esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Table 1 lists relevant works on adaptive modulation methods in MIMO systems, covering target BER-based SNR thresholds [35; 36], convolutional neural network (CNN)-based spectrogram classification [37; 38], artificial neural network (ANN)-driven power spectral density (PSD) learning [39], channel state information (CSI)-driven adaptive algorithms [40], supervised learning [41], and signal-to-interference noise ratio (SINR)-based modulation coding scheme (MCS) selection. These MIMO schemes primarily focus on frequency domain estimation. These strategies have resulted in higher system throughput, spectrum efficiency, and BER performance. However, their reliance on computationally demanding preprocessing operations such as Fourier transforms and windowing causes delays, increases complexity, and makes them sensitive to dynamic channel conditions. In frequency domain SNR estimation, approaches such as CNN-based PSD analysis [38] and ANN-based AMC [39] require significant computer resources to produce and interpret PSD images or extract spectral characteristics. Similarly, approaches like CSI estimation [40; 41] suffer challenges in high-mobility or non-line-of-sight (NLOS) situations, where acquiring accurate CSI becomes increasingly challenging. Furthermore, supervised learning models [37; 41] rely on huge, high-quality datasets, making them less adaptable to practical situations with varying fading scenarios and mobility. Transitioning SNR estimation to the time domain offers a compelling alternative, especially in MIMO-OFDM systems where time synchronization is crucial. For example, employing CAZAC sequences initially intended for synchronization to perform SNR estimation provides two benefits. These time synchronization preambles have strong autocorrelation properties, allowing for precise and consistent SNR estimation while reducing overall system complexity [15; 27; 34; 42]. Using CAZAC sequences for SNR estimation eliminates the need for additional preambles, simplifying the system's design and increasing efficiency. Time domain SNR estimation with CAZAC sequences also solves important issues in adaptive modulation. Their strong and consistent estimates make dynamic and optimal threshold selection for AMC possible, even in demanding situations with multipath fading, Doppler effects, and noise fluctuations. High-accuracy SNR estimation is especially useful for modern applications such as 5G [43], massive MIMO systems [36], unmanned aerial vehicle (UAV) communications [37], Internet of Things (IoT), and telemedicine systems [40], which require real-time adaptability and low latency. To summarize, while previous SNR estimating techniques have greatly improved AMC in wireless systems, time domain estimation employing CAZAC sequences is a viable and efficient alternative. These approaches use the synchronization preamble for SNR estimation, which promises decreased complexity, robust performance, and seamless integration with adaptive modulation, making them a significant enabler for future wireless technologies across multiple domains.
## 3 Fundamentals of GLI-Split
### Multicast Support  
At present, video streaming, IPTV, and content delivery networks account for a large and still growing fraction of Internet traffic. In this area, IP multicast is gaining more and more popularity. We therefore explain how IP multicast traffic is handled with GLI-Split. IPv6 offers native support for multicast and requires the Multicast Listener Discovery (MLD) protocol [31] and the Protocol Independent Multicast-Sparse Mode (PIM-SM) protocol [32]. MLD is used by hosts to join or leave a multicastgroup while PIM-SM is used between routers to build the multicast delivery tree state. PIM-SM creates unidirectional trees which are rooted at a group specific rendezvous point. The multicast delivery model can be either any-source multicast (ASM) or source-specific multicast (SSM). The ASM mode relates to many-to-many group communications while the SSM mode relates to one-to-many group communications. In the ASM mode, a host uses the (*,G) state to join all sources in the multicast group with multicast IPv6 address G. In contrast in the SSM mode, a host uses the (S,G) state to join only the source with IPv6 address S in the multicast group with IPv6 multicast address G.  
In PIM-SM, multicast group join requests are sent towards the rendezvous point for which the address may be obtained from the multicast group address [33]. In GLI-split, the address of the rendezvous point is a global address so that also classic IPv6 nodes in non GLI-domains are able to join the multicast group. By sending the PIM-SM join request towards the rendezvous point, the required (S,G) state is established in all routers on the path from the receiver to the rendezvous point. In case the rendezvous point is in a GLI-domain, the GLI-gateway translates the address of incoming PIM-SM join requests to the local address of the rendezvous point. To send multicast data to the multicast group, the source forwards the data to the rendezvous point which further distributes the multicast data according to the established (S,G) tree state. In this way, no modifications are required for PIM-SM and MLD, and multicast handling is the same as in the ordinary IPv6 architecture.
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 4 Numerical Analysis Methods
### Nearest Neighbor Search for Trajectory Decision  
After the hovering points are decided, the trajectory which passes through all hovering points with the minimum flight time needs to be decided. However, the TSP is known as an NP-hard problem, which requires a huge amount of calculations to obtain a precise solution. Thus, for simplicity, we adopted the nearest neighbor search (NN), which is an approximate algorithm used in many applications. While the TSP is known as an NP-hard problem, which requires a large scale of computation, NN provides relatively good solutions with very low computational costs. We acknowledge that other algorithms, e.g., 2-opt, genetic algorithm (GA), etc., may achieve better solutions, so we have evaluated the performance of NN and GA in [39]. The simulation results revealed that GA provides only a slight improvement in terms of the total completion time for collecting the sensing data. Therefore, in this paper, we selected NN as a reasonable algorithm with low complexity for TSP in post-disaster areas where complex calculations should be avoided.  
As shown in Figure 10, according to the concepts of NN, the UAV always selects the nearest hovering point as the next destination.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 1 Introduction and Related Work
#### Our Contribution  
This paper is a complete analysis of the synchronization process of two replicas of a theoretical filesystem. Our filesystem model together with the commands considered  
Figure 1: Outline of the synchronization process. Identical replicas of the original filesystem \(\Phi\) are updated (modified) yielding the divergent replicas \(\Phi_{1}\) and \(\Phi_{2}\). The _reconciler_ uses the update information \(\alpha\) and \(\beta\) extracted by the _update detectors_, and generates the synchronizing instructions \(\alpha_{1}\) and \(\beta_{1}\). These create the identical merged state \(\Psi\) when applied to the replicas. The update detectors determine the update information \(\alpha\) and \(\beta\) either by comparing the different states of the replicas (e.g., \(\Phi_{1}\) vs. \(\Phi\)), or by having access to the update instructions \(\alpha_{0}\) and \(\beta_{0}\) that were applied to \(\Phi\).  
such as _create_, _rmdir_, or _edit_--are discussed in Secion 3. The changes between the original replica and the locally updated versions are captured by a special command set as defined in Section 4, which also defines algorithms generating this update information.  
The important question of which filesystems can, in general, be considered to be a synchronized state of two divergent replicas is tackled in Section 5. Our definition captures this notion in its full generality without prescribing how the synchronized state can be reached. Providing such a declaration-based definition of the synchronized state is one of our main contributions. Section 6 presents the generic synchronization algorithm based on conflict resolution. By using different strategies to resolve conflicts, any of the synchronized states allowed by our definition, and only those, can be the result of the algorithm. Finally, Section 7 summarizes the results and lists open problems and directions for future research.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 2 Related Work
WDFAD-DBR not only reduces the probability of encountering void holes in the local sparse area but also saves the energy consumption. However, these routing protocols basically do not consider the problem of obstacle avoidance. In particular, marine animals which use the acoustic signals to communicate interfere with communication among sensor nodes. In this paper, we propose an energy-efficient and obstacle-avoiding routing (EOAR) protocol that mainly focuses on the network scenarios of marine animals as obstacles. We use a fuzzy logic system to calculate the forwarding probability of each candidate forwarding relay independently by considering the propagation delay, residual energy and included angle of each relay, and we use a priority-based forwarding method to reduce packet collisions.
# Improving the Phosphatase-Catalyzed Synthesis of 5'-Nucleotides: A Reaction Engineering Approach
###### Abstract  
5'-Phosphorylation of nucleosides is a reaction as important in nature and in industry as it is cumbersome to be performed. Whilst chemical phosphorylation relies on the use of harsh reagents, solvents, and conditions, as well as on the need for protection-deprotection steps, biocatalysis can be a tool to achieve one-step phosphorylation reactions, which are selective, protecting group-free, and occurring under mild and sustainable conditions. In this work, the wild-type non-specific acid phosphatase from _Morganella morganii_ (PhoC-_Mm_) was expressed, purified, and used for the synthesis of inosine 5'-monophosphate (IMP), an important food additive, by using pyrophosphate (PPi) as an inexpensive phosphate donor in a fully aqueous medium at 30 \({}^{\circ}\)C. Via the fine-tuning of the reaction set-up taking into account the type of buffer, amount of PPi, mode/time of PPi addition, and enzyme and substrate concentration, PhoC-_Mm_ could be used for catalyzing the phosphorylation of inosine (I) to IMP in a good yield and high purity (62% yield). The catalysis of the hydrolytic reaction direction, which is the primary function of phosphatases in nature, was here reversed to a certain extent by a reaction engineering approach, without the need for protein engineering strategies.  
non-specific acid phosphatase (NSAP) from _Morganella morganii_; wild-type enzyme; inosine 5'-monophosphate (IMP); enzymatic phosphorylation; transphosphorylation; fed-batch; disodium pyrophosphate dibasic; sustainable synthesis; regioselectivity; reaction engineering
## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)
### Methodology of SSECRF
#### 3.2.2 Multi-Feature Extraction from the Visible Imagery
The quality of the segmentation is related to the scale of the mergence and segmentation options, and the performance of the segmentation has a direct influence on the classification map. Considering the distribution of the land-cover types and the description rules of the spatial attributes, compactness, roundness, form factor, and rectangular fit are defined as the object-based features in our method. The object-based features we extract from the visible image can be shown as:  
\[F_{spatial}=\left[x_{i(m,n)}^{compactness},x_{i(m,n)}^{roundness},x_{i(m,n)}^{ form\_factor},x_{i(m,n)}^{rectangular\_fit}\right]^{T} \tag{8}\]  
where \(x_{i}\) denotes the pixel site of the image, and \((m,n)\) is the optimum scale of the mergence and segmentation in the above formula.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Routing Protocol Configuration  
The process of configuration was carried out by combining the values of the RREQ_RETRIES and MAX_RREQ_TIMEOUT AODV routing protocol. A list of the combinations of the two values is shown in Table 3. From that, the values were simulated by using a test design. The value selection aimed to compare the change in the RREQ_RETRIES value to the MAX_RREQ_TIMEOUT value and vice versa. The simulator determined the default values for both parameters: 3 for RREQ_RETRIES and 10 s for MAX_RREQ_TIMEOUT. RREQ_RETRIES and MAX_RREQ_TIMEOUT parameters do not exist in the OLSR protocol  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 5 \\ Amount of traffic & 1 \\ Node movement & One moving node \\ Node speed & 5, 8, 11, and 20 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario details.  
Figure 2: (a) Node starting position; (b) node final position.  
because it is categorized as proactive routing. This study's OLSR routing protocol had HELLO values and Topology Control (TC) Intervals of 2 and 5 s.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 4 Application of Probiotics in Functional Foods
### Plant-Based and Fruit and Vegetable Products  
#### 4.2.1.  
Probiotic Fermentation of Fruit and Vegetable Juice  
Fruits and vegetables, which are rich in vitamins, minerals, dietary fiber, and other nutrients, provide an ideal environment for probioticic growth. After processing, fruit and vegetable juices with a roughly neutral pH exhibit a relatively short shelf life and limited storage time, leading to suboptimal flavors and textures [102]. By acidifying the raw juice [103], the pH value was reduced from 6.5 to 4.2, extending the shelf life by 14 days and effectively prolonging the storage time, thereby enhancing the flavor when consumed directly. During the production of fermented fruit and vegetable products, adding LAB and yeast probiotics to the juice results in a gradual increase in lactic acid content [104]. Simultaneously, the concentrations of amino acids, polyphenols, organic acids, extracellular polysaccharides, vitamins, bacteriocins, minerals, and various biologically active enzymes in fruit juices increase [105]. These nutrients and beneficial components not only enhance the flavor of fruit and vegetable juices but also provide various health benefits to consumers, including antioxidant, antimicrobial, and hepatoprotective effects [106], the optimization of gastrointestinal function, a reduction in blood glucose levels [107], and enhanced immune function [108]. Nowadays, a wide variety of functional fruit and vegetable juices are emerging and gaining public attention due to their unique formulations and flavors. [PERSON] et al. [109] inoculated _L. plantarum_, _L. fermentum_, _L. acidophilus_, _L. rhamns_, _L. casei paracasei_, and _B. longum_ into orange juice and fermented the mixtures for 48 h. The results showed that all _Lactobacillus_ strains grew well, and the fermented orange juice exhibited enhanced bioactive constituents, aroma compounds, total antioxidant activity, and sensory characteristics, with _L. casei paracasei_ fermentation producing the best aroma and flavor. These findings provide a reference for the further processing of other fruit and vegetable juices. These innovative products not only enhance the health benefits of yogurt but also meet consumer expectations for product variety and personalization.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Sd-Wan
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Category** & **Survey Techniques** & **Challenges** & **Solutions \& Performance** & **Technological Interventions** \\ \hline \multirow{6}{*}{Security-based Techniques} & Intrusion Detection and Prevention (IDPS), VPNs & High false positive rates, low detection latency & AI-driven threat detection [53; 54], automated mitigation, Improved WAN optimization [4; 37; 72] & Deep learning-based intrusion detection [6], early network monitoring tools [74] \\ \cline{2-5}  & Zero Trust Network Architecture (ZTNA) & Implementation complexity, integration with legacy systems & Micro-segmentation [67], continuous authentication [8] & Cloud-native Zero Trust [81] \\ \cline{2-5}  & Cybersecurity Mesh Architecture (CSMA) & Data inconsistency, real-time analysis difficulty & Data aggregation and tool integration [89], threat intelligence [90] & Threat-sharing platforms [91], federated learning for cybersecurity [39] \\ \hline \multirow{6}{*}{Cloud and SDN-based interventions} & Cloud Networking & Security breaches, Cost management & Multi-cloud strategies [92], hybrid cloud security models [38] & Software-defined cloud networking, SASE [4; 69] \\ \cline{2-5}  & Software-Defined Networking (SDN) & Control plane vulnerabilities, compatibility issues & Open-source SDN controllers [93], policy-based automation [83] & AI-driven network orchestration [65], intent-based networking [94] \\ \hline \multirow{6}{*}{Cloud and SDN-based interventions} & Software-Defined WAN (SD-WAN) & Complexity in deployment, security risks, high power consumption & AI-based traffic routing [95], AI-driven integrated security SASE [72], Energy-saving control [80], Ultra-low latency [88] & Cloud-native SD-WAN [84; 85], Hybrid \& Multi-cloud networking [37; 38], AI-based optimization [75] \\ \hline \multirow{6}{*}{AI-Driven and Automation solutions} & AI-based network Optimization & High computational costs, trustworthiness of AI decisions & Federated learning [39], cloud-based AI platforms [74] & Predictive analytics [75], real-time network self-healing [1; 75] \\ \cline{2-5}  & Edge Computing, Network automation & Difficult policy translation, scalability concerns & Adaptive control mechanisms [43] & AI-powered policy automation [96; 97] \\ \cline{2-5}  & Self-Healing Networks & Complexity in automation, fault prediction accuracy & Reinforcement learning models [77] & Autonomous networks \& Self-configuring AI agents [76] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of survey techniques for enterprise networking optimization.
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 3 Results  
### Tube Current of 77 mAs  
Figure 6 shows the 1D NPS at each filter iteration. The NPS at a frequency of about 0.2 mm\({}^{-1}\) decreased significantly with the number of filter iterations. The peak frequency is shifted to a lower value, and the NPS at very high frequencies (>0.4 mm\({}^{-1}\)) was almost completely suppressed by the 5 th iteration. This shows the effectiveness of the bilateral filter in filtering noise at high frequencies.  
Figure 7 shows the noise of the original image and the filtered image in five iterations with a tube current of 77 mAs. The noise magnitude decreased while the percentage of reduced noise increased with the number of filter iterations. The peak frequency and mean frequency shifted down gradually from 0.19 mm\({}^{-1}\) to 0.11 mm\({}^{-1}\) and from 0.24 mm\({}^{-1}\) to 0.17 mm\({}^{-1}\), respectively. These results indicate a decrease in NPS at a certain frequency as a result of repeated filtering at low tube currents.  
Figure 5: Anthropomorphic phantom image of the head. **(a)** ROI for measuring noise, **(b)** ROI for observing spatial resolution.  
Figure 6: NPS on repeatedly filtered images at a tube current of 77 mAs.  
Figure 8 shows the spatial resolution of each filter iteration at a tube current of 77 mAs. Table 3 shows the MTF50 and MTF10 values. MTF curves that coincide after each filter iteration indicate that no degradation of the spatial resolution of the image due to iteration. This was also confirmed by the MTF50 and MTF10 values which did not change significantly. This shows the ability of bilateral filters to maintain the spatial resolution of the image.  
Figure 8: The MTF curve obtained from the edge of the image in 5 filter iterations at a tube current of 77 mAs.  
Figure 7: Noise measurement on homogeneous phantom with tube current of 77 mAs. (**a**) Noise, (**b**) Percentage of reduced noise, (**c**) Peak frequency, and (**d**) Mean frequency.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### Data Recording in Movement
#### 2.2.2 Motion Compensation  
Motion compensation is achieved by shifting the entire image according to the \"true\" motion direction (Equation (1)) with respect to the \(x\)- and \(y\)-axes of the image coordinate system. Let \(\alpha\) be the motion direction along the \(x\)-axis. Then:  
\[\Delta x=\cos(\alpha)\cdot\Delta s;\,\Delta y=\sin(\alpha)\cdot\Delta s. \tag{2}\]  
Hence, the image must be shifted (image by image, \(n\) = 1) by \(-\Delta s\) = (\(-\Delta x\), \(-\Delta y\)).  
In order to compensate for the sensor motion, Equation (1) can be used to construct a correction function for the 2D images. The parameters \(c,f,\,n,ps\), and \(b\) are known, whereas  
Figure 2: Sheets with ArUco markers combined with circle grids used for calibration.  
\(v\), \(d\), and \(\alpha\) must be estimated. For simplicity, \(d\) should be set to a constant value obtained by an estimation of the average distance of the object measurement points. This assumption can be made because the points on the object surface are at a similar distance to the sensor, and the distance change within one scan sequence (few milliseconds) is negligible.  
This kind of motion compensation is valid for observations where all object points are close to a virtual plane in the space, perpendicular to the main observation direction of the sensor. When the sensor is tilted by an angle, \(\tau\), a systematic distance shift is caused from lower to upper image regions, and distance shift of distinct lines must be treated differently depending on the angle, \(\tau\).
# Development of a Cone Penetration Testing Apparatus with a Textured Shaft
## 2 Laboratory Experiments
### Cone Penetration Test Apparatus  
A laboratory model chamber and small-scale cone system are developed to quantitatively evaluate the shear resistance according to the load direction and textured shaft. The inner dimensions of the model chamber used in the tests are 700 \(\times\) 700 \(\times\) 700 mm (length \(\times\) width \(\times\) height). The front and rear of the chamber are made of 50 mm acrylic, while the sides are constructed with 50 mm thick steel to maintain a zero lateral strain condition. Six 8 mm diameter drainage holes are installed at the bottom of the chamber, which are connected to plastic valves and polyurethane hoses to create a drainage system. To avoid the plastic zone formed around the cone during the first penetration test from affecting subsequent tests, the distance between cones (S) is designed to satisfy S/D \(\geq\) 11 (where D is the cone diameter), as suggested in centrifuge model cone penetration tests [21]. A loading plate measuring 40 mm thick and made of steel is manufactured to apply a uniform load, with four 100 mm diameter holes created in the center to allow cone penetration without soil loss. Note that four penetration and pull-out tests are conducted under the same sand conditions. Two hydraulic cylinders with 35 kN capacity and a diameter of 80 mm are installed at opposing ends of the load application plate to apply vertical load without eccentricity. A reaction frame, 600 mm high, is integrated with the model chamber using a bolt-nut system to withstand the resistance forces generated during cone penetration and to provide sufficient space for cone penetration.