esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### Architecture Overview  
The proposed architecture is structured into five distinct layers, each meticulously designed to ensure secure, scalable, and efficient IIoT operations:  
* Physical Layer: This foundational layer comprises IIoT sensors, actuators, and edge computing devices responsible for real-time data collection and processing. Edge computing is pivotal in reducing latency by performing localized computations and ensuring timely responses in industrial settings where delays can lead to significant operational and financial losses [34].
* Network Layer: The network layer facilitates reliable communication between devices using standardized protocols such as MQTT (Message Queuing Telemetry Transport), HTTP (Hypertext Transfer Protocol), and OPC-UA (Open Platform Communications Unified Architecture). By enabling seamless data exchange across local and remote components, this layer ensures efficient coordination in distributed IIoT systems, laying the groundwork for robust interoperability [35].
* Storage and Cloud Layer: This layer manages data storage, quality control, and ontology modeling. Leveraging cloud-based tools, it processes large volumes of IIoT data to extract actionable insights, driving informed decision making and operational optimization [33; 34; 35].
* Blockchain Layer: At the core of the architecture lies the blockchain layer, which provides decentralized security and data integrity through distributed ledger technology (DLT). This layer prevents unauthorized access and data tampering by employing consensus algorithms, cryptographic encryption, and identity management. It mitigates Distributed Denial of Service (DDoS) attacks by distributing computational tasks across edge nodes and blockchain miners, enhancing system resilience [36; 37; 38; 39].
* Application Layer: The application layer oversees system operations, including monitoring, control, and logging. Real-time interaction tools notify operators of failures or anomalies, ensuring continuous system functionality and minimizing downtime. This layer is the interface between the system and end users, fostering trust through transparent and tamper-proof data transactions [37].  
By integrating blockchain technology [40; 41; 42; 43; 44], the architecture ensures transparent and immutable data transactions, fostering stakeholder confidence while enabling secure device identity management. Smart contracts enhance system resilience by automating access control and enforcing security policies, thereby improving overall system security and reliability. As illustrated in Figure 3, this additional security layer promotes robust access control and data protection.
## 2 Instability of Shock Wave-Sub-Millimeter Liquid Droplet Interfaces
### The Influence of Weber Number and Initial Diameter on the R-T Instability
Variations in growth rates among the different wavelengths would become prominent. At this stage, the substantial growth rate increase in the R-T instability waves accelerated the R-T instability development, rendering the sub-millimeter liquid droplets more prone to fragmentation. Figure 1: Growth and development process of the R-T instability. Figure 2: Schematic of the sub-millimeter liquid droplet center of mass displacement. In Figure 4, a comparison is presented between this study and the experiments conducted by [PERSON] [15] and [PERSON] [16]. It shows how R-T instability wave growth rates vary with the wavenumbers for sub-millimeter liquid droplets of varying initial diameters under identical Weber number conditions. Evidently, decreasing the initial diameters of sub-millimeter liquid droplets leads to gradual increases in both the peak growth rates and wavenumbers of the R-T instability waves. This implies that, under a constant Weber number, reducing the droplet diameter causes the R-T instability waves on the droplet's surface to evolve toward smaller scales and higher growth rates. The analysis indicated that the growth of R-T waves was primarily linked to acceleration, where a higher acceleration led to faster R-T instability development. Under a constant Weber number, reducing the initial diameters of sub-millimeter liquid droplets increased the airspeeds around the droplets, consequently leading to greater acceleration for smaller droplets compared to larger ones in the same time frame. Consequently, experimental observations revealed that sub-millimeter liquid droplets with smaller initial diameters displayed reduced-scale surface-piercing behaviors and underwent more rapid development. Furthermore, it became apparent that with increasing Weber numbers, the disparities in the peak growth rates and maximum wavenumbers of the R-T instability waves between sub-millimeter liquid droplets with different initial diameters gradually diminished. This suggested that when the Weber number exceeded a specific threshold, variations in the initial diameters of sub-millimeter liquid droplets had a relatively minor influence on the progression of R-T instabilities.
## 1 Introduction
It can be seen in Figure 1 that some rectifiers are power factor correctors (PFC), keeping the THD as low as possible, complying with the established standards [7, 8]. In most cases, another converter is associated with the rectifier, to control the current. There is also the possibility to associate an active power filter to the rectifier's input terminals, in order to compensate the currents generated by the rectifier, thus obtaining a good quality sinusoidal current [9, 10] at the rectifier input. Some studies are focused on improving the efficiency of the active power filter and simplifying the control system, as in [10]. Other studies focus on implementing the hysteresis, called also Bang-Bang control [11]. In this case study, the modular three-phase rectifiers associated with a boost converter will be analyzed, as this combination improves the power factor (PF). This effect is achieved by shaping the input currents sinusoidally, thus guaranteeing a minimum THD at the input.
## 2 Materials and Methods
### Nutrient Manipulation Experiment, Gross Primary Production and Ancillary Data
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Campaign** & **Fertilization** & **PAR** & **VPD** & **Ta** & **SWC** & **SZA** \\  & & & \(\mu\)mol s\({}^{-1}\) m\({}^{-2}\) & **hPa** & \({}^{\circ}\)C & **\%** & \({}^{\circ}\) \\ \hline
20-03-2014 & 1 & No & \(\textbf{1604.82}\pm 11.33\) & \(\textbf{12.59}\pm 0.38\) & \(\textbf{24.2}\pm 0.2\) & \(\textbf{19.01}\pm 0.27\) & \(\textbf{41.86}\pm 0.23\) \\
15-04-2014 & 2 & Yes & \(\textbf{1842.92}\pm 32.63\) & \(\textbf{15.12}\pm 0.59\) & \(\textbf{30.09}\pm 0.55\) & \(\textbf{22.58}\pm 0.58\) & \(\textbf{31.83}\pm 0.85\) \\
7-05-2014 & 3 & Yes & \(\textbf{1342.1}\pm 93.73\) & \(\textbf{22.4}\pm 1.98\) & \(\textbf{32.1}\pm 0.91\) & \(\textbf{4.78}\pm 0.09\) & \(\textbf{25.69}\pm 0.6\) \\
27-05-2014 & 4 & Yes & \(\textbf{1417.15}\pm 104.4\) & \(\textbf{15.83}\pm 1.2\) & \(\textbf{27.89}\pm 0.47\) & \(\textbf{6.57}\pm 0.09\) & \(\textbf{21.4}\pm 0.82\) \\
04-03-2015 & 5 & Yes & \(\textbf{1411.29}\pm 18.05\) & \(\textbf{7.01}\pm 0.36\) & \(\textbf{23.9}\pm 0.48\) & \(\textbf{21.49}\pm 1.91\) & \(\textbf{49.66}\pm 0.49\) \\
23-04-2015 & 6 & Yes & \(\textbf{1842.64}\pm 25.23\) & \(\textbf{16.38}\pm 0.84\) & \(\textbf{29.98}\pm 0.37\) & \(\textbf{6.7}\pm 0.11\) & \(\textbf{31.21}\pm 0.98\) \\
27-05-2015 & 7 & Yes & \(\textbf{1955.21}\pm 35.25\) & \(\textbf{23.2}\pm 1.56\) & \(\textbf{36.33}\pm 0.73\) & \(\textbf{1.14}\pm 0.02\) & \(\textbf{24.26}\pm 1.87\) \\ \hline \hline \end{tabular}  
* PAR is the photosynthetic active radiation, VPD is the Vapor Pressure Deficit, Ta represents the mean air temperature, SWC is the soil water content and SZA is the solar zenith angle. Medians and one standard error are shown for each variable.  
\end{table} 
Table 1: Summary of the main meteorological data collected in each field campaign. \[\delta^{13}\mathrm{C}\ =\ \frac{\left(13R_{sample}-\ 13R_{standard}\ \right)}{13R_{ standard}} \tag{4}\]  
where 13R\({}_{\mathrm{sample}}\) and 13R\({}_{\mathrm{standard}}\) are \({}^{13}\)C/\({}^{12}\)C ratio of the sample and of the standard, respectively.
## 2 Material and Methods
### Drought Identification Using SMAI  
Based on the considerations in Section 2.1 the SMAI is designed as a 6-level threshold scheme to account for moistening conditions. SMA is expressed by digital values ranging from 0 to 5, which are correspondingly color-coded, Table 1:  
The SMAI is calculated for the region of each synoptic station from the operational NIMH network, indicated by symbol (\(\cdot\)) in Figure 1. Values are visualized accordingly to provide information about the land surface state in each one of the 28 main administrative regions of Bulgaria. The inverse distance weighted (IDW) method, GDAL_grid library, was used to interpolate soil moisture in the mesoregions. This method was chosen due to its simplicity and efficiency as demonstrated in applications that aimed to interpolate soil moisture data [46].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **SMAI Values** & **SMAI Color-Coding** \\ \hline Drought & 0 & Red \\ Drought risk & 1 & Orange \\ Dry & 2 & Yellow \\ Optimum & 3 & Green \\ Wet & 4 & Light blue \\ Overmoistening & 5 & Dark blue \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categories of dry/wet classes by the soil moisture availability index (SMAI) following [42].  
Considering that the soil moisture monitoring observational network in Bulgaria was established to obtain relevant information for use in agricultural productivity modeling, in situ measurements are organized in stations up to 800 m altitude [47]. For that reason, mountain regions are excluded in SMAI maps. In SMAI calculations, the moisture at 50 cm soil depth was used as a reference point, since it is indicative of vegetation activity during persistent dry anomalies.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Qualitative Comparison  
We conducted qualitative comparisons with state-of-the-art models--HRNet-W32 and YOLOv5s--and present the results in Figures 3 and 4. As shown in Figure 3, YOLOv5s without HFIP failed to detect distant and small targets. However, YOLOv5s with HFIP showed improved detection results and successfully identified these small targets with higher accuracy. As illustrated in Figure 4, the results clearly show that HRNet-W32 without HFIP produced misplaced and mispredicted results. In contrast, HRNet-W32 using HFIP effectively addressed these issues and achieved more accurate results.  
Figure 3: The qualitative results of small-object detection. To facilitate comparison, we enlarged areas within red boxes in the input images (the first column). We visualized the detection boxes for YOLOv5s (second column) and YOLOv5s-HFIP (third column) on the enlarged areas. The notable differences is highlighted with red arrows (third column).
## 2 Design Method of Conventional Switched Type Attenuators
### Conventional T-Type Attenuator  
Figure 2 shows the conventional T-type attenuation structure and its equivalent circuits.  
In the reference state, transistor _M_s is on, and transistor _M_p is off. The insertion loss is determined by the on-resistance of transistor _M_s and the series resistance _R_s. Consequently, the corresponding transmission S-parameter can be expressed as follows:  
\[S_{21,REF-T}=1-\frac{R_{s}R_{s,on}}{(2R_{s}+R_{s,on})Z_{0}+R_{s}R_{s,on}} \tag{1}\]  
where _Rs,on_ represents the on-resistance of the transistor _M_s, and \(Z\)0 is the characteristic impedance.  
In the attenuation state, transistor _M_s is off, and transistor _M_p is on; the insertion loss is determined by the T-type attenuation network and the on-resistance of transistor _M_p. Consequently, the corresponding transmission S-parameter can be expressed as follows:  
\[S_{21,ATT-T}=\frac{2Z_{0}(R_{p}+R_{p,on})}{2(Z_{0}+R_{s})(R_{p}+R_{p,on})+2Z_{ 0}R_{s}+{Z_{0}}^{2}+{R_{s}}^{2}} \tag{2}\]  
where _Rp,on_ represents the on-resistance of the transistor _M_p.  
The relative attenuation _AS_21 can be expressed as follows:  
\[\Delta S_{21}=\frac{S_{21,REF-T}}{S_{21,ATT-T}} \tag{3}\]  
Furthermore, it is imperative that the \(S\)11 of both the reference state and attenuation state be as minimal as possible to ensure impedance matching. Notably, the transistor _M_s, typically larger in size, is designed to minimize insertion loss. Consequently, this results in a superior impedance matching of the reference state. The \(S\)11 of attenuation state can be expressed as follows:  
\[S_{11,ATT-T}=\frac{2R_{s}(R_{p}+R_{p,on})+{R_{s}}^{2}-{Z_{0}}^{2}}{2Z_{0}(R_{ p}+R_{p,on})+2Z_{0}R_{s}+2R_{s}(R_{p}+R_{p,on})+{R_{s}}^{2}+{Z_{0}}^{2}} \tag{4}\]  
When the value of \(S_{11,ATT-T}\) = 0, it allows for the computation of the following:  
\[R_{s}=\frac{Z_{0}(1-S_{21,ATT-T})}{(1+S_{21,ATT-T})} \tag{5}\]  
\[R_{p}=\frac{2Z_{0}S_{21,ATT-T}}{(1-S_{21,ATT-T}^{2})}-R_{p,on} \tag{6}\]  
Based on (1), (3), (5), and (6), we can calculate _Rs_ and _R_p under the optimal impedance matching condition.  
Figure 2: (**a**) Schematic diagram of the conventional T-type attenuation structure and its equivalent circuits. (**b**) Reference state. (**c**) Attenuation state.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSN) can accurately and timely obtain the production information of crops, and provide data basis for intelligent agriculture. The dynamic crop state and unstable climate environment make it difficult to predict the connectivity probability of wireless links. Therefore, this paper studies an energy-saving opportunity routing transmission strategy under the influence of dynamic link interaction. The protocol establishes an importance model based on algebraic connectivity to reduce the energy consumption of network key nodes. At the same time, based on the improved Bellman-Ford algorithm, a method of constructing candidate sets is studied. It converts the opportunistic routing transmission cost of farm WSN into anycast link cost and the remaining opportunistic path cost affected by energy consumption. The priority queue is used to determine the nodes participating in the iteration, thereby reducing the computational overhead. The protocol also designs a backoff strategy considering the current residual energy to select the only forwarding node and reduce the unnecessary packet copies in the transmission process. Simulation results show that the studied method is superior to the existing opportunistic routing schemes in terms of packet overhead, network lifetime, energy consumption, and packet delivery rate.  
f 2022111936103390  
In an agricultural environment, the dynamic change of wireless links will lead to unreliable communication and affect energy efficiency. The traditional routing protocols obviously do not consider how to deal with the problem of poor link connectivity. Opportunistic routing selects candidate forwarding nodes. These nodes have a chance to become the next hop in the given priority order. Multiple forwarding nodes cooperate to form space diversity, which greatly improves transmission reliability [6]. The current studies on opportunistic routing mainly focus on the minimum transmission cost with little consideration of energy consumption and link interaction.  
The main contributions of this paper are as follows:  
1. This method introduces algebraic connectivity to quantify the importance of the nodes in the farm WSN. When constructing the candidate set, the key nodes are not frequently used, which prolongs the network lifetime.
2. Considering the opportunistic forwarding of candidate sets, the transmission cost is transformed into anycast link cost, and the remaining path cost is affected by energy consumption. Design construction rules of the candidate set are based on factors such as node energy consumption, the importance of the node, and distance to sink.
3. We adopt the priority queue to calculate the transmission cost iteratively to construct candidate sets. Then, the invalid copies are reduced by defining the backoff time, so the transmission efficiency in WSN is further improved.
## 4 Test Results and Discussion
### Relationship between Compressive Load and Relative Slippage of the Two Connecting Plates  
Figure 8 shows the curves between compressive load and relative slippage of the two connecting plates of each specimen. Before the first slip, the curves were rather straight, indicating that the specimens were in elastic state. The curves nosedived at the first slip, causing the PTM to unload. With the growing compressive load, the MC-60 curves zigzagged into the yield stage and then into the slip-unload hardening stage, while the MC-45 and MC-35 directly entered into the hardening stage. Eventually, the bolts were cut off once the load surpassed the ultimate strength of specimens.  
Figure 5: The structure of a bolt force sensor.  
Figure 6: The test setup.  
Figure 8: The relationship between the compressive load and relative slippage of specimens at different shear–compression ratios.  
Figure 7: The specimen failures observed in the tests.  
It could be concluded that: (1) the MC-60 curves covered four stages: elastic stage, yield stage, hardening stage and failure stage; (2) the MC-45 and MC-35 specimens had large initial slippages, pressing the bolt to the hole wall of the connecting plate, which is why these specimens skipped the yield stage before entering the hardening stage; (3) after the initial slip under a huge load, buckling of local rough contact surfaces occurred, the degree of mechanical occlusion also changed, the friction coefficient of MC-35 contact surfaces decreased, and the anti-slip bearing capacity reduced. The mechanical properties of specimens were weakened, and their ultimate strength fell below the initial friction load; therefore, the MC-35 curves cover only two stages: elastic stage and failure stage; (4) the growing shear-compression ratio suppressed the initial friction load and intensified curve fluctuations, shortened time to enter the hardening stage and reduced the total slippage and (5) there was no \"slip-unload-load-slip\" process on the buttered specimen, and the zigzag curve did not fluctuate obviously. It could be concluded that the friction coefficient had a great influence on the load-displacement curves.
## 2 Wireless Multimedia Sensor Networks
### Real-Time Video Data
#### 2.2.2 Reliability  
Reliability is another important QoS measure for real-time video delivery over the lossy wireless medium. Ideally, all video data needs to be delivered to the sink successfully since data loss could result in missing events of interest. In general, reliability guarantee can be provided either by redundancy or by retransmission [13], as shown in Figure 1.  
Depending on how redundant data is introduced, we classify redundancy oriented reliability into bit-level redundancy and packet-level redundancy. In bit-level redundancy, extra bits or segments are added to an existing packet. In case the packet is damaged or some segments are missing, the receiver can use the redundant bits to recover the packet or utilize the redundant segments to reconstruct the packet. Regular bit-level redundancy techniques include forward error correction (FEC) and erasure codes. On the other hand, packet-level redundancy generates redundant and standalone copies of packets. There are two commonly used techniques for packet-level redundancy. The first is multipath routing, where redundant copies of the same packet are sent from the source via multiple disjoint paths to the sink. Naturally this increases the chance that at least one copy of the packet is successfully delivered, therefore improving reliability. The second is by applying random linear network coding (RLNC) [52; 53]. RLNC creates \(m\) coded packets out of every \(n\) native non-coded packets with \((m\geq n)\). As long as \(n\) out of \(m\) coded packets are correctly received at the destination, all of the native packets are guaranteed to be successfully received after a decoding process.  
Another way for providing reliable data delivery is to retransmit the lost or damaged packets. Communication protocols can either introduce redundancy to packets or deploy mechanisms for retransmission to guarantee reliable communication. However, it is worth to note that the cost of introducing redundancy and retransmission must be considered since sending extra bits or redundant packets requires more resources.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 3 Methods
### Computing the 2D Projection Area  
After pruning (i.e., removing redundant or insignificant Gaussian primitives), each remaining Gaussian primitive can be treated as a brushstroke unit that is ready for use in the style transfer process. In this section, we describe how to compute the bounding box of the projected 2D Gaussian primitive in the image plane. This process is crucial for ensuring that the 2D Gaussian is mapped accurately with the texture, as the bounding box defines the spatial region where the Gaussian will contribute to the rendered texture.  
Equations (1) and (2) show how to compute the mean and covariance of the 2D Gaussian after applying the projection. The mean represents the center of the Gaussiandistribution, and the covariance matrix characterizes the spread or orientation of the Gaussian, defining how the Gaussian is distributed in 2D space.  
The next step is to convert the 2D Gaussian's elliptical shape into a rectangular bounding box for the convenience of texture mapping (Figure 4), as rectangular boxes are easier to handle and align within the 2D image space when placing textures. To achieve this, we first compute the major axis (the longest axis) and minor axis (the shortest axis) of the 2D Gaussian ellipse, which together define the size of the ellipse in 2D space. The axis can be computed using the following formulas:  
\[Axis=\sqrt{mid\pm\sqrt{max(0.1,mid^{2}-det)}}, \tag{8}\]  
where _mid_ and _det_ are defined as follows:  
\[mid =\frac{\sigma_{x}^{2}+\sigma_{y}^{2}}{2}\] \[det =\sigma_{x}^{2}\cdot\sigma_{y}^{2}-\sigma_{xy}^{2}. \tag{9}\]  
Here, _mid_ represents the average of the diagonal elements of the 2D covariance matrix, which corresponds to the mean scale of the ellipse. The determinant (_det_) represents the area of the ellipse and provides information about the spread or \"flattening\" of the Gaussian. A higher determinant means a larger spread, while a smaller determinant indicates a more concentrated Gaussian distribution.  
Now, the computed rectangle area needs to be further aligned with the original ellipse area by applying a rotation. This rotation ensures that the major and minor axes of the rectangle match the orientation of the ellipse in the image space.  
The rotation can be computed as follows:  
\[R(\theta)=\begin{pmatrix}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{pmatrix}\begin{pmatrix}x-\mu_{x}^{x}\\ y-\mu_{y}^{y}\end{pmatrix}, \tag{10}\]  
where \(\theta\) can be computed as follows:  
\[\theta=\frac{atan2(2\sigma_{xy}^{2},\sigma_{x}^{2}-\sigma_{y}^{2})}{2}. \tag{11}\]  
Figure 4: Computing the area. By calculating the major and minor axes, we convert the Gaussian distribution from an elliptical to a rectangular area, making it easier for texture mapping.