esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Specific Case of DnaA in the _E. coli_ Cell Cycle
### Central Carbon Metabolism (CCM)  
The central carbon metabolism is the set of biochemical pathways responsible for the transport and oxidation of main carbon sources and, in _E. coli_, comprises the phosphotransferase system, glycolysis, gluconeogenesis, the pentose-monophosphate bypass with the Entner-Dudoroff pathway, Krebs cycle with the glyoxylate bypass and the respiratory chain. A relationship between the enzymes involved in CCM and the elongation step in DNA replication was first observed in _B. subtilis_[132]. This has now been extended to _E. coli_ where it has been found that the temperature sensitivity of a strain containing the _dnaB(ts)_ mutation could be suppressed by dysfunction of _pgi_ or _pta_; _dnaE486(ts)_ by dysfunction of _ktR_B; _dnaG(ts)_ by dysfunction of _gpmA_, _pta_ or _ack4_; _dnaN159(ts)_ by dysfunction of _pta_ or _ackA_[133]. In particular, a full suppression of the thermosensitivity of _dna446(ts)_ was observed in _pta_ (encoding phosphate acetyltransferaseense) or _ack4_ (encoding acetate kinase) double mutants. The acetyl phosphate level, which may be reduced in the Pta- and raised in the AckA\({}^{-}\) contexts, is thought to act as a global signal in two-component systems linking metabolism with other systems [134]. Could this relationship between DnaA and Pta/AckA reflect intensity sensing? Suppose that free Pta/AckA were to bind DnaA. Then, if the formation of a FDS containing Pta and AckA (and perhaps other CCM enzymes too) were to depend on the intensity of functioning of these enzymes, the availability of ATP-DnaA might increase as the proportion of free Pta/AckA decreased. There is, however, no evidence for direct interaction between DnaA and CCM enzymes and other explanations are possible, as discussed by the authors [132,133].
## 1 Introduction
### Research Question  
The main objective of the research is to shed light on the facilitation of virtual desktop infrastructure, which means a computer in the cloud and its impact on the regular computer science course itinerary.  
The research question considered here is:  
**RQ:** What are the student's expectations and impact of a Virtual Desktop Infrastructure (VDI) solution designed and adapted for a Computer Science laboratory in the first course.  
Different delivery methods for building knowledge are used in the classroom. These days teachers deliver content using infographics, videos, discussions, students pitches, teacher-led sessions, interactive guides, group participation and discussion, hands-on exercises, on-the-floor training, mentor shadowing, or huddles, for mentioning some of them [13; 14]. Changing face-to-face models to synchronous mixed online approaches requires a different viewpoint [15]. This is especially relevant when the COVID-19 pandemic arises. Many of the activities carried out face to face must switch to online scenarios which have an important impact in laboratory work [16; 17; 18]. Given our intention to use virtual desktop infrastructure in our courses, it is necessary to evaluate usability and acceptability first.  
This study poses four main hypotheses that should be validated attending our usability and acceptability premises:  
**Hypothesis 1 (H1)**.: _The VDI perceived usability by the students is considered correct using a predefined scale._  
**Hypothesis 2 (H2)**.: _There is a significant relationship between Perceived Usefulness of student and Attitude Towards Technology._  
**Hypothesis 3 (H3)**.: _There is a significant relationship between the Perceived Usefulness of students and the Perceived Ease of Use._  
**Hypothesis 4 (H4)**.: _There is a significant relationship between Perceived Ease of Use and Attitude Towards Technology._  
The H1 hypotheses will be validated with the system usability scale [19]. Hypotheses H2, H3, and H4 are handled and validated with the technology acceptance model [20].  
Collaterally, it is performed a brief Sustainability analysis of the approach from an economic perspective. It provides an alternative vision when providing virtual desktops to students. Highlight that SUFFER [21] was completely deployed in a private server located in group premises.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 4 Discussion
### Analysis of Yield Prediction Potential for Individual Growth Periods  
The spatial heterogeneity of the soil and physiological characteristics of crops change during different growth stages [18; 56; 57; 58]. Figure 8 illustrates the accuracy trends of various yield prediction models for winter wheat. In this study, we observed significant differences in the accuracy of winter wheat yield predictions across different growth stages. The accuracy of yield predictions gradually increased with the growth of winter wheat, peaking at the heading stage before declining. The peak prediction potential at the heading stage is likely due to the formation of spikes and ears, which stabilize the plant's morphology and structure. Nutrient accumulation and growth conditions play a crucial role in the final yield of winter wheat. The subsequent decline in prediction accuracy may be attributed to the inclusion of vegetation index data among the features. After the heading stage, nutrients are transferred from the stems and leaves to the grains, leading to a decrease in chlorophyll in the leaves. This reduction in chlorophyll affects the vegetation index  
Figure 10: Spatialization of winter wheat yield prediction in Henan Province, 2021.  
data related to chlorophyll, thereby reducing its correlation with winter wheat yield and resulting in decreased accuracy of the yield prediction models.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation
#### 3.2.4 Shunt Regulator  
Figure 18 shows the location of the implemented shunt regulator inside the microphotography of the whole self-powered tag. Moreover, a zoomed-in view of its layout is included. The required silicon area is 115 \(\upmu\)m \(\times\) 90 \(\upmu\)m. This circuit was designed to deliver a regulated output voltage of \(V_{DD}=1\) V when \(V_{REC}=1.2\) V, showing a discharge current of \(I_{LIM}=560\) nA and allowing a deliverable current \(I_{VDD}=5\) A. The dimensions of the transistors of the shunt regulator are listed in Table 6.  
The post-layout simulation response of the shunt regulator is shown in Figure 19. The output voltage \(V_{DD}\) is presented in Figure 19a, while the consumed and deliverable current \(I_{LIM}\) and \(I_{VDD}\) are depicted in Figure 19b. It can be noted from Figure 19a that the maximum output voltage is \(V_{DD}=1.45\) V for a maximum output current of \(I_{VDD}=6.7\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Value** & **Element** & **Value** & **Element** & **Value** \\ \hline \(m_{1}\) & (11.2 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 25 & \(m_{4}\) & (7.82 \(\upmu\)m/45 \(\upmu\)m) \(\times\) 4 & \(m_{7}\) & (7.59 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 10 \\ \(m_{2}\) & (3.62 \(\upmu\)m/4.5 \(\upmu\)m) \(\times\) 4 & \(m_{5}\) & (1.375 \(\upmu\)m/18 \(\upmu\)m) \(\times\) 2 & \(R_{1}\) & 25 \(K\Omega\) \\ \(m_{3}\) & (0.9 \(\upmu\)m/45 \(\upmu\)m) & \(m_{6}\) & (12.315 \(\upmu\)m/4.5 \(\upmu\)m) \(\times\) 4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Elements and transistor sizes of the shunt regulator.  
Figure 17: LDO voltage regulator post-layout simulation. (**a**) Line regulation. (**b**) Load regulation. (**c**) Dynamic load regulation.  
Figure 18: Location of the implemented shunt regulator inside the microphotography of the whole self-powered tag and a zoomed-in view of its layout.  
when \(V_{REC}=1.8\) V. For \(V_{REC}\geq 2.4\) V the magnitude of the current discharge path is \(I_{LIM}\geq 80\) uA, adjusting the output voltage to no more than 1.1 V.
## 5 Clinical Presentation and Instrumental Diagnosis
In an emergency, examination of the distal portion of the colon is sufficient because ischemia affects the left hemicolon in more than 90% of patients [55]. Computed tomography (CT) imaging can provide indirect and additional clinical information, such as detecting thickening of the colonic wall, the presence of toxic megacolon, and reduced perfusion of the intestinal wall [56], while also excluding other intra-abdominal pathological findings [57] (Figure 3). Although several intraoperative methods are available to assess intestinal perfusion, none can definitively predict the development of ischemia. Clinical evaluation during open surgery, based on bowel color and peristalsis, is often insufficient, prompting the use of more objective and quantitative techniques [58]. Doppler ultrasound can assess blood flow in mesenteric or marginal arteries, but its sensitivity is limited, with high rates of false negatives and positives. More advanced techniques, such as visible-light spectrophotometry (VLS) and near-infrared spectroscopy (NIRS), as well as measuring tissue oxygen saturation (StO\({}_{2}\)), can be useful. [PERSON] et al. demonstrated that persistently low mucosal StO\({}_{2}\), measured intraoperatively with VLS, may indicate early colonic ischemia [59]. However, a precise StO\({}_{2}\) threshold predictive of ischemia has yet to be established, and variability in measurement algorithms and interference from intestinal contents (bile, stool, or food) remain critical challenges [60]. Fluorescence imaging using intravenous fluorescein or indocyanine green (ICG) dye has also been applied to visualize intestinal perfusion. ICG, in particular, allows for repeatable measurements due to rapid hepatic clearance, but requires specific equipment and is influenced by intestinal contents. Limiting thresholds for irreversible ischemia have not been yet standardized [61]. Figure 3: CT findings of CI. (**A**) Ideal air-fluid levels (**B**) Bowel wall pneumatosis. Optical coherence tomography (OCT) has emerged as a high-resolution, non-invasive tool for intraoperative intestinal viability assessment; however, this technique needs to be further validated [62].
## 2 Instrument
### Rayleigh-Mie-Raman Lidar  
To examine the performance of the CDWL in aerosol measurements, an RMRL was running near the CDWL simultaneously. The lidar uses an Nd: YAG power laser, which generates 20 pulses per second with a pulse energy of 250/350/350 mJ at the wavelength of 355/532/1064 nm. The backscatter signals are collected by a 450 mm diameter Cassegrain telescope and then separated into several channels using dichroic beam splitters, including the elastic channels (355, 532, and 1064 nm) and the nitrogen Raman (386 nm) channels. During the daytime, the elastic channels detect backscattered light with interferometric filters of 0.3 nm bandwidth to reduce the solar light, while the inelastic channels do not work due to the weak Raman scattering and strong background noise. The backscatter signals are detected by the photomultiplier tubes (PMT), and recorded using multichannel scaler (MCS) boards with a resolution of 30 m and 60 s.
## 4 Consistency Repaired Methods for an HMPR
Therefore, we can obtain the following simplified optimization model:  
\[(\mathbf{M}-\mathbf{4})\min J_{2}=\sum_{i<j}\left(d_{ij}^{-}+d_{ ij}^{+}\right) \tag{13}\] \[s.t.\left\{ \begin{array}{c}\ln f(p_{ij})+d_{ij}^{-}-d_{ij}^{+}= \ln w_{i}-\ln w_{j},i<j,\\ \sum_{i=1}^{n}w_{i}=1,w_{i}>0,i\in N.\end{array} \right.\]By using MATLAB or LINGO, we obtain the priority vector \(w=\left(w_{1},w_{2},\cdots,w_{n}\right)^{\text{T}}\) and optimal nonzero deviation values \(\widetilde{d}_{ij}^{-},\widetilde{d}_{ij}^{+}\), then we have \(\ln f\left(p_{ij}\right)+\widetilde{d}_{ij}^{-}-\widetilde{d}_{ij}^{+}=\ln w_ {i}-\ln w_{j}\), i.e., \(f\left(p_{ij}\right)\cdot\exp\left(\widetilde{d}_{ij}^{-}-\widetilde{d}_{ij}^ {+}\right)=w_{i}/w_{j}\). Therefore, the following Algorithm 2 is designed to improve the consistency of the HMPR \(P\):  
```
Step 1'. See Algorithm 1; Step 2'. According to model \(\left(\mathbf{M}-4\right)\), we get the optimal nonzero deviation values \(\widetilde{d}_{ij}^{-}\) and \(\widetilde{d}_{ij}^{+},i,j\in N\), and the priority weight vector \(\widetilde{w}^{(t)}=\left(\widetilde{w}_{1}^{(t)},\widetilde{w}_{2}^{(t)}, \cdots,\widetilde{w}_{n}^{(t)}\right)^{\text{T}}\); Step 3'-4'. See Algorithm 1; Step 4. If \(CI(P^{(t)})\leq\delta_{0}\) or \(t>t_{\text{max}}\), then go to Step 7. Otherwise, go to Step 5; Step 5'. Let \(P^{(t+1)}=\left(p_{ij}^{(t+1)}\right)_{n\times n}\), where \(p_{ij}^{(t+1)}=\left\{\gamma_{ijk}^{(t+1)}\middle|k=1,2,\cdots,\left|p_{ij}^{(t +1)}\right|\right\}\), \(\gamma_{ijk}^{(t+1)}=\left(w_{i}^{(t)}/w_{j}^{(t)}\right)^{\theta}\cdot\left( \gamma_{ijk}^{(t)}\right)^{1-\theta}=\left(\gamma_{ijk}^{(t)},\exp(\widetilde{d }_{ij}^{-}(t)-\widetilde{d}_{ij}^{+})\right)^{\theta}\cdot\left(\gamma_{ijk}^ {(t)}\right)^{1-\theta}=\gamma_{ijk}^{(t)}\cdot\exp(\theta(\widetilde{d}_{ij} ^{-}(t)-\widetilde{d}_{ij}^{+}))\) Step 6'-8'. See Algorithm 1. ```  
**Algorithm 2**The consistency adjusting process of HMPR based on linear optimization model  
Next, we will prove that the developed Algorithm 2 is convergent. **Theorem 2**. : _Let \(P=\left(p_{ij}\right)_{n\times n}\) be an HMPR, \(\theta(0<\theta<1)\) be the adjusted parameter, \(\left\{P^{(t)}\right\}\) be a collection of HMPRs in Algorithm 2, \(CI(P^{(t)})\) be the consistency index of \(P^{(t)}\), then we have_  
\[CI(P^{(t+1)})<CI(P^{(t)})\text{ for each }t,\text{ and }\lim_{t\to\infty}CI(P^{(t)})=0 \tag{14}\]  
**Proof. ** The proof of Theorem 2 is similar to Theorem 1. \(\square\)
## 3 Results and Discussion
### Descriptive Analysis  
The sensory profiles of the oils under study were established by a trained panel (Table 6). Olive oils from the 2019 season had higher fruity and pungent intensities than those obtain during the 2018 season. In general, water-stressed trees (T\({}_{3}\) and T\({}_{2}\)) led to olive oils with a higher intensity of the fruity, bitter and pungent notes compared to those of the control samples, T\({}_{0}\). Previous studies reported that water stress in olive trees negatively affected the attributes of \"bitterness\" and \"pungent\" ([PERSON] et al., 2019); however, this was not the case in the irrigation treatments applied to the super-high-intensive 'Arbequina' orchard under study, where water stress led to oils of even higher sensory quality than the control samples. A similar trend has also been reported by [PERSON] et al. (2019). An important characteristic for the oils to be classified as \"extra virgin\" is the absence of defects ([PERSON] et al., 2019). Regarding the overall defects presented in the samples under study (rancid, moldy, oxidized, humid and others), none of the studied samples showed significant values of the attribute \"defect\". \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Factor** & **Fruity** & **Bitter** & **Pungent** & **Overall Defects \({}^{\text{\textdagger}}\)** & **Commercial Classification** \\ \hline \multicolumn{5}{c}{**ANOVA \({}^{\text{\textdagger}}\)**} \\ \hline
**Season** & *** & **n.s.** & *** & **n.s.** \\
**Irrigation** & *** & ** *** & **n.s.** \\
**Season \(\times\) Irrigation** & *** & ** *** & **n.s.** \\ \hline \hline \end{tabular} \({}^{\text{\textdagger}}\) n.s. = not significant at \(p>0.05\), \({}^{\text{\textdagger}}\)**, **\({}^{\text{\textdagger}}\)** and *** significant at \(p<0.05\), \(p<0.01\) and \(p<0.005\); \({}^{\text{\textdagger}}\) Values followed by the same letter, within the same row, were not significantly different (\(p>0.05\)), according to [PERSON]’s least significant difference test. \({}^{\text{\textdagger}}\) RT = retention time; RE\({}_{\text{\textdagger}}\) = experimental retention index; RE\({}_{\text{\textdagger}}\) = literature retention index.  
\end{table} 
Table 6: Sensory profile (fruity, bitter, pungent and overall defects) by the trained panel of ‘Arbequina’ olive oil samples obtained from a super-high-density olive tree orchard with deficit irrigation.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## Security Proofs
### Other Attacks  
In MANETs, due to the lack of a centralized structure, denial of service (DoS) attacks can be frequent. In order to protect the proposed scheme from DoS attacks, although the communication produced with the application is through a non-secure channel, only legitimate nodes of the networks are able to send and decrypt valid messages.  
The proposed scheme is resistant against a brute force attack. A brute force attack, also called exhaustive key search, is a trial-and-error method used to obtain relevant data through the generation of a large number of consecutive guesses about the desired data. In the proposal described in this paper, since the desired data are contained in the last segment of the sent package, a brute force attack on the key used to encrypt the last segment would be required. In order to make it possible that a brute force attack could be used to discover the key used to encrypt such a segment, it could be necessary to check all of the possible keys. The key used to encrypt each segment has a fixed size defined by the hash function used to obfuscate the response to the previous segment challenge. SHA-3 has been used as a hash function in the implementation, so since its smallest output is 224 bits, a brute force attack would involve checking \(2^{224}\) combinations. If the scheme is used as a key agreement protocol, since in that case, interaction between parties exists, a maximum response time could be defined in order to prevent possible brute-force attacks.  
Another dangerous attack in MANETs is the sibling attack, which occurs when a node illegitimately uses multiple identities. This problem is avoided in the proposed scheme thanks to the distributed nature of the used NIZKP.  
Finally, the proposal is also resistant to identity theft because node access is controlled by an NIZKP.  
In a nutshell, usual attacks have no harmful effect on the proposal, because its security is supported by NIZKP based on complex mathematical problems and current standard hashing and encryption.
); andres.ecchini@upredu (A.C.)2 Department of Engineering Sciences and Materials, University of Puerto Rico at Mayaguez, Mayaguez, PR 00681, USA; basir.shafiq@upredu (F.A.J.-A. Typical vibration testing is a damage evaluation technique that uses frequency changes and modal property variations [22; 23]. This method can also be implemented to assess fatigue lifetime [24; 25; 26; 27; 28; 29; 30; 31; 32]. However, the stress patterns produced in beam vibration testing do not generally match those generated in standard three-point bending test procedures, as described in [33]. Therefore the patterns produced in vibration testing are not consistent with standard test results of flexural fatigue lifetime tests. This research proposes a novel vibration technique that can be used to determine the fatigue life of a sandwich composite. Instead of vibrating a standard prescribed shape, such as a rectangular beam, a geometric optimization of the test specimen is performed that creates a desired stress pattern emulating three-point bending flexural fatigue testing [33]. The hypothesis behind this idea is that the stress pattern produced by vibration fatigue testing should yield results matching conventional fatigue testing, as long as the frequency effects do not affect the material properties significantly. The main difference between this approach and work performed previously [34; 35; 36] is that, for the first time, this technique is actually verified against low frequency three-point bending tests and is applied to a sandwich core composite as opposed to homogeneous metals. Additionally, this procedure uses flexural vibration modes whereas other references [34; 35; 36] use torsional modes of vibration. Traditional fatigue testing machines operate between 1 Hz and 15 Hz; however, inertial jig effects usually limit testing frequencies below 5 Hz for most engineering materials. The literature provides contrasting opinions on whether an increase in frequency can lead to an improvement or a reduction in the fatigue life [19; 20; 21]. According to some authors [11; 12; 19; 37], temperature increase affects material properties and reduces fatigue life in sandwich composites. This is mostly produced by hysteretic heat generation that occurs between the different constitutive layers of the sandwich composite. In the case of foams in an environment with constant temperature, the temperature rise is typically prevented by the heat dissipation [1; 2; 37]. Vibration tests performed on a polyurethane foam showed a temperature increase of no more than 2 \({}^{\circ}\)C [37], an amount that is considered small enough not to affect the properties of such materials. The proposed methodology was used to study the fatigue life characteristics of polyurethane foam sandwich composites, where carbon/epoxy face sheet constituents are significantly stiffer, and the effects of temperature increases in testing are negligible. The results show reasonably good correlation data to justify the use of this technique as a viable substitute for conventional low frequency (1-5 Hz) fatigue testing in practical applications.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Initial Parameter Variation  
In this subsection, the sensitivity of the filtering probability to the initial parameter variation is derived based on SMF. Let us consider the sensitivity functions \(p(\mathbf{x}_{\
u}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta_{\gamma})\) for a filtering probability and variation in the initial parameter \(\theta_{\gamma}=\gamma_{\tau}\). From Equation (6), the recursive filtering probability sensitivity expression, for \(t=1\), we have \(p(\mathbf{x}_{\
u}^{1},\mathbf{y}_{\mathbf{e}}^{1})(\theta_{\gamma})=o_{\
u, \mathbf{e}^{1}}\cdot\gamma_{\tau}(\theta_{\gamma})\), and for \(t>1\),  
\[p(\mathbf{x}_{\
u}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta_{\gamma})=o_{\
u, \mathbf{e}^{1}}\cdot\sum_{z=1}^{n}a_{z,\
u}\cdot p(\mathbf{x}_{\
u}^{t-1}, \mathbf{y}_{\mathbf{e}}^{1:t-1})(\theta_{\gamma}) \tag{8}\]