esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Establishment of Intelligent Control Systems for DHSs  
Along with the increase in the energy efficiency of thermal energy production, it is crucial to pay special attention to energy saving. This involves equipping DHSs with an appropriate intelligent control system. When dealing with this issue, it is important to consider the specificities and variations in consumer requirements within the public utility industry across various countries.  
The level of energy saving can be determined by the comfortable temperature maintained in apartments. It is imperative to highlight that in countries with higher levels of gross domestic product (GDP) per capita, the temperature maintained in apartments is lower. Figure 10 shows the active participation of household consumers in energy conservation [113].  
Decrease in thermal energy consumption for heating apartments is achieved through the use of control devices in individual heating systems. In DHS, there can be a combination of system-wide and individual approaches to managing thermal energy consumption, but this requires the modernization of DHSs and their control systems.  
The primary focus of modernizing DHS control systems lies in their digitalization and intelligitization. Without intelligent control systems for the production and consumption of thermal energy, it is difficult to ensure optimal coordination of the interests of producers, suppliers, and consumers of thermal energy. An efficient technological interaction between  
Figure 10: The relationship between GDP per capita and temperature in apartments by country.  
various sources of thermal energy and heat receivers is crucial for a successful transition of DHSs to the level of 4 GDH and 5 GDH. If consumers are not provided with the opportunity to actively and equally participate in the thermal energy market, and if they are not encouraged to use their sources of thermal energy for supply to the DHS, then ensuring the development of DHSs in the domestic sector will be a challenging task.  
Global climate change associated with global warming heightens the need for a cooling supply to consumers in many countries, which requires the adaptation of DHSs to new challenges. The possibility of expanding the DHS functions into the area of cooling supply to consumers should be considered to enhance its economic viability.  
The creation of a flexible and efficient DHS that integrates a host of different sources of thermal energy using various energy resources, including renewable ones, allows the DHS to transition to a more energy-efficient generation [114; 115]. To ensure the transition of the DHS to 4 GDH and 5 GDH level, it is recommended to commence it in regions and countries that offer favorable preconditions for the development of integrated energy systems based on a low-power CHPP for household consumers.
## 3 Results
### Main Species Distribution  
According to the PCA analysis, the first two components contain 68.70% of the global variation. The first component accounts for 53.86% of the global variation, while the second explains only 14.84%. As illustrated in Figure 7, Axis 1 has a strong positive loading for the soil elevation and soil water content variables, and strong negative loadings for the mean flood duration, frequency of flooding, and water depth, whereas Axis 2 is influenced by strong positive loadings for salinity and inversely by the redox potential. As a result, this analysis points toward a negative correlation between the hydrodynamic variables, soil elevation, and water content, and similarly, between the redox potential and salinity.  
Figure 5: Elevation-dependent Shannon’s entropy, _S_(2), for the studied salt marshes.  
A further assessment of this analysis, in conjugation with the complementary PERMANOVA analysis, revealed significative differences in variance between species, salt marshes, and most of their joint interaction (_p_-value < 0.05; Table 3). The data suggests a marked distinction between the distribution of _S. maritimus_ and the other two studied  
Figure 6: Relative frequency distribution of the main species over different soil elevation _z(m)_ intervals for each studied salt marshes.  
Figure 7: Principal component analysis (PCA) grouped by species for physicochemical and hydrodynamic variables. Mean flood duration (_FD_); frequency of flooding (_F_); water depth (_WD_); water content (_WC_); soil elevation (_2_); _pH_; salinity (PSU); and redox potential (_Et_). The data are representative of a period of 10 months, from December 2021 to September 2022.  
species. Although, within a broad range of values, _S. maritimus_ tended to be in lower areas subjected to a higher frequency of flooding, extended mean flood duration, and deeper water depth, typically, with reduced soil water content and slightly more reduced redox potential. _S. perennis_, on the other hand, inhabited regions with intermediate values of altitude subjected to the widest variance in most of the examined parameters. Lastly, in opposition to the other species under investigation, the studied variables linked to _A. portulacoides_ exhibited a reduced range of variation, and the variability that occurred appeared to be more correlated to seasonal changes in salinity and redox potential.
# A Method for the Design of Concrete with Combined Steel and Basalt Fiber
## 4 Experimental Results
### Influence of Steel and Polypropylene Fiber Contents on Concrete Strength  
Using PF, which is a low modulus fiber, yields almost the same compressive strength and decreases the flexural tensile strength 5 to 40%, compared to the control specimens. Results, presented in Table 3, demonstrate that using PF as a component of composite reinforcement yields a lower effect, compared to BF. It is explained, first of all, by worse PF distribution in concrete structure, which avoids a uniform distribution of SF. Non-uniform distribution of fibers was observed in the structure of destroyed concrete specimens, and in some zones, clumped fibers. Concrete mixtures with PF were more prone to fiber balling at mixing and molding. Additionally, examination of the specimens' destruction nature indicated poorer adhesion of PF with cement matrix, resulting in specimens' failure due to individual fibers withdrawal. These disadvantages were not observed when using BF for hybrid fiber reinforcement.  
BF is closer to the concrete matrix than PF from the viewpoint of density and elastic characteristics. Therefore, at an optimum ratio with SF it forms a kind of bearing frame that holds the SF, prevents mixture segregation and yields uniform fiber distribution in the concrete structure. All this, in turn, affects the strength characteristics of HFRC. The results obtained in this study add valuable data. [PERSON] et al. [16] also have analyzed in detail the mechanical properties of HFRC, which included SF, BF, and PF. Their results showed that mixing BF and SF yield a better synergistic effect than for PF and SF.
## 3 Results and Discussion  
### Statistical Analysis  
One of the first steps in exploratory research is usually constructing a correlation matrix [31]. Frequently, its analysis allows eliminating the irrelevant parameters and may be a starting point for PCA analysis. Apart from that, the correlation matrix may reveal some dependencies between particular variables, their mutual relations, and potential redundancies [32]. To visualize such information, a thermal map was applied. By analyzing the correlations presented in Figure 2, a very weak relation of failure from all other parameters (the last row of the matrix) can be observed.  
The highest correlation coefficient at 0.26 had a parameter of a current meter value. Such a correlation value was relatively small and is usually omitted. On this basis, it can be concluded that failures did not directly depend on any single meter feature, but perhaps on some nonlinear combination of state variables.  
Figure 2: Correlation matrix: heat map.  
A very strong correlation can be observed between the meter age and the number of billings (nearly one). Presumably, one of these parameters would be unnecessary (redundant) in further analysis. A little smaller, but also significant correlation can be seen between the maximum and the current energy consumption. Furthermore, this relation was rather apparent and did not require any more explanations.  
There were no rows below the current meter value, which demonstrated higher correlation with other parameters and could be treated as linearly independent. Attention is drawn only to the small correlation between the manufacturer and the type of communication, which was at 0.4. This indicates the situation where manufacturers specialize themselves in making meters for particular communication types, but also the maintenance company is supplied with particular types of meters not only by the selected manufacturers.  
During the analysis of the correlation matrix, it should not be forgotten that a strong correlation does not necessarily imply that correlation is causation.
## 3 Main Findings
### Ingredients and Their Effects on Gluten-Free Dough Rheology and Bread Quality
#### 3.3.4 Improvers for Gluten-Free Doughs and Baked Products
Various types are widely used in the baking industry, to enhance the formation of protein networks [17]. Amylases, proteases, cyclodextinase, hemicelllases, lipases, and oxidases have been investigated by several authors. The effect of starch-hydrolyzing enzymes from the genus _Bacillus_ was reported in [14; 15; 17]. The latter studies note decreased amylopectin retrogradation, a significant anti-staling effect, and an increase in loaf specific volume. Transglutaminase (TGase) seems to be another promising option. TGases have been reported to catalyze acyl-transfer reactions, which can induce covalent cross-linking between proteins, peptides, and various primary amines, mimicking the gluten network [15]. Moreover, they can link proteins of different origin, such as casein and albumin from milk, animal proteins from eggs and meat, etc. [14; 15]. Typically, TGases used in baking applications are microbial aminotransferase that are able to create a network-like structure, with significant benefits for the quality of the final product [14; 15]. The effect of cyclodextinase addition to rice flour was reported in [PERSON] and [PERSON] (2012) [17]. The final product had good specific volume and very soft crumb texture, due to, among other effects, hydrolysis and cyclization [17]. Cyclization, in particular, produced cyclodext that formed complex molecules with rice proteins, increasing their solubility [17]. Cyclodext can also form inclusion complexes with lipids, creating films with the ability to retain gases, with a consequent increase in bread volume and better crumb texture [17]. The addition of glucose oxidase has been found to have a positive effect on dough rheology. In particular, [PERSON] and [PERSON] (2012) [17] highlighted that it increased the viscous and elastic moduli of rice flour dough. The authors suggested that this might be related to its ability to catalyze the oxidation of glucose to gluconic acid and hydrogen peroxide, which, in turn, induces the gelation of water-soluble pentosans in rice flour [17]. The authors noted that this could promote protein cross-linking, increasing bread volume.
## 3 Modelling and Parameterisation
### Multiscale Geographically Weighted Regression (MGWR) Model
#### OLS  
The OLS model is a fundamental and widely used model in station ridership prediction. Its model expression is as shown in Equation (1) below:  
\[y_{i}=\beta_{0}+\sum_{j=1}^{n}\beta_{j}x_{ij}+\epsilon_{i} \tag{1}\]  
where \(y_{i}\) represents the dependent variable corresponding to the \(i\)-th rail transit station; \(x_{ij}\) represents the \(j\)-th independent variable corresponding to the \(i\)-th rail transit station; \(\beta_{0}\) is the intercept; \(\beta_{j}\) is the regression coefficient for the \(j\)-th independent variable; n is the number of independent variables; \(\epsilon_{i}\) is the random error.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 2 Related Work
### Single-Path Direct Optimization Method  
The single-path direct optimization method typically transforms the local path planning problem into an optimization problem. These algorithms usually consider multiple factors, such as path length, the robot's kinematic constraints, distance from obstacles, smoothness of speed and acceleration, and travel time. By integrating these factors, the algorithm can generate paths that are safe, comfortable, and efficient.  
In traditional solutions, a feasible initial path is first generated by a path planner. This initial path is then refined through optimization algorithms. For example, initial paths generated by planners based on ant colony optimization [17; 18], rapidly exploring random trees [19], artificial potential fields [20], particle swarm optimization (PSO) [21], and Bezier curves [22] have been optimized further. However, in dynamic environments, these methods require the initial path to be regenerated, necessitating the design of replanning strategies to ensure the robot follows the global path. The time-elastic-band (TEB) algorithm proposed by [PERSON] et al. [23; 24] optimizes the entire solution space to ensure the local path is optimal under given constraints. The main idea of the TEB algorithm is to treat the global path as an elastic band that deforms under internal and external forces, adjusting the global path based on the position and shape of obstacles. Different constraint parameters can be set to adapt to the complexity of the scenario. Therefore, the TEB algorithm is also one of the most widely applied methods.  
Although the single-path direct optimization method effectively addresses multi-objective optimization problems and can more easily find the optimal path solution compared to multi-path generation and evaluation methods, it also requires substantial computational resources and time to find the optimal solution. If inappropriate optimization algorithm parameters and cost function weights are selected, the algorithm's performance and results may be adversely affected. This can lead to issues such as failure to converge or becoming trapped in local optima.
## 3 IEEE 802.15.4
### Physical Layer  
The IEEE 802.15.4 physical layer is responsible for (de)activating the radio transceiver, data reception and transmission, channel frequency selection, energy detection within a channel and determining whether or not the communication channel is occupied by a transmission (_i.e._, Clear Channel Assessment, CCA). IEEE Std 802.15.4-2011 [9] defines a total of 15 different PHY modes. The PHY mode specifies which frequency band and modulation are used for data transmission.  
An 802.15.4 compliant radio typically operates at one of the following license-free frequency bands: 868-868.6 MHz (e.g., Europe), 902-928 MHz (e.g., North America) or 2,400-2,483.5 MHz (worldwide, _i.e._, ISM band). O-QPSK and BPSK are the most commonly used constellations for (de)modulating the signal. Almost all of the defined PHY modes apply a spreading technique that allows spreading out the signal so that it occupies a larger bandwidth. By using these simple constellations and the spreading technique, the communication is more robust and has an increased resistance to interference and (narrow-band) noise even when using a low transmission power.  
Depending on the environment and the PHY mode, the communication range varies between 10 and 100 meters. In 802.15.4-2011 the maximum PHY data rate for the ISM band is limited to 250 kbps when using _Direct-Sequence Spread Spectrum_ (DSSS) or 1,000 kbps when using _Chirp Spread Spectrum_ (DSSS).  
Spectrum_ (CSS) respectively as a spreading technique. CSS is, however, an optional feature of the standard and in most applications 250 kbps is the maximum PHY data rate.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 3 Case Study  
In order to ensure the stable operation of the sensor and obtain more accurate vibration data near the blasting source, the blasting monitoring area was selected in the section with three parallel tunnels. The tunnels on both sides were constructed first, and the progress of the middle tunnel was slightly slow, with a difference of 50-80 m. Considering the site geology and construction conditions, the velocity sensors were assembled at the bottom of five parallel measuring holes in the rock wall with a thickness of 6 m between the right-line and the middle-line tunnel. One end of the sensing line was connected to the sensor connector, and the other end extended out of the hole through the hollow steel pipe. The depths of these holes were 5 m, 4 m, 3 m, 2 m, and 1 m and filled with a mixture of cement and coal ash. The interval between the holes was 1 m. The TC-4850 blasting vibration meter and TCB-B3 large-range triaxial vibration velocity sensor were used in this test. The layout of sensors and measuring points is shown in Figure 1.  
The rock conditions of each construction cycle are different, so the parameters such as the total charge used in the actual blasting or the total charge of the blast holes in each detonating period are different. Hundreds of groups of data were measured under the conditions of different charge quantities and different distances from the explosion center. Due to space limitations, some vibration monitoring data are listed in Table 2.  
Figure 2 is a box chart showing the logarithm of vibration velocity in three directions. It can be found that the overall distribution of data is reasonable except for individual maxima.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Maximum Charge** & **Distance from the** & **Transverse Vibration** & **Longitudinal Vibration** & **Vertical Vibration** \\
**per Delay Q/kg** & **Detonation Center** & **Velocity** & **Velocity** & **Velocity** \\  & **R/m** & \(V_{\text{X}}\)/cm\(\cdot\)s\({}^{-1}\) & \(V_{\text{Y}}\)/cm\(\cdot\)s\({}^{-1}\) & \(V_{\text{Z}}\)/cm\(\cdot\)s\({}^{-1}\) \\ \hline  
\hline \hline \end{tabular}  
* (Taking the advancing direction of the working face as the front, \(R\) is positive indicates that the measuring point is in front of the working face, and \(R\) is negative indicates that the measuring point is behind the working face.)  
\end{table}
Table 2: Blasting vibration monitoring data.  
Figure 1: Layout of testing sensors and measuring points: (**a**) speed sensors; (**b**) placement of measuring points; (**c**) distribution of measuring points.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.8 Bayesian Classifier  
This classifier represents the linked probability of a set of attributes graphically, making it easy for the user to interpret. This algorithm learns the conditional probabilities based on the labeled dataset on which it is trained [41].  
Naive Bayes (NB) is another type of Bayesian classifier that is based on probabilities. This classifier requires prior probability information about NTLs obtained from general statistics [6; 39; 43; 58; 59]. However, in [59], after evaluating 15 classifiers, it was determined that NB has a success rate below other classifiers such as DT, SVM, or K-NN.
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 3 Method
### Case Study: RF-tree
#### 3.3.1 Structure of RF-tree  
**RDMA-Friendly Search Layer.** In RF-tree, we make the search layer RDMA-friendly by allocating the search layer in a log-structured manner in the memory server. Thus, RF-tree can read multiple internal nodes using one RDMA Read operation. Moreover, RF-tree divides the search layer into multiple chunks and distributes the chunks to multiple memory servers to balance the load of each memory server. Each internal node is appended to the end of memory space or added to the free page in the selected memory server. RF-tree manages the free page using a linked-list-based _free list_ (Section 3.3.2). Each client checks the free list, and, if there is any free page, then the client removes the free page from the free list and uses it.  
**Uniformly Distributed Data Nodes.** The data layer is composed of data nodes (i.e., leaf nodes). The data node keeps the key and value pair. Since the data node and the target data will be accessed together, our approach stores the data node and the data in the same memory server to reduce the communication overhead. The memory server will be selected in a round-robin manner to uniformly distribute the data and data nodes.
## 4 Results and Discussion
### Data Set 2 Benchmarking Instances
#### 4.2.2 Instances with Ratio Greater Than 0.25  
Due to the above-mentioned complexity inherent to this class of instances, to facilitate a more thorough search, each run of a particular test instance is allocated 60 iterations. For this set of instances, the average processing time is far lower than that of the average intermachine distance. Hence, for temporal savings, it is more likely for a job to have several consecutive operations processed within the same machine, as compared to different machines. This is because the usage of different machines for a job is highly likely to incur extra waiting time for transporters alongside traveling time, which is far greater than the respective processing times of machines subtending the transportation route.  
From the above reasoning, it is likely for good machine-operation assignments to be present within the solution space, where continual operations are allocated to the same machine. To this end, a surrogate objective is proposed to replace the original objective of minimizing makespan:  
\[Min\;\eta(C_{max})-\sum_{l\in M_{i+1};l=k}\sum_{k\in M_{i}}\sum_{i:l\leq|I_{j} |-1,\forall j\in J}Y_{i,i+1,k,l} \tag{69}\]  
The augmented negative summation of \(Y_{i,i+1,k,l}\) terms guide the search towards maximising the number of consecutive operations assigned to be processed within the same machine. There are, however, an exorbitant quantity of possible assignments along with the need to minimize makespan. Both are addressed by  
1. Incorporating the makespan within the objective to ensure the minimization of the makespan.
2. Weighting the makespan with noise, \(\eta\), which is a random number in the interval [0.05, 0.25] in increments of 0.01 to expedite the search process through diversification.  
Table 5 shows the performance comparisons against those by the MILP model and LAHC of [PERSON] and [PERSON] [18] and the DE of [PERSON], [PERSON] and [PERSON] [11].  
Out of ten optimal solutions obtained by the MILP model, the two-phase heuristic was able to obtain six of them. Compared to LAHC and PDE, the two-phase heuristic was able to locate the optimal solution of EX_52. Of the remaining four MILP optimal solutions, the results of the two-phase heuristic were close to those of LAHC and PDE. For the remaining eighteen instances, nine instances obtained solutions that were as good as the incumbent best solution amongst LAHC and PDE. Eight of the remaining nine instances provisioned solutions that are at least as good as the remaining solutions by LAHC or PDE.  
The two-phase heuristic is more robust than LAHC for most instances, as evidenced by having percentage makespan standard deviation values that are mostly zero in value or are lower than its LAHC counterpart, except for two instances.