esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Random Analysis
### Effect of Pillar Depth \(P\)  
The changes in the mean values of the normalized maximum tunnel responses with increasing \(P\) under different \(\theta_{E}\) (\(\theta_{E}\) = 1D, 3D, 10D) are shown in Figure 16. Previous case studies indicate that the pillar depth typically varies between \(0D\) and \(0.8D\)[5]. It is seen from Figure 16 that the mean values of \(w_{\rm m}\) gradually increase, and the mean values of \(M_{\rm m}\) and \(Q_{\rm m}\) decrease when \(P\) increases from \(0.2D\) to \(1.8D\). The profiles of the greenfield soil settlement and the tunnel settlement become flattened and wide due to the increase in \(P\), which results in smaller internal forces of the existing tunnel. Figure 17 presents the variations in the COVs of the normalized maximum tunnel responses with increasing \(P\) under different \(\theta_{E}\) (\(\theta_{E}\) = 1D, 3D, 10D). As shown in Figure 17, the increase in \(P\) leads to an approximately linear decrease in the COV of the normalized responses. For example, the COV of \(w_{\rm m}\), \(M_{\rm m}\), and \(Q_{\rm m}\) under \(\theta_{E}\) =3\(D\) decreases by 48%, 37%, and 37%, respectively, due to the increase in \(P\) from \(0.2D\) to \(1.8D\). Therefore, the increasing \(P\) weakens the effect of the spatial variability of the soil elastic modulus. It is also observed that the variation in \(\theta_{E}\) has nearly no effect on the slope of variation in the COVs of the normalized tunnel responses.  
Figure 15: Effect of \(\mu_{E}\) on COVs of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.  
Figure 16: Effect of \(P\) on mean values of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.  
Figure 17: Effect of \(P\) on COVs of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.
# A 2.4-GHz Fully-Integrated GaAs pHEMT Front-End Receiver for WLAN and Bluetooth Applications
###### Abstract  
This paper describes a 2.4-GHz fully-integrated front-end receiver including a single-pole triple-throw (SP3T) switch and a low-noise amplifier (LNA) with bypass function, which was fabricated in a 0.25 \(\upmu\) GaAs pHEMT process. An asymmetrical SP3T switch architecture is incorporated to enable the receiver to operate in four modes. The exploration of impedance and voltage gain behavior of the proposed LNA help to establish the matching network and alleviate the trade-off between noise figure (NF) and gain performance. In LNA high gain mode, the implemented front-end receiver shows 1.7 dB of NF and 6 dBm of input third-order intercept point (IIP3) with 20 dB of power gain drawing 11 mA of current from 5 V power supply at 2.4 GHz. All input and output return loss had exceeded 10 dB with fully on-chip impedance matching network. In bypass mode, the measured insertion loss of typically 7.5 dB is achieved.  
low noise amplifier; SP3T switch; GaAs pHEMT; WLAN; front end +
Footnote †: journal: Applied  
sciences  
Footnote †: journal: Applied
## 3 Results and Discussion
### Reducing Power  
The reducing power assay based on the reduction of Fe\({}^{3+}\) to Fe\({}^{2+}\) by donating an electron could be used as an important indicator to evaluate the potential antioxidant activity of polysaccharides [44]. A direct correlation between the antioxidant and reducing capacity has been reported. Reducing properties are generally associated with the presence of reductones, which can donate a hydrogen atom and exert antioxidant action by breaking the free-radical chain. Reductones are also reported to react with certain precursors of peroxide, preventing peroxide formation. Thus, the reducing capacity of a compound or extract may be a significant indicator of its potential antioxidant activity. According to the reports which claimed that the reducing power is positively related to the antioxidant activities [45], the antioxidant capacity of GPPP was evaluated using the K\({}_{3}\)Fe(CN)\({}_{6}\) reduction method at 700 nm. The reducing power of GPPP and ascorbic acid was shown in Figure 4B. A higher absorbance value means greater reducing ability of samples. The reducing power of GPPP was increased from 0.5 to 0.63 in the range of 0.1-0.9 mg/mL. It is clear that the reducing power of GPPP correlated well with sample concentrations. According to Figure 4B, the reducing power of GPPP was significantly lower than that of ascorbic acid. It was revealed that the reducing power was generally associated with the presence of reductones. Therefore, this result suggested that GPPP might contain reductone-associated and hydroxyl groups [15; 46]. The data of reducing power suggested that GPPP could act as electron donors and can react with free radicals to convert them to more stable products [47].
## 4 Discussion
Thus, ex situ conservation of microorganisms collected from monuments can ensure that all strains with their unique properties are preserved and maintained over time. In accordance with other studies, we observed that the genera most frequently identified as colonizers of several stone substrates are _Aspergillus_, _Fusarium_, and _Penicillium_[31]. The action of these fungi, included in the orders Capnodiales and Pleosporales, could lead to aesthetic alteration and biopitting of stone materials [32]. Moreover, some of the fungal isolates belonging to _Alternaria_ section _Alternata_, _Cladosporium sp._, _Fusarium solani_ species complex, and _Penicillium_ section _Aspergillus_ are known to contribute to acidification and the dissolution of stone by accreting organic acids. In particular, oxalic acid secreted by fungi can dissolve limestone calcium carbonate, producing calcium oxalates, one of the most severe biodeterioration processes affecting limestone monuments [33]. In addition, recently it has been shown that the genus _Purpurecillium_ may have halotolerant characteristics [34], which may further broaden the impact these fungi have on stone structures.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 7 Experimental Results  
### Prototype  
We implemented our prototype using discrete components and off-the-shelf chips to prove the feasibility of the future integrated circuit (IC) design, as shown in Figure 10. The coils and the inductive link circuit in the prototype are the same as those we optimized in Sections 4 and 5. The power feeding circuit is implemented by a class-E power amplifier. The external receiving filter is implemented by a four-stage Butterworth low-pass filter. The external BPSK demodulator is digitally implemented in an MCU (STM32F207, STMicroelectronics, Geneva, Switzerland). The implant uplink power amplifier is merged in an H-bridge modulator to achieve low cost and high efficiency [24]. The implant BPSK modulator is implemented in an FPGA (Cyclone IV, Altera, San Jose, CA, USA).
## 4 Discussion
### BC of Lemongrass Might Regulate Axillary Meristem Growth and Development  
Many studies evidence the anticancer activity of citral, limonene, citronellal, geraniol, geranyl acetate, and \(\beta\)-carophyllene. All these were detected in the EO of lemongrass (Table 2). Still, scarce data are available on their specific mode of action to induce apoptosis and block the proliferation of carcinogenic human cell lines [3,53]. In addition, each BC induces apoptosis using different mechanisms of action even to stop cell proliferation and cell cycle arrest in various types of cancer. To be updated, we recommend the reader to consult the following reviews [2,70]. It has been reported that limonene and geraniol inhibit the activity of HMGR in various human cancer cell lines. A reduction of isoprenoid and terpenoid biosynthesis is the consequence, arresting the cell cycle and stopping the growth of culture tumor cells [71,72]. [PERSON] and colleagues [73] also reported that geraniol induces apoptosis, including DNA and nuclei fragmentation in cultured shoot primordia  
Figure 5: MEP/DOX and mexolonate pathways in plants. The two main biosynthetic routes involved in terpenoid biosynthesis are shown. The figure shows the chemical structures of the main monoterepenes and \(\beta\)-carophyllene. GPPS geranyl diphosphate synthase, GES geraniol synthase, GPPD geranyl diphosphate diphosphate, GAT geraniol acetyl-CoA acetyltransferase, GAE geranyl acetate esterase, GeDH geraniol dehydrogenase, FPPS farnesyl diphosphate synthase, SPS sesquiterpene synthase, MEP methyl erythroid phosphate, DOX deoxy-xylulose 5-phosphate [68,69].  
of _Matricaria chamomilla_, and [PERSON] et al. [74] said that geraniol induces senescence in tomatoes. In addition, [PERSON] et al. found that microtubules are the intracellular targets of plant terpene citral [75].  
Furthermore, \(\beta\)-cyclocitral has recently been reported to induce root apical meristem cell division and promote root growth in _Arabidopsis_[76]. Based on these reports, we also suggest exploring the possibility that these lemongrass BC could regulate the cell cycle in plants by inhibiting or increasing HMGR activity and, by the way, be involved in the control of axillary meristem growth and development. Therefore, a better understanding of the effects of CKs on the regulation of plant terpen biosynthesis, and the mode of action of these terpen on regulating plant cell cycle and axillary meristem growth, should let us establish and improve lemongrass in vitro propagation protocols. These protocols can permit the manipulation of in vitro shoot outgrowth and increase plant biomass and the content of BC in the EO. Finally, we are addressing a gene network study involving plant mevalonic acid pathway, CK biosynthesis, CK activity and signaling, plant BC and cell cycle checkpoints to help us elucidate the regulation of axillary meristem growth and development by BC to increase lemongrass biomass and BC production.
## 2 Methodology
### Model Validation  
To ensure the accuracy of our mChl-\(a\) model, we compared it against in situ Chl-\(a\) data. If discrepancies were observed, regional adjustments were proposed. For algorithm validation, we employed several metrics: the coefficient of determination \(R^{2}\), the adjusted coefficient of determination \(R^{2}_{a}\), the sum of squares error (SSE), and the root mean squared logarithmic error (RMSLE). Additionally, the Akaike information criterion (AIC) was computed to compare the modeled results.  
\[\text{AIC}=-2 ln(L)+2k \tag{6}\]  
where \(L\) is the log-likelihood and \(k\) is the number of parameters in the model [26]. The AIC, commonly used for model selection, compares the performance of a set of models by estimating their quality based on error, data size, and the number of parameters. The smaller the value of the AIC, the better the quality of the model.
## Materials and Methods
### Questionnaires  
The frequency and quantity of consumption of foods containing polyphenols and Mediterranean diet adoption was investigated by two validated questionnaires. The first was a semi-quantitive food frequency questionnaire (FFQ), based on the NHANES study FFQ [40] after some modifications. Changes were made in particular with the goal of including more natural foods high in polyphenols, without changing the type of questions, based on previously published studies on natural functional food consumption in the Greek population [23; 39]. The questionnaire included four parts. The first contained questions about gender, age, occupation, and other demographic characteristics. The second part contained questions about the nutritional background of the participants, smoking, water consumption, use of medication, and supplements. In the third part, the frequency and the quantity of foods that contain polyphenols were investigated. A total of 109 foods and food groups were evaluated and included fruits, vegetables, legumes, cereals, nuts, herbs, spices, olive oil, wild greens, wine, especially tomatoes, grapes, raisins, red wine, black chocolate, broccoli, lentils, etc. The possible answers were \"every day\", \"4-5 times per day\", \"2-3 times per day\", \"1 time per day\", \"5-6 times per week\", \"3-4 times per week\", \"1-2 times per week\", \"3-4 times per week\", \"1-2 times per week\", \"3-4 times per month\", \"1-2 times per month\", and \"never\" [23; 39; 40]. The filling of the questionnaires took place with assistance by the researchers, using pictures with food portions, and aimed to identify the frequency of each item consumption, the number of items, and the quantity consumed each time. For seasonal fruits and vegetables, the volunteers were informed to fill the consumption only for the season that the foods consumed.  
The second questionnaire was the Mediterranean Diet Score that was proposed by [PERSON] et al. (2006) [41], and consisted of 11 main components of the Mediterranean diet (non-refined cereals, fruits, vegetables, potatoes, legumes, olive oil, fish, red meat, poultry, full-fat dairy products, and alcohol). For the consumption of items closer to the Mediterranean diet, scores of 0, 1, 2, 3, 4, and 5 were assigned when a participant reported no consumption, rare, frequent, very frequent, weekly, and daily, respectively. For the consumption of foods away from this pattern, the same scores were assigned on the reverse scale. Then, the total Med Diet Score from 0 to 55 was calculated. A high Med diet score indicates high Mediterranean diet adherence, while a low Med diet score indicates low Mediterranean diet adherence [41]. The Med Diet Score was also grouped into three categories (low 1-17, moderate 18-36, and high adherence 37-55).
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### Kalman Filter
The innovative approach can be described as:  
\[\hat{y}^{\prime}{}_{n,I}(n|n-1)=x_{n,I}(n)-H_{I}(n)\hat{y}^{\prime}{}_{n,I}(n| n-1), \tag{48}\]  
\[\hat{y}^{\prime}{}_{n,Q}(n|n-1)=x_{n,Q}(n)-H_{Q}(n)\hat{y}^{\prime}{}_{n,Q}(n| n-1), \tag{49}\]  
The estimation error can be explained by the equation below, when \(x_{n,I}(n)\) and \(x_{n,Q}(n)\) is estimated by \(\hat{y}^{\prime}{}_{n,I}(n)\) and \(\hat{y}^The efficiency and performance of the FD transceiver are calculated based on link budget analysis, thereby computing powers of different components of SI signal and SINR.
## 2 Gravity Calculation Method
### Polyhedral Method  
For the polyhedral gravity field, the potential of spacecraft at position \(\mathbf{r}\) is [7]  
\[U(\mathbf{r})=\frac{1}{2}G\sigma\Bigg{(}\sum_{c\in\text{edges}}\mathbf{r}_{e} \cdot\mathbf{E}_{e}\cdot\mathbf{r}_{e}\cdot L_{e}-\sum_{c\in\text{faces}} \mathbf{r}_{f}\cdot\mathbf{F}_{f}\cdot\mathbf{r}_{f}\cdot\omega_{f}\Bigg{)}. \tag{4}\]  
where the density \(\sigma\) is assumed to be a constant and \(\mathbf{r}_{e}\) is a vector from the field point to any point on edge e. \(\mathbf{r}_{f}\) a vector from the field point to some fixed point in the face plane f. Suflixes \(e\) and \(f\) denote the edge and face of the polyhedron. The face's outward-pointing normal vector is \(\mathbf{\hat{n}}_{f}\), and its dyad \(\mathbf{\hat{F}}_{f}=\mathbf{\hat{n}}_{f}\mathbf{\hat{n}}_{f}\) and \(\mathbf{\hat{n}}_{e}^{f}\) is outward edge normal, which is on the plane \(f\) and perpendicular to the edge \(e\). For the edge connecting vertices i and j shared by faces A and B, the edge dyad is \(\mathbf{\hat{E}}_{ij}=\mathbf{\hat{n}}_{A}\mathbf{\hat{n}}_{i}^{A}+\mathbf{ \hat{n}}_{B}\mathbf{\hat{n}}_{ji}^{B}\), with similar \(\mathbf{E}_{e}\)s. If \(\mathbf{r}_{i}\) is the vector form of the spacecraft-to-polyhedron vertex \(P_{i}\), \(r_{i}\) is its magnitude. \(e_{ij}\) is the length between vertices \(P_{i}\) and \(P_{j}\), and an edge factor \(L_{e}\) is defined as  
\[L_{e}=\int_{e}\frac{1}{r}\text{d}\mathbf{s}=\int_{P_{i}}^{P_{j}}\frac{1}{r} \text{d}\mathbf{s}=\ln\frac{r_{i}+r_{j}+e_{ij}}{r_{i}+r_{j}-e_{ij}}. \tag{5}\]  
\(\omega_{f}\) is a dimensionless face factor bounded by the triangular face \(f\):  
\[\omega_{f}=\iint_{\text{triangle}}\frac{\Delta z}{r^{3}}\text{d}\mathbf{s}=2 \arctan\frac{\mathbf{r}_{i}\cdot\mathbf{r}_{j}\times\mathbf{r}_{k}}{r_{i}r_{ j}r_{k}+r_{i}(\mathbf{r}_{j}\cdot\mathbf{r}_{k})+r_{j}(\mathbf{r}_{k} \cdot\mathbf{r}_{i})+r_{k}(\mathbf{r}_{i}\cdot\mathbf{r}_{j})}. \tag{6}\]  
We note that many researchers know that the polyhedral method can calculate the gravity outside or near the surface of an asteroid. Some of them may know that it can be used to calculate the gravity even on the surface. But very few of them know that it can calculate the gravity inside the asteroid with some modification of the codes.
## 4 Experiment Results and Comparison
### The Experimental Setup  
The \(n\)-fold cross-validation strategy is applied to test the performance of the proposed approach, as well as baselines on each data set, by partitioning it into \(n\) equal-size sub-sets; then, one of these \(n\) sub-sets is used as a testing set at each time, while the rest \(n\)-1 sub-sets are used as a training set. Then, the final result will be calculated as a mean value of the results of all \(n\)-fold. In this experiment, \(n\) is set to be (10), as suggested by [32], in order to reliably demonstrate the efficiency of any proposed algorithm. The 10-fold cross-validation is repeated (M = 10) times; each time, the order of the data set instances is randomised. This is because many algorithms are influenced by data order, i.e., specific orders significantlyupgrade or degrade the performance. In our case, we repeated the following experiment 10 times. We randomly selected a small subset which consists of 0.1% of the total data set to represent the labeled data for the proposed approach. Each selected subset is distinct from the previous selected ones. In each iteration, we used the selected subset as a small labeled data, while the remaining data set was considered as an unlabeled data set. Therefore, the proposed approach will utilise the small labeled data for the labeling process.  
The environment of our experiment is as follows: Windows 10 Enterprise. The types of system are as follows: 64-bit operating system, x64-based processor; Intel(R) Core(TM) i7-9750H CPU@ 2.6 GHz-2.59 GHz; RAM: 16 GB. The proposed approach and baseline algorithms were implemented in Python 3.7 using libraries such as numpy, pandas, and Scikit-Learn (sklearn) [33]. In all experiments, we used a single core and the default configurations of the backend libraries, where the adapted classifiers are run on a single core by default. In the following, all experiment details are broadly explained.
## 4 Experimental Outcomes
### XAI Interpretation
#### 4.3.1 t-SNE and UMAP  
The extracted features from both pretrained and fine-tuned networks are visualized in order to understand what patterns emerge in low-dimensional spaces after having employed nonlinear dimensionality reduction techniques such as t-SNE and UMAP.  
In Figure 9, the t-SNE embedding plots for both pretrained and fine-tuned DenseNet-161 and SqueezeNet architectures are pictorially represented. Similarly, Figure 10 presents the UMAP embedding plots for both pretrained and fine-tuned DenseNet-161 and SqueezeNet models. In the pretrained version, no clear patterns arise from both embedding plots, showing that features learned from ImageNet dataset are not necessarily well discriminative for radiological image applications.  
Nonetheless, after 50 epochs of fine-tuning on the designated train set, the clusters appear more distinctive. In fact, with trained CNN features, both UMAP and t-SNE allow to visualize different clusters for all four considered classes: _None, Ori, Oro,_ and _Ost_.  
Figure 9: The t-SNE embedding plots of the features extracted from pretrained (**a**,**c**) and fine-tuned (**b**,**d**) DenseNet-161 and SqueezeNet, respectively, on the validation set of 1 st fold. It is clearly visible that the fully TL paradigm does not allow a clear clustering of the features in low-dimensionality space, whereas the finetuned model is able to discover more discriminative features with respect to its pretrained-only version.  
As described in Section 3, the distance between the clusters cannot be interpreted by using the t-SNE visualizations. For instance, it cannot be inferred from Figure 9 that clusters are dissimilar to each other when one cluster is closer to the other. However, it can be stated that points closer to each other are more similar objects than the points at farther ends, whereas Figure 10, thanks to the local and global feature representation capability of the UMAP, clearly plots the points that can be interpreted as distinguishing clusters and the position of the points.
## 7 Dephosphorylation of RIPK1
Additionally, it will be interesting to investigate if the PPP1R3G/PP1\(\gamma\) holoenzyme removes inhibitory phosphorylation of S415. Figure 4: Regulation of RIPK1 activity through phosphorylation and dephosphorylation. This schematic illustrates the dynamic regulation of RIPK1-dependent cell death pathways. Inhibitory phosphorylation events on RIPK1, catalyzed by multiple kinases, serve as important checkpoints for cell death activation. Following cell death induction, the PPP1R3G/PP1\(\gamma\) holoenzyme is recruited to complex I to remove the inhibitory phosphorylation on RIPK1. This process enables RIPK1 autophosphorylation to activate its kinase activity. Consequently, activated RIPK1 triggers downstream signaling cascades, leading to apoptosis and necroptosis. The balance between inhibitory phosphorylation and PPP1R3G/PP1\(\gamma\)-mediated dephosphorylation serves as a key regulatory mechanism for RIPK1-dependent cell death processes. The red stripes in the diagram of RIPK1 and RIPK3 indicate the RHIM domain. A recent study revealed that the PPP6C (protein phosphatase 6 catalytic subunit) is essential for necroptosis, identified through a CRISPR whole-genome knockout screen [98]. As previously reported [99], PPP6C is recruited to complex I through TAB2, and dephosphorylates TAK1 to prevent inhibitory phosphorylation of RIPK1, thus activating TNF-induced necroptosis. Gastrointestinal tract-specific deletion of one allele of _PPp6c_ in mice could partially alleviate cecum damage caused by TNF-induced SIRS, confirming its role in necroptosis activation. Recent findings have partially corroborated this result, as evidenced by a CRISPR screen that identified the protein phosphatase 6 (PP6) holoenzyme as an activator of TAK1 inhibitor-induced PANoptosis [100]. The study demonstrated that ablation of the catalytic subunit PPP6C, or combined deletion of its regulatory subunits PPP6R1, PPP6R2, and PPP6R3, resulted in enhanced inhibitory phosphorylation of RIPK1 at S321 and reduced auto-activating phosphorylation at S166, consequently suppressing PANoptosis. However, the research did not conclusively determine whether the PP6 complex directly dephosphorylates RIPK1 at S321 or if it acts on TAK1, as proposed in the previous study [98]. This leaves open questions regarding the precise mechanism of PP6 in regulating RIPK1 phosphorylation.
## 3 Improvements in HY-2B POD  
Based on the processing standards described in Section 2.3, assessments of each processing chain are described in this section. In addition to satellite dynamics, improving the geometric strength is a powerful means of improving the orbital accuracy of LEO satellites. GPS antenna offset calibration and an assessment of single-receiver ambiguity resolution were performed for the HY-2B satellites, as we describe in this section. An internal consistency analysis and external validations are provided for different orbit solutions. The GPS data covering the period since the initial GPS receiver operation (November 2018) to the end of 2019 were used.