esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Stage 2: Assessing the Relevance of NPD Practices in the Company's Contextual Circumstances
#### 4.2.1 The Prioritization of NPD Objectives  
This step is carried out using [PERSON]'s [65] AHP method under the IT2F environment. The five employees from the systems engineering, R&D, production, and quality divisions were invited to participate in the prioritization process.  
After being introduced to the decision-making problem, participants were requested to articulate their perceptions concerning the company's commitment to the following NPD objectives: NPD_Obj1--reduced development cycle; NPD_Obj2--higher-quality products; NPD_Obj2--reduced expenses; and NPD_Obj4--elevated degree of innovation.  
The participants articulated their perceptions as preferential relations, modeled by linguistic characterizations that are mathematically described using IT2F logic (refer to Table 3). As a result, five individual IT2F matrices of preferential relations (\(\overset{\approx}{A}^{k}=\left\{\overset{\approx}{a}^{k}_{ij}\right\}_{n\times n}\), \(k=1,\ldots,K\)) (9) were established; here, \(K\) denotes the total number of participants involved in the prioritization process. A consistency test was performed to detect and eliminate eventual inconsistencies in the estimates, following the procedure outlined by [PERSON] [125]. The test results indicate that all individual preference relation matrices are inconsistent within acceptable limits (\(CR_{k}<10\%\)).  
This was followed by the consolidation of individual preferences (\(\overset{\approx}{a}^{k}_{ij}\)) and the creation of an IT2F aggregate matrix of preference relations (9). Subsequently, the IT2F geometric mean (\(\overset{\approx}{F}_{i}\)) was computed for each row of the matrix (\(\overset{\approx}{A}\)) following Equation (15), and from these values, the IT2F weight vectors (\(\overset{\approx}{w}_{i}\)) of the NPD objectives were derived according to (16). The findings are summarized in Table 6.  
The prioritization results indicate that the company's NPD strategy mainly focuses on creating higher-quality products (enhanced product compliance with customer demands, industry standards, and environmental regulations), with other NPD objectives (reduced development cycle, reduced expenses, and elevated degree of innovation) given roughly equal importance.
###### Abstract  
Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN's QoS, which can further reflect the quality of data information. Additionally, the coverage requirements of regional monitoring target points are different in real applications. On this basis, this paper proposes an energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism (EECRP-HQSND-ICRM) in WSNs. First, this paper proposes formula definitions for information integrity, validity, and redundancy from the coverage rate and introduces a node deployment strategy based on twofold coverage. Then, in order to satisfy the uniformity of the distribution of cluster heads (CHs), the monitoring area is divided into four small areas centered on the base station (BS), and the CHs are selected in the respective cells. Finally, combined with the practical application of the WSN, this paper optimizes the Dijkstra algorithm, including: (1) nonessential paths neglecting considerations, and (2) a simultaneous introduction of end-to-end weights and path weights, achieving the selection of optimal information transmission paths between the CHs. The simulation results show that, compared with the general node deployment strategies, the deployment strategy of the proposed protocol has higher information integrity and validity, as well as lower redundancy. Meanwhile, compared with some classic protocols, this protocol can greatly reduce and balance network energy consumption and extend the network lifetime.  
Keywords:wireless sensor network; network coverage; quality of services; inter-cluster routing mechanism; Dijkstra algorithm +
Footnote †: journal:  
_Article_  
An Energy-Efficient Clustering Routing Protocol Based on a High-QoS Node Deployment with an Inter-Cluster Routing Mechanism in WSNs
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Oxygen and Carbon Dioxide Effects  
Oxygen and carbon dioxide are considered the most important gases because of their global influence on metabolism which has a huge impact on quality of packaged vegetables. These two gases are a crucial part of respiration and photosynthesis processes in vegetables. After cutting vegetables, photosynthesis stops, while breathing continues. In respiration, the oxygen is consumed through chemical reaction to provide energy for vegetables, emitting carbon dioxide as an outcome of that reaction. The ratio between these gases inside the package affects the freshness, firmness, and the color of the packaged product [16]. Thus, keeping these gases' combination within a certain range inside the package, which is known as modified atmosphere packaging (MAP), is essential to prolong storage duration and shelf-life of packaged vegetable. Three main process of how these gases affect the packaged vegetables are briefly discussed.
# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Methodological Perspective
#### 3.2.2 Logic-Based Approach  
In logic-based explainability, the focus is on capturing the logical relationships and dependencies between various pieces of information involved in fact verification. This includes representing knowledge in the form of logical axioms, rules, and constraints to provide justifications for the verification results. For example, refs. [6; 32] are recent studies that focus on the explainability of fact verification using logic-based approaches. In [6], a logic-regularized reasoning framework, LOREN, is proposed for fact verification. By incorporating logical rules and constraints, LOREN ensures that the reasoning process adheres to logical principles, improving the transparency and interpretability of the fact verification system. The experimental results demonstrate the effectiveness of LOREN in achieving an explainable fact verification. Similarly, ref. [32] highlights the potential of natural logic theorem proving as a promising approach for explainable fact verification systems. The system, named ProoFver, applies logical inference rules to derive conclusions based on given premises, providing transparent explanations for the verification process.  
The experimental evaluation shows the efficacy of ProoFVer in accurately verifying factual claims while also offering interpretable justifications through the logical reasoning steps.  
It is important to acknowledge certain limitations and drawbacks associated with this logic-based approach. First, the complexity and computational cost of logic-based reasoning can limit its scalability and practical applicability in real-world fact verification scenarios. Furthermore, while logic provides a structured and interpretable framework for reasoning, it may not capture all the nuances and complexities of natural language and real-world information. This means that the effectiveness of these approaches heavily relies on the adequacy and comprehensiveness of the predefined logical rules, which may not cover all possible scenarios and domains. Lastly, the interpretability of the generated explanations may still be challenging for non-expert users. They may involve complex logical steps that require expertise to fully understand and interpret.
## 4 Surface-Immobilized Peptides on Medical Implants
There is a great variability in how different studies assess the potential of antimicrobial coatings, in terms of in situ antimicrobial activity on the implant and at the peri-implant area, in vitro and in vivo, biocompatibility, etc. The majority of studies reported solely in vitro assays, with only a few in vivo studies [97; 98; 113; 114; 115; 116; 117]. The methodology for evaluating the in vitro antibacterial and anti-biofilm activity is not unified among the literature, with studies testing the bacterial load directly by measuring the colony forming units, others by performing live-dead staining or fluorescent based staining methods (Table 4). Critically, most of the in vitro work reviewed in the present article, in order to assess the effects of the antimicrobial coatings on the targeted bacteria, used static or mild agitation culture conditions. Those culture conditions do not simulate the conditions of biofilm formation and growth in vivo, in a human body, therefore the results must be interpreted accordingly. For example, the constant flow of biological fluids in the human body applies significant shear forces and supplies sustained nutrition to the bacteria. These conditions impact the bacterial growth and biofilm formation; therefore, it is of high importance to take them into account. [PERSON] and colleagues showed that the formation and growth of _E. coli_ biofilm on surfaces in a drip flow bioreactor was twice that obtained in a shaker [118]. This indicates that biofilm grown in the drip flow bioreactor system mimics the in vivo conditions better and presents a greater challenge to the antimicrobial peptide coatings than standard culture conditions. A few studies have used flow systems for studying the antibacterial effect of biomaterial coatings, with the example of [PERSON] and colleagues, who used a drip flow biofilm reactor system to show the antimicrobial activity of covalently bound GL13K peptide on titanium discs against _Streptococcus gordonii_ biofilm [119]. Titanium and titanium alloys confer excellent mechanical properties and biocompatibility, due to the formation of stable oxide layer on its surface [120]; therefore, titanium is widely used for the manufacturing of orthopedic and dental implants. In the following sections different immobilization strategies of AMPs on titanium implants are explored.
## 5 Summary and Conclusions  
This paper proposes a two-speed transmission based on dual planetary gear sets for EVs, and the gear ratio optimization and gearshift control problems of the two-speed transmission are studied. The main results can be summarized as follows:  
1. From the Pareto front graph obtained using the NSGA-II approach, the dynamic and economic performance of the two-speed transmission is conflicted with the other. Thus, a compromise solution is chosen as the final gear ratio combination.
2. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and electric energy consumption compared with the fixed-ratio transmission.
3. Compared with the conventional constant-input-torque control (CITC), the proposed constant-output-torque control (COTC) comes with advantages of keeping the driving torque on the output shaft constant during the whole gearshift process.
4. The disturbance observer is integrated to the feedforward-feedback slip speed controller to enhance the robustness, and the effectiveness is validated through comparison with the non-disturbance-compensation method. Thus, the controller is suitable for practical engineering use.  
The proposed control scheme is also applicable to other clutch-to-clutch shift-based transmissions. Our future work directions include the mechanical structure development of this transmission and the validation of the proposed gearshift method through bench and real vehicle tests.  
Conceptualization, W.H. and J.H.; data curation, W.H.; funding acquisition, C.Y.; methodology, W.H. and J.H.; project administration, C.Y.; validation, W.H. and J.H.; writing--original draft, W.H. and J.H.; writing--review & editing, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Technology R & D Program of China, grant number 2015 BAG04B01.  
The authors declare no conflict of interest.  
Figure 12: Comparison with non-disturbance-observer (DO)-based controllers.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme
### Existing DCTF-Based CNN Structures
#### 3.1.2 Multi-Channel DCTF-Based CNN Structure  
In [21], the multi-channel DCTF-based CNN demonstrated outstanding performance for LTE terminals. This inspired us to apply it to the 5G terminals. The multi-channel approach consists of generating separate DCTFs from the transient on and off parts and the steady state part in order to enhance the recognition of transient features. Consequently,  
Figure 7: Single-channel DCTF-based CNN structure.  
each signal was transferred to three DCTF features. As shown in Figure 8, these three DCTFs were the inputs of the multi-channel CNN. The components of each CNN channel were same as those in the single-channel CNN, and the outputs of the three channels were connected through the concatenation layer.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Effect of Ammonia  
Ammonia, which is the end-product of anaerobic digestion of proteins, urea, and nucleic acids [94], plays a major role in the performance and stability of the anaerobic digestion process [6]. Optimal ammonia concentration ensures sufficient buffer capacity for the methanogenic medium, especially for nitrogen-rich organic feedstock. However, excess ammonia concentration is usually reported as the fundamental cause of digester failure when it exceeds the inhibition threshold levels [95; 96; 97; 98; 99]. Inhibition is caused by total ammonia nitrogen (TAN) concentration, which is a combination of free (unionized) ammonia nitrogen (FAN) and ionized ammonium nitrogen (NH\({}_{4}\)\({}^{+}\)) [6; 100; 101; 102].  
TAN concentrations between 1500-7000 mg N/L were found to be inhibitory for anaerobic digestion [98; 103]. This broad range of inhibiting concentrations might be explained by different causes, including differences in substrates composition, inoculum origin, environmental and operational conditions (temperature, pH), and acclimation periods [97]. However, it should be noted that any probable inhibition by ammonia in the anaerobic digestion process should not only be directly attributed to the TAN concentration, but to the FAN levels, as this is believed to be the prime cause of inhibition of methanogenic microflora [104; 105].
## 1 Introduction
Then, the optimum schedule of controllable loads such as batteries is solved with using tabu search (TS) algorithm considering uncertainties for finding charge or discharge control of a small fixed battery and EV systems according to the condition of the PV generation power, where the PV power output is re-forecasted every several hours by the neural network (NN) [15] forecasting method. In addition, the scheduling in the optimization process is re-planned every several hours according to re-forecasted PV output. An additional purpose of this paper is to reveal how much the expected operational cost can be reduced by introducing re-forecast and re-plan in addition to consideration of the uncertainties. This paper provides a robust optimal scheduling decision method of controllable loads which are battery, EV and heat pump (HP) in a smart home, which can address the uncertainties by applying scenario-based approach in a stochastic optimization. Additionally, re-forecast and re-plan are considered in the optimization process to further decrease the expected operational cost. In order to verify the effectiveness of the proposed method, the results obtained in four different cases are compared and discussed in the simulation results. Finally, the optimal schedules obtained in the four cases are tested for the PV output involving uncertainties in order to confirm the robustness. This is done using Monte Carlo simulation for the various scenarios. The comparison of simulation resultsobtained in four cases is stated and examined. The statistical analysis mentions the significance of consideration of the uncertainties, re-forecast and re-plan in the scheduling of the controllable loads, and it implied that the capacity of batteries can be reduced by the proposed optimization method in the statistical analysis. The effectiveness of the proposed method is validated with using MATLAB(r). The rest of the paper is organized as follows. Section 2 describes the power system model involved in the smart home. Section 3 presents a formulation of the optimization problem with the TS algorithm, including the uncertainties. Section 4 discusses simulation results which are derived from the optimization method. Furthermore, the results are statistically confirmed to verify the effectiveness of the optimal solution by using Monte Carlo techniques. Section 5 presents the conclusion and future research avenue.
## 2 The IoV Scenario and Use Cases
### Use Cases
#### 2.2.1 Vehicles Platooning  
Vehicle platooning is defined as the creation of groups of vehicles, one of them playing the role of _leader_, which exchange sensors' information to increase the driving efficiency, reducing the inter-vehicle distance, reducing the fuel consumption, and allowing limited automated driving. The main research challenges in this area reside in how messages are disseminated within the members of the platoon and how the platoon leader/manager communicates with the network. The platoon must support up to five vehicles and, given that other V2X services can be run in the vehicles, 19 users can be using other V2X applications in the platoon. The reliability of communications must be between 90% and 99.99%, depending on the degree of automation, and the maximum latency is 10-20 ms upper bounded for platoon members' cooperation.
# Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength Is Not Enough
## 5 Exploiting Sparse Arrays
It is also interesting to see that the spreading of the condition number is reduced when using a non-equispaced array: the result in Figure 14, where the case with \(K=50\) is analyzed, shows a conditioning significantly better than the one presented in the equispaced case of Figure 7, for any average inter-element spacing. Finally, also the analysis of the distribution of the singular values for the \(K=50\) case confirms the advantages of the non-equispaced array (see Figure 15). \[x_{s,n}=L\left(\left(1-\sum_{p=1}^{P}\alpha_{p}\right)u_{n}+\sum_{p=1}^{P} \alpha_{p}T_{2p+1}(u_{n})\right) \tag{7}\]0 Distribution of the condition number as a function of the average distance \(d\) for \(K=50\) and the sparse array with \(\alpha_{1}=-0.03\). Each column of the matrix in the image map is a histogram of the condition number. \[x_{s,n}=L\left(\left(1-\sum_{p=1}^{P}\alpha_{p}\right)u_{n}+\sum_{p=1}^{P} \alpha_{p}T_{2p+1}(u_{n})\right) \tag{7}\]1 Mean condition number of the channel matrix as a function of the average inter-element distance \(d\) and the number of users \(K\) for a non-equispaced array with \(\alpha_{1}=-0.03\). \[x_{s,n}=L\left(\left(1-\sum_{p=1}^{P}\alpha_{p}\right)u_{n}+\sum_{p=1}^{P} \alpha_{p}T_{2p+1}(u_{n})\right) \tag{7}\]2 Singular values analysis for \(d_{0}=2\lambda\), \(\alpha_{1}=-0.03\) and \(K=50\). (**Left**) Image map of the distribution of each one of the 50 singular values; (**Right**) solid line, average singular values; dashed lines, average singular values \(\pm\) the standard deviation of the distribution. It is important to underline that, whatever the optimization approach used to define the position of the elements in the sparse array, those positions are fixed: their choice has to be done only once, when designing the array. It is also worth underlining that in the simulations shown, no smart terminal selection techniques [18] have been employed. This means that we can achieve an even better conditioned channel matrix by the application of these approaches.
## 3 Results
### Final Calibration Functions  
The best performing calibrated functions with eliminated bias for each site and layer are presented in Table 5. These functions are applied to the respective FDR sensors of the LII plots to construct the \(\theta_{\text{FDR}}\) time series and may be considered the end-product of this study. For the 60 layers examined, a linear model (LM) was the best choice for 58% of these layers and 27% of them a LM with temperature correction. Quadratic models seemed more appropriate for 10% (QM) and 5% (QMTC) of all layers. The resulting time series of all calibrated models for the 2015-2017 period is given in Figures 11-5.  
On average, calibration functions explained 56% (median 61%) of the total variance of the calibration dataset with a maximum of 96%, but also with no variance explained at all (R\({}^{2}\) = 0.003) in the special case of extremely clayey substrates. The RMSPE ranges from 0.012 to 0.117 m\({}^{3}\) m\({}^{-3}\) with a mean of 0.043 m\({}^{3}\) m\({}^{-3}\) and median 0.041 m\({}^{3}\) m\({}^{-3}\). Hence, regardless of specific soil conditions, measurements of one sensor per layer will not suffice to predict \(\theta_{\text{v}}\) better than the 0.03 m\({}^{3}\) m\({}^{-3}\) accuracy threshold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**WIJ**} & \multicolumn{3}{c}{**RAV**} & \multicolumn{3}{c}{**BRA**} & \multicolumn{3}{c}{**GON**} & \multicolumn{3}{c}{**HOE**} \\ \hline
**Layer** & **A** & **B** & **C** & **A** & **B** & **C** & **A** & **B** & **C** & **A** & **B** & **C** & **A** & **B** & **C** \\ \hline M01/OFH & 21 & 20 & 3 & 19 & 23 & 21 & 19 & 23 & 17 & 20 & 22 & 24 & 21 & 21 & 22 \\ M12 & 11 & 13 & 6 & 17 & 20 & 11 & 17 & 20 & 12 & 17 & 17 & 20 & 13 & 11 & 21 \\ M24 & 12 & 19 & 8 & 10 & 19 & 10 & 10 & 19 & 22 & 14 & 20 & 20 & 8 & 5 & 22 \\ M48 & 10 & 6 & 19 & 8 & 12 & 9 & 10 & 3 & 22 & 14 & 20 & 23 & 7 & 9 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of consecutive months required for stable removal of bias (MPE < 0.01 m\({}^{3}\) m\({}^{-3}\)) and adequate calibration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **WIJ** & **RAV** & **BRA** & **GON** & **HOE** \\ \hline OFH & 2 & & & **4** & \\ M01 & **16** & **5** & 3 & **5** & 3 \\ M12 & **6** & **4** & 2 & **5** & 1 \\ M24 & 1 & 3 & 2 & **4** & 2 \\ M48 & 1 & 1 & 2 & **10** & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Estimated number of FDR sensors required to reduce average measurement error well below 0.03 m\({}^{3}\) m\({}^{-3}\) for each plot and layer, based on the maximum SDPE found within each layer. The numbers in bold refer to an increased number of sensors compared to the current sampling setup.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 3 Trajectory Compensation Based on Stiffness Modeling  
To solve the relationship between the stiffness and the space position error caused by it, first, the Jacobian matrix of the manipulator was solved by using the vector product method. Second, the stiffness matrix of the manipulator was solved with the given generalized force and manipulator parameters. Last, the mapping model of the six angles of the manipulator and the errors caused by the joint stiffness of the manipulator were established.