esa-sceva/satcom-chunk-collection huggingface dataset:
# Response Analysis of Asymmetric Monostable Harvesters Driven by Color Noise and Band-Limited Noise
## 4 Response of the Asymmetric MEHs under Band-Limited Noise
When the frequency bandwidth is 0.025, the average output power is obtained when the center frequency equals 0.175. When with the frequency bandwidth increases to 0.05, 0.1, and 0.15, maximum power is achieved for the center frequency of 0.19, 0.216, and 0.23, respectively. Therefore, the increase in frequency bandwidth makes the system generate considerable power in a wider frequency range and leads to an increase in the center frequency at which the maximum output is reached. Furthermore, Figure 13 illustrates the effect of frequency bandwidth on the output performance of the asymmetric MEH excited by band-limited noise with different center frequencies. When the center frequency is small and far from the COF-2, the output power shows an increasing trend with the increase in frequency bandwidth. For a center frequency of 0.2 near the COF-2, the power output increases quickly to a certain value and then maintains a slower growth trend with the increase in frequency bandwidth. For a center frequency of 0.4, much larger than COF-2, the generated power first remains small and then quickly increases when the frequency bandwidth is about 0.25. The reason for this phenomenon is that the increase in frequency bandwidth leads to the frequency range around COF-2 being contained in the excitation frequency. Furthermore, it was observed that the power remains unchanged when the frequency bandwidth further increases. Figure 11: Influence of frequency bandwidth on the output performance of the asymmetric MEH excited by band-limited noise with different noise intensity (CF = 0.18). Figure 12: Influence of center frequency on the output performance of the asymmetric MEH excited by band-limited noise with different frequency bandwidth. In addition, we consider the effect of asymmetric degree on the output performance of the MEH, and Figure 14 explains the influence of center frequency on the output power of the MEH excited by band-limited noise with different values of the quadratic coefficient. For all MEHs, the output performance is firstly enhanced and then degraded with the increase in center frequency and the phenomenon was similar to the response under harmonic excitation. For a symmetric MEH with \(\beta\) equaling 0, the average power reaches a maximum value at center frequency 0.22. When the asymmetric degree i for \(\beta\) increases to 1, 1.5, and 1.9, the maximum power is achieved at a frequency of 0.211, 0.191, and 0.166, respectively. In other words, the increase in asymmetric degree leads the MEH to generate larger output power at lower frequencies and this condition is similar to the response under down-sweep frequency harmonic excitation.
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 1 Introduction
### Precursor and Peptides  
The orexinergic system includes orexins A and B and the orexin receptors type 1 (OXR1) and 2 (OXR2) [14], and integrated with other systems, such as leptin and ghrelin systems, controls the energy homeostasis [13], and is essential for the normal consolidation of sleep and waking [18]. Orexin A and orexin B (also known as hypocretin 1 and 2, respectively) are encoded by the same gene (_hctrl_) on chromosome 17 [1] and are constituted of two exons [16] being the enzymatic cleavage products of a single 130-residue precursor, prepro-orexin, and share a 46% amino acid identity [1; 19; 20]. Both peptides have an amidated C-terminal end and include two \(\alpha\)-helices domains linked by a small flexible domain [15; 21]. Orexin A includes residues 28-66 of the prepro-orexin (33-residue peptide) and shows two intramolecular disulfide bridges within the N-terminal that are not essential for its full activity [22]. The C-terminal domain (sequence 20 to 33) is essential for its activity, and the deletion of the central domain between residues 15 and 19 strongly reduced the peptide activity [22]. Orexin B is a linear 28-residue peptide, corresponding to prepro-orexin residues 69-97 [1; 19; 20], without disulfide bridges [1]. The C-terminal domain of orexin B is crucial for its activity, but the deletion of the first six residues of the peptide have no impact on it [22; 23]. It has been reported that some single-nucleotide polymorphisms corresponding to missense mutation, mainly located in the C-terminal domain of both peptides, may have a negative impact on the peptide activity [24].  
In an adult brain, in situ hybridization displayed, via cRNA probe, neurons containing prepro-orexin distributed in hypothalamic (lateral and posterior hypothalamic areas and peritoneal nucleus) and subthalamic (zona incerta, subimertal, and subthalamic nuclei) areas [25; 26]. In addition, neurons containing the peptides were detected by immunohistochemistry in the dorsal lateral hypothalamic area, and immunoreactive fibers were observed in the arcuate hypothalamic nucleus [25]. These neurons display extensive and wide projections to numerous regions of the central nervous system [25; 26], and thus, orexins can regulate many physiological functions, although the heaviest projections are related to regions that control arousal and wakefulness [13]. The hypothalamic neurons producing orexins can be segregated into distinct populations (anatomically, genetically, and functionally) which are involved in the different functions regulated by orexins, such as reward, stress, emotions, feeding, sleep/wakefulness cycle, or arousal [27]. In addition, orexin has also been detected in the enteric nervous system and in the enteroendocrine gut cells [28], as well as in the reproductive tract, pituitary, adipocytes, thyroid, pancreas, adrenal gland, testis, and ovary [29].
## 3 Results
### Systematic Evaluation of the Identified Literature
#### 3.4.1 Enabling Concepts  
A total of 17.86% of the identified articles were assigned to the cluster \"enablers and implementation concepts\". [PERSON] et al. present several steps to transform logistics systems toward logistics 4.0, whereby the development of factory management strategies from reactive to predictive and proactive approaches is described. To enable those strategies, the implementation of DT technologies is suggested [45]. These DTs can be developed and applied for all phases during a product's life cycle, which can be divided into product design, product manufacturing, and product service. To illustrate the possible usage, different cases are described for the design of bicycles, shaft manufacturing, and power  
Figure 8: Digital Twin components [10,13,20,49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster** & **Records** & **Records [\%]** \\ \hline Enablers and Implementation Concepts & 5 & 17.86 \\ Areas of Operation and State of Development & 3 & 10.71 \\ Virtual Model Creation & 4 & 14.29 \\ Enterprise Information Systems & 6 & 21.43 \\ Decentralized Applications & 7 & 25.00 \\ Machinery Prognostics and Maintenance Management & 3 & 10.71 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Clusters according to the content of the paper.  
transformer service [13]. For shop-floor processes, on the other hand, different stages toward a DT can be described. In the last stage, the virtual and physical spaces interact and converge. For the manufacturing phase, the interaction between physical entity, VM, and Service System are shown in an activity diagram; the operations are differentiated into before production, during production, and after production. To ensure the convergence between physical and virtual space, the VM must evolve during operation [17]. In addition, [PERSON] and [PERSON] demonstrate a new concept for mobile digital shop-floor management at the ESB Logistics Learning Factory. For this concept, an indoor positioning system, a social platform for communication and knowledge transfer between employees, mobile devices, and a large mobile interactive board are used. It is intended to investigate the impact of organizational structure, leadership, and communication of the new concept on efficiency and productivity [48]. Regarding the implementation of DTs, [PERSON] et al. mention the special situation of small and medium-sized enterprises (SMEs), which face more difficulties in implementing a DT for production systems. SMEs are characterized by a low degree of automation and interlinking of their production, as well as insufficient machine and process data acquisition. To overcome these obstacles for logistics processes, a combination of sensor-based tracking and machine vision technologies is suggested and described [29].
## 3 Results and Discussion
### RSID(tm) Semen and RSID(tm) Reader Experiments  
Assuming 100% extraction efficiency, 50 \(\mu\)L of semen deposited on a sterile cotton swab extracted in 1 mL of RSID(tm) extraction buffer will produce an extract that contains 50 nL of semen per microliter of extract. Using RSID(tm) extraction buffer, three dilutions of the extract were made: 1:20, 1:10, and 1:5. In order to generate 2.5, 5, and 10 nL test volumes of semen, 2 \(\mu\)L of each of the semen dilutions (1:20, 1:10, and 1:5) were adjusted to a total volume of 200 \(\mu\)L with RSID(tm) running buffer. In order to generate 50 nL test volumes of semen, 2 \(\mu\)L of undiluted semen extract were adjusted to a total volume of 200 \(\mu\)L with RSID(tm) running buffer. Final tested (calculated) volumes of semen were 2.5, 5, 10 and 50 nL. Two different Readers were evaluated in this study, each with two sets of RSID(tm) cassette replicates. All results were concordant.  
Figure 4 shows results of RSID(tm) Semen as they appear in a digital photograph (Nikon's Coolpix 5600) and in the digital images generated by the RSID(tm) Reader. Extraction negative controls (Figure 4, cassettes 1 and 2) and blood-saliva-urine cross-reactivity controls (Figure 4, cassettes 11 and 12) were read as \"negative\" by both RSID(tm) Readers tested. Samples with calculated amounts of 2.5, 5, 10 and 50 nL of semen were read as \"positive\" by both RSID(tm) Readers tested (Figure 4, cassettes 3-10).  
Cassettes 3 and 4, on which an estimated 2.5 nL of semen were tested had very light positive signal, which was expected since 2.5 nL of semen is at the detection limit of RSID(tm) Semen test [4]. To measure reproducibility of RSID(tm) Reader, cassette 3 was analyzed three times on both RSID(tm) Readers. Both RSID(tm) Readers detected signal in all three replicas.  
Figure 3: Results of RSID(tm) Saliva and RSID(tm) Reader Experiments. Results of two replicates for each analyzed saliva volume from one Reader are shown. Cassette number and calculated volume of saliva added to each cassette are shown **(top)**. Digital camera capture of cassettes and RSID(tm) Reader images are shown below, respectively. **Top—**Digital photograph of the cassettes (C—Control; T—Test lines) with analyzed saliva volumes indicated above. **Bottom—**Images taken from RSID(tm) Reader. RSID(tm) Reader results (positive [+] or negative [-]) are indicated under each RSID(tm) Reader image. Note: Camera capture or RSID(tm) Reader images are slightly _less_ sensitive than visual observation of the cassettes.
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 4 Experiment Results and Discussion  
### UAD Model Preselection Results  
The dataset in Table 3 is used to train and validate UAD models mentioned in Section 2.1 with the proposed model preselection method in Section 3.3 to select the most suitable UAD model. The validation false negative rate (_FNR_) of different UAD models is calculated by Equation (15), except for validation accuracy, to help prove the reliability of the proposed preselection method.  
\[FNR=\frac{FN}{TP+FN} \tag{15}\]  
Table 4 shows the ten-fold cross-validation accuracy obtained by different UAD methods on multi-channel data, and Table 5 presents the corresponding _FNR_. The bold data indicate the highest cross-validation accuracy and the lowest _FNR_. According to the results in Table 4, AKPCA (fusion) achieves the highest mean ten-fold cross-validation accuracy at 0.83, making it the most suitable UAD model as selected by the model preselection algorithm. Table 5 shows that AKPCA (fusion) also achieves the lowest _FNR_ at 0.144. Although ISF (fusion) attains a mean accuracy greater than 0.8 and a mean _FNR_ less than 0.4, its performance is still inferior to that of AKPCA (fusion). _FNR_ is another evaluation indicator in addition to accuracy; an _FNR_ of 0.4 is the qualified limit for determining whether an anomaly detection model is acceptable.  
Figure 5: The ESWP condition monitoring framework based on UAD and FD.
## 3 Proposed Solution
### Trust Calculation in Proposed Methodology
Reactive updates, on the other hand, offer a quick way to respond to any abrupt or unexpected changes in node behavior that might point to a security risk. Combining the two strategies enables CPS networks to efficiently respond to dynamic changes in node behavior. ```
Input:\(G_{m}(T)\): Gini index value of node \(m\) at time \(T\) \(P_{m}(T)\): Packet loss rate of node \(m\) at time \(T\) \(L_{m}(T)\): Latency of node \(m\) at time \(T\) \(EC_{m}(T)\): Energy consumption of node \(m\) at time \(T\) \(T_{m}(T)\): Throughput of node \(m\) at time \(T\) \(\theta_{p}\): Predefined threshold for packet loss \(\theta_{l}\): Predefined threshold for latency \(\theta_{E}\)C: Predefined threshold for energy consumption \(\theta_{T}\): Predefined threshold for throughput Output: Potential greyhole nodes
1 Characteristic 1: Gini Index Deviation;
2 forach node \(m\)do
3 Gini_Deviation \(\leftarrow\)\(|G_{m}(T)-G(T)|\);
4 ifGini_Deviation is significantly different from 0 then
5 Add node \(m\) to PotentialGreyholeNodes;
6 Characteristic 2: Packet Loss;
7 forach node \(m\)do
8 if\(P_{m}(T)>\theta_{P}\)then
9 Add node \(m\) to PotentialGreyholeNodes;
10 Characteristic 3: Latency;
11 forach node \(m\)do
12 if\(L_{m}(T)>\theta_{L}\)then
13 Add node \(m\) to PotentialGreyholeNodes;
14 Characteristic 4: Energy Consumption;
15 forach node \(m\)do
16 if\(EC_{m}(T)>\theta_{E}C\)then
17 Add node \(m\) to PotentialGreyholeNodes;
18 Characteristic 5: Throughput;
19 forach node \(m\)do
20 if\(T_{m}(T)<\theta_{T}\)then
21 Add node \(m\) to PotentialGreyholeNodes;
22 return PotentialGreyholeNodes;
```  
**Algorithm 3**Detection of greyhole node.
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 5 Results and Discussion  
### \(c_{\rm L}\) Measurement at a Single Temperature  
#### 5.1.1 Uncertainty Analysis  
In this work, an ultrasonic transducer with a 5 MHz center frequency was employed to measure the ultrasonic velocities in all of the prepared ULE samples. Figure 6 gives an example of the received ultrasonic signals, which formed a series of ultrasonic longitudinal waves with a sampling rate of 2.5 GHz. High-frequency noise was found to be present in the actual ultrasonic signals. However, since the correlation of the noisy signal was very small, the effect of such noise was removed via the correlation calculation. The results of the TOF calculations obtained using this method are depicted in Figure 6. Again, there is a peak in the correlation coefficient distribution plot corresponding to a TOF of \(\Delta t\) = 17.3383 \(\mu\)s.  
The uncertainty in measuring ultrasonic velocity was also investigated, and the expression is shown in Equation (23).  
\[u_{c_{\rm L}}=\sqrt{\left(\frac{2}{\Delta t}u_{d}\right)^{2}+\left(\frac{2d}{ \left(\Delta t\right)^{2}}u_{\Delta t}\right)^{2}} \tag{23}\]  
where \(u_{c_{\rm L}}\), \(u_{d}\), and \(u_{\Delta t}\) are the uncertainties regarding the \(c_{\rm L}\), \(d\), and TOF in the ULE glass, respectively.  
As illustrated in the first item of Equation (23), the uncertainty related to the \(d\) was \(u_{c_{\rm L}}(d)=(2/\Delta t)\Delta u_{d}\). A typical value of \(\Delta t\) is 17.3562 \(\mu\)s. When measuring the \(d\) of the ULE sample, \(u_{d}\) was evaluated by the measurement precision of a micrometer (0.001 mm), thus \(u_{c_{\rm L}}(d)=0.12\) m/s. After substituting the measured values into the second item of Equation (23), the uncertainty introduced by the TOF (\(\Delta t\)) was obtained: \(u_{c_{\rm L}}(\Delta t)=0.332\times 10^{9}u_{\Delta t}\)  
Figure 5: \(c_{\rm L}\) measurement results of the ULE samples 1#-6# at frequencies of 3.5 and 5 MHz.  
Since \(u_{\Delta t}\) was evaluated by the sampling period of the data acquisition card (0.4 ns), the typical \(u_{\mathrm{CL}}(\Delta t)\) was 0.13 m/s. Therefore, when measuring the ULE glass using the proposed experimental setup, the calculated total uncertainty was \(u_{\mathrm{CL}}=0.2\) m/s, ensuring that the ultrasonic velocity measurements at a single temperature had high reliability.
## 3 Instantaneous Multi-Constellation Relative Positioning: Methodology and Performance  
Our first goal in this section is to briefly present a multi-constellation geometry-based relative positioning model. This is followed by an empirical evaluation of the instantaneous positioning with a focus on the application of Galileo E14 and E18 satellites.
## 3 Results
### Thr-Prx Are More Widely Distributed and Ser-Prx Is More Prevalent in Bacteria
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Thr** & & & & **Ser** & \\ \hline
**Bacteria** & & & **Eukarya** & & **Bacteria** & & **Eukarya** & \\ \hline Acidobacteria & 15 & Alveolata & 107 & FCB group * & 14 & Amoebozoa & 1 \\ Aquificae & 16 & Amoebozoa & 83 & Proteobacteria & 15 & Euglenozoa & 12 \\ Calditrichaeota & 2 & Apusozoa & 2 & PVC group \(\lx@sectionsign\) & 12 & Opishokonta & 75 \\ Chrysiogenetes & 1 & Cryptophyta & 1 & Spirochaetes & 2 & Rhodophyta & 2 \\ Deferribacteres & 5 & Euglenozoa & 105 & Synergistetes & 1 & & \\ Elusimicrobia & 18 & Heterolobosea & 1 & Terrabacteria & 803 & & \\ Environmental samples & 3 & Opishokonta & 2079 & Unclassified & 4 & & \\ FCB group * & 1455 & Parabasalia & 13 & & & \\  & & (parabasalids) & & & & \\ Fusobacteria & 78 & Rhiz & 4 & & & \\ Nitrospinae/Tectomicobia & 11 & Rhodophyta & 116 & & & \\ group & 15 & Stramenopiles & 136 & & & \\ Proteobacteria & 4890 & Viridiplantae & 302 & & & \\ PVC group & 220 & & & & & \\ Spirochaetes & 149 & & & & & \\ Synergistetes & 10 & & & & & \\ Terrabacteria group & 3564 & & & & & \\ Thermodesulfobacteria & 2 & & & & & \\ Unclassified & 245 & & & & & \\ \hline \hline \end{tabular}  
* * Superphylum that comprises Fibrobacteres, Chlorobi, and Bacteroidetes phyla, \(\lx@sectionsign\) Superphylum that comprises Plantcomycetes, Verrucomicobia, and Chlamydia phyla.  
\end{table} 
Table 3: Distribution of Thr-Prx or Ser-Prx among the living groups. Figure 4: Distribution of the typical 2-Cys Prx containing Thr or Ser as part of the catalytic triad. The analysis of 15,034 unique sequences of typical 2-Cys from the NCBI revealed 14,092 Thr-Prx (93.7%) and 941 Ser-Prx (6.3%) sequences. Considering each domain, Ser-Prx are more abundant at bacteria (7.4%), followed by eukaryotes (2.7%), and archaea (1.4%). The left Y-axis is referent to Thr-Prx, while right Y-axis is related to Ser-Prx. Additionally, as _S. cerevisiae_, some bacteria (e.g., _Bacillus subtilis_) possess both Thr-Prx and Ser-Prx isoforms [60], as _S. cerevisiae_.
## 5 Experiment and Analysis
### Evaluation of Candidate Point Extraction  
The TUM RGB-D dataset [29] contains several dynamic image sequences with an image collection frequency of 30 Hz and resolution of \(640\times 480\). Each image matches the high-precision pose groundtruth provided by the motion capture system. For the traditional direct visual odometry based on the assumption of static environment, the dynamic objects in the scene will affect the final positioning results.  
Figure 7 shows the comparison of Dynamic-DSO and DSO for extracting candidate points in keyframes. Two pedestrians walk around in front of the camera. The candidate points extracted by DSO (Figure 7c) include pedestrians. The proposed method segments dynamic pedestrians (Figure 7b) and only extracts candidate points from the static area (Figure 7d), which ensures that interference from dynamic objects is avoided when calculating photometric error.  
Figure 7: Comparison of Dynamic-DSO and DSO for extracting candidate points in keyframes. (**a**): Keyframes containing dynamic objects. (**b**): Semantic images containing dynamic objects obtained by Dynamic-DSO. (**c**): Candidate points extracted by DSO. (**d**): Candidate points extracted by Dynamic-DSO.
## 5 Analysis of Key Factors Influencing the Cyclic Operation Time
### Time Gaps in Cylinder Actions  
The actuation time of the cylinder is related to the pump pressure. The greater the pressure, the faster the cylinder expansion and the shorter the actuation time of the cylinder. Under a given pressure, the time required for a complete operation cycle can be reduced by minimizing the time gap in cylinder actions. The relationship between the two is plotted in Figure 8.  
It can be seen in Figure 8 that for mechanized and automated solid backfilling operations, the time of cyclic operation is directly proportional to the time gap in cylinder actions. However, for intelligent operation, the time gap in cylinder actions approaches zero. When the time gap in cylinder actions is 5 s, the times of cyclic operation for mechanized, automated, and intelligent solid backfilling technologies are 3.13, 2.52, and 1.92 h, respectively. Compared with mechanized backfilling, the backfilling efficiency of automated and intelligent operations is improved by 19% and 39%, respectively.
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 3 Analysis of Results
### Prediction Models of Resilient Modulus
#### 3.2.2 Andrei Model  
In the practical problem, the intermediate and the minor principal stresses are not equal. In order to consider the influence of both of them on the resilient modulus, [PERSON] model [40] introduced the octahedral shear stress to replace the deviatoric stress in the [PERSON] and Witczak model [71]. This operation provides a better explanation of the stress state of the materials, and further generalize the nonlinear constitutive model, as shown in Equation (3).  
\[M_{R}=k_{1}P_{a}\bigg{(}\frac{\theta}{P_{a}}\bigg{)}^{k_{2}}\bigg{(}\frac{ \tau_{oct}}{P_{a}}+1\bigg{)}^{k_{3}} \tag{3}\]  
where \(\tau_{oct}\) is the octahedral shear stress, \(P_{a}=\) atmospheric pressure and \(k_{1},k_{2}\) and \(k_{3}\) are regression coefficients.  
\(\theta\) is the mass stress that is given as  
\[\theta=\sigma_{1}+\sigma_{2}+\sigma_{3}\]  
\(\tau_{oct}\) is given as  
\[\tau_{oct}=\frac{1}{3}\sqrt{(\sigma_{1}-\sigma_{3})^{2}+(\sigma_{1}-\sigma_{ 2})^{2}+(\sigma_{2}-\sigma_{3})^{2}}\]  
where \(\sigma_{1}\) is the major principal stress; \(\sigma_{2}\) is the intermediate principal stress and \(\sigma_{3}\) is the minor principal stress or confining pressure.  
In this prediction model, the influence of bulk stress and octahedral shear stress on the resilient modulus was fully taken into account. Compared with Equation (2), the Andrei model is essentially consistent with the Uzan-Witzzak model. The difference between them is that the term \(\frac{\tau_{oct}}{P_{a}}\) in the [PERSON] model is followed by an additional constant of 1, eliminating the existing problems of the Uzan-Witzzak model.