esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 4 Accessibility of Genes for Transcription during Fruit Development and Ripening
### DNA Methylation  
DNA methylation is influenced by DNA methylases and demethylases, and, in general, hypermethylation of DNA is associated with gene repression. In 1991, differential DNA methylation was first reported in two tomato genera by [PERSON] et al. [196]. [PERSON] et al. [197] confirmed that epigenome status was not static during fruit development, and [PERSON] et al. [198] demonstrated an association between tomato fruit development and genomic DNA demethylation that was governed by DNA demethylylase 2 (DML2). In addition, a transient increase in DNA methylation was observed during chilling of tomato fruits [83], which represses expression of flavour genes. DNA methylation also represses gene expression during fruit development in grape [199], strawberry [200] and orange [201].  
Methylation occurs on the 5 position of the cytosines in CG, CHG and CHH contexts (where H = A, T or C), often in gene promoters, and is maintained by methyltransferase1(MET1) and chromomethylases (CMT3 and CMT2) [202; 203]. DML2 actively removes methyl groups from methylated DNA during fruit ripening [198], and genome-wide DNA methylome analysis of the _sldml2_ mutant of tomato has shown a strong direct correlation between the demethylation of hundreds of ripening-associated genes and their expression [204], including important ripening regulators such as tomato SPL-CNR, MADS-RIN and NAC-NOR in tomato [197; 198; 200; 201; 204], which are discussed in detail in Section 6. Recently, the _gs_ mutant form of _GREEN STRIPE_ (_GS_) in tomato has been identified as a methylated isoform of _TAGL1_, an important fruit development and ripening TF, which regulates chloroplast development and carotenoid accumulation in tomato fruit [205]. In this mutant, the _TAGL1_ gene is methylated and the mutant fruit have patches where ripening is repressed. High methylation of the _TAGL1_ promoter is correlated with downregulation of its expression, which increases chlorophyll content and delayed appearance of PSY1 and carotenoid accumulation, leading to dark green stripes on ripening tomatoes, with light green stripes in regions where _TAGL1_ is less methylated. This stripe pattern is retained and is converted to yellow and red stripes as the fruit ripen.
_J. Sens. Actuator Netw._**2013**, \(2\), 235-287; doi:10.3390/jsan2020235  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan/  
_Review_  
**IETF Standardization in the Field of the**  
**Internet of Things (IoT): A Survey**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON],**  
**[PERSON], [PERSON] and [PERSON]**  
Department of Information Technology (INTEC), Ghent University - iMinds,  
[PERSON] 8 Bus 201, Ghent 9050, Belgium; E-Mails: [EMAIL_ADDRESS] (D.C.);  
[EMAIL_ADDRESS] (G.K.T.); [EMAIL_ADDRESS] (J.H.);  
[EMAIL_ADDRESS] (F.V.A.); [EMAIL_ADDRESS] (E.D.P.);  
[EMAIL_ADDRESS] (I.M.); [PERSON]] (P.D.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +32-933-14-900; Fax: +32-933-14-899.  
_Received: 15 February 2013; in revised form: 2 April 2013 / Accepted: 9 April 2013 / Published: 25 April 2013_  
**Abstract:** Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6 LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities.  
**Keywords:** Internet of Things; standardization; survey; constrained devices; IETF ROLL; IETF CoRE; IETF 6 LoWPAN; RPL; CoAP; Web of Things
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
## 4 Analysis of the Calculation Results of the Station Structure
### Vertical Displacement of the Structure  
The vertical displacement of the station structure is shown in Figure 10. Under the action of the design earthquake, the amount of structural uplift in the deep liquefaction case is greater than that in the non-liquefaction case, but the difference between the two is not significant. The difference between the amount of structural uplift in the deep liquefaction case and that found in the non-liquefaction case is large under the high-level earthquake. This indicates that the liquefaction behavior of the deep liquefied soil layer has an effect on the uplift of the structure, and the higher the degree of liquefaction, the more obvious the observed uplift trend in the structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Points** & **Case 1** & **Case 2** & **Case 2 – Case 1** & **Amplitude/\%** \\ \hline T1 & 41.82 & 43.99 & 2.17 & 4.9 \\ T2 & 67.18 & 89.18 & 22.00 & 24.7 \\ T3 & 100.85 & 132.88 & 32.03 & 24.1 \\ T4 & 153.98 & 186.63 & 32.65 & 17.5 \\ T5 & 221.40 & 220.95 & \(-\)0.45 & \(-\)0.2 \\ T6 & 285.80 & 279.98 & \(-\)5.82 & \(-\)2.0 \\ T7 & 348.97 & 320.13 & \(-\)28.84 & \(-\)8.3 \\ T8 & 410.39 & 349.67 & \(-\)60.72 & \(-\)14.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effective stresses in the x-direction of the soil layer under the design earthquake (kPa).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Points** & **Case 3** & **Case 4** & **Case 4 – Case 3** & **Amplitude/\%** \\ \hline T1 & 49.71 & 71.69 & 21.98 & 30.7 \\ T2 & 95.42 & 122.55 & 27.13 & 22.1 \\ T3 & 127.16 & 184.61 & 57.45 & 31.1 \\ T4 & 185.45 & 362.31 & 176.86 & 48.8 \\ T5 & 260.93 & 223.94 & \(-\)36.99 & \(-\)14.2 \\ T6 & 329.38 & 294.93 & \(-\)34.45 & \(-\)10.5 \\ T7 & 393.29 & 349.94 & \(-\)43.35 & \(-\)11.0 \\ T8 & 450.56 & 395.65 & \(-\)54.91 & \(-\)12.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effective stresses in the x-direction of the soil layer under the high-level earthquake (kPa).  
Figure 9: Arrangement of station structure measurement points.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
###### Abstract  
Despite advances in image captioning, existing models struggle to generate captions that include accurate numerical information, especially the number of objects. One reason for this issue is that the dataset used for training has a limited number of samples with numerical information about the image. To address this issue, we propose a new framework, the Numerically Aware Captioning (NuCap) model, to enhance numerical reasoning in caption generation. We extract dual features by combining a region-attended object encoder for finer-grained object features and a spatially attended grid encoder for encoding spatially distributed global features. We also propose a number-focused cross-entropy loss component to increase sensitivity to numerical tokens, and introduce CountCOCO, a dataset for structured understanding of numerical information. Experiments show that our method achieves statistically significant counting performance improvements over state-of-the-art image captioning models while maintaining similar captioning performance. Despite the significant improvement in numerical reasoning power, our proposed approach has significantly fewer parameters and lower inference latency than large-scale vision language models, demonstrating both computational efficiency and stability. NuCap is an image captioning model that can represent specific numerical information in a given image, making it more suitable for applications that require precise object enumeration, such as automated surveillance, store monitoring, and scientific documentation.  
multi-modal learning; image captioning; numerical reasoning 2020 IEEEexample: accepted: 26 March 2025
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 4 Evaluation Method  
Despite the range of methods that have already been used to evaluate foaming characteristics, to date a shared procedure has yet to be developed to assess the potential risk of formation of stable foams on the surface of the aeration basin of a WWTP [62]. There are several different methods for generating and characterizing foams on a small scale (including whipping, stirring, plunging, shaking, oscillating, pouring and bubbling) [20; 68] and the most used are (i) sparging or diffusing gas (gas is sparged inside a tube with a glass sinter at the bottom into which a known volume of test liquid is introduced) and (ii) shaking (liquid/gas mixture in a bottle is shaken for a specified period of time). To date, the most widely used procedure consists in the bubbling of gas through the liquid, which consists of the basic practice that characterized the Bikerman test, one of the first experiments on foams: introduction of air at a fixed flow rate through a disc of porous glass on the bottom of a glass column [20; 69]. In recent years, the scientific community has been moving in this direction, mainly carrying out a series of foamability tests that simulate the aeration conditions in a plant [21]. This approach allows obtaininga series of useful information on the quantitative and qualitative characteristics of foams that are produced following aeration.  
Foams evaluation methods include direct methodologies (analysis of the foams following their formation), including those that can be applied directly on-site at an aeration basin. There are also other methods that allow the assessment of foams by indirect means: quantification of surface tension, evaluation of the concentration of EPS and filamentous bacteria and the characterization of hydrophobicity and viscosity.
# Medical Applications of the GEMPix
## 3 Conventional Radiation Therapy
* The spatial resolution obtained with GEM detectors is at least an order of magnitude better than with ion chamber (IC) matrix arrays. With this spatial resolution, no interpolation software is needed to calculate the intermediate dose values as it is the case for IC arrays. * The pixel pitch of the GEMPix is even better (55 \(\upmu\)m) than the one of EBT gafchromic films (340 \(\upmu\)m) [5]. The gamma index showed excellent agreement between the GEMPix and gafchromic films. * The GEMPix is able to perform fast real-time IMRT treatment plan verifications. It has been demonstrated that it is also able to measure single pulses. Then, IMRT fields both in'step and shoot' and in continuous mode can be reconstructed in time with a very high resolution [21]. No other devices can reach these performances. The drawback is represented by the active area which is limited to a few cm\({}^{2}\) essentially due to the wire bonding of the chip, while larger fields are used in radiation therapy  
Figure 6: The GEMPix placed in position for irradiation with the Elekta Synergy Linac at the Tor Vergata Radiotherapy Centre (**left**) and the EBT gafchromic film placed on the detector window after irradiation (**right**) [21]. (for example, 16 \(\times\) 21 cm\({}^{2}\) is the maximum field size for the Elekta Synergy Linac used in Tor Vergata). A large area GEMPix is under development, as discussed in Sections 7 and 8.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology
### Requirements Analysis  
The following section describes the hardware and software components required for implementing the prototype.  
* **Single-board computer:** This device is essential for implementing an IP-based communication system, so it must support the installation and efficient execution of the required software. It must create a wireless network to ensure data transmission, have connectivity interfaces such as Wi-Fi to establish links with other devices, and have a compact and lightweight design, ideal for placement inside the UAV. It must also be energy-efficient to ensure greater autonomy during system operation.
* **IP telephony software:** Open-source software is required to provide a suitable interface for configuring the IP telephony-based communication system. This software must be compatible with the operating system of the selected device and have detailed documentation to facilitate installation and customization.
* **UAV:** A lightweight UAV of simple design must be connected to a ground power supply system via a cable, significantly increasing flight time. This UAV must be capable of carrying a payload of approximately 1.5 kg to support the single-board computer, its battery, and the associated power cable. It must also be equipped with an integrated control system and a GPS module to ensure stable and controlled navigation during operation. It must also include a remote control for ease of operation and real-time monitoring, allowing in-flight adjustments.
* **Power supply system:** A ground-based power supply is required to allow the UAV to operate for an extended time. The proposed UAV-enabled communications system assumes the availability of a power grid (AC voltage), which feeds the power supply located at the GCS. In a future approach, the entire system could be portable and entirely battery-powered, as detailed in our previous work [30]. The power supply considered in the prototype must provide a constant voltage and current suitable for operating the UAV. In addition, it is crucial to select an appropriate cable to carry power between the power supply and the UAV, which must not only be capable of carrying the required voltage and current but also have an appropriate gauge to ensure the stability of the power supply, considering an objective distance of approximately 10 m. The cable should be lightweight to minimize the additional load on the UAV, thus optimizing its flight capability.
## 3 Proposed Protocol  
In this section we present the _Network basEd aUtonomic clusteRing and rOutiNg (NEURON)_ protocol, aiming to address challenges that are accrued from the current trends in the WSNs evolution and the existing approaches for efficient deployment, operation and maintenance of large in scale wireless sensor networks. NEURON is designed in order to achieve the following goals:  
* _Support autonomic functionalities in a WSN_: sensors deployed in the sensor field, establish independently communication with their neighbours, become part of the WSN and gain access to the provided data and services without human intervention.
* _Self-organize the nodes into clusters_: clusters are self-formulated according to the current conditions in the network, while cluster heads rotation extends the network lifetime.
* _Achieve fault tolerance and avoid dependence from specific purpose nodes_: clusters are formulated automatically in case of failures, since each node may be elected as CH.
* _Reduce energy consumption through hierarchical routing based on an overlay among the CHs_: information about the current CHs is disseminated among the WSN and routing is provided through them.
* _Achieve scalability by taking advantage of autonomicity, decentralization and probabilistic techniques_: there is no performance degradation as the size of the sensor network increases.  
It is important to note that NEURON does not make any assumptions for its operation. It may be applicable to heterogeneous environments, in which nodes with diverse computational and storage capabilities may be present. It does not require prior knowledge about location based information or the pre-assignment of specific roles in the sensor nodes. Its operation is based on the knowledge of network based parameters within the WSN that could be estimated in an autonomous manner by using an _Autonomic Estimation Algorithm_, as described in Section 3.1. NEURON may be effectively applied in static and dynamic topologies where nodes continuously join or leave the network or they are to some extent mobile. Furthermore, its clustering and routing mechanisms--as presented in Sections 3.2 and 3.3--make it energy efficient since they suppress the number of messages that are required for its operation and they support multi-hop communication.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs  
The main components of PDMS architecture comprise physiological signal collection, data processing, feature extraction, and classification methods for driver's state assessment. In this study, the aim was to focus on the first component of PDMS and analyze various methods for collecting physiological signals from drivers, which differ in precision, accuracy, convenience, cost of implementation, and applicability in real-world driving conditions. First, a review of the measurement methods, techniques, and sensor modalities used for monitoring the physiological state of the driver in publications from recent years will be provided. Then, based on the review, a taxonomy will be proposed at the end of this section.  
Specifically, the focus was on the methods for acquisition of physiological signals applied in DMS design and development. This included scientific articles in the recognized scientific databases Scopus and Research Gate over the past twenty years that used methods, techniques, and sensors to monitor physiological parameters considered important for the assessment of the driver's health status, were implemented and tested in laboratory-simulated or real driving scenarios, and were written in English.  
Studies using cameras to analyze facial expressions and body positions for the driver's state assessment were excluded. Also excluded were studies and analyses that emphasized different approaches for data processing instead of methods, techniques, and sensors for signal collection, papers that duplicated already described measurement methods and sensors, and numerous conference proceedings with minor contributions. Finally, abstracts of selected papers were personally examined to verify their relevance to this study.
## 4 Discussion
### Scientometric Analysis Insight: Role of Co-Citation Reference and Keyword Co-Occurrence Analysis
#### 4.2.5 Climate Change Impacts Forest Net Carbon Exchange and WUE  
Forest ecosystems can significantly be impacted by climate change and influence WUE and net carbon exchange (NCE) [6; 133]. This impact is motivated by various climatic factors such as elevated temperature levels, varied precipitation patterns, and higher atmospheric CO\({}_{2}\) levels [6; 60]. Climate change-related increases in temperature can have a major impact on forest NCE by altering photosynthesis and respiration rates [134]. An increase in temperature up to the optimal level can improve photosynthesis; but, beyond this level, it starts to decline due to higher respiration rates and heat stress [135]. Furthermore, increased temperature might lead to longer growing seasons, which may boost carbon uptake in some places [6; 136]. However, the cumulative impact of increased temperatures on net NCE is a complex phenomenon that is influenced by photosynthesis and respiration.  
Any changes in precipitation patterns such as frequency and intensity can significantly impact forest WUE and NCE. For instance, less precipitation results in drought conditions which reduce soil moisture and water availability for trees and consequently decreases photosynthesis rates. This decline in photosynthesis can result in decreases in carbon sequestration and higher mortality [137]. On the other hand, excessive precipitation can lead to waterlogging which has negative effects on tree roots and carbon uptake [138]. Hence, the precipitation pattern variability poses significant challenges to maintaining WUE and NCE.  
Elevated atmospheric CO\({}_{2}\) levels can increase photosynthesis rates via the CO\({}_{2}\) fertilization effect and potentially enhance forest carbon-sequestration capacity [139]. However, this effect is subjected to environmental factors and nutrient availability. For instance, inadequate nitrogen can restrain the positive effects of elevated atmospheric CO\({}_{2}\) on photosynthesis rates and tree growth [140]. Furthermore, elevated atmospheric CO\({}_{2}\) levels can influence stomatal conductance, which may impact WUE. Studies have shown that CO\({}_{2}\) fertilization can boost carbon uptake; however, the overall impact is altered by interactions with various climatic factors on forest NCE [141; 142].  
Since WUE is a ratio of carbon assimilation to water loss (transpiration), it is a critical consideration in understanding forest responses to climate change in terms of NCE [60]. WUE is influenced by rising temperatures and varied precipitation patterns through changes in stomatal behavior and transpiration rates [12]. For example, elevated temperatures caused by climate change can increase vapor pressure deficit (VPD), which results in reduction in WUE and higher transpiration rates [143]. In contrast, higher CO\({}_{2}\) levels can decrease stomatal behavior, potentially improving WUE.
## 3 Algorithm Description
### Reception phase  
When a reader receives a REQUEST1 or REQUEST2 message on the control channel, its response varies based on its current state.  
**Response to REQUEST1:**  
* If the reader receives a REQUEST1 message, it updates its table entry by adding the TS value from the ELIMINATE_TS field of the corresponding RESPONSE1(TS) frame to the TS_Disable column. This is achieved by the equation TSDisable = TSDisable + RESPONSE1(TS).
* Subsequently, if there are available Time Slots (IsEmpty(TSEnable) = 0) in its table, the reader sends a RESPONSE1 message. It selects one of the available TSs and stores it in the TS_Enable field (TSEnable = TS). If there are no available TSs, the reader does not participate in the process.  
**Response to REQUEST2:**  
* If the reader already has a TS and is unaffected (IsEmpty(TSEnable) = 0), it acts as a forwarding node. The reader rebroadcasts the REQUEST2 frame to its neighboring readers.
* Upon receiving a RESPONSE2 message, the reader forwards it to the sender of the original REQUEST2 message and waits for a response containing the TS value to be forwarded.
* However, if the reader is affected because it does not have a TS (IsEmpty(TSEnable) = 1), it sends a RESPONSE2 message and waits for a TS_select response. This response provides the necessary TS for the reader to participate in the process.  
By following these actions, the readers effectively respond to different control channel messages, either updating their table entries or forwarding messages to ensure a coordinated and efficient allocation of TS.