esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Input Data and Definition of Reference Case  
A global flight plan of the year 2018 as derived from Sabre Market Intelligence Data Base (www.sabre.com/products/market-intelligence, accessed on 15 June 2021) is the basis for all following calculations. This flight plan contains information on origin and destination airports, aircraft type, airline information, seats capacities, and frequencies of the different flights. The missions to be investigated are derived in a pre-processing step. As this study is performed in the context of the European research project ClimOP, we limit our analysis to flights with either origin or destination airport in a member state of ECAC [32]. Moreover, only long-range flights with a great circle distance of more than 2500 NM are selected in accordance with previous studies, which do not show the benefits of ISO for shorter missions [7; 8]. We restrict the study to the most relevant aircraft types in terms of available seat kilometres (ASK). This allows a coverage of 97.7% of the ASK of all long-range flights to or from the ECAC area and ensures high representativeness of the results for the focused region. All in all, we investigate a set of approximately 811,000 flights that cover 18% of all global ASK.  
In the following, we define the reference case as the sample of all non-stop connections along the great circle between origin and destination. Flight altitudes are characterised by fuel-optimal step climbs.  
Figure 1: Modelling workflow to assess climate mitigation potential of different ISO scenarios.
## 2 The Csr Network
### Building up the Csr Network  
In the years following CsrA identification, additional components of the Csr network were identified, notably sRNAs and proteins regulating either its activity or its expression. CsrA acts as a dimer [10, 36] and forms ribonucleoprotein complexes with two sRNAs, CsrB [37], and CsrC [38]. Interaction of CsrA with these regulators leads to CsrA sequestration and inhibition of activity. These sRNAs are structurally similar although they carry a variable number of CsrA binding sites [39, 40]. CsrB is 366 nt long containing 22 putative CsrA binding sites. It was shown that this sRNA is able to bind up to 18 CsrA dimers (see below), as shown by molecular weight estimation (256 kDa) [37]. The CsrC sRNA (242 nt long) contains 14 putative CsrA binding sites and is able to bind nine CsrA dimers [38].  
Experimental data indicate that up to one-third of CsrA may be bound to the CsrB sRNA [41]. As CsrA affinity for these sRNAs is higher than that for mRNA targets and its concentration is higher than CsrA-target dissociation constant [41, 42], it is inferred that CsrB and CsrC levels determine the concentration of \"active\" CsrA. Regulation of expression of these sRNAs is thus crucial to control CsrA activity. A negative feedback loop is involved in this regulation. CsrA indirectly activates _csrB_ and _csrC_ transcription through the BarA/UvrY two-component system (TCS) [38, 41, 43]. The BarA-associated response regulator, UvrY, activates transcription of the regulatory sRNAs [38, 43, 44, 45]. The Csr network is composed of additional feedback loops _i.e._, CsrA directly represses its own translation, while it indirectly activates its own transcription by an unknown mechanism [46]. These regulatory loops provide a rapid mechanism to reduce _csrA_ expression when concentration of free CsrA reaches critical levels. The physiological signals that trigger the Csr network through the membrane-bound BarA sensor kinase have been identified. Interestingly, signals are products of carbon metabolism such as formate, acetate, other short-chain fatty acids, and Krebs cycle intermediates, which correlates with the crucial roles played by CsrA in central carbon metabolism [47, 48].  
Another important player in the Csr network is the CsrD protein. CsrD is predicted to be a membrane-bound protein containing degenerate GGDEF and EAL domains [49]. It controls the decay of CsrB and CsrC in an RNase E-dependent manner and in a c-di-GMP-independent manner, although both domains appear to be necessary for CsrD activity [19, 49]. The \"destabilizing\" activity of CsrD on CsrB and CsrC has a positive effect on CsrA activity [49]. In addition, CsrA negatively controls _csrD_ expression, which indicates an additional feedback loop in the Csr network [21, 49, 50]. It is not excluded that other types of regulation might exist within the Csr network. For example, in _Bacillus subtilis_, CsrA activity is regulated by direct interaction with the FliW protein, rather than sRNAs, in order to regulate motility [51].
## 2 Materials and Methods  
All rock blocks were collected from the outcrops of sedimentary rock sequences of limestone, Dammam Formation, Middle to Late Eocene, aged 49-34 Myr, at Jabel Hafit Mountain in Al Ain City. Each rock block was prudently inspected for macroscopic flaws such as fractures, cracks, bedding planes, and alteration zones in order to attain standard testing samples for laboratory studies. As the UPV is greatly affected by nonhomogeneous and anisotropic features of testing samples, this task is critical for rock block assortments. All rock blocks were kept under the laboratory condition (around \(25\pm 1\)\({}^{\circ}\)C) until sample preparation and testing. To avoid natural occurrence effects, all test samples were inspected prior to testing to ensure that they were free of any macroscopic or visible defects such as fractures or cracks. For the UPV test, core samples with diameters of 24.9, 38.1, 47.7, 53.9 (NX size), and 63.2 mm and various lengths ranging from 78.9 to 108.3 mm were acquired from the selected rock blocks perpendicular to bedding plane using a core drill and trimming machine. For good coupling, both end surfaces of each core sample were smoothed for standard testing, and the diameter and length of all core samples were measured and recorded. A total of 190 P-wave velocity tests were performed on the prepared core samples, with 39 24.9-mm-diameter samples, 36 38.1-mm-diameter samples, 29 47.7-mm-diameter samples, 63 53.9-mm-diameter samples, and 23 63.2-mm-diameter samples. An accurate P-wave measurement requires perfect matching between the transducers and the end surfaces of the test samples, which enables good coupling. The UPV tests were conducted on the core samples using a Pundit Lab (Proceq, PL02-002-0276 B0) pulse generator unit with two transducers (54 kHz, 38.6 mm diameter). Each sample was evaluated under different environmental conditions, i.e., natural, dry, and saturated conditions, according to the American Society for Testing Materials (ASTM) standard [43]. To define the natural P-wave velocities, \(\mathrm{V^{n}}_{\mathrm{P}}\), 190 samples were tested under the assumption of in situ conditions. The dry P-wave velocities, \(\mathrm{V^{d}}_{\mathrm{P}}\), of the samples were measured after the samples were dried in an oven for 12-16 h at 105 \({}^{\circ}\)C; subsequently, all samples were cooled under laboratory conditions for approximately 48 h. For the saturated P-wave velocity, \(\mathrm{V^{s}}_{\mathrm{P}}\), the samples were saturated with distilled water for 48-50 h by soaking them in distilled water tank prior to P-wave velocity measurements. As UPV tests are nondestructive, all samples used for UPV tests were further evaluated to determine the mechanical properties of limestone after being maintained under laboratory conditions for approximately 2 weeks. In total, 87 UCS, 50 \(\mathrm{PLI^{A}}\), 45 \(\mathrm{PLI^{D}}\), 102 ITS, and 190 SHV tests were performed following the suggested ASTM standards [44; 45; 46; 47], (see Table 1, second column).
## 2 Related Work
### Lightweight Hybrid Architecture Super-Resolution Network  
Hybrid architectures synergize multiple technical approaches. They employ strategies that integrate local feature extraction with global dependency modeling, achieving performance breakthroughs under lightweight constraints.  
Early hybrid frameworks predominantly adopted CNN backbones augmented with attention modules to enhance feature selectivity. For example, CBAM [29] fused channel and spatial attention mechanisms to prioritize critical information in feature maps. SAN [30] introduced second-order channel attention and non-locally enhanced residual groups to concurrently capture long-range dependencies and local textures. LatticeNet [31] drew inspiration from lattice filter theory, proposing lattice blocks with multi-scale attention to amplify feature discriminability. As researchers began to recognize the limitations of Transformers, researchers increasingly explore hybrid architectures combining CNNs and Transformers. ESRT [14] reduced the computational complexity of self-attention by using group-wise attention, which limited attention computation within local feature groups. SAFMN [32] enhanced VIT blocks with a spatial adaptive feature modulation mechanism for dynamic feature selection. The emergence of the Mamba architecture has provided further solutions for SISR tasks. DVMSR [33] integrates Mamba with knowledge distillation to optimize inference speed without sacrificing performance.  
In addition, several recent studies explore hybrid frameworks combining Mamba and Transformers to enhance low-level vision tasks. For example, Contrast [34] proposed a dual-branch design integrating SSM and a vision Transformer for image restoration. MatIR [35] introduced a Mamba-Transformer fusion model to balance expressiveness and efficiency. In the SR domain, FAMSR [36] incorporated frequency priors into a Mamba-based network to improve detail recovery in remote sensing images, while MPSI [37] leveraged pixel-wise sequential interaction to enhance positional context modeling. MambaCSR [38] adopted a dual-interleaved scan mechanism with Mamba to handle severe compression artifacts. Although these models do not prioritize lightweight design, they highlight the growing trend of combining convolution, attention, and structured state modeling.  
Hybrid architectures now demonstrate clear superiority over single-paradigm approaches, emerging as a focal research area. In this context, we propose a dense residual fusion architecture that effectively combines the advantages of convolutional operations, attention mechanisms, and the Mamba architecture. This framework enables efficient multi-level spatial information fusion and effectively mitigates high-frequency texture degradation.
## 4 DASH7 Communication System
### Packet Structure  
Every DASH7 packet is preceded by a power ramp-up and succeeded by a power ramp-down, which is used to meet the band stop channel requirements. The power ramp-up is the time that is needed before the first symbol transmission. This means that the carrier frequency is ramped from idle power to transmit power and settles to a stable state. The reverse operation occurs when ramping down. If this ramp-down is too fast, a part of the encoded frame might be affected, and therefore, the packet can become unrecoverable. Typically, these ramps have a period of 8 symbols but can go up to 32 symbols.  
When looking into the Data Link Layer of DASH7, we see two types of frames defined: background frames and foreground frames. Background frames have a static payload size of six bytes. These frames are used to send broadcast messages, which are applied for  
Figure 4: Three–dimensional spectrogram of ten modulated DASH7 packets seen over 1.4 s and a bandwidth of 200 kHz.  
ad-hoc group synchronisation. Foreground frames can have a length of up to 256 bytes and are used for data requests or contain data.  
The preamble is an unencoded static structure of alternating binary symbols starting with '1' and can have a variable length of up to 64 bits. This is used to settle the receiver by calibrating its data rate circuits. The DASH7 specification defines a typical preamble length of 32 bits for the Lo-Rate and Normal-Rate classes while recommending 48 bits for the Hi-Rate channel class [23].  
The sync word is an unencoded structure of 16 binary symbols used to identify the start of the payload. The physical layer supports two sync word classes which depend on the type of frame that is used. Background frames use Sync Word Class 0, while foreground frames use Sync Word Class 1. A second categorisation that makes up a specific sync word is the applied type of encoding. It can be seen from Table 5 that only Coding Scheme 0 (CS0) and Coding Scheme 2 (CS2) are used. In future releases, this can be extended with two additional coding schemes.  
Finally, the payload field, which can have a length between 5 and 256 bytes, contains at least a length byte, a subnet byte, a control byte (CTRL), the packet data, and two Cyclic Redundancy Check (CRC16) bytes. The CRC operation is applied only to the payload before the encoding process. Its operation is extensively explained in Appendix A. Furthermore, the payload is the only encoded part of the packet. This encoding step is achieved with a PN9 code or a combination of a PN9 code and \(\frac{1}{2}\)-Forward Error Correction Code (FEC), which are described in Appendices B and C. Figure 5 shows the general composition of a DASH7 packet.
## 3 Pathogenesis of AD
### Inflammatory Hypothesis  
Advances in the field of AD research seem to indicate that the pathogenesis of the illness does not exist only in the neuronal compartment but has a significant interplay with immune systems in the brain [100]. Neuroinflammation in AD is not a passive system triggered with the emergence of senile plaques and neurofibrillary tangles, but rather is due as much to pathogenesis as plaques and tangles themselves [101]. Numerous findings indicating genes encoding immune receptors, such as TREM22 and CD33, linked to AD, highlight the importance of neuroinflammation [102; 103]. A critical step in AD is believed to be the ability of microglia to attach to soluble A\(\beta\) oligomers and fibrils through cell-surface receptors such as \(\alpha\)6\(\beta\)1 integrin, CD36, CD14, SCARA1, CD47, and Toll-like receptors, namely TLR2, TLR4, TLR6, and TLR9 [79; 104; 105]. Activation of microglia leads to the production of proinflammatory cytokines and chemokines, which cause binding of A\(\beta\) with CD36, TLR4, and TLR6 [79; 106; 107]. In this respect, genetic deletion of these three genes (CD36, TLR4, and TLR6) limits A\(\beta\)-induced cytokine production in vitro and blocks the aggregation of amyloid proteins and the activation of inflammosomes [107; 108; 109]. Following the binding of A\(\beta\) to this receptor complex leads to intracellular signaling results in the signal transduction of the nuclear factor-\(\kappa\)B (NF-\(\kappa\)B) from the cytoplasm to the nucleus [101]. In addition, this leads to the initiation of protein kinase A, response element-binding protein [111; 112], which results in the transcription of numerous pro-inflammatory cytokines, cyclooxygenase-2 (COX-2), and NO-synthase [111; 113]. Recently, A\(\beta\) has been reported to induce NLRP3 inflammasome, high-molecular-weight protein complexes that are involved in several inflammatory pathophysiologies in microglial cells in vitro and in vivo, thus identifying a novel mechanism that could contribute to the development of AD [111; 112]. As a result, stimulation of NLRP3 inflammasome by A\(\beta\) in microglia is essential for IL-1\(\beta\) maturation and subsequent inflammatory events [114]. Increasing our understanding of the ways that these immune processes affect AD might lead to future therapeutic or preventative approaches.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Study of TE and TM Polarization  
Transverse Electric (TE) and Transverse Magnetic (TM) modes are utilized to characterize the properties of the MA to explain its absorption. In TE Mode incident wave propagates in the direction of the z-axis. The propagation of the magnetic field vector is along the z-axis. The magnetic and electric field vector is along the y and x-axis, respectively. In TM Mode incident wave propagates in the direction of the z-axis. Also, the electric field vector propagates along the z-axis. The magnetic and electric field vectors are along the x and y-axis. Figure 7 shows that the MA exhibit identical absorption characteristics for both TE and TM modes throughout the resonating bandwidth. These characteristics indicate that the structure proposed in this work can be employed as an ideal solar energy harvester, sensors, stealth technologies, and micro-imaging equipment.  
Figure 5: The relative impedance of the MA.  
Figure 6: The reflectance (red), transmittance (blue), and absorption (black) characteristics of the MA.
###### Abstract  
The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart meters continue to struggle from consumers' acceptance. To promote smart meters' successful deployment, research is needed to better understand consumers' acceptance of smart metering. Motivated by these concerns, a smart meter acceptance model is developed to evaluate the moderation role of experience and personal innovations factors among residential consumers. A cross-sectional research design was used in this study. Data were collected using a self-administrated questionnaire from 318 smart meters consumers who have had experience in using it. Hypothetical relationships were assessed and validated using partial least squares structural equation modelling. The empirical findings exert the moderating role of experience and personal innovativeness of smart meter acceptance that achieved an acceptable fit with the data, and specifically, five out of nine hypotheses were supported.  
smart meter; internet of things; personal innovativeness; users experience; technology acceptance +
Footnote †: journal: Applied Sci. 2021  
11
## 10 Conclusions and Future Directions
### Implication for the Industry  
This tool can be utilised in a very effective way in the industries which deals with IoT and real-time operations. One particular example in which this tool can contribute much is in \"Industry 4\". The tool can allow users and stakeholders to track and monitor the life cycle of critical manufacturing activities and their scheduling footprints will enable them to understand the processes better. If some products manufacturing is not as critical as other, it can also be prioritised. The development of these activities can be monitored from anywhere, and the users need not be on the premises. Additionally, this tool can also help in other IoT fields which requires continuous tracking of certain processes. For instance, in smart farm such as a greenhouse, it can track and monitor the growth of plants and based on the progress the environment can be tuned. Similarly, in smart health industries, it will also have a massive impact due to the remote support of tracking and monitoring.
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 4 Case Study
### Study Objective and Hypothesis  
The aim of the case study is to analyse whether the proposed framework is able to address the current challenges faced by the e-government project in Nepal. In particular, the framework will be evaluated as to whether it is able to integrate the isolated e-government system (Data silos) in efficient (financially and technologically) way while supporting the government to provide services in more citizen-centric approach. We have set two hypotheses that will be tested against the findings of the study.  
**Hypothesis 1:**_Government-to-government (G2G) approach would allow governments in developing countries to provide citizen-centric e-government services._  
**Hypothesis 2:**_The integration of the government departments for G2G e-government could be achieved by adopting cloud computing platform with less cost and time._
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 3 Distributed Integration of Multi-Source Remote Sensing Data
### The ISO 19115-Based Metadata Transformation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **ISO Metadata** & **HDF-EOS Metadata** & **CCRSDA Metadata** \\ \hline \multirow{4}{*}{Metadata information} & Creation & FILE\_DATE & - \\  & LandFusion & & - \\ \hline \multirow{8}{*}{Image Information} & MD\_Identifier & LOCALGRANULIED & - \\  & TimePeriod\_position & RangeBeginDate + RangeBeginTime & imagingStartTime \\  & TimePeriod\_andPosition & RangeInedingDate + RangeInedingTime & imagingTime \\  & Platform & AssociatedPlatformsDRName & satelliteId \\  & Instrument & AssociatedInstrumentMember & satelliteId \\  & Sensor & & - \\  & Datacenter & PROCISSON/CENTR & \\  & re-sciathol & STATION ID & re-sciathol \\  & spatialResolution & NADIDDATARSOLUTION & pixelSpacing \\  & westInst/ContagDate & WISROUNDINGCORDDSTATE & productUpreferLeft.Long \\  & eastIound\_reading & EASRTONIDNICCORDSTATE & productUpreferLeft.Long \\  & southIound\_latitude & SOUTHIOUNDINGCORDSTATE & productUpreferLeft.Long \\  & northIound\_latitude & NORTHIOUNDINGCORDSTATE & productUpreferLeft. \\  & centerLongdate & - & sceneContext.Long \\  & oernet\_altitude & - & scene\_relat. \\  & scene\_rish & WRS\_PATH & scenePath \\  & sceneRow & WRS\_ROW & sceneRow \\  & re-cerenecyverslendIdentifier & PROJECTION\_rRAMETERS & northIodd\_majorDetection \\  & cloud\_over\_percentage & - & cloud\_over\_percentage \\  & imq\_QualityCode & - & overscanQuality \\  & processing\_level & DATA\_TYPE & product\_level \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ISO 19115-2:2009-based uniform metadata format and transformational rules. ISO: International Standardization Organization; CCRSDA: China Center for Resources Satellite Data and Application; HDF-EOS: Hierarchical Data Format Earth Observation System.
###### Abstract  
The Internet of Things (IoT) is comprised of connected devices which are equipped with sensors, actuators, and applications to provide services for enabling those objects to connect and exchange data. For these connected devices and the IoT network environment, heterogeneous protocols and frameworks have been published and applied to provide novel IoT services in our daily life. On the Internet, most of the services are provided by existing web service providers which are based on the high-performance processor, storage, and stable power supply. However, devices of the IoT are developed for constrained environments using the small size of equipment to provide seamless services ubiquitously. For accessing the constrained devices and existing web service providers using the clients in the IoT networks such as smart homes, the services of servers from the devices and web service providers shall be discovered by the clients using a consistent discovery service. In this paper, a consistent registration and discovery scheme is proposed for the devices and web service providers in the Open Connectivity Foundation (OCF)-based IoT network. For supporting the proposed scheme, an embedded resource directory (RD) server is proposed to provide a consistent registration service that is used for publishing information of devices and web service providers. For the registration, a unified profile format is used that is based on the RESTful API Modeling Language to describes the information of devices and web service providers. Furthermore, the discovery service provides the consistent interface to discover the registered devices and web service providers by the client using the unified user interface. Accordingly, the client can access the resources of devices and web service providers based on the discovered information.  
Internet of Things (IoT); resource directory (RD); RESTful API modeling language (RAML); Open Connectivity Foundation (OCF); web service provider (WSP); constrained network +
Footnote †: journal: _Journal of the Operational Society of America_