esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Disaggregating Functionalities and Migrating SDN as Microservices
### Components and Interfaces as Microservices  
In this proposal, the SDN controller deploys a set of microservices in several Docker containers, as shown in Figure 6. The upper layer corresponds to the SDN controller, the middle layer represents the communication topology, and the bottom layer is the electrical system. The container's functionality includes traffic routing, topology management, and event handling. The ability to segregate the controller functions as microservices allows a distributed system to scale according to topology demand. If the number of containers increases, a platform for orchestrating multiple pods is required.  
Figure 5: Main functionalities of the SDN controller.  
Figure 6: Communication framework for microservices implementation.  
Figure 6 presents the proposed topology and considers the aggregation of microservices deployed in three Raspberry pi 4 (with ARM architecture). Given the increased computational capacity of the latest versions of Raspberry and its lower cost compared to other computing devices, this platform is selected to operate as a worker in the Kubernetes cluster and an OpenFlow communication device. OpenFlow is a communication protocol SDN uses to standardize the interfaces between the control and data planes [48]. This research uses OpenFlow because it is a standard supported by multiple communication devices, adding great flexibility and programmability to the network. The flexibility of OpenFlow minimizes the effort and resources necessary to manage complex networks, including those in microgrid hierarchical control.  
The Kubernetes cluster is deployed in K3s [49] to obtain the SDN controller's global functionality. K3s is a lightweight Kubernetes distribution built for IoT and Edge computing. The three Raspberrys are physically independent and configured as a high-availability cluster to improve the controller operation and provide good resilience through microservices. The synchronization of each container and the global administration of the SDN controller is orchestrated by Rancher [50]. Rancher is a Kubernetes-based orchestration software integrating container monitoring and management tools through a simple graphical interface.  
The northbound interface (NB) allows the interaction between the SDN controller and external applications. On the other hand, the southbound interface (SB) standardizes the operation of the communication protocols, as in the case of OpenFlow [51]. REST APIs are communication channels that use the HTTP protocol to carry out operations such as GET, POST, or DELETE on data. It has high scalability, good performance, and the ability to decouple its functions, making it an ideal candidate for developing microservices.
The majority of AIMS techniques use an external ionisation source to generate ions prior to introduction into the mass spectrometer without the need for liquid or gas chromatography effluents. These techniques, including the direct analysis in real time-high resolution mass spectrometry (DART-HRMS), facilitate the simultaneous characterisation of a vast array of bioactive compounds that could potentially enhance the nutritional value of plant and food by-products [28]. In fact, such advanced techniques offer a rapid, sensitive, and versatile approach to measure the chemical signatures of food compounds. In particular, as a rapid non-targeted foodomics technique able to profile the chemical changes in pre-processed foods from both vegetable [29] and animal raw materials [30; 31], DART-HRMS has represented a significant advancement in food analysis, impacting a number of different fields, including food safety [32], quality control, and product authentication issues [33]. Thanks to a near real-time analysis and minimal sample preparation, DART-HRMS is a key technology at our disposal to provide consumers worldwide with safer and higher-quality food [34]. That is why DART-HRMS metabolomic profiling should be considered for a reliable assessment of the changed chemical signatures occurring during the recycling process of the RM into an optimised functional powdered ingredient. Therefore, the aim of this study is to provide a novel insight into the metabolomic changes in a rapeseed meal (RM) by-product when it is first processed into an ethanol-wash solute (EWS) and then submitted to an ultrasound treatment for optimisation (optimised EWS, OEWS). To reveal the statistically significant modifications in the bioactive compounds, the metabolomic fingerprinting of EWS and OEWS samples was explored via a chemometric approach based on DART-HRMS combined with a partial least square-discriminant analysis (PLS-DA) to retrieve the most significant biomolecules that could potentially track the specific chemical pathways for the tested optimisation process.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
###### Abstract  
Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination. The selection of the cluster head (CH) plays a crucial role in gathering and aggregating the data from the cluster members and forwarding the data to the sink node. The inefficient CH selection causes packet failures during the data transfer and early battery depletion nearer to the sink. This paper proposes a cluster tree-based routing protocol (CT-RPL) to increase the life span of the network and avoid the data traffic among the network nodes. The CT-RPL involves three processes, namely cluster formation, cluster head selection, and route establishment. The cluster is formed based on the Euclidean distance. The CH selection is accomplished using a game theoretic approach. Finally, the route is established using the metrics residual energy ratio (RER), queue utilization (QU), and expected transmission count (ETX). The simulation is carried out by using a COOJA simulator. The efficiency of a CT-RPL is compared with the Routing Protocol for Low Power and Lossy Networks (RPL) and energy-efficient heterogeneous ring clustering routing (E2 HRC-RPL), which reduces the traffic load and decreases the packet loss ratio. Thus, the CT-RPL enhances the lifetime of the network by 30-40% and the packet delivery ratio by 5-10%.  
clustering methods; energy consumption; energy efficiency; game theory; Internet of Things; routing protocol +
Footnote †: journal: Sensors  
0000-0002-4000-0000-00002-0000][PERSON]  
1  
[PERSON]  
1 Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India; [EMAIL_ADDRESS]  
2 Department of Information Technology, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad 500090, India; [EMAIL_ADDRESS]  
3 Department of Electrical and Communication Engineering, The PNG University of Technology, Lae 411, Papua New Guinea; [EMAIL_ADDRESS]  
4 Graduate School, Duy Tan University, Da Nang 550000, Vietnam5  
5 Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam6  
6 Robotics and Internet of Things Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia; [EMAIL_ADDRESS]
## 4 Application in the City of Dortmund
### System Dynamics Simulation Model
#### 4.1.2 System Conceptualization  
From a qualitative point of view, we use an SDSM to understand the behavior and interdependencies of the components of APL systems. From the existing literature on APIs (mainly based on case studies and field data), we define the main components that have an impact on the system. Following SD standard procedures [19], we use the software tool Vensim to create the causal-loop and stock-flow diagrams. Figure 5 describes the main APL system components that third-party logistics service providers will need to consider for future APL applications.  
The CLD describes that the market size is positively influenced by the population and the population growth rate. The potential number of e-customers is positively influenced by the e-shoppers growth rate and balanced by the number of APL users. In turn, the number of deliveries is positively reinforced by purchases per month and number of APL users. In turn, the purchases per month are positively reinforced by the average purchases per month and the online purchase rate.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 3 Emerging Security Vulnerabilities in IoMT-Driven 6G Healthcare Scenarios  
With the rapid evolution toward 6G, healthcare applications powered by the IoMT are set to become more intelligent, interconnected, and latency sensitive. As illustrated in Figure 2, some of these 6G usage scenarios are extensions of the 5G framework, while others, such as ubiquitous connectivity, native AI, and integrated sensing and communication, are entirely new scenarios. For further technical details, readers may refer to the ITU-R IMT-2030 framework [4].  
While 6G advancements offer great potential for healthcare, they also create significant security challenges. Each usage scenario involves distinct operational dynamics that expose unique vulnerabilities. Figure 3 highlights these scenarios alongside associated threats such as data tampering, denial-of-service (DoS) attacks, lat  
Figure 1: The proposed explainable security framework.  
Figure 2: The six usage scenarios for 6G (source: ITU-R M.2160).  
poisoning [9; 16; 27; 28]. These risks are particularly critical in IoMT-driven environments, where security breaches can quickly propagate and impact patient safety and care delivery.  
As healthcare systems transition to fully connected, intelligent 6G environments, the increasing reliance on AI can hinder threat detection and strategic decision-making for risk management [11; 29]. Integrating XAI is essential for ensuring transparency, building trust, and maintaining secure, resilient infrastructures [30]. In the following sections, we will discuss the methods, materials, experiments, and results, demonstrating how XAI contributes to developing effective mitigation strategies for 6G usage scenarios, ultimately enhancing security and system resilience.
## 3 Spaceborne GNSS-R Raw IF Data and Processing  
### GNSS-R Raw IF Data from Different Missions  
In a GNSS-R receiver, the direct and reflected signals are received by the up- and down-looking antennas, and amplified and down-converted to IF (or baseband) in the radio frequency (RF) front-ends. These IF signals are digitized using a set of analog-to-digital converters (ADCs) to generate the raw IF data streams, which are the first and most fundamental data available following the antennas (as shown in Figure 2). Due to their high data rate (a few mega samples per second to a few tens of mega samples per second), the raw IF data streams are normally not recorded continuously but processed directly by the digital signal processing unit to derive lower sampling rate (a couple of Hz) measurements (e.g., positioning observations from the direct signal and power DDM of the reflected signal). As the data density is reduced from IF data to the final products, information is irretrievably lost. Fortunately, as mentioned in Section 1, most of the GNSS-R missions are capable of recording and down-linking the raw IF data streams occasionally, which allows the recovery of much more flexible GNSS-R measurements through on-ground post-processing than these generated by the onboard real-time processors.  
To date, the ICE-CSIC/IEEC team gets access to the raw IF data sets collected by the TDS-1, CYGNSS, BF-1 A/B and SPIRE RO missions, which are either publicly available or obtained with the support of different projects. The main features of these raw IF datasets are summarized in Table 1, which shows that these data sets can complement each other in terms of the geographic coverage, bistatic geometry and frequency band of the received signal. First, the raw IF data collected by the CYGNSS and BuFeng-1 missions are limited within low latitude regions due to their orbit inclinations (i.e., 35\({}^{\circ}\) and 45\({}^{\circ}\), respectively). While the TDS-1 and Spire's GNSS RO satellites are in polar orbits, which can provide opportunities for the collection and analyses of reflected signals from sea ice and ice sheet at high latitude. Second, Bufeng-1 and SPIRE GNSS RO satellites are capable of receiving direct and reflected signal at a second frequency band other than only GPS L1. GNSS-R measurements at different frequency bands can be potentially combined to improve the performance for different observations, e.g., the ionospheric delay can be corrected in altimetric retrieval by combining delay observations at GPS L1 and L2 frequencies. Finally, dedicated GNSS-R missions, i.e., TDS-1, CYGNSS and BuFeng-1, can only receive reflected signals from near nadir direction with enough antenna gain. In contrast, reflected GNSS signals at low elevation angle can be collected with enough SNR though the antennas facing toward the Earth's limb onboard Spire's GNSS RO satellites, which makes it possible to demonstrate GNSS-R carrier phase altimetry at grazing angle.  
Figure 2: Collection and Processing Scheme of spaceborne GNSS-R Raw IF Data.
###### Abstract  
The multi-hop amplify-and-forward free-space optical communication (FSOC) system is studied in random fog using the \(I\)-function, considering Gamma-Gamma atmospheric turbulence and Beckmann pointing error. Outage probability, average bit error rate and average ergodic channel capacity are obtained. Channel-state-information assisted relay performs better than fixed-gain relay under high transmitted power. Increasing the hop number significantly improves the performance. More hops are needed in medium fog than in light fog to achieve the same performance. In addition, on a single-hop link, the influence of fog channel on system performance is dominant, while atmospheric turbulence intensity, normalized jitter standard deviation and normalized boresight error have little effect on the system performance. However, on a multi-hop link, atmospheric turbulence intensity, normalized jitter standard deviation and normalized boresight error have serious effects on system performance. Compared with correcting the normalized boresight error, compensating the normalized jitter standard deviation greatly improves the multi-hop FSOC system performance. Furthermore, optimizing beam width can further improves the performance. To ensure good communication, the system should select a low-order modulation scheme.  
free-space optical communication; multi hop; fog; atmospheric turbulence; generalized pointing error; outage probability +
Footnote †: journal: _Photonics_
## Appendix A  
This section presents more extensive results from (1) the attribute identification process and (2) the empirical Q-matrix validation:  
* Table 1 presents an overview of the 9 attributes that resulted from the attribute identification process, which are comprised of 33 learning objectives. The table includes references to empirical studies that show the relevance of the addressed concepts and the occurrence of misconceptions for each learning objective. In the Supplementary Material, a more detailed description of each attribute and the references to the empirical studies is provided. * Table 2 presents all items for which modifications to the Q-matrix were suggested in the empirical Q-matrix validation. Argumentation to retain or modify entries is provided along with the final \(q\)-vectors. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}}
**A. ** & **Understanding center \& spread** & - & [73] \\
1. & Understanding of the idea of variability in samples and populations. & - & [27], Chapter 9 \\
2. & Ability to describe and interpret measures of center (mean, median, mode). & - & [28, 74] \\
3. & Ability to describe and interpret measures of spread (range, interquartile range, variance, standard deviation). & - & [73] \\
4. & Understanding of how center and spread are affected by data transformations. & - & [28] \\
5. & Understanding of how center and spread are affected by handling of outliers. & - & [27], Chapters 9 and 10 \\
**B. ** & **Interpreting univariate graphical representations** & - & [28, 74] \\  & Ability to describe and interpret graphical representations of sample data in common univariate graphical & - & [28, 74] \\
6. & displays (histogram, boxplot, dotplot, bar chart), including the ability to reason about center and spread in sample data based on graphical representations.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques  
### High-Speed Techniques  
There are two difficulties in the design of 100 Gb/s DSP. One of the difficulties is the strict timing of the DFE structure. The data input in parallel must be processed and output in one cycle, so as to ensure that the parallel data of the next cycle can be connected when it arrives. This means that the time required for each DFE to process a channel of data is actually less than or equal to the symbol width (20 ps for 100 Gb/s PAM4 modulation). Another difficulty of DSP is the 16-tap FFE structure because its final output is composed of each tap data and their corresponding tap weights multiplied and then added, so this problem can be solved by inserting enough registers [10]. However, an FFE with a 16-tap structure needs to insert 15 registers. For parallel data, the number of registers that need to be inserted will be more, which will make the delay in obtaining the result of the FFE very long, and its occupied area and power consumption will also be larger. These are all things that need to be avoided in high-speed, low-power designs. This section aims to address these two problems by developing FFE structures for parallel operations, speculative DFEs with custom MUXs, and optimizing them with some low-power algorithms during data processing.
## 3 Artificial Intelligence Methods for GWL Modelling
### Artificial Neural Networks
The input data included monthly rainfall, temperature, aquifer recharge/discharge, and piezometric data. The multi-layer Feed-Forward Network was identified as the best method in a study by [20] in Montgomery County, PA, USA. The input data included humidity, rainfall, and temperature. Similarly, ref. [15] in Odisha, India, showed that evaporation and runoff were critical factors in predicting GWL. A Recurrent Neural Network (RNN) was used, with input data including rainfall, temperature, humidity, runoff, and evaporation. In South Korea, ref. [21] showed that a Feed-Forward Artificial Neural Network (FFANN) successfully predicted hourly GWL. The input data included surface water levels and groundwater extraction parameters. Ref. [22], in the Shamil-Ashtor Plain, Iran, demonstrated that an Extreme Learning Machine (ELM) was significantly more accurate than a Radial Basis Function Neural Network (RBFNN), Multiple Linear Regression (MLR), and Autoregressive Moving Average (ARMA) in predicting monthly GWL. The input data included hydrological and climatic data. Ref. [23], in Southwest Germany, conducted a study where the nonlinear autoregressive network (NARX) effectively predicted GWL in wells unaffected by human activities. The input data included rainfall and temperature. Ref. [21], in Longyan City, Fujian Province, China, showed that an RBFNN was more efficient in predicting monthly GWL. The input data included daily groundwater levels. In Canada, [24] showed that the ELM outperformed the Support Vector Regression (SVR) method in predicting monthly GWL. The input data included meteorological and hydrological data. Ref. [25], in Taiwan, demonstrated that a Feed-Forward Back Propagation Neural Network (FFBPNN) effectively simulated GWL fluctuations. The input data included daily groundwater levels. Ref. [26], in Jenderam Hilir, Selangor, Malaysia, showed that the multi-layer perceptron (MLP) successfully predicted GWL in tropical areas. The input data included hydrological and climatic parameters. Table S1 (Supplementary Materials) summarises the evaluated articles that used ANN methods and draws a general conclusion.
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 2 EMD between Two Histograms
### Basic EMD  
[PERSON] et al. (2011) first introduced EMD for measuring the difference between texture and color, in which the EMD was applied to the distributed signatures instead of histograms directly. As a matter of fact, a histogram can also be considered to be a special kind of signatures.  
The classical EMD between two histograms is the lowest cost of transporting one histogram into another, in which the cost is usually defined as that the amount of weight multiplies the ground distance between two histograms. This formalization is easily generalized to two normalized histograms with the same number of bins ([PERSON] and [PERSON], 2011).  
Given two n-bin histograms, \(H^{1}=\{h_{i}^{1},i=1,2,3,\ldots,n\}\) and \(H^{2}=\{h_{j}^{2},j=1,2,3,\ldots,n\}\), \(H^{1}\) is transformed into \(H^{2}\) by moving \"mass\" from \(h_{i}^{1}\) to \(h_{j}^{2}\) for every pair of \((i,j)\), such that the difference of two histograms is minimized. Let another -bin all-zero histogram be \(T\), and we denote the flow \(f_{ij}\) as the amount, which is moved from the \(i\)-th bin in \(H^{1}\) to the \(j\)-th bin in \(T\). Then, we can define the EMD metric between \(H^{1}\) and \(H^{2}\) to be the minimum amount flow that is demanded to make the histogram of \(T\) to be identical with \(H^{2}\). Therefore, the EMD between \(H^{1}\) and \(H^{2}\) is expressed in mathematically as follows:  
\[\text{EMD}(H^{1},H^{2})=\min_{\{f_{ij},i,j=1,2,3,\ldots,n\}}\sum_{i=1}^{n} \sum_{j=1}^{n}f_{i,j}d_{i,j}, \tag{1}\]  
subject to \(\sum\limits_{j=1}^{n}f_{i,j}=h_{i}^{1}\), \(\sum\limits_{i=1}^{n}f_{i,j}=h_{j}^{2}\), \(f_{i,j}\geq 0\) and \(i,j=1,2,3,\ldots,n\), where \(d_{i,j}\) is the ground distance between bin \(i\) and bin \(j\). Let \(d_{i,j}=|i-j|\) to be the \(L^{1}\) distance in this section.