esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 7 Conclusions  
In this paper, we propose a novel data collection scheme based on opportunistic node connections in network with a mobile sink, which can be used in smart industrial fields. In the scheme, the mobile sink can launch a data collection task anywhere in network at any time by broadcasting a tag message, a node that receives this message can forward its probe message that includes its source information to the mobile sink by using the energy efficient probe message forwarding mechanism to minimize the overall energy consumption. On receiving these messages, the mobile sink first adopts the random graph theory to constructs an opportunistic connection random graph, then calculates the optimal  
Figure 18: The illustration of two cases when two mobile sinks collect data in network simultaneously, (**a**) the data collection scopes of the mobile sinks are non-overlapped, (**b**) the data collection scopes of mobile sinks are overlapped.  
path from itself to each node in this graph, hence a spanning tree is generated where the mobile sink is the root node, and it finally broadcasts this spanning tree to all nodes under its long radio range so that they can obtain the optimal paths from themselves to the mobile sink. Accordingly, a routing protocol which adapts to different nodes operating status is proposed to improve the reliability of data transmission. The simulation results show that the proposed scheme works better concerning the packet delivery ratio, energy consumption and network lifetime.  
The possible future work are, first, studying the synergy between mobile sinks, and exploring the impact of the comprehensive factors such as the number of mobile sinks and the their long radio ranges on data collection efficiency. Second, studying the energy efficient cross-layer protocol design [37], so that it could be used more widely. Third, inspired by work in Reference [38], of which the authors proposed a fog structure composed of multiple mobile sinks that act as fog nodes to bridge the gap between WSNs and the Cloud, it can consider using mobile sinks to upload data to the cloud and use their powerful computing and storage capability to improve the efficiency of constructing the OCRG and calculate the optimal paths.  
Formal analysis, X.H.; Investigation, Z.P.; Methodology, G.Y. and X.H.; Project administration, G.Y.; Software, Z.P.  
The research was funded by [National Natural Science Foundation of China] grant number [61602305], [61802257], and [61572325], [Natural Science Foundation of Shanghai] grant number [182R1426000], jointly.  
The authors would like to appreciate all anonymous reviewers for their insightful comments and constructive suggestions to polish this paper in high quality.  
The authors declare no conflict of interest.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### AI-Driven Autonomous Video Processing (2023-2025)  
The current stage of VBDA marks a shift toward autonomous, AI-native systems capable of handling complex video analytics with minimal human intervention. Advanced generative models, including GANs, ViT, and diffusion architectures, are increasingly applied in tasks such as anomaly detection, semantic summarization, and predictive surveillance [31; 32]. Although these models offer promising performance, their applicability in real-time systems is constrained by substantial computational overhead.  
Concurrently, blockchain technologies are being leveraged to support secure metadata logging, transparent access control, and tamper-resistant video sharing [33]. The integration of privacy-preserving mechanisms such as Zero-Knowledge Proofs and decentralized consensus further reinforces compliance with data protection standards. Future advancements, such as 6G networks and quantum-enhanced video processing, are expected to drive the emergence of scalable, resilient, and privacy-conscious VBDA systems [5].  
In summary, VBDA has evolved from centralized, rule-based systems to hybrid, AI-driven architectures that effectively address latency, scalability, and data privacy challenges. Key milestones, ranging from cloud-based batch processing to edge computing and FL, have laid the foundation for more adaptive and resilient video analytics frameworks. Modern platforms now integrate generative models, semantic indexing, and decentralized training to enable real-time, autonomous video understanding. Looking forward, advances in explainable AI, privacy-preserving computation, and next-generation infrastructure such as 6G networks and quantum-enhanced processing are expected to drive the development of secure, intelligent, and context-aware VBDA systems for critical real-time applications [9; 20; 32].
# Experimental and Numerical Studies on the Structural Performance of a Double Composite Wall
## 4 Analysis Results and Discussions
### Practical Shear Strength Equation  
Previously, the shear behavior of DCW was analyzed in detail through the finite element analysis shown in Figures 10 and 12. However, in practice a straightforward calculation method is required for the shear design of the DCW. In this regard, this study proposed a method to estimate the shear strength of a DCW section composed of PC panels, CIP concrete, and bolts based on the ACI 318-19 and KCI 2017 codes [15; 25].  
The ACI 318-19 and KCI 2017 codes specify that the shear strength of concrete (\(V_{c}\)) and the contribution of shear reinforcement (\(V_{s}\)) in RC members with stirrups should be calculated, respectively, as follows:  
\[V_{c}=\frac{1}{6}\sqrt{f_{c}^{\prime}}b_{w}d_{s}, \tag{15}\]  
\[V_{s}=A_{v}f_{vg}\frac{d_{s}}{s_{v}}, \tag{16}\]  
where \(f_{c}^{\prime}\) is the compressive strength of concrete; \(b_{w}\) is the web width; \(d_{s}\) is the effective depth of tensile reinforcement; \(A_{v}\) and \(f_{vg}\) are the sectional area and yield strength of shear reinforcement, respectively; and \(s_{v}\) is the spacing of transverse reinforcing bars. As shown in the upper part of Figure 13, if the compressive strengths of the PC panel (\(f_{c,PC}^{\prime}\)) and  
Figure 12: Comparison of the test and analysis results (DCW-S specimens): (**a**) DCW-SN specimen; (**b**) DCW-SB(1) specimen; (**c**) comparison of the bolt contributions in DCW.  
CIP concrete (\(f^{\prime}_{c,CIP}\)) are applied to the red and blue areas, respectively, and the stress of the bolt at shear failure is assumed to be 65% of its yield strength (\(f_{yb}\)) based on the test and analysis results, the shear strength of the DCW section can be represented by the following equation:  
\[V_{n}=V_{c,PC}+V_{c,CIP}+0.65V_{bolt}=\frac{1}{6}\Big{(}\sqrt{f^{\prime}_{c,PC} }b_{w}d_{s}+\sqrt{f^{\prime}_{c,CIP}}b_{CIP}d_{s}\Big{)}+0.65\frac{A_{br}f_{ yb}d_{s}}{s_{b_{0}}}, \tag{17}\]  
where \(b_{w}\) and \(b_{CIP}\) are the web widths of the PC panel and CIP concrete, respectively. The lower part of Figure 13 shows that Equation (17) can reflect the shear contribution of the bolt very reasonably, considering the partial interaction between the PC panel and the bolt. Meanwhile, it should be noted that for the shear contribution of concrete (\(V_{c}\)), the proposed equation underestimated the test result because the ACI 318-19 and KCI 2017 code equations were derived to obtain the shear strength estimation results on the safe side [18]. This could be deemed a safety margin in practical design. Consequently, in Equation (17), the shear contributions of the concrete and steel elements constituting the DCW section were reasonably considered, and the shear strength of the DCW-SB(1) specimen with bolts was evaluated conservatively. Therefore, it is expected that economical and reasonable design results can be obtained using Equation (17) when estimating the shear strength of the DCW section.
## 4 Methodology
### The 5-Parameter Transformation  
This transformation is also called the Molodensky transformation because [PERSON] conducted differential equations that connect two ellipsoidal coordinate systems [2]. Different approaches to those equations resulted in two types of this transformation [4]:  
* Standard 5-parameter Molodensky transformation, which represents the transformation between two ellipsoidal coordinate systems, including translations along three coordinate axes as well as changes of the semi-major axis and flattening between two ellipsoids (Figure 3) [4];
* Abridged 5-parameter Molodensky transformation, which represents simplified expressions of standard Molodensky trasnformation where ellipsoidal heights of points in the input coordinate system are unknown [4].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Standard Deviation** \\ \hline \(t_{x}\) (m) & 644.192 & 0.058 \\ \(t_{y}\) (m) & \(-\)213.131 & 0.058 \\ \(t_{z}\) (m) & 442.788 & 0.058 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parametersof 3-parameter transformation from HDKS to HTRS96.  
Standard and abridged 5-parameter Molodensky transformations represent the approximation of a 3-parameter transformation, so they can be considered similarity transformations. An advantage of these transformations over 3-parameter transformations is the avoidance of conversion between ellipsoidal and Cartesian coordinates, and errors caused by approximation are negligible compared to the uncertainty of a 3-parameter transformation [35].
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 5 Simulation Details and Results
### Evaluation Metrics  
WSN performance can be evaluated by a variety of metrics. Corresponding to our requirements discussed in Section 3.2, and considering those used by most of the researchers, we used the following metrics:  
1. Energy consumed: Average energy consumed by all sensors is depicted using this metric. The simulator measures energy consumption by considering the amount of time sensor radio has been in receive or transfer mode [37]. It is independent of data transfer or receive activity in a mode rather depends on duration the sensor is in specific mode. Table 2 displays the energy consumption of CC2420 Radio we simulated, in different modes [38].
2. Network lifetime: This metric also depicts energy consumed, but shows the lifetime of the whole network. Using a conservative approach, the network is considered non-functional whenever the first node dies.
3. Nodes Alive: Shows the percentage of sensor nodes alive at specific simulation time.
4. Packet reception rate: This metric shows the rate of number of packets received by the sink as compared to the number of packets sent by each of the sensors. Any duplicate packet is discarded by the sink. The packet loss is there because, in all kinds of wireless communication, as we are using realistic simulation models (Section 5.2), it is common that there is some packet loss.
# Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors
## 5 Development of RNA-Based Sensors for Intracellular Metabolites: A Challenging Endeavor
In addition, a second fluorogenic aptamer has been recently introduced into a light-up sensor for the first time. Here, a natural aptamer with a 3 WJ motif was used as a scaffold to select a new generation of stable fluorogenic aptamers named _Squash_. A yellow Squash was connected to a SAM-sensing aptamer, and this RNA device was integrated with Broccoli in a self-circularizing Tornado framework [72]. This light-up sensor incorporates most design improvements to enhance the stability, folding, and signal emission in a single device and represents a promising tool in metabolite sensor development.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 5 Contributions  
A service management framework is designed to incorporate the QoS penalty and energy consumption of fog jobs waiting for execution in resource queues of the cloud environment such that cost of service and energy is mitigated. The framework employs an analytical model for communication and computational performance metrics derived to calculate the service cost. In this perspective, the communication bandwidth is the performance metric that affects the QoS delivered to fog clients. A high bandwidth allocated to fog nodes can, for instance, mitigate the latency incurred from the transmission time of data jobs. Moreover, the resource demands of fog jobs proportionally influence transmission and computational energy, and as a result, energy constraints affect job latencies in that fog jobs with high resource demands demand high communication and computational energy consumption.  
The management framework employs the following: (i) the allocation cost of the communication bandwidth assigned for a job delivered from a fog node; (ii) the waiting cost of a fog job in the resource queues of the cloud computing environment; (iii) the execution cost of a fog job in the cloud resource allocated for it; and (iv) the SLA violation cost if the service of a fog job does not meet its QoS deadline and tardiness constraints. The contributions of this paper are summarized as follows:  
* Designing a cost model based on a performance metric derived by utilizing QoS obligations and energy demands of fog jobs transmitted for execution in the cloud computing environment, in which the performance of energy efficiency is optimized based on the QoS of fog jobs;
* Employing information of resource usage required by fog workloads to decide on their optimal allocation to cloud resources, so as to serve the demands of fog nodes such that the cost of service is mitigated;
* Considering mutual performance impacts between quality metrics of fog jobs allocated for execution and factors of energy consumption required to service such jobs, so as to achieve pragmatic client satisfaction and mitigate the gross energy cost of job workloads queued for execution across the cloud-fog environment;* Mitigating the management complexity of the scheduling model so that schedules of minimum cost of QoS and energy penalties are evolved in a reasonable time.  
Overall, schedules are formulated based on a joint energy- and QoS-optimized performance wherein the performance is predicted and evaluated using the cost of service and energy. Optimal schedules are thus formulated on queues of cloud resources such that the cost of service and energy are minimized, so as to maximize the probability of satisfied clients. The system performance is assessed on modeled fog jobs generated with heterogeneous service characteristics, energy demands, and SLA penalties.
## 4 Discussion
Additionally, researching the long-term stability of these systems in real-world climatic circumstances would provide greater insight into their practical usage. Another area worth investigating is combining DBD plasma polymerization with other environmentally friendly technologies to create integrated systems for resource recovery or pollution management.
###### Abstract  
Underwater deployed sensors nodes are energy-constrained. Therefore, energy efficiency becomes crucial in underwater wireless sensor networks (U-WSNs). The adverse channel corrupts the packets and challenges their reliability. To handle these challenges, two routing schemes are introduced in this paper. They are effective energy and reliable delivery (EERD) and cooperative effective energy and reliable delivery (CoEERD). In EERD, the packets follow single-path routing and the best forwarder node is selected using a weight function such that packets are transferred via the reliable paths with low energy usage. Packet transfer via a single route in EERD has, however, compromised reliability as the undersea links bear harshness and unpredictability. Therefore, the CoEERD scheme adds cooperative routing to EERD, in which a relay node is introduced between a source-destination pair. The destination requests the relay when the packets it gets from the source are corrupted beyond a threshold value. Selection of weight function is unique and considers many factors to ensure low energy usage with reliability while considering nodes for data transfer. This also helps in selecting a single relay node rather than many relays in the conventional cooperative routing model. Based on simulation results, the EERD and CoEERD protocols have improved performance in energy usage, reliable packet transfer and delay.  
energy efficiency; network reliability; routing protocols; U-WSNs +
Footnote †: journal: Energy-Effective Cooperative and Reliable Delivery Routing Protocols for Underwater Wireless Sensor Networks
## 4 Temperature Finite Element Model and Test Comparison  
### Temperature Finite Element Model  
A three-dimensional transient finite element model (FEM) of the U-shaped girder bridge was established using the ANSYS 16.0 to simulate the temperature distribution under solar radiation effect [40]. The model uses SOLID70 hexahedral heat conduction elements for volumetric meshing to ensure both the accuracy and computational efficiency of the model [41]. The SURF152 elements are used to simulate the surface of the U-shaped girder bridge, allowing for the application of convective and radiative heat loads [42].  
Figure 8: Partial shading FEM model of the U-shaped girder bridge: **(a)** 8:00; **(b)** 10:00; **(c)** 12:00; **(d)** 14:00; **(e)** 16:00; **(f)** 18:00.  
In addition, the temperature field shows a nonlinear distribution inside the structure, where high temperatures are only found within a shallow depth of the surface and quickly decrease as the distance from the surface increases. To accurately simulate the nonlinear distribution of the temperature field and ensure computational precision, it is critical to choose the appropriate mesh size. This study selected a mesh size of 5 cm to balance accuracy with the complexity of the calculations. The U-shaped girder bridge comprises a total of 1,690,605 nodes and 1,819,758 elements of SOLID70 and SURF152, and the FEM of the U-shaped girder bridge is shown in Figure 9.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 1 Introduction  
Large-aperture mirrors are commonly used in ground-based telescopes, and gradually applied to space optical remote sensors with the development of launch vehicle technology. Image quality improves with the increased mirror aperture. The processing difficulty, processing costs, and time increase at the same time. In addition, repairing remote sensors during operation in space is difficult and costs a great amount. Hence, the quality of the mirror assembly must be considered to ensure the service time of remote sensors. Support structures must be flexible during the design stage to eliminate the influences on mirror surface error caused by temperature fluctuation and assembly stress. Grooving and reducing the cross section are effective methods for minimizing the rigidity and releasing designated degrees of freedom. However, a flexible design will partially increase stress and strain while generating stress concentration and causing local plastic deformation on the structure. Stress concentration may cause fatigue failure, reduce the reliability of the optical system, and finally affect the imaging quality of the whole camera [1,2]. The stress concentration \(\mathrm{K_{T}}\) and the fatigue notch \(\mathrm{K_{f}}\) factors are typically used to describe the effect of stress concentration on fatigue strength under static force or simple alternating load. However, describing the complicated random vibration fatigue life with only these two coefficients is difficult because the flexible structure will bear uncertain random vibration load during experiment, transportation, and launching [3].  
A determinate function is unable to describe the excitation and the response of a random vibration, and the instantaneous value is also unpredictable. However, they follow certain statistical laws and can be described using statistical theory. Power spectral densityfunction (PSD) is usually applied to describe the load of random vibration. Knowing the geometric characteristics of parts, fatigue property of materials, PSD of excitation signal, and surface state of components is necessary during fatigue analysis. Obtaining the time history of stress and strain responses through finite element analysis software, S-N curve of materials, and surface condition of products is the main method for calculating fatigue life [4]. However, neither influences of size parameters nor influences of temperature and residual stress on fatigue life are considered in traditional design methods [5; 6]. Fatigue life is analyzed in this work by exploring the failure mechanism. In addition, analysis mode of the flexible structure is established to improve the reliability of the mirror assembly.
# The Impact of the Stringent Response on TRAF GTPases and Prokaryotic Ribosome Assembly
## 7 Inhibition of rRNA Transcription and Processing by the Stringent Response
Late-stage processing of the 16S rRNA is a crucial aspect of quality control, preventing subunit degradation by RNase R upon correct maturation [140], hence the stringent response not only shuts down de novo production of the ribosome constituents but also encourages the degradation of extant ribosomal precursors upon induction. Secondly, GTP is one of two CodY cofactors, along with branched chain amino acids (isoleucine, leucine and valine) [141]. CodY is a gene repressor in Gram-positive bacteria with a large regulon [142]. As the GTP pool in the cell drops during the stringent response, the repression of CodY is  
Figure 5: The effect of the stringent response on transcription in Gram-negative and Gram-positive bacteria. (**A**) In Gram-negative organisms, (p)ppGpp (yellow) and DksA (green) bind to RNA polymerase to alter the transcription of amino acid biosynthesis genes (activated) and rRNA genes (repressed). (**B**) In Gram-positive bacteria (p)ppGpp bind to and inhibit multiple enzymes involved in GTP synthesis. This coupled with the decrease in branched chain amino acid (BCAA) levels during the stringent response results in a derepression of the CodY regulon as well as a decrease in transcription of _rm_ and other genes with a G residue at the +1 site. released resulting in a very large change in the transcriptional profile. In _S. aureus_, 150 genes are up-regulated during the stringent response, however, only seven of these do so independently of CodY derepression [143]. A total of 161 genes are down-regulated during the stringent response induced by leucine/valine starvation and all of these are regulated independently of CodY. This is perhaps unsurprising seeing as how CodY generally acts as a gene repressor. This clearly highlights that, in Gram-positive bacteria, CodY is an important factor for determining gene up-regulation during the stringent response but not down-regulation.