esa-sceva/satcom-chunk-collection huggingface dataset:
# Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic
## 1 Introduction
### Aims of the Study  
The main objectives of this study are:  
* Identify the key stakeholders in COVID-19 vaccine research and investigate the content of their Instagram posting, as well as how this is perceived by users;
* Detect any similarities/differences between the respective companies posting on both textual and visual features;
* Detect any similarities/differences between the respective users' perception, by means of hashtags;
* Perform user posts' intent classification, to explore a potential predictive modelling application for detecting what users desire to post;
* Perform user posts' sentiment analysis, to quantify their feelings and opinions.  
As shown later, the only companies offering vaccine with an Instagram account are Pfizer, Astrazeneca, and Johnson & Johnson. Pfizer Inc. is an American multinational pharmaceutical corporation that was founded 172 years ago in New York City. Pfizer, one of the largest pharmaceutical companies in the world, in cooperation with BioNTech created a vaccine that is based on mRNA technology. This technology introduces part of the genetic material of the SARS-CoV-2 virus in the form of messenger RNA (mRNA) [46].  
AstraZeneca is a science-led biopharmaceutical company producing innovative medicines that are used by millions of patients around the world [47]. The AZD1222 vaccine is 'a replication-deficient simian adenovirus vector, containing the full-length codon-optimized coding sequence of SARS-CoV-2 spike protein along with a tissue plasminogen activator (tPA) leader sequence' [48].  
Johnson & Johnson is multinational company that was founded in 1886 in USA. Their range of products includes medical devices, pharmaceuticals, and consumer goods [49]. The Johnson & Johnson vaccine is 'based on the virus's genetic instructions for building the spike protein. Unlike the Pfizer-BioNTech and Moderna vaccines, which store the instructions in single-stranded RNA, the Johnson & Johnson vaccine uses double-stranded DNA' [50].
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 4 Erase Algorithm
### Improving the Effect of Lateral Migration  
In 3D NAND, the charge trap layer (CTL) is entirely connected, from the upper WL to the lower WL, and so charges are also trapped in the CTL in the spaces between WLs during repetitive program/erase cycling [85]. In addition, since 3D NAND forms a virtual junction by fringing field, the Vt of the cell is sensitively affected not only by the charges stored in the CTL but also by the charge in the space region. In particular, as the WL pitch becomes smaller and smaller, more holes are programmed in the CTL in the space region during the erase operation due to an increase in the fringing field by the adjacent WLs. This leads to an acceleration of retention loss due to the lateral migration effect.  
To improve the above problems, [PERSON] et al. proposed a two-step annealing pulse scheme [40], shown in Figure 13. In conventional NAND, the equivalent voltage (0 V) is applied to all WLs when the erase voltage is applied to the channel during erase operation. However, in the proposed method, at first, 0 V is applied to even WLs, while biasing odd WLs. Next, 0 V is reversely applied to odd WLs while biasing even WLs. It is demonstrated that retention loss can be improved by suppressing the formation of holes by the fringing field in the space region. However, in this case, the two-step erase operation can deteriorate the erase time, and so there is a trade-off between erase performance and reliability gain, which must be carefully controlled.  
Figure 12: (**a**) Experimental fail bit count at fresh, after 1 K P/E cycles and 3 K P/E cycles. Reprinted/adapted with permission from Ref. [84], 2021, IEEE. (**b**) Id-Vg sweep with Cell Vt is programmed to \(\sim\)3 V. Reprinted/adapted with permission from Ref. [84], 2021, IEEE. (**c**) The recovery phase of conventional and proposed program operation, respectively. Reprinted/adapted with permission from Ref. [84], 2021, IEEE.  
Whereas the above-mentioned approach minimizes the occurrence of hole traps in the space area, [PERSON] et al. proposed a deep erase compensation scheme to reduce holes under the gate [36]. Recently, holes underneath adjacent WLs, as well as holes in the space area, have been reported to critically deteriorate the lateral migration effect [86]. In the proposed method, by softly verifying the deep erased cells toward the high Vt, retention loss by lateral migration can be enhanced. However, in this method, if the soft verifying sequence is added to all WLs within the entire erase operation, the erase time becomes too long. Therefore, adding this sequence to the program operation would be a more realistic solution.
## 3 Simulation Implementation
### Implementation of the Communication System  
Key ingredients of the communication systems are:  
1. analog-to-digital conversion (ADC) of the load voltage,
2. modulation of the digital signal,
3. coupling/decoupling, and
4. demodulation.  
A detailed description of the implementation of the above features is provided in the next paragraphs.
## 2 Test Setup  
### R/C Beams and Strengthening Methods  
Four R/C beams, each with distinct strengthening conditions, were tested under cyclic quasi-static loading along vertical direction. The length of the tested beams was 3200 mm, while the cross-sectional dimensions were 150 mm by 250 mm. Since the primary focus of this study was to investigate the contribution of the FRP materials to the flexural behavior of the beams, the reinforcement arrangement was intentionally designed to have insufficient flexural capacity. The reinforcement details of the beams are presented in Figure 1.  
The beams were strengthened by the externally bonding FRP technique using two different materials, namely GFRP and CFRP. Prior to the strengthening process, the beam surface intended for contact with the strengthening material (i.e., bottom surface of the beam) was thoroughly roughened and cleaned to improve adhesion. Pre-cut woven glass or carbon fibers, having dimensions of 150 mm by 2800 mm, were bonded to the beam with an adhesive. Two layers of glass or carbon fibers were applied in all strengthening procedures. During the bonding process, each layer was meticulously flattened and pressed with a roller to remove air bubbles and ensure full saturation. A thin layer of adhesive was applied between each fiber layer. After completing the strengthening process, the beam was maintained at room temperature for a week to cure the strengthening material and ensure a good bond between the beam and the strengthening material. The FRP thickness in the beams strengthened with GFRP was 2.6 mm, with a fiber volume fraction of 11%, while the FRP thickness in the beams strengthened with CFRP was 2.1 mm, with a fiber volume fraction of 19%. Examples of GFRP and CFRP applications on beams are provided in Figure 2.  
The materials used for strengthening each beam, as well as the timing of the strengthening process, are summarized in Table 1. In this context, the term 'pre-strengthened' refers to a beam that was strengthened before any damage was induced (i.e., in the undamaged state). In contrast, the term 'post-strengthened' indicates that the strengthening process was applied after a beam had sustained a pre-defined level of damage.  
\begin{table}
\begin{tabular}{c c c} \hline
**Beam Name** & **Strengthening Material** & **Strengthening Timing** \\ \hline Beam\_GFRP\_PreStr & GFRP & Pre-Strengthened \\ Beam\_GFRP\_PreStr & CFRP & Pre-Strengthened \\ Beam\_GFRP\_PostStr & GFRP & Post-Strengthened \\ Beam\_GFRP\_PostStr & CFRP & Post-Strengthened \\ \hline \end{tabular}
\end{table}
Table 1: Strengthening materials and timing for each beam.  
Figure 1: Reinforcement details of the beams.  
Figure 2: (a) GFRP and (b) CFRP applications on beams.
# BIM Policy Trends in Europe: Insights from a Multi-Stage Analysis
## 3 Results
### BIM Policy in Europe
#### 3.2.25 Switzerland  
The mandatory use of BIM in Switzerland follows a more bottom-up approach, with notable examples such as the SBB Swiss Federal Railways. The railway company committed to using BIM based on the SN EN ISO 19650 1-2018 methodology for cubature buildings with budgets exceeding CHF 5 million, starting in 2021. Additionally, there are plans to extend this mandate to railway infrastructure by 2025. The Federal Office of Communications (_Bundesamt fur Kommunikation_, _Office federal de la communication_, _Ufficio federale delle convin-cazioni_) published the Digital Switzerland Strategy in 2020, encouraging digitalization [107]. The country has also launched a cross-sector initiative, _digitalswitzerland_, to support digital transitions, similar to the efforts of The Swiss Association of Engineers and Architects (SIA, _Der Schweizerische Ingenieur- und Architektenveretin_).
# Sustainable Management of Water Resources in Supplementary Irrigation Management
###### Abstract
[8] add that the relationship between SWC and physical properties might not be unique and may differ along with drying and wetting cycles, especially in finer-textured soils. [PERSON] et al. [9] claim that each sensor may perform differently when used in real measurement operations in a specific region. Soil moisture sensors perform differently not only with different soil types, but also with different soil depths [10], different parts of the field and different growing conditions [11]. Because of the above-mentioned factors, without the site-specific calibration, it is hard to obtain accurate data on SWC. Site-specific calibration would not only significantly increase the precision of sensors [12], it is also essential in the modeling of water flow and nitrogen transport in soils [13]. In the present study, the performance of the following devices for measuring SWC was tested: Granular Matrix Sensor (GMS, Watermark 200 SS), Tensioneter both manufactured by Irrometer Co. (Riverside, CA, USA) and Time Domain Reflectometry (FieldScout, TDR300) manufactured by Spectrum Technologies, Inc. Watermark is an electrical resistance device in a gypsum wafer surrounded by a granular matrix material. It is a relatively low-cost GMS, which is easy to use and install and can function consistently over a range of soil water tension from \(-10\) kPa to \(-200\) kPa [14]. The Watermark sensor operates on the same principles as other electrical resistance sensors. Water content inside the sensor changes corresponding to soil water content. The changes with the sensor are reflected by differences in electrical resistance between the electrodes [15]. Tensioneter measures soil water potential and comprises a tube filled with water attached to a ceramic cup on one end and a vacuum gauge on the other end [16]. The last sensor used in this study was Time Domain Reflectometry (TDR300). TDR was chosen because of its increased usage for field soil water estimation because the measurement is non-destructive and less affected by soil texture, bulk density and temperature [17]. TDR is a technology in which an electromagnetic pulse is propagated down a parallel rod, which acts like a balanced transmission line [18]. The advantage of TDR is its ability to accurately measure the permittivity of material and the ability to estimate SWC and measure bulk soil EC simultaneously [19]. The sensor measures the travel time of a step pulse of electromagnetic radiation from a reference node along the guide rods inserted into the soil and back to the reference node [20]. With TDR, the soil dielectric constant (_Ka_) is measured and related to Volumetric Water Content (VWC) using a calibration equation [21]. The most important issue is to ensure that the rods are fully inserted straight into the soil. The main goal of this study was to (i) evaluate the performance of sensors for measuring SWC in supplementary irrigation scheduling, (ii) to perform field calibration and (iii) to perform laboratory calibration to reduce errors.
## 2 Materials and Methods
### How Cognitive Radio Engage with the Industrial IoT?
#### 2.1.1 LPWAN for IIoT  
LPWAN is considered an efficient technology for IIoT applications, which promises high-energy efficiency and coverage capability with low power consumption [31]. The LPWAN approaches such as Sigfox, LoRA, NBIoT, and other licensed cellular IIoT technologies standardized by 3 GPP such as eMTC (LTE-Cat-M1) and 5G cellular IIoT are all represented for IIoT applications and expected to be part of the new 5G framework by 2020 [32]. Multiple access levels have been applied using LPWAN for IIoT applications. In a mixed LPWAN network, Sigfox can collect the IIoT data using LoRaWAN [33] intermediary devices to facilitate connectivity to the IIoT core network, as shown in Figure 5. The LTE and 5G as wireless Access technology based on the Internet of Things connect the cells such as NBIoT, WiFi, and LoRa [38]. The comparison is shown in Table 1.  
In Mixed LPWAN, device management functions are: registration, authentications, data traffic, speed transmission, and bandwidth performance using the core network. The consistency of network servers in LPWAN occurs to control the LoRaWAN base stations' traffic flow to provide received payload [39]. Base stations forward the collected data to the IIoT SaaS to manage the database contents. Generally, LPWA networks are suffering from latency limitations that are mitigated, and the highest rate demand is replaced by long coverage range and low consumption [40]. The inverse demand for the two technologies such as IIoT concerns big applications, and it is accessing that traditional cellular networks will still be a resolution, for example, non-energy-constrained issues that request high data rates [41]. CR delivers capacity through spectrum sensing. So, CR obtains the best frequency through the interaction with its radio
## 4 Design of TLQoS
### Reliability Module  
The reliability module chooses a forwarder locally that ensures the higher reliability for \(Rc\) and \(Cr\) traffic type. Algorithm 2 shows the route selection procedure at this module.  
**INPUT** (a packet \(p\in Cr,Rc\), NT)  
```
1.if\(p.HC>HC^{max}\) {Condition-2} then
2.\(\gamma\)=\(p.\gamma\)
3.\(\gamma=\gamma-0.1\)
4.\(p.\gamma\)=\(\gamma\)
5.else
6.\(\gamma\)=\(p.\gamma\)
7.endif
8.forall\(n_{j}\) in NT do
9.if\(T_{n_{j}}^{avg}<T^{thr}\) {Hotspot Avoidance} then
10.if\(n_{j}\
otin RS\) {Condition-1} then
11.\(c_{n_{i},n_{j}}=dst_{n_{i},n_{j}}\)
12. Calculate \(P_{n_{i}}^{r}\) and \(P_{n_{j}}^{r}\) according to Equation (7)
13. Calculate \(P_{n_{j}}^{Hr}\) according to Equation (13)
14. Calculate \(F_{h}(n_{i},n_{j})\) according to Equation (3)
15. Calculate \(F_{r}(n_{i},n_{j})\) according to Equation (8)
16. Calculate \(F_{Hr}(n_{i},n_{j})\) according to Equation (19)
17.endif
18.endif
19.endfor
20. Select \(n_{j}\) with MAX-\(F_{Hr}(n_{i},n_{j})\), MIN-\(P_{n_{j}}^{Hr}\), Random in turn
21. Call Queuing Manager with \(p\in Cr\) or \(Rc\) as parameter
```  
**Algorithm 2** Route selection algorithm at reliability module  
The algorithm follows a similar procedure to the delay module in applying Condition-1, Condition-2 and Hotspot avoidance mechanism. Since reliability is the concerned metric here, the algorithm measures the related reliability potential and forces as shown in line (12-16). The algorithm chooses the potential forwarder that has the maximum hybrid reliability force, and takes into account a neighbor with minimum hybrid reliability potential if more than one neighbor has the same maximum value of \(F_{Hr}(n_{i},n_{j})\). Unlike the delay module, we ignore the minimum hop-count potential value as an option,as delay is not the concerned metric in this case. The algorithm goes for random choice if none of the mentioned condition works. After determining an appropriate forwarder node ensuring the reliability, the algorithm calls the queuing manager module to place the \(Cr\) or \(Rc\) packet in the relevant queue.
## 3 Results
### Crop Classification
In summary, although the estimated areal proportions obtained from the classification algorithm had a large discrepancy with the reported area proportions, the growth curves of the three annual crops reflected their typical phenological patterns with contamination by other crops. Figure 3: Crop growth profiles from March to October, illustrated using two-band enhanced vegetation index (EVI2) extracted for winter wheat (**a**), corn (**b**), soybean (**c**), and the three crops combined (**d**). The curves were derived from 2016 for three representative counties in Southern (Chathem-Kent), Western (Perth), and Central (Durham) Ontario (refer to Figure 1).
## 2 Methods
### Brain Age Gap Based on Cross-Frequency Coupling  
We predicted brain age and estimated the brain age gap separately for each of the 34 functional networks, considering the baseline and capacity cross-frequency coupling (CFC) metrics separately. To predict brain age from cross-frequency phase-amplitude coupling (PAC) features, we employed Relevance Vector Regression (RVR), a sparse Bayesian machine learning method [38]. While sharing operational principles with Support Vector Regression (SVR), RVR incorporates probabilistic inference, typically yielding a more parsimonious model that uses fewer input features (or \"relevance vectors\") and is less prone to overfitting. In this study, the RVR model was trained using these CFC features derived from all participants, with chronological age as the target variable for prediction. The analysis was performed using the _sklearn-rvm_ Python package, with the RVR configured to use a polynomial kernel of degree 3.  
To estimate the brain age gap for each network and CFC metric, we used 10-fold cross-validation. We divided participants into ten equal groups, trained the model on 90% of the data, and tested it on the remaining 10%. This process was repeated ten times, ensuring that each participant's brain age was predicted without their data being included in the training set.  
After obtaining brain age predictions, we retained only the networks that showed robust associations with chronological age. We computed a distance correlation t-test for each network between chronological and predicted age using the _distance_correlation_t_test()_ function from the _door_ Python package [39]. We corrected network-specific _p_-values for multiple comparisons using a false discovery rate (FDR) threshold of 5%. In subsequent analyses examining correlations between the brain age gap and psychometric performance, we included only those functional networks where predicted age showed a robust correlation with chronological age.  
We computed the brain age gap (BAG) for each selected functional network as the deviation of a participant's predicted brain age from the population average. Specifically, we applied a quadratic regression for each CFC metric and functional network to model the relationship between chronological age and predicted brain age. We then calculated the BAG for each participant by subtracting the predicted brain age from the regression-based population average.  
Positive BAG values indicated slower ageing, meaning that the participant's cross-frequency coupling profile resembled a younger individual's. Conversely, negative BAG values reflected accelerated ageing, where the participant's CFC profile appeared older than expected for their chronological age. Each participant was ultimately characterized by two sets of network-specific brain age gap estimates, one for each CFC metric. Notably, the same participant could exhibit both accelerated and slowed ageing effects across different functional networks.
- regenerative potential of skeletal muscle is further compounded by mitochondrial dysfunction, increased extracellular matrix stiffness, and maladaptive immune responses [4; 5; 6; 7; 8; 9]. As global life expectancy rises, sarcopenia poses an escalating burden on public health systems [6; 7; 10]. While resistance exercise and adequate protein intake remain the cornerstone of prevention, these strategies may be insufficient for many older individuals due to metabolic resistance or comorbidities. Therefore, functional nutrients that target the molecular drivers of sarcopenia are of increasing interest [10; 11; 12; 13; 14; 15; 16; 17; 18]. Among them, nicotinamide (vitamin B3) and pyridoxine (vitamin B6) have emerged as promising candidates due to their regulatory roles in redox balance, energy metabolism, inflammatory control, and muscle stem cell activity [19; 20; 21; 22]. Nicotinamide is a precursor of nicotinamide adenine dinucleotide (NAD\({}^{+}\)), a cofactor required for mitochondrial function, sirtutin activity, and DNA repair [20; 23; 24]. Pyridoxine is a coenzyme in amino acid metabolism, neurotransmitter synthesis, and glutathione production [25; 26; 27]. Both vitamins are involved in pathways relevant to sarcopenia pathophysiology, including Nuclear factor-kB (NF-kB) signaling, Nrf antioxidant responses, and protein kinase B (AKT)/ mechanistic target of rapamycin (mTOR)-mediated protein turnover [28; 29; 30; 31]. Despite their clinical relevance, the therapeutic potential of B3 and B6 in muscle aging remains underexplored in an integrated, mechanistic framework. This review addresses this gap by providing the first comprehensive synthesis of nicotinamide and pyridoxine as dual nutritional modulators of muscle regeneration in aging. We emphasize their converging actions on NAD\({}^{+}\) metabolism, redox signaling, and immune modulation, highlighting synergistic effects and translational relevance for biomarker-guided, personalized nutrition strategies in sarcopenia prevention.  
Figure 1: Multifactorial contributors to sarcopenia. Sarcoma results from interconnected molecular and systemic factors, including mitochondrial dysfunction, hormonal dysregulation, chronic inflammation, neuromuscular and neurological impairment, as well as lifestyle-related contributors such as poor diet, obesity, and physical inactivity. Age-related decline and genetic predisposition further exacerbate muscle loss and functional impairment.