esa-sceva/satcom-chunk-collection huggingface dataset:
### Previous Research on Criteria-Based Method Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\  & **[PERSON]** & **[PERSON]** & **[18]** & **[19]** & **et al.** & **et al.** & **and [PERSON]** & **et al.** & **et al.** \\  & **[16]** & **[17]** & & & & & & **[13]** & **[12]** \\ \hline \multicolumn{12}{c}{**Examined Methods**} \\ \hline PIOT & X & & & & & & X & & \\ SFA & X & X & & & X & X & X & X \\ MFA & X & & & & X & X & X & X \\ Energy Analysis & & & X & & & & & \\ ENA & & & & & & & & \\ LCA & X & X & X & X & X & X & X & X \\ Carbon Footprint & & & & & & & & \\ Water Footprint & X & & & & & X & \\ Ecological Footprint & X & & X & & X & X & X & X \\ MIPS & & & X & & X & & \\ _Emergy_ Analysis & X & & X & & X & & X & X \\ Exergy Analysis & X & & X & & X & & X & X \\ \hline \multicolumn{12}{c}{**Criteria**} \\ \hline Life Cycle Thinking & & & & & & & X & X & \\ Inventoried Flows & & X & & & & & X & \\ Indicators Provided & X & & & X & X & X & X & X \\ Efficiency Concept & X & X & X & & X & & & \\ Temporal Resolution & X & X & & X & & & \\ Spatial Resolution & X & X & & X & & & X & \\ Scientific Soundness & & X & & X & & X & \\ Formalization & X & & & & & X & \\ Data Availability & & X & & & & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of criteria-based comparison approaches for energy and environmental assessment methods. Most studies refer to a particular application area. For example, several authors address agriculture, farming regions, or territories [12; 13; 25]. In this area, spatial differentiation is crucial. [PERSON] and [PERSON] [16] refer to'spatial entities with anthropogenic activities' in general. Furthermore, [PERSON] et al. [19] examine four environmental assessment methods referring to the energy sector. [PERSON] and [PERSON] [26] analyze the suitability of sustainability indicators for urban energy systems. The assessment of petrochemical processes is examined by [PERSON] and [PERSON] [27]. [PERSON] and [PERSON] [15] consider a whole group of studied objects: traded goods and services. In summary, no criteria-based analysis of the energy and environmental assessment methods for cooling technologies has been conducted as of yet. Thus, no standardized method provides all-embracing criteria for that purpose. Furthermore, previous studies defined criteria and presented them as parallel and mostly independent.
## 3 Testing Results Analysis
### Analysis of Experimental Results of Lower Step Specim  
#### 3.2.1 Space-Time Evolution Process of Deformation and Fracture of Surrounding Rock  
Figure 11 is the experimental stress-time curve of the lower step specimen. Figure 12 is the rockburst process image of the lower step specimen. Figure 13 is the total displacement cloud diagram of the lower step specimen. In general, before the rockburst, the average and maximum values of the total displacement basically showed the characteristics of circumferential distribution (Figure 13A-I). Starting from the buried depth of 850 m, the deformation and fracture of the lower step specimens were initiated at the inner walls of the left and right sides (Figure 12A-E), and the shear deformation could be judged from the direction of the displacement vector (Figure 13A-E). With the further increase of the buried depth (1050-1250 m), the cracks would extend to the deep part of the surrounding rock, and intersect and connect with each other in the process of expansion. The surrounding rock near the left spandrel, haunch, right spandrel and arch footing was cut into a fractured rock mass containing different blocks of wedges, that is, a zonal fracture phenomenon similar to \"logarithmic spiral\" (Figure 12F-H). When the buried depth was 1350 m, the average and maximum values of the total displacement of the specimen reached the peak at the critical moment (Figure 13I). Subsequently, slight rockburst damage occurred at the left spandrel, haunch, right haunch and arch footing (Figure 12). The rockburst at the right haunch and arch footing only showed the shedding of the specimen skin, the ejection speed was slow, and no obvious pit was formed. When the step method was used for construction, the upper step usually adopted the method of shotcrete to seal the surrounding rock. Therefore, the slight rockburst phenomenon of the upper step would be obviously weakened in the actual construction.  
Figure 11: Experimental stress–time curve of lower step specimens.
# Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
###### Abstract  
The Global Positioning System (GPS) is not the only way to solve connected objects' geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5 th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of [PERSON] are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.  
reference signal; received power; micro-cell; geo-localization; SS-RSRP; 5G
# Software Fault Localization Based on SALSA Algorithm
## 6 Limitation and Future Work  
In the current research on software defect localization, the methodology proposed in this paper has shifted from single-spectrum analysis to a more complex analysis of the linking relationships between program entities, but it still has some obvious limitations. Firstly, the mining of internal program information is not limited to the linking relationships between program entities; it also includes the interactions between program entities in the program execution network. Secondly, this study is constrained by the analysis tools and can only be conducted when the test subject is written in the Java language. This restricts the widespread application of software defect localization techniques. Additionally, the SALSA algorithm used in this paper selects the entire set of program entities executed in failed test cases as the root set, which can lead to a problem where in some cases, the root set is particularly large, resulting in reduced efficiency of defect localization.  
In response to these limitations, future research directions could include the following: First, developing defect localization tools that support multiple programming languages. Given the diversity of programming languages, developing a defect localization tool that can adapt to various programming language environments will have broad application prospects. Secondly, exploring more network analysis algorithms that can fully utilize the effective information within the program is possible. Lastly, the selection method for the root set can be optimized to obtain a high-quality root set. Overall, with the increase in software fault localization research and the publication of a large number of related papers, it is expected that more researchers will focus on identifying fault localization issues and providing specific solutions in the future.
# Development and Analysis of an Intensified Batch-Fed Wine Fermentation Process
## 2 Materials and Methods  
### Flask Fermentations  
To determine YAN uptake rate over the course of fermentation, flask fermentations were performed in triplicate in 500 mL Erlenmeyer flasks charged with 300 mL must at room temperature (22-23 \({}^{\circ}\)C). Frozen 2012 UC Davis Chardonnay juice was used as the fermentation medium, 24.4 Brix, 310 mg/L YAN, and pH 3.45. Fermentations were run in triplicate in a two-factorial experiment: low YAN (310 mg/L) or high YAN (1100 mg/L); low biomass (0.25 g/L) or high biomass (10 g/L). YAN was adjusted with Fermaid K (Scott Labs, Petaluma, CA, USA), fermentations were inoculated with _Saccharomyces cerevisiae_ strain EC1118 (Scott Labs, Petaluma, CA, USA).  
Brix, optical density (OD), and YAN were measured every 12 h until the end of fermentation, defined as no change in Brix over two concurrent measurements. Total and viable cell counts were performed at the end of fermentation. Brix was measured via a DMA-35 handheld densitometer (Anton Paar GmbH, Graz, Austria). Optical density (OD) was measured in a Genesys 10S UV-VIS spectrophotometer (Thermo Scientific, Waltham, MA, USA) at 600 nm via disposable cuvette, with samples diluted to the 0.2-0.4 OD linear range for biomass measurement, blanked against similarly diluted unfermented juice [15]. YAN concentrations were determined in the UC Davis Pilot Winery Lab in Davis, CA as the sum of free amino nitrogen and ammonia ion concentrations in the fermentation juice. Free amino nitrogen (K-PANOPA kit, Megazyme, Wicklow, Ireland) and ammonium ion (Ammonia-Rapid kit, Megazyme, Wicklow, Ireland) kits were purchased from BSG Wines, Napa, CA, USA for use in a Gallery Discrete Analyzer (Thermo Scientific, Waltham, MA, USA). The samples were collected by filtering the cell suspensions with a 0.45 \(\upmu\)m PVDF Millex-HV Syringe Membrane filter (Burlington, MA, USA) prior to YAN analysis. Samples were stored at \(-20\)\({}^{\circ}\)C until YAN analysis. Cell suspensions were diluted to appropriate concentrations and studied using a Bright-Line hemacytometer (Hausser Scientific, Horsham, PA, USA) under a Zeiss light microscope at 400\(\times\) magnification to estimate total and viable cell concentrations. Cell slurries (20 \(\upmu\)L) were added to 20 \(\upmu\)L of methylene blue solution (0.4% methylene blue, 10% ethanol [95%], 0.4 M KH2 PO4) and this mixture was contacted for 1 min before analysis. Blue cells were counted as dead while the cells with no color were counted as live.  
Microsoft Excel was used to analyze flask fermentation data on a Windows 10 PC. YAN consumption rate per degree brix drop per unit OD, normalized to current YAN concentration, was expressed as a function of fermentation Brix and an exponential fit was applied. This fit was then integrated to estimate the maximum YAN consumption for the remainder of the fermentation as a function of current YAN, current Brix, and current biomass OD.
# Extraction of Catastrophe Boundary and Evolution of Expressway Traffic Flow State
## 3 Catastrophe Boundary Extraction with Spectral Clustering Analysis Algorithm
### Analysis of Catastrophe Characteristics of Traffic Flow  
As a research hotspot of machine learning, the spectral clustering analysis algorithm can address the inability of traditional dynamic clustering methods to deal with non-spherical cluster data [26; 27], cluster traffic flow states efficiently, and provide more accurate and reliable results. The specific process is described below.  
1. Define the data sample set. \[X=\{x_{i}|i=1,2,3,\ldots,n\},\] (9) where \(x_{i}\) is the \(i\)-th sample point in the data.
2. Take the data to scatter points as an overall network graph, where each scatter point is a node in the network graph, and the connected nodes are edges in the network graph. The similarity between nodes is the weight value of the edge. The similarity calculation formula is as follows: \[\omega_{ij}=e^{\frac{-|x_{i}-x_{j}|^{2}}{2\sigma^{2}}},\] (10) where \(\sigma\) is the sample standard deviation, and the value is 0.9.
3. Arrange the similarity into a similarity matrix. \[W=\big{\{}\omega_{ij}|1\leq i\leq n,1\leq j\leq n\big{\}}.\] (11) To exclude self-similarity, make the diagonal of matrix \(w\) equal to 0. \[W(i,j)=0,i=j.\] (12)  
Figure 2: Flow chart of the method for extracting the catastrophe boundary of speed.  
In order to prevent a single node from being eliminated in the iterative process, a normalized diagonal matrix \(D\) is established. \[D(i,i)=\sum_{j=1}^{N}x_{ij}.\] (13) (5) Calculate the Laplacian matrix. \[L=D^{\frac{1}{2}}WD^{\frac{1}{2}}.\] (14) (6) Determine the number of clusters as \(K\); \(K\) = 3, representing free flow, synchronized flow, and congested flow. Arrange the vectors with the largest eigenvalues of the first \(K\) into the eigenmatrix \(U\). \[U=\{u_{1},u_{2},\ldots,u_{k}\},\] (15) where \(u_{1},u_{2},\ldots,u_{k}\) is the first \(K\) eigenvector. (7) Normalize the eigenmatrix to get matrix \(Y\). \[Y_{ij}=\frac{u_{ij}}{\sqrt{\frac{\sum_{j}u_{ij}^{2}}{2}}}.\] (16) (8) Using each row vector in the matrix \(Y\) as a data point, perform clustering through the \(K\)-means algorithm, thus obtaining \(K\) clustering results \(C_{1},C_{2},\ldots,C_{k}\).
## 3 Results
### Digestate Characteristics  
TS removed by the anaerobic digestion process ranged from 54.8 \(\pm\) 2.1% in Variant 1 (MA100+MS0) (with the final TS at 4.7 \(\pm\) 1.3%) to 87.1 \(\pm\) 4.2% in Variant 6 (MA0 + MS100) (final TS: 3.9 \(\pm\) 0.9%) (Table 5). Decreases in VS were also noted in these variants, ranging between 19.2 \(\pm\) 2.3% and 32.0 \(\pm\) 2.6% (final values: 70.9 \(\pm\) 2.5% TS to 63.8 \(\pm\) 3.2% TS) (Table 5). The \(\eta_{\rm F}\)MSS ranged from 60.27 \(\pm\) 1.82% in Variant 4 (MA40+MS60) to 71.84 \(\pm\) 2.12% in Variant 6 (MA0 + MS100) (Table 5). The same variants produced the lowest and highest values of \(\eta\)VS at 55.24 \(\pm\) 1.95% in Variant 4 and 67.11 \(\pm\) 1.45% in Variant 6, respectively (Table 5). TN peaked (45.3 \(\pm\) 3.1 mg/gTS) in Variant 1 (MA100+MS0), whereas the lowest TN levels were recorded for Variant 6 (MA0 + MS100) (13.1 \(\pm\) 2.0 mg/gTS). For TP, the observed levels ranged from 4.0 \(\pm\) 1.0 mg/gTS to 2.0 \(\pm\) 0.8 mg/gTS (Table 5). The C/N ratio was between 6.9 \(\pm\) 0.2 in Variant MA100+MS0 to 24.0 \(\pm\) 0.6 in Variant MA0 + MS100. The pH was near-neutral at 6.7 \(\pm\) 0.2 in Variant 1 (MA100+MS0) to 7.5 \(\pm\) 0.2 in Variant 6 (MA0 + MS100) (Table 5).  
The digestate characteristics depend primarily on the feedstock used in the fermentation process [63]. The content of the main fertilization index, N:P:K, is directly related to the concentrations of these compounds in the biomass and falls within a very wide range [64]. The highest amount of nitrogen is in digestate from the fermentation of slaughterhouse waste and animal manure [65]. The highest concentrations of phosphorus and potassium are observed in plant substrates [66]. The degree of digestion, the level of organic compounds removed and their final concentration in the digestate depend mainly on the technological parameters of the process [67]. The organic load rate of the anaerobic chamber, the hydraulic retention time and the temperature are among the factors that determine the stabilization level of post-fermentation sludge [68]. Other important aspects are the pretreatment techniques used and the support of the hydrolysis process [69]. A high degree of digestion is important to reduce the negative impact of sludge on the environment, including reducing the susceptibility to rotting and limiting the spread of odors [70].  
Figure 5: Forecasted plane-plotted correlation between C/N ratio or microbial share and biogas production.
## 4 Experimental Results of Applying Robust-EKF
### Experimental Results
To give tangible evidence on that, we compared the two methods and the results are illustrated in Figure 6.  
The Figure 6 presents the position errors obtained from the two methods using the combined GPS, Galileo and GLONASS data. This WLSE method was developed in our previous work in [8], where the weight matrix is defined using the \"Exponential model\". The position errors of WLSE are wavering; In particular, the positions around 23h-24h are unavailable because of high HPLs. On the other hand, the position errors of robust-EKF are stable and small. Although the robust-EKF can greatly improve the position accuracy, its outputs might contain errors that cannot be estimated. Therefore, an algorithm that can predetermine the errors of robust-EKF behaviour is needed. For this purpose, a de-noising filter method is presented in Section 5.
# Identification of the Wear Margin of a Pipeline-Machine Subsystem
## 3 The Hydrodynamic Model of an Open Pipeline-Machine Subsystem
### Power Output and Machine Efficiency  
The hydraulic power of fluid resulting from machine work is referred to as machine power output.  
For conditions making gases incompressible, the power output of a compressor [30] can be calculated from the following formula:  
\[N_{u}\ =\ 10^{-3}\dot{V}\cdot(p_{d}-p_{s}) \tag{15}\]  
with \(\dot{V}\) in cubic meters per second and \(p\) in pascals.  
The power output of a pump [31] is given by  
\[N_{u}\ =\ \dot{V}\cdot\rho\cdot g\cdot H\cdot 10^{-3}\ (\mathrm{kW}) \tag{16}\]  
\[H\ =\ h_{2}-h_{1}\ (\mathrm{m}) \tag{17}\]  
The machine efficiency \(\eta_{\mathrm{mach}}\) is determined as the ratio of power output \(N_{u}\) to power input measured on the machine shaft \(N_{\mathrm{shaft}}\).  
\[\eta_{\mathrm{mach}}\ =\ \frac{N_{u}}{N_{\mathrm{shaft}}} \tag{18}\]  
Power measurement on the machine shaft requires a torque measuring device to be installed between the machine shaft and engine shaft. To determine power output, we need to measure the volume flow rate and differential pressure. It is best to measure pressure drops (differential pressure) by electronic manometers. A number of various flowmeter design solutions are offered for measurements of volume flow rate. Depending on the design, flowmeters measure volume flow rate directly or indirectly by determining flow rate from pressure difference or fluid velocity. Measurement accuracy varies depending on the principle of operation used in a flowmeter. Constriction flowmeters, which measure differential pressure at the constriction, are the most accurate. Measurements of fluid flow by Venturi flowmeters (measuring orifices) inserted in circular cross-section conduits running full are specified in standards [32; 33].  
In a pipeline-machine subsystem, significant periodical pressure pulsations and nonperiodical flow pulsations may occur, resulting in measuring instrument errors. Rotodynamic pumps and compressors generate pulses of low amplitude and frequency corresponding to the passing rotor blades. There are often no apparent symptoms of pulsation, as commonly used flowmeters and pressure or flow transducers generally have a large time constant [34]. Pulsations of volume flow rate may additionally cause mass losses in a machine and additional pressure losses in pipes [22].  
To reduce the amplitude of pulsations in pipelines, additional devices are installed, namely equalization tanks or choking elements before the measurement point. It is also possible to eliminate measuring errors caused by pulsation [35] by using fast pressure and differential pressure transducers (constriction flowmeters) combined with a signal processor generating a signal proportional to the root of instantaneous values of pressure and the value of differential pressure.  
Pressure pulsations can be used as diagnostically useful signals. The impacts of valve and piston-cylinder leaks on the value of pressure pulsation in a discharge pipe are presented in [36].
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 4 Discussion
### NLoS Communication  
NLoS communication was observed in a number of measurements at distances of 20 km and more from the base station. The most likely propagation mechanisms are knife-edge diffraction over mountain ridges and reflections on higher mountain tops. Tropospheric effects are not likely, as these occur mainly in lower and mid-latitude regions on Earth. Moreover, the repetitiveness of the phenomenon indicates that propagation is determined by constant factors.  
While NLoS propagation is shown to be possible in the Antarctic, the practical setup of such links would require field measurements or ray-tracing simulations to determine suitable antenna locations.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 1 Introduction
### Related Work
In [29], three-dimensional acoustic and electric conversion channels based on an ultrasonic transducer array were constructed, and an effective method for an array amplitude weighting algorithm was proposed to achieve efficient underwater wireless energy transmission. Despite the realization of wireless power transfer in the underwater environment, these works have their drawbacks and disadvantages. Specifically, the methods proposed in [21; 22] involve harvesting energy from natural energy sources, which depends on weather and other natural environments, resulting in the instability, uncontrollability, and unreliability of the energy harvesting, as well as difficulties in hardware implementation in marine environments. The scheme proposed in [24] is highly susceptible to the influence of the marine environment (such as ocean currents and seawater temperature) and can only operate within millimeter-level distances. Similarly, the wireless power transfer methods in [28; 29] can only operate within a distance of a few meters. As a promising wireless power transfer technology, WPCN has attracted notable and growing interest since [PERSON] first proposed the concept of transporting information and energy simultaneously [30]. In [31], the authors proposed a dynamic power-splitting receiver where the received signal was split with an adjustable power ratio for energy harvesting and information decoding separately. In [32; 33; 34], different schemes were proposed to enhance the performance of the WPCN systems in different channels and scenarios. Specifically, joint optimization of downlink wireless power transfer (WPT), uplink wireless information transfer (WIT), and power allocation in the Rayleigh channel have been studied in [32] to acquire the maximum information rate and balanced throughput. In [33], the same optimization problem as [32] was analyzed in the Nakagami-\(m\) channel. In [34], the intelligent reflecting surface (IRS) was introduced to the full-duplex (FD) WPCN to enhance the performance of the system, where an FD hybrid node sends information in the downlink and receives energy signals in the uplink simultaneously. Meanwhile, different WPCN protocols have also been studied in [3; 35], where the authors proposed a \"harvest then transmit\" protocol and derived an optimal time allocation parameter for throughput maximization in [3], while in [33], the authors proposed a \"transfer then receive\" protocol for the WPCN system based on time division multiple access (TDMA) and analyzed the throughput maximization for the system.