esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Foundations of Algorithms  
### HSV Model Analysis  
HSV (hue, saturation, value) [17] is created from the intuitive properties of a colour, which is also commonly referred to as the hexagonal cone model, as shown in Figure 1. Each colour is represented using the three characteristics of hue, saturation and colour luminosity.  
The hue, H, parameter indicates the image's colour information, i.e., the position of the spectral colour in which it is located; the saturation, S, indicates the shade of the paint; and the value, V, indicates the brightness of the colour. This time, instead of using the traditional RGB mode for image processing, the image is converted in HSV space for enhancement, where specific attributes of the image are enhanced or diminished, which is more conducive to the processing of underground monitoring images.  
The formula for converting from RGB space to HSV space is shown in Equations (1) and (2):  
\[\left\{\begin{aligned} & R=R/255\\ & G=G/255\\ & B=B/255\end{aligned}\right. \tag{1}\]  
\[H=\left\{\begin{aligned} &\frac{60\times(G-B)}{V-\min(R,G,B)},if\ V=R\\ &\frac{60\times(B-R)}{V-\min(R,G,B)}+120,if\ V=G\\ &\frac{60\times(R-G)}{V-\min(R,G,B)}+240,if\ V=B\\ & S=\left\{\begin{aligned} &\frac{V-\min(R,G,B)}{V},if\ V\
eq 0\\ & 0,else\end{aligned}\right.\end{aligned}\right. \tag{2}\]  
Figure 1: HSV model.  
In the above equation, max and min represent the maximum and minimum values in RGB space.  
The formula for converting from HSV space to RGB space is shown in Equations (3) and (4):  
\[\begin{array}{l}H=H\times 2;\ S=S/255;\ V=V/255\\ h_{i}=floor(\frac{H}{60});\ f=\frac{H}{60}-h_{i}\\ p=V\times(1-S);\ q=V\times(1-f\times(S))\\ t=V\times(1-(1-f)\times(S)\\ \end{array} \tag{3}\]  
\[(R,G,B)=\begin{cases}(V,t,p),if\ h_{i}=0\\ (q,V,p),if\ h_{i}=1\\ (p,V,t),if\ h_{i}=2\\ (p,q,V),if\ h_{i}=3\\ (t,p,V),if\ h_{i}=4\\ (V,p,q),if\ h_{i}=5\\ \end{cases} \tag{4}\]  
where floor means rounding down.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 3 Modeling of MIMO System in SystemVue
### Transmitter Modeling
The signal is transmitted through an antenna after a Butterworth bandpass filter, a multistage RF amplifier and an isolator. The specific parameters of each component at the RF transmitter module are listed in Table 2. The input and output impedances of all components are default to 50 ohms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Attenuator 1 Loss & 2 & dB \\ Band-pass Filter & 3 & order \\ Passband Range & 490–510 & MHz \\ Filter Insertion Loss & 8 & dB \\ Attenuator 2 Loss & 3 & dB \\ Amplifier 1 Gain & 12 & dB \\ Local Oscillator Power & \(-\)3 & dBm \\ Resonant Frequency & 2180 & MHz \\ Amplifier 2 Gain & 20 & dB \\ Cut-off frequency & 2200 & MHz \\ Amplifier 3 Gain & 30 & dB \\ Isolator Loss & 0.5 & dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RF transmitter module parameter. Figure 4: Diagram of transmitter module. Figure 5 compares the input and output spectrum of the RF transmitter, with input center frequency of 220 MHz and the peak power of \(-32\) dBm. The output center frequency is 2400 MHz, with peak power of 14 dBm. The RF link at the transmitter can effectively realize the up-conversion process and the output power of each component agrees with the parameters set up previously.
## 3 EC-BER Algorithm
When weighting is applied based on Table 1, errors were corrected perfectly on an SNR of 3 [dB]; in the other case, errors were corrected on an SNR of 6 [dB]. We knew 3 [dB] of performance gain was obtained by applying the weighting algorithm. Finally, we confirmed the proposed method is effective to measure the performance of each band and improved the performance of the multi-band transmission method in underwater channel environments. With respect to the computational complexity compared to the state-of-the art, such as ESNR and PES described in [13], in contrast to ESNR and PES which need several complex operations (\(N\) squaring and divide operations), the EC-BER algorithm only needs a shift register operation in the turbo re-encoding processing and to compare logic. Furthermore, it can predict the reliability of the coded data field exactly in time-varying underwater channels. However, in deciding the optimal weighting values using EC-BER, the values listed in Table 1 are a little different depending on amount of multi-path and Doppler spread.
# Dissimilar Rotary Friction Welding of Inconel 718 to F22 Using Inconel 625 Interlayer
## 3 Results and Discussion
### Mechanical Properties
Both the weld joints prepared in this study failed in the F22 base metal, and hence the fracture features were similar to the F22 base metal. In this study, F22 was joined with the Inconel 625 during the first stage of the joint preparation. Subsequently, F22/Inconel 625 joint was welded with Inconel 718 to produce the weldment with an interlayer. However, studies are needed with similar joints prepared by welding Inconel 718 to Inconel 625 in the first stage of experiment, which is to be welded with F22 in the second stage. This kind of change in sequence could have a significant impact on the weldment properties. Further, future studies are needed to investigate the effect of the second weld heating cycle on the microstructure of already formed first weld for the Inconel 718/F22 weld joints with Inconel 625 interlayer. In the investigation of the precipitate formation during welding, detailed high-resolution, microscopic studies would shed more light on the microstructure-property correlations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Yield Strength** & **Ultimate Tensile** & **Elongation** & **Charpy Impact** \\  & **(MPa)** & **Strength (MPa)** & **\%** & **Energy Value (J)** \\ \hline
718 base metal & \(485\pm 7\) & \(940\pm 2\) & \(30\pm 0.5\) & \(58\pm 2\) \\ \hline F22 base metal & \(540\pm 5\) & \(659\pm 5\) & \(17\pm 1\) & \(185\pm 6\) \\ \hline
718-F22 joint & \(535\pm 3\) & \(643\pm 6\) & \(12\pm 1.5\) & \(25\pm 4\) \\ \hline
718-F22 joint with & \multirow{2}{*}{\(542\pm 4\)} & \multirow{2}{*}{\(640\pm 1\)} & \multirow{2}{*}{\(14\pm 0.2\)} & \(45\pm 5\) * \\
625 interlayer & & & & \(75\pm 2\) ** \\ \hline \hline \end{tabular}  
* Notch at 718/625 interface; ** notch at 625/F22 interface.  
\end{table} 
Table 3: Results of tensile testing of base metals and 718-F22 friction weld joints with and without 625 interlayer (average properties from three tests in each case).
## 1 Introduction
These datasets are already being used to monitor crop photosynthetic activity and gross primary production (GPP) [51; 52; 53], estimate surface turbulent fluxes [42; 54], and improve LSM estimates of photosynthesis [55; 56]. However, their potential to understand the effects of environmental stress on global transpiration has not been evaluated. Here, we present a novel application of SIF observations to diagnose transpiration efficiency (\(\tau\)), understood as the ratio between transpiration (T) and potential evaporation (E\({}_{\text{p}}\)). We also establish an in situ validation dataset of \(\tau\) across eddy-covariance sites to test the performance of satellite retrievals. Analogous \(\tau\) estimates from a set of four offline LSMs, one hydrologic model and one transpiration retrieval model from the earthH2 Observe database [57] (see Section 2.4) are compared against in situ and satellite retrievals. This is done with the ultimate goal to evaluate the potential of SIF to benchmark the representation of transpiration in traditional LSMs.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 1 Introduction
[2] state, current approaches are tailored to their target scenario and then adapted afterwards. SOLIOT presents a Digital Twin concept primarily based on the standardized Web of Things data model, the self-descriptive interaction patterns of LDP and the data protection mechanism as developed by the SOLID approach. To the best of our knowledge, no other framework has yet regarded these aspects as the corner stones of a consistent, comprehensive solution instead of unrelated developments. SOLIOT shows how the commonly shared principles driving these technologies actually outline a broadly followed trend towards a distinct set of core principles and how they can be combined to implementable Digital Twins, and which steps are still missing. Several works have already translated the concepts and patterns for Web-based architectures and map them to IoT protocols. This work extends these activities with the following core contributions:  
* Analysis and structure of the core IoT requirements
* Examining the Digital Twin characteristics of SOLID
* A mapping and extension of the SOLID stack towards IoT requirements
* Presenting a thereby specified Digital Twin representation, its potential and open gaps  
The mapping of SOLID features to two different IoT protocols, the Message Queuing Telemetry Transport (MQTT, [3]) and the Constraint Application Protocol (CoAP, [4]) illustrates the potential of SOLIOT. Both protocols explicitly target different communication patterns. While CoAP follows a request/response process, MQTT is based on publish/subscribe. Both CoAP and MQTT contain only a minimal set of added requirements, which makes them optimal representatives to illustrate the SOLIOT core principles. The novelty of SOLIOT is therefore not provided by the CoAP or MQTT mapping but by the compliant merging of their underlying concepts and the restriction to self-descriptive entities. The SOLID reference project in NodeJS is extended with support for both protocols to provide two proof-of-concept developments and general showcases. Up to ten instances of the servers have been started in a federated test bed. Therefore, the scalability of the two approaches is compared to the plain SOLID on HTTP solution. Furthermore, an analysis is applied investigating which of the identified requirements can be fulfilled by the SOLIOT approaches. The rest of the paper is organized as follows. Section 2 gives an overview of the state of the art and outlines the foundations. Section 3 illustrates the regarded scenario and supplies a running example, followed by an examination of requirements of IoT systems in Section 4. Section 5 introduces the current state of the SOLID specification with respect to these requirements. SOLIOT is presented in Section 6 together with a prototypical implementation (Section 7) and the evaluation of the proposed ideas (Section 8). Section 9 concludes the work by summarizing the contributions, discussing the advantages, limitations and opportunities for future work.
## 3 The Proposed Algorithm
### Merging Period
#### 3.2.1 Methods of Similarity Measurement
And \(con_{cluster_{i}}(cluster_{j})=\sum_{i=1}^{cluster_{j}}KNN(cluster_{i}) \cap cluster_{j}\) denotes the number of \(k\)-nearest neighbors of the subcluster \(cluster_{i}\) near the subcluster \(cluster_{j}\). Similarly, \(con_{cluster_{j}}(cluster_{i})=\sum_{i=1}^{cluster_{j}}KNN(cluster_{j}) \cap cluster_{i}\) denotes the number of \(k\)-nearest neighbor sample points of the subcluster \(cluster_{j}\) near the subcluster \(cluster_{i}\). In order to have a clearer understanding of the proposed similarity metric, a further analysis of Equation (9) is made. First, \(SNN\) denotes the number of shared nearest neighbors between two clusters. This is one of the important metrics to measure the similarity  
Figure 5: Merge subclusters with maximum shared nearest neighbor similarity. between two clusters. If two clusters have more shared neighbors, they are likely to belong to the same taxon. Secondly, \(con_{cluster_{i}}(cluster_{j})\) denotes the total number of points of k-nearest neighbors of \(cluster_{j}\) that are connected to \(cluster_{i}\). This term can be viewed as the strength of the connection between two clusters. If a cluster has a large number of points connected to another cluster, then the two clusters are likely to be similar. Lastly, \(nums_{cluster_{i}}\) is used to represent the size of \(cluster_{i}\). When calculating similarity, it is important to consider the size of the cluster because larger clusters are likely to have more connected points. By dividing the cluster size, we can obtain a normalized similarity metric that allows direct comparison of clusters of different sizes. In summary, the proposed similarity metric takes into account the number of shared nearest neighbors between clusters, the strength of connections, and the size of the clusters, which is helpful in merging subclusters with maximum result similarity.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 5 Discussion
### Hybrid Platform  
Another positive outcome of this study was the design and implementation of a hybrid platform that combines UE and Max, expanding the possibilities for higher-order Ambisonics (HOA) reproduction within VR environments. To ensure the stability of the hybrid platform performance, it is crucial that the operating systems of the HMD and the computer used to run the test are compatible and up to date. In this case, addressing this technical issue proved to be a significant challenge in the development and timely implementation of the test.  
The decision to use Max and _Spat_ spatialization tools proved effective for HOA audio rendering in the tests, offering both stability and detailed control over the spatial attributes of complex Ambisonics encoding and decoding processes. Considering the current limitations of HOA development in UE, these tools hold the promise of being a highly valuable and adaptable addition, paving the way for advanced Ambisonics capabilities in virtual reality and gaming applications.
# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
###### Abstract  
The Internet of Things has gained substantial attention over the last few years, because of connecting daily things in a wide range of application and domains. A large number of sensors require bandwidth and network resources to give-and-take queries among a heterogeneous IoT network. Network flooding is a key questioning strategy for successful exchange of queries. However, the risk of the original flooding is prone to unwanted and redundant network queries which may lead to heavy network traffic. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. IoT devices consume more energy and high computational time. More queries leads to consumption of more bandwidth, cost, and miserable QoS. Current existing approaches focused primarily on how to speed up the basic routing for IoT devices. However, solutions for flooding are not being addressed. In this paper, we propose a cluster-based flooding (CBF) as an interoperable solution for network and sensor layer devices which is also capable minimizing the energy consumption, cost, network flooding, identifying, and eliminating of redundant flooding queries using query control mechanisms. The proposed CBF divides the network into different clusters, local queries for information are proactively maintained by the intralayer cluster (IALC), while the interlayer cluster (IELC) is responsible for reactively obtain the routing queries to the destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in term of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to the standard flooding techniques. In this research work, we used Cooja simulator to evaluate the performance of the proposed CBF. According to the simulation results the proposed technique has superiority in term of traffic delay, QoS/throughput, and energy consumption, under various performance metrics compared with traditional flooding and state of the art.  
QoS; redundant query; Internet of things; network flooding; energy efficiency +
Footnote †: journal: Computer Systems
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Third Computational Approach Analysis  
The third computational approach, which emerged as the most reliable, utilized five-fold cross-validation to systematically evaluate model performance across multiple training and testing subsets. This approach not only provided more robust estimates but also refined the optimization process with stricter parameter constraints, resulting in greater stability and interpretability. The results showed that the 1 PL-IRT standard model once again outperformed all alternatives, achieving the lowest AIC (1416.44), BIC (1574.11), and highest AUC (0.804). The TP-IRT model followed closely, maintaining a similar AUC (0.793) while exhibiting slightly higher AIC/BIC values compared to the 1 PL-IRT model. This suggests that while subtracting a scaled time factor does not drastically improve performance, it does not negatively impact the model either. The TWD-IRT and NRT-IRT models once again showed poor performance, with AUC values as low as 0.772 and 0.759, respectively, alongside elevated AIC/BIC values. The ART-IRT model performed the worst in this approach, with an AUC of 0.734, indicating that the multiplication-based transformations continued to degrade performance. The DTA-IRT model, however, showed a slight improvement in stability, with AIC/BIC values comparable to the 1 PL-IRT standard model, suggesting that a subtractive transformation may be more theoretically justified than a multiplicative one. Test information function (TIF) values remained within a reasonable range for most models, with the standard model maintaining the highest reliability in ability estimation.
## 3 Materials and Methods
### SDN-Based Transport Layer in the Proposed System  
The proposed system incorporates Software-Defined Networking (SDN) principles to optimize communication in a decentralized IoT ecosystem by separating the data and control planes. The data plane manages real-time encrypted communication using IOTA Streams, ensuring confidentiality, integrity, and low latency. Lightweight cryptographic algorithms make it suitable for resource-constrained IoT devices. The control plane oversees access control and key management, leveraging IOTA Stronghold for the secure storage and dynamic allocation of cryptographic keys. This integration enhances scalability and fault tolerance, allowing the system to adapt to network changes, prioritize critical tasks, and ensure seamless device communication. The SDN control plane provides centralized management while leveraging the decentralized trust of the IOTA Tangle, enabling efficient resource allocation and dynamic policy enforcement. In addition, the transport layer supports multiple communication protocols and provides fault tolerance by reconfiguring data flow in the event of network failures, maintaining service continuity and transaction integrity.
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 3 Methods  
### Overall Network Architecture  
The proposed DAIN, as illustrated in Figure 1, is composed of three phases. Stage 1 strives to obtain shallow feature information; Stage 2 strives to obtain and merge deeper features; and Stage 3 strives to merge dense features and reconstruct underwater images. The input of our network is an LR image (\(X\in R^{H\times W\times 3}\)) and its output is an HR image (\(S\in R^{H\times rW\times 3}\)). The height and width of the image are denoted by \(H\) and \(W\), respectively. The scale factor is represented by \(r\).  
In Stage 1, we first use a 3 \(\times\) 3 convolution to extract shallow information:  
\[F_{0}=H_{\text{SFE}}(X) \tag{1}\]  
where \(H_{\text{SFE}}(\cdot)\) is the 3 \(\times\) 3 convolution operation and the deviation term of the convolutional layer is omitted for simplicity. \(F_{0}\) is then delivered to Stage 2 as an input to the DAEM, which uses two MCCBs and an STL to model the multi-scale and long-range dependenciesof features. More detailed information will be described in Sections 3.2 and 3.3. Assuming the amount of DAEMs is \(D\), the result of the dth DAEM \(F_{\text{d}}(1\leq d\leq D)\) can be expressed as:  
\[F_{d}=H_{DAEM}^{d}(H_{DAEM}^{d-1}\cdots((H_{DAEM}^{1}(F_{0})))) \tag{2}\]  
where \(H_{DAEM}^{d}(\cdot)\) is the operation of the dth DAEM and \(F_{d}\) denotes the result of the dth DAEM. More importantly, all the results of these DAEMs are combined and transmitted to the dense feature fusion block (DFFB), which aggregates all the hierarchical features to generate more expressive feature representations. To ease the learning difficulties, we add a global residual learning strategy. This specific process is defined as follows:  
\[F_{DFFB}=H_{DFFB}[F_{0},F_{1},F_{2},\cdots,F_{D}] \tag{3}\]  
where \(H_{DFFB}(\cdot)\) denotes the operation of the DFFB, which contains a 3 \(\times\) 3 convolution and a 1 \(\times\) 1 convolution. \([\text{F}_{1},\text{F}_{2},\cdots,F_{d}]\) is the combination of all the characters produced by the DAEM. Finally, Stage 3 functions as the element for recuperation. An upsampling operation is utilized to upgrade the depth detail to the HR image required. We use 3 \(\times\) 3 convolutional layers and sub-pixel convolution to reconstruct the SR image \(S\) as follows:  
\[S=H_{\text{U}P}(F_{DFFB}) \tag{4}\]  
where \(H_{\text{U}P}(\cdot)\) is the function of upsampling.  
Based on the existing image SR, the L1 loss can be integrated into the training of the DAIN, which helps maintain rich texture and local structure [51].  
\[L=L_{1}(G)=E_{X,S}[\left\|S-D(X)\right\|_{1}] \tag{5}\]  
Here, \(\left\|\cdot\right\|_{1}\) represents the L1-norm function. The mapping relationship between LR and HR is implemented by \(D:\{X\}\to S\).
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 2 Materials and Methods
### Irrigation Water Characterization  
In pursuit of elucidating the retention and/or immobilization capacity of boron and sodium--elements commonly prevalent in irrigation waters and notorious for their adverse effects on agriculture, particularly in citrus cultivation--samples were drawn from an irrigation reservoir situated on a commercial citric farm in Ojos municipality, Murcia, Spain. The preference for these authentic natural waters rested on their compositional intricacies, posing a significant challenge to replicate accurately in laboratory conditions using synthetic counterparts. The presence or absence of diverse compounds and contaminants in these waters can distinctly shape, modify, or entirely transform the reactions and interactions of dissolved species [24].  
The irrigation water samples, obtained in non-sterile 15 L polypropylene containers from accessible points in the reservoir, were immediately transported and stored at a constant temperature of 4 \({}^{\circ}\)C until their characterization and use on the same collection day. A rigorous characterization of all irrigation water samples was carried out before and after the experiments using inductively coupled plasma mass spectrometry (ICP-MS Model 2030, Shimadzu, Kyoto, Japan), selected for its precision and well-established recognition in the characterization of liquid samples. Additionally, continuous real-time monitoring and characterization of all agronomically significant ions (sodium, potassium, pH, electrical conductivity, chloride, nitrate, magnesium, calcium, and ammonium, among others) present in the effluents were meticulously carried out throughout the experiments. This detailed monitoring was conducted using the Imacimus Multi Ion 10 portable multi-ion nutrient analyzer (Imacimus NT Sensors, El Catllar, Spain), which provides readings of the agronomic parameters of interest every 2 min. It is noteworthy that this probe is specialized for field measurements and is widely utilized in the agricultural estates of the region [25,26].