation  
From (21), we know that the optimal receive power of the IoT users is relevant to the received power of the legacy satellite system. With perfect power control, we could adjustthe receive power \(q\) of the legacy satellite users to maximize the sum rate of the IoT users. Thus, we formulate the joint resource allocation problem as **P4**:  
\[\max_{p,q} \frac{K}{N}\log(1+\gamma_{s})\] s.t. \[\gamma_{p}\geq\beta^{*}\] \[p\leq\bar{P}.\]  
For every given \(q\), there must be an optimal IoT user receive power \(p\) which can ensure the maximization of IoT system's sum rate. To overcome the joint resource allocation problem, we will find the influence of \(q\) to \(\gamma_{s}\) with the optimal IoT user receive power \(p\), based on which we provide the following analysis.  
**Lemma 2**.: _When \(q\in[q_{0},q_{1})\) with the optimal IoT user receive power \(p\), if \(q_{1}>q_{2}\), then, we can have \(\gamma_{1,s}>\gamma_{2,s}\)._  
Proof.: The proof is given in Appendix B.  
**Lemma 3**.: _When \(q\in[q_{1},\infty)\), with the optimal IoT user receive power \(p\), if \(q_{1}>q_{2}\), then, we can have \(\gamma_{1,s}<\gamma_{2,s}\)._  
Proof.: The proof is given in Appendix C.  
Note that the values of \(q_{0}\) and \(q_{1}\) will be discussed later. From Lemmas 2 and 3, we know that when \(q\in[q_{0},q_{1})\), \(\gamma_{s}\) increases with \(q\), and when \(q\in[q_{1},\infty)\), \(\gamma_{s}\) decreases with \(q\). This indicates that the sum rate of the IoT systems is quasi-concave over the legacy satellite receive power. As a result, when \(q=q_{1}\), \(p=\bar{P}\), the sum rate of the IoT system is maximized. Meanwhile, if we have \(q_{0}>q_{1}\), the IoT users can not transmit signals i.e., \(p=0\).  
Next, we will focus on the investigation of the values of \(q_{0}\) and \(q_{1}\). When the legacy satellite system cannot tolerate the interference of the IoT system, the receive power of legacy satellite system is minimal, i.e.,  
\[\beta^{*}=\frac{q_{0}}{\sigma^{2}+\frac{q_{0}\alpha_{1}}{1+\beta^{*}}}. \tag{24}\]  
Solving this equation for \(q_{0}\), we can get  
\[q_{0}=\frac{\sigma^{2}}{\frac{1}{\beta^{*}}-\frac{\alpha_{1}}{1+\beta^{*}}}. \tag{25}\]  
When \(q=q_{1}\), (21) and (23) both hold, which gives  
\[\bar{P}=\frac{\frac{q_{1}}{\beta^{*}}-\sigma^{2}-\frac{\alpha_{1}q_{1}}{1+ \beta^{*}}}{\alpha_{2}+\frac{\sigma^{2}\beta^{*}}{q_{1}}+\frac{\alpha_{1} \beta^{*}}{1+\beta^{*}}-1}. \tag{26}\]  
By solving (26), we can get the result of \(q_{1}\).  
As aforementioned, the optimal joint resource allocation scheme is related to the primary and secondary system loads, i.e., \(\alpha_{1}\) and \(\alpha_{2}\). Intuitively, the growth of the number of IoT users will contribute to the new increments in the sum rate, however, leading to the increase in interference. Thus, there exists a tradeoff in the number of IoT users. In the following, we aim to exploit the optimal user number of the IoT system to maximize the sum rate of the IoT system for a given \(N\).
## 2 Description of Proposed Algorithms
#### 2.2.2 Step-by-Step Algorithm  
Two variants of the TL have been implemented: the original (v.1, \(N_{LOC}=0\)) and the hybridized with the modification of trust region (v.2, \(N_{LOC}>0\)) method for local descents. See Algorithm 2.
```
1. Set the algorithmic parameters. Choose starting point \(x^{0}\in X\). Suppose \(x^{REC}=x^{0}\), where \(x^{REC}\) is the record point. Compute \(f^{REC}=f(x^{REC})\). Suppose \(K=0\), where \(K\) is the algorithm iteration number.
2. Create a random initial set of agents (a group of students) \(P^{0}=\big{\{}p_{1}=x^{0},p_{2},\ldots,p_{m}\big{\}}\), \(p_{j}\in X,\,\,j=\overline{1,m}\).
3. Carry out a selection of the initial population: perform \(m\) local descents, each of \(N_{LOC}\) iterations of the local search method, each starts with its agent (student) of the initial population, get \(\{s_{1},s_{2},\ldots,s_{m}\}\).
4. Improve the record point, take \(x^{REC}=\text{argmin}\big{\{}f(s_{i}),l=\overline{1,m}\big{\}}\).
5. Substitute in the group \(P^{0}\) of the students \(\{p_{1},p_{2},\ldots,p_{m}\}\) by the agents \(\{s_{1},s_{2},\ldots,s_{m}\}\). On the \(K\)-th iteration:
6. Calculate a measure of biodiversity \(M_{bio}=1/(m-1)\sum\limits_{l=1}^{m-1}\|p_{l+1}-p_{l}\|\).
7. If the stoppage criterion \(M_{bio}<\epsilon_{bio}\) is satisfied, then output \(x^{REC}\), the algorithm is terminated.
8. Teacher stage: ```
**Algorithm 2** TL

### Algorithm Based on Teacher-Learner and Local Descent Methods

#### 2.2.1 Parameters

* \(m\in[5;\,10000]\subset Z\)--number of students;
* \(N_{LOC}\in[0;\,10000]\subset Z\)--number of iterations of the local search method;
* \(\epsilon_{bio}\in[0;\,0.1]\subset R\)--biodiversity factor.

#### 2.2.2 Step-by-Step Algorithm

Two variants of the TL have been implemented: the original (v.1, \(N_{LOC}=0\)) and the hybridized with the modification of trust region (v.2, \(N_{LOC}>0\)) method for local descents. See Algorithm 2.
```
1. Set the algorithmic parameters. Choose starting point \(x^{0}\in X\). Suppose \(x^{REC}=x^{0}\), where \(x^{REC}\) is the record point. Compute \(f^{REC}=f(x^{REC})\). Suppose \(K=0\), where \(K\) is the algorithm iteration number.
2. Create a random initial set of agents (a group of students) \(P^{0}=\big{\{}p_{1}=x^{0},p_{2},\ldots,p_{m}\big{\}}\), \(p_{j}\in X,\,\,j=\overline{1,m}\).
3. Carry out a selection of the initial population: perform \(m\) local descents, each of \(N_{LOC}\) iterations of the local search method, each starts with its agent (student) of the initial population, get \(\{s_{1},s_{2},\ldots,s_{m}\}\).
4. Improve the record point, take \(x^{REC}=\text{argmin}\big{\{}f(s_{i}),l=\overline{1,m}\big{\}}\).
5. Substitute in the group \(P^{0}\) of the students \(\{p_{1},p_{2},\ldots,p_{m}\}\) by the agents \(\{s_{1},s_{2},\ldots,s_{m}\}\). On the \(K\)-th iteration:
6. Calculate a measure of biodiversity \(M_{bio}=1/(m-1)\sum\limits_{l=1}^{m-1}\|p_{l+1}-p_{l}\|\).
7. If the stoppage criterion \(M_{bio}<\epsilon_{bio}\) is satisfied, then output \(x^{REC}\), the algorithm is terminated.
8. Teacher stage: ```
**Algorithm 3** TL
## 1 Introduction  
Global extreme climate change, with both drought and precipitation, occurs frequently, which causes intensive changes in the hydrological cycles at both global and regional scales [1]. Such phenomena occur regularly in arid and semiarid areas [2, 3]. Nitrogen (N) is often a limiting macronutrient in many territorial systems, and its transformation is largely driven by fluctuations in the soil moisture, such as drying-rewetting (DW) [4]. Soil DW can significantly affect the soil microbial activity and alter the N transformation, which has attracted great attention during the last few decades in the context of extreme climate change [5]. Soil organic matter (SOM) is an important pool of organic N. It is mineralized to simple inorganic forms by microbial communities [6]. Soil organic N mineralization is the most significant process, which determines the absorption of N available to plants and is influenced by abiotic factors [7]. Soil water content is one of the most important abiotic factors, which is easily influenced by the soil DW and affects the N availability, from organic N to inorganic N [7]. Interestingly, laboratory studies of N transformation have quantified wetting-drying cycles in recent decades [8]. These studies mainly focused on wetland soil, grassland soil, and forest soil, while there has been less research on agricultural soil [9; 10; 11]. Agricultural soil plays an important role in national production and life and should receive great attention. Cotton (_Gossyvium hirsutum_ L.) is one of the most important cash crops in Xinjiang autonomous province, located in northwestern China, an arid region [12]. Most of the cotton plant types in Xinjiang use drip fertigation. Affected by the small amount of single irrigation and high frequency of drip irrigation, the soil experiences periodic alternation between dry and wet during cotton growth.  
The rate of nitrogen mineralization in the soil increased after the DW cycles compared with that kept moist [5]. DW cycles promoted N mineralization in alpine wetlands, while nitrogen mineralization decreased after repeated wet and dry conditions [13; 14]. During the soil DW cycle, plants and microbes can have different sensitivities to soil moisture, which may cause temporal shifts and delays in the net N mineralization by microbes and the N uptake by plants, with long-term implications for N loss and plant productivity [15]. This excitation effect may be facilitative, inhibitory, or ignored during the DW cycle, and the mechanisms are still unclear.  
The objectives of this study were: (1) to define the dynamics of nitrate nitrogen (NO3--N) and ammonium nitrogen (NH4+--N) in different DW cycle treatments under drip irrigation in an arid area; (2) to explore the characteristics of N mineralization from the wet to dry and dry to rewet processes under drip irrigation in an arid area; and (3) to demonstrate which treatment benefits N transformation under drip irrigation in an arid area. The results of the study will be particularly helpful for reasonable water and nitrogen management.
## 4 Experimental Testing
### Manufacturing Workshop Data Collection Terminal Test  
This article uses three servers to build a test platform. Two of the servers integrate PDC components as OPC UA clients, namely, PDC1 and PDC2. Each PDC is responsible for detecting the underlying equipment data under the manufacturing unit. The other server uses PostgreSql as the data storage function, which is responsible for pulling the data collected by the two PDC servers from the Kafka Cluster. The Web front-end component realizes data visualization by subscribing to the specified Topic. The entire framework uses JSON format for data exchange, and each piece of data has a uniquely identified UUID sequence defined to facilitate the distinction of a large number of devices in the workshop, as shown in Figure 12. Each piece of collected data contains the data change time, variable value, data type, and node identifier UUID of the node.  
In a smart factory environment, large amounts of data from OPC UA device nodes need to be frequently collected and transmitted. In order to achieve good cross-platform  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CPU** & **RAM** & **HDD** & **OS** & **Kafka** & **PostgreSql** \\  & & & & **Version** & **Version** \\ \hline
4 \(\times\) 3 & 16 GB & 50 GB & Ubuntu22.04.4 LTS & 2.13–3.7.0 & 16.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test configuration of the server.  
Figure 12: Sample data received from the database.  
compatibility and readability, this study uses JSON format as the serialization method for Kafka messages. However, JSON itself has the problems of redundant field names and large encoding volume, which may cause a waste of transmission bandwidth and processing delay in high-frequency data scenarios. Therefore, compressing the message size and improving the transmission efficiency of Kafka while maintaining the flexibility of the system are key issues to consider.  
In order to improve the efficiency of message transmission, this study tests and compares three mainstream compression algorithms: gzip, lz4, and snappy, with the help of the message compression mechanism natively supported by Kafka. The experimental results are shown in Table 3.  
Since the average size of a single JSON message required for transmission in this paper is 520-600 bytes, lz4 is finally selected as the Kafka message compression algorithm based on the compression effect and real-time processing performance. The algorithm has an extremely fast compression and decompression speed while maintaining a high compression ratio, which is suitable for the latency-sensitive real-time data processing requirements of smart factories.
# Influence of Mix Proportions on Rheological Properties, Air Content of Wet Shotcrete--A Case Study
## 3 Results and Discussion
### Summary of Influence of Admixtures or Additives on Pumpability and Shootability  
According to the above experimental analysis, free paste and round particles are the key factors affecting the pumpability and sprayability of wet shotcrete. As shown in Figure 22, cement paste has three functions: coating particles, filling gaps, and lubricating pipe wall. The \"ball effect\" and \"free paste effect\" have compound effects on the properties of wet shotcrete. In theory, these two effects, i.e., enough free paste or enough balls, can reduce the yield stress and improve the fluidity of concrete, which is called positive effect. However, in the fresh concrete system, if the free cement paste or smooth ball is insufficient, it is called negative effect, which will reduce the fluidity of fresh concrete. For example, in Figure 7, it can be concluded (from SR50 to SR60) that the negative paste effect is greater than the positive ball effect, resulting in an increase in yield stress and a decrease in slump.  
[PERSON] et al. [72] also found that the rheological properties of fresh concrete changed significantly between hydrodynamic and friction control. When the cement paste volume is small, the fluidity is mainly determined by the direct friction between particles. When the cement paste volume is large, the interaction between fluid and particles or between particles is hydrodynamic. The movement of the particles causes the paste to flow, where additional energy is dissipated. Therefore, with the increase of paste volume fraction, the plastic viscosity decreases. The additional paste volume significantly reduces the negative effect of coarse aggregate on the fluidity of fresh concrete [54]. Various factors affecting shotcrete properties are summarized in Table 4.  
According to the above test results, to meet the different workability, such as pumping, spraying, or compressive strength, the general mix proportion range is given. Finally, the optimum mix proportion is recommended, as shown in Table 5.  
Figure 22: Free paste effect and ball bearing effect in fresh concrete system.