esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 4 Discussion
### Prospects for Automatic In Situ Hyperspectral Sensing Network  
The automatic in situ hyperspectral sensing network is a technology developed based on the on-site hyperspectral monitoring technology of water components and water quality, combined with automation and wireless communication technology [43; 44; 45; 46]. It is not only applied in water quality monitoring [28; 29; 47], but also plays an important role in satellite remote sensing and drone remote sensing calibration [47; 48; 49]. It is an important infrastructure for integrated remote sensing monitoring networks that connect space, air, and ground.  
Using an automatic in situ hyperspectral sensing network to monitor water and sediment has the following prominent characteristics: (1) Non-contact: The in situ non-contact spectral method does not require direct contact with the water. It can obtain the spectral information of the water from a distance through remote sensing technology, avoiding the need for direct sampling in traditional monitoring methods, reducing interference with water samples, and helping to maintain the original characteristics of the water; (2) Real-time: The in situ non-contact spectral method can achieve remote, real-time monitoring. Through spectral instruments, it can, in real-time, obtain the spectral characteristic parameters of the water, promptly reflecting changes in composition and concentration in the water, and providing data support for rapid response to water changes; (3) Efficiency: Compared to traditional sampling and laboratory analysis, the in situ non-contact spectral method for monitoring water and sediment is more efficient, reducing sampling, transportation, pre-processing, and other links, shortening the monitoring period, and improving monitoring efficiency [50]; (4) Environmental friendliness: The in situ non-contact spectral method does not require a large number of chemical reagents and laboratory analysis equipment, reducing environmental pollution and resource waste, in line with the environmental requirements of energy conservation and emission reduction.  
In summary, using the in situ non-contact spectral method to monitor water and sediment has significant advantages. It can improve monitoring efficiency, reduce monitoring costs, and achieve remote, real-time, and comprehensive monitoring. It provides new technical means and ideas for water and sediment monitoring and water resource management.
# Compression of Next-Generation Sequencing Data and of DNA Digital Files +
## 4 DNA Compression
Each file in the test data set was compressed in nine different ways: by using the standard, lossless, one-dimensional, compression utilities _zip_, _gzip_, _bzip2_, by using our coding strategy, our coding in combination with the _BWT_ transform, our coding strategy in combination with _zip_, our coding strategy in combination with both the _BWT_ transform and _zip_. We have not used _Quip_ in our experiments or other FASTA compressors because they are not targeted to this kind of input. The two tables show that the best standard utility to compress this type of data is, as expected, _bzip2_. In fact, _bzip2_ outperforms _zip_ and _gzip_ on all the files but not on the first file, which is the smallest. Our coding strategy compresses the files with a compression ratio that is in general close in the range from _4 to 1_, outperforming by itself _zip_, _gzip_, and _bzip2_. There is still space for compression improvements. In fact, if we pair our strategy to a standard compressor like _zip_, or if we order the data by using the Burrow Wheeler Transform, or if we do both things, we constantly get an improvement in compression. This improvement, on the other side, corresponds to a higher computational cost (in our experiments, using the _BWT_ slowed down the compression time even up to four times than using only our coding strategy). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**File** & **Algorithm** & **Compressed size** & **Compression Ratio** \\ \hline \multirow{8}{*}{\begin{tabular}{c} Lambda\(\_\)Airrus.fa \\ (48,502 bytes) \\ \end{tabular} } & zip & 14,465 & 3.35 \\ \cline{2-4}  & gzip & 14,316 & 3.39 \\ \cline{2-4}  & bzip2 & 13,249 & 3.66 \\ \cline{2-4}  & Our coding & 12,220 & 3.97 \\ \cline{2-4}  & BWT + our coding & 12,218 & 3.97 \\ \cline{2-4}  & Our coding + zip & 12,236 & 3.96 \\ \cline{2-4}  & BWT + our coding + zip & 13,325 & 3.63 \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Homo\(\_\)sapiens.GRCh38 dna\(\_\)sm \\ (3,072,712,323 bytes) \\ \end{tabular} } & zip & 847,980,783 & 3.62 \\ \cline{2-4}  & gzip & 847,980,647 & 3.62 \\ \cline{2-4}  & bzip2 & 775,099,444 & 3.96 \\ \cline{2-4}  & Our coding & 748,228,083 & 4.11 \\ \cline{2-4}  & BWT + our coding & 707,234,325 & 4.34 \\ \cline{2-4}  & Our coding + zip & 726,112,596 & 4.23 \\ \cline{2-4}  & BWT + our coding + zip & 646,265,050 & 4.75 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} SRR74141\(\_\)2 \\ (7,982,945,875 bytes) \\ \end{tabular} } & zip & 2,254,125,397 & 3.54 \\ \cline{2-4}  & gzip & 2,254,125,141 & 3.54 \\ \cline{2-4}  & bzip2 & 2,040,455,140 & 3.91 \\ \cline{2-4}  & Our coding & 1,987,213,134 & 4.02 \\ \cline{2-4}  & BWT + our coding & 1,804,446,210 & 4.42 \\ \cline{2-4}  & Our coding + zip & 1,926,300,241 & 4.14 \\ \cline{2-4}  & BWT + our coding + zip & 1,764,562,905 & 4.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results (1).
## 2 First Principle Model of System  
### System Structure and Working Principle  
The main structure diagram of the HHHTS can be seen in Figure 1. It can be seen from the figure that the main body of the HHHTS is a wind tunnel, including a combustor, test section, stable section, diffuser connection section, and contractile section. In order to generate a temperature field and realize the gas temperature control, in addition to the main body, the entire test system also includes the measurement and control subsystem, the air supply subsystem, the fuel supply subsystem, and the cooling system. The operating principle of the whole test system is as follows: firstly, the aviation kerosene provided by the fuel supply subsystem and the high-speed air flow provided by the air supply subsystem burn in the combustor, generating high-temperature high-speed hot air flow; then, the hot air flows through the middle section of the wind tunnel and finally forms a uniform and stable temperature field in the test section; finally, the thermal test of the specimen can be completed in the test section, and the performance data can be obtained.
## 1 Introduction  
The research on air-induced dynamic deformations of the corneal surface has so far relied exclusively on limited air pulses strengths proposed in commercial tonometers [1, 2, 3, 4, 5]. Commercial air-puff tonometers measure corneal deformation indirectly, based on the amount of light reflected from the corneal surface [6]. Over a decade ago, improvements enabling direct measurements of these deformations were proposed. Notably, [PERSON] et al. combined an air-puff chamber with optical coherence tomography (OCT) [7]. This approach was explored in both ex vivo and in vivo measurements over the years [8, 9, 10, 11, 12]. Shortly after the first publication on air-puff OCT, Oculus GmbH introduced the commercial Corvis ST device, which uses a Scheimpflug camera [13; 14]. Similar to the OCT-based system, the Corvis ST prompted numerous studies focusing on clinical applications, data analysis and interpretation [15; 16; 17; 18], as well as computer simulations (modeling) of the biomechanical properties of corneal and scleral structures [19; 20; 21].  
To date, the application of custom air pulses with different spatial and temporal properties, and their impact on corneal-surface deformations, has not been explored. Such customization could be motivated by the need to optimize the eye and air-puff stimuli distance, which is particularly important when imaging beyond the central cornea (i.e., the corneal apex) [22]. However, achieving pulse customization requires specialized tools to characterize its spatial and temporal properties.  
Because corneal response to air-pulse stimulation also depends on intraocular pressure (IOP), air-puff OCT studies are typically performed at various IOP levels. These levels may be reached through its natural fluctuations (in vivo measurements) [23] or by using well-controlled IOP adjustment systems (ex vivo measurements) [24].  
To focus solely on the corneal surface, one must exclude (or subtract) the dynamic behavior of the rest of the eye. For in vivo measurements, methods such as peripheral corneal displacement and ultra-long-range OCT have been proposed to correct for eye retraction [11; 25]. However, for ex vivo porcine eye studies, postmortem changes (e.g., the dramatic reduction in crystalline-lens transparency) necessitate alternative approaches.  
In this paper, we evaluate how the spatial and temporal characteristics of custom air pulses influence the dynamic displacement of the ex vivo porcine corneal surface under various IOP levels. To isolate corneal dynamics from overall eyeball movements, we developed a dedicated dual-arm air-puff OCT system. Finally, we investigate how the customization of these air pulses affects the parameters extracted from the resulting temporal displacement profiles.
## 4 Role of Ap4A: Functions in Normal and Cancer Cells
### Interaction with Other Proteins  
Thanks to advancements in technology, unusual and novel Ap4A roles in metabolic events have been explored. Recent studies have shown that this alarmone may play multiple roles in cellular processes by interacting with and modifying other proteins. These findings, which could have potential therapeutic applications, will be briefly discussed to highlight emerging directions in the field.  
A significant upregulation of Ap4A concentration was observed in dendritic cells (DCs) in an in vivo model using CD11c+ mice, which lack Ap4A hydrolase. DCs are immune cells known for their advanced migratory abilities, and they activate CD8+ T cells during an immune response by presenting antigens in secondary lymphoid organs [72]. In addition, it was previously reported that Ubiquitin-like Modifier-Activating Enzyme 1 (UBA1) is capable of synthesizing Ap4A in vitro [10]. Chemical proteomic profiling also revealed a variety of proteins involved in gene expression, translation, and protein targeting that interact with diadenosine triphosphate and tetrapphosphate. UBA1, an essential enzyme in the ubiquitin system, was among the most significantly enriched hits for Ap4A [73]. In addition to Ap4A's previously discussed effects on P2X and P2Y receptors, an increase in lactoferrin levels has also been noted. A single application of Ap4A has been shown to increase lactoferrin production, a protein known for its significant anti-inflammatory and antimicrobial properties [74]. Therefore, diadenosine tetraphosphate appears to support immune homeostasis on the ocular surface and may strengthen the tear film barrier.  
Furthermore, it was recently demonstrated that Ap4A is a superior co-substrate for the auto-AMPylation of the FIC enzyme (FICD) in vitro. While FICD has been implicated in neurogenesis, its best-characterized in vivo target remains BiP, linking AMPylation to the cellular stress response. Notably, the authors found that FICD utilizes Ap4A more efficiently than ATP for auto-AMPylation, leading to a complete conversion of the enzyme into its AMPylated form, whereas ATP resulted in only partial AMPylation, leaving residual non-AMPylated FICD. These findings connect AMPylation to Ap4A alarmone formation as a novel cellular stress response mechanism [75]. Another study showed that Ap4A could act as a diabetogenic factor, inducing a diabetic state by decreasing Glucose Transporter Type 4 (GLUT-4), a protein responsible for glucose transport into cells, and reducing glucose uptake. These findings are particularly interesting as diadenosine polyphosphates have been shown to affect blood pressure, and the link between hypertension and diabetes remains unclear [76]. Given that cancer cells require more glucose to fuel their rapid growth, downregulation of GLUT-4 could hinder glucose uptake, potentially limiting tumor cell proliferation.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 4 Experiment  
We evaluate our method on four core tasks of point cloud processing: reconstruction (Section 4.1), pose change estimation (Section 4.2), classification (Section 4.3), and segmenta  
Figure 2: A diagram of the framework of the VN-Transformer. Its structure is similar to the standard Transformer framework. The left is the flow of tokens in the whole autoencoder framework, and the right is the internal structure of each block and the calculation process of VN-Attention.  
tion (Section 4.4), and also test it on a classification task with few-shot learning (Section 4.5), which is used to demonstrate the robustness of our network. These tasks are not only rich and diverse but also encompass most of the different application scenarios of the equivariant framework. Reconstruction and pose change estimation are rotation-equivariant tasks, while classification and segmentation are rotation-invariant tasks. Moreover, the pose change estimation of 3D point clouds has been less explored in the previous research.  
**Dataset:** We mainly use ShapeNet [62] and ModelNet40 [63] datasets for the evaluation. The ShapeNet dataset contains more than 50,000 different 3D models of point clouds, which can be divided into 55 categories. The ModelNet40 dataset consists of 12,311 3D point cloud models covering 40 object classes. For the segmentation task, we use the ShapeNetPart dataset for partial segmentation, which contains 16 shape classes and more than 30,000 models. Since there is currently no publicly available clean human point cloud dataset, we utilize point clouds sampled from the HumanBody dataset [64]. The HumanBody dataset comprises 399 meshes. For our study, we select 397 complex mesh models (two of the mesh models are too simple to sample effectively) and randomly sample 4096 points on each model. At the same time, we also add random noise perturbation during the sampling process.  
**Rotation Settings for Training/Testing:** The three rotation settings in training and testing are i, z, and SO(3), where i means no rotation of the data, z means rotation only around the z-axis, and SO(3) means any rotation in 3D space. We test all tasks under the three rotation settings.
# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 2 Social-Ecological Systems and Evolution: Theoretical Framework
### Social-Ecological Systems and Resilience: Origins and Scope  
Social-Ecological Systems is _\"an ecological system intricately linked with and affected by one or more social systems\"_[19]. The SES framework further elaborates [PERSON]'s Institutional Analysis and Development (IAD) and [PERSON] and colleagues' ones [10; 19; 20]. It was intended to specify the biophysical aspects of the IAD framework [10]. The IAD framework has been updated through a rearrangement of the list of relevant attributes of governance systems to make it applicable to broader policy settings [9]. Some emerging research aims to combine or integrate the IAD and SES frameworks to help overcome their individual limitations (SES framework as too static and the IAD framework as underspecified) and offer a more powerful framework that allows for deeper multilevel analyses of dynamic changes in social-ecological circumstances by making direct connections between institutional changes and social-ecological outcomes [21].  
The SES perspective is closely linked to resilience thinking [22; 23]. While the SES framework relates to the structure of a system, resilience focuses on evolution. Social-Ecological System and resilience thinking conceive systems as moving across stability domains according to internal and external disturbances. Key variables of interest are interlinkages among the components of the system and the adaptation of the later according to its resilience. We adopt the social-ecological resilience thinking [24]. Resilience is the magnitude of disturbance that can be absorbed before the system changes its structure by changing variables and processes that control behavior [24]. Social-ecological resilience extends the ecological resilience [25] to the social world and distinguishes from engineering resilience [26]. Engineering resilience considers changes as predictable and focuses on stability near to steady-state equilibrium. The key variable of interest is the recovery time, i.e., the speed of return to the steady-state equilibrium after a disturbance. In that perspective, the system does not change its structure.  
The SES framework and resilience thinking together provide a consistent set of concepts for the analysis of co-evolution between humans and nature. Consequently, most researchers use these frameworks as a _\"checklist of concepts from which they could qualitatively or quantitatively measure their variables for the sake of case descriptions, case comparisons, or statistical analysis\"_ ([10], p. 243). To move toward more explanatory power, there is a need for internal theoretical models, especially regarding evolution and change [27; 28].
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 2 Methods
### Integrated Application of Industry 4.0 Technologies  
Synergies of technology application at an Industry 4.0 Level: putting together separate solutions under one roof can be better explained while noting that, collectively, Industry 4.0 technologies demonstrate a more powerful effect in introducing sustainability into manufacturing in comparison to their individual applications. Another example of improving safety and training can be seen in the integration of MES with IIoT. This combination enables real-time tracking of worker activities and machine operations, helping to identify any unsafe behaviors or conditions before they lead to accidents. Predictive analytics generated by IIoT can alert safety personnel about equipment that requires maintenance, thereby preventing accidents caused by machinery failures. This proactive approach to safety management reduces downtime and enhances the overall safety culture within the organization. Furthermore, combining Additive Manufacturing with Information and Communication Technology (ICT) enables resource efficiency and sustainable manufacturing throughout the whole product development cycle--from the design stage through the production stage to recycling [23; 24].  
Case Study: proper implementation of the interplay of the technologies: the notable roles companies such as famous manufacturers have played in the successful combination of technologies are evidence enough of the effective multi-technology integration. Citing another example, we can use the case of a major electronics manufacturer that has achieved comprehensive production optimization through the inclusion of MES, IIoT, and AM technologies [25]. An outcome is that the MES system interfaces with the IIoT to enable immediate change in production motifs through real-time data and sensor data, resulting in less material wastage and downtime. Simultaneously, AM is utilized to model and manufacture tailored goods, which not only reduces material waste but also results in lower production and transport emissions. As a result of technology incorporation into their processes, the organization has used resources more efficiently and more sustainably, decreased their environmental influence, and managed to uphold economic performance. This is sufficiently supported by the practical scores.  
The architecture of science and technology management: with the high expectation of the integration of industrial technology into business operations, this study comes up with a concept, \"Technology Integration Maturity Model\" (TIMM), to guide companies in a more feasible approach (Figure 7). Such an evaluation includes four major aspects: the level of success in technology usage, the integration in data exchange, the improvement in decision making, and the tangible versus non-tangible results. Via this model, companies can identify areas of strength and weakness concerning the integration of technology, hence making use of their Industry 4.0 technology initiatives to maximize the benefits accruing from sustainable development [26].
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 6 The Test Cases  
### First Test Case  
The set-up of the first test case at the manufacturing experimentation facility is shown in Figure 7a. A mobile robot is responsible for fetching objects to a robotic bi-manipulator. The objects are located at a set of shelves, where a human operator is responsible for manually loading the objects on the mobile robot. Both the robot and the operator are aware of which object is currently needed at the bi-manipulator, as there is a centralized wireless or wired communication infrastructure, coordinated by a CNC, and the data can be sent and received through the communication links. However, due to harsh conditions in several industrial environments, it is not unusual for the main centralized operational network to go offline, for a variety of reasons. When there is a situation like this in the current scenario, the mobile robot and human operator cannot be aware of which object is currently needed at the bi-manipulator, which in turn results in a failure of the production process. In order to address this problem, we suggested the adoption of a distributed data management approach. More specifically, we employed a secondary, lightweight data distribution layer operating using direct communications between the involved nodes, such that data exchange can survive disruptions of the main network infrastructure. We implemented it by using small, low-cost wireless sensor motes. The proof of this concept was showcased by placing three motes in the network as shown in Figure 7b: one on the bi-manipulator, one on the mobile robot and one on the set of shelves. The motes used were IEEE 802.15.4 enabled, a fact that renders them compatible with typical industrial networking protocols, such as IEEE 802.15.4e and WirelessHART. This test case is demonstrating that we can achieve high fault tolerance and reliability with a low-cost approach.  
Figure 6: Performance for variable number of consumers. (**a**) Success rate; (**b**) average latency; (**c**) maximum latency; (**d**) maximum average latency.  
Figure 7: First test case set-up. (**a**) Backbone network operational; (**b**) backbone network network operational.