esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 AI-Based Solutions for SDN Consistency Synchronization
### AI-Based SDN-Related Work
An application of interest is inter-domain routing. The controller synchronization frequencies directly impact the quality of implemented routing paths. They specify the synchronization budget as the maximum number of other controllers that an SDN controller can synchronize with at any given time. Inspired by the success of the DRL technique in [44], the researchers in [45] introduced a controller synchronization framework. They called it MACS: deep reinforcement learning-based SDN controller synchronization policy design. As far as we are aware, MACS (Multi-Armed Cooperative Synchronization) is the first DRL-based SDN controller synchronization scheduler that produces fine-grained synchronization rules. It is aimed at communication and computes resource optimization [45]. They investigated the problem of controller synchronization with a restricted synchronization budget. In their work, to avoid the significant overhead created by the frequent distribution of synchronization messages between controllers, only a limited number of synchronization messages at a given time were exchanged [45]. This is in contrast to the existing implementation of the distributed controller, such as ONOS [24], where the entirety of the state information is exchanged between the synchronized controllers. They modeled their work after Dueling Network Architectures [57] and Action Branching Architectures for DRL [58]. However, MACS [45] suggested a central controller whose only role is to set synchronization policies and regulate controller synchronization--either a standalone control unit or one of the distributed controllers that are already in use. MACS's single-agent DRL is built on logic with centralized control, which is insufficient for distributed control in SDN [59]. The work presented by MARVEL focused on enabling controller load balancing in software-defined networks with multi-agent reinforcement learning as per [59]. MARVEL attempts the first distributed solution of the NP-Complete Software Migration Problem (SMP). This work proposes a scheme to dynamically control the load balancing between the distributed controllers in the large SDN environment. The distributed processing characteristic of MARVEL makes it desirable for the communications and decision-making of the distributed control plane in SDN. MARVEL uses the DRL technique with each agent. Following thorough training, the MARVEL agents in the controllers can decide on the SMP control approach.
## 2 System Model of DBT-MC-FTN  
Based on our previous work in [11; 12], FTN signaling is further applied in both time and frequency domains. In this work, a unique DFT-based block transmission for multi-carrier FTN signaling, i.e., DBT-MC-FTN has been proposed. Figure 1 depicts the schematic block diagram of the DBT-MC-FTN transceiver.  
Figure 1: Block diagram of the DFT based block transmission for multicarrier faster-than-Nyquist (DBT-MC-FTN) transceiver: (**a**) transmitter: Scheme I: Channel estimation using DBT-MC-FTN pilots; Scheme II: Channel estimation using orthogonal pilots; (**b**) receiver.
## 6 Energy-Efficient, Priority-Based Forwarding (EPF)
### System Model
#### 6.4.2 Topology Discovery and Content Placement  
At first, All the nodes broadcast L_info pkt to their neighbor nodes (in one-hop network range) after controllers' announcement with its ID. After updating LITs, all the nodes then send L_info pkt to controller having information of their content names, ID, neighbor ID and the remaining energy of the nodes by enabling controller ID. Controller then updates the table entries of its GIT using the information sent by each node in L_info pkt. After GIT setup, the actual forwarding mechanism starts. This whole process of topology discovery is presented in Figure 11 and Algorithm 1.  
Figure 11: Content placement in EPF controller.  
Figure 10: EPF controller announcement in IoT.
## 3 Proposed Algorithm
\[\log[\hat{F}(i,j,k)]=\log[F(i,j,k)]\times M(i,j) \tag{5}\]  
In the formula, \(log[\hat{F}(i,j,k)]\) represents the pixel value located at the \(i\)-\(th\) row and \(j\)-\(th\) column of the \(k\)-\(th\) image, which has undergone a mask processing. \(M(i,j)\) denotes the pixel value located at the \(i\)-\(th\) row and \(j\)-\(th\) column of the binary mask image, which can only be either 0 or 1. During the element-wise multiplication operation, the pixels with a value of 0 in the binary mask will result in the corresponding pixel value in the image being set to 0, while pixels with a value of 1 will have no effect. After this, the Constant False Alarm Rate (CFAR) detection algorithm is applied on the image sequence to detect targets, and the number of pixels containing targets is extracted. The CFAR detection algorithm is a target detection technology commonly used in the field of radar signal processing [27]. Its main purpose is to effectively detect the area where the target exists while maintaining a constant false alarm rate. The basic idea is to adaptively adjust the detection threshold so that the system can adapt to changes in different backgrounds so as to achieve target detection while maintaining a certain false alarm rate [28]. Finally, the number of targets is obtained by multiplying the number of pixels containing targets by the proportional coefficient. In the experimental images, each pixel represents an area of 10 m by 10 m on the ground: that is, each pixel represents a square area of 10 m by 10 m. In the field inspection of parking lots, the number of vehicles parked in each pixel can be counted to determine the true value and proportional coefficient of the number of vehicles in a single pixel. The main processing steps are from logarithmic transformation to CFAR detection; their details are introduced as follows.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Evaluation Framework
#### 3.3.1 Diarization Error Rate  
The DER remains the primary metric for overall system performance evaluation and is computed as:  
\[\text{DER}=\frac{\text{Miss}+\text{FA}+\text{Speaker Error}}{\text{Total Speech Duration}} \tag{4}\]  
where Miss represents missed speech detection, FA indicates false alarm speech detection, and Speaker Error quantifies incorrect speaker attribution, all measured in seconds of audio. While this metric provides a general assessment of system performance, it may obscure specific areas of strength or weakness in the diarization process. Following standard practice, a 250 ms collar is typically applied around speaker boundaries to accommodate for annotation inconsistencies. In our final results, we present results both with and without this collar to provide a more comprehensive performance assessment.
# IoT Services for Monitoring Food Supply Chains
###### Abstract  
Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product integrity, leading to deterioration and an increased risk of foodborne illness. Monitoring agrifood supply chains is essential, from packaging to last-mile delivery. Distribution methods that rely on non-automated monitoring systems, such as manual temperature measurements, are error-prone due to the failure of manual treatments and increase the likelihood of product deterioration. Emerging sensor technologies and the rapid development of Information and Communication Technologies offer new possibilities for real-time tracking, enabling stakeholders to maintain optimal conditions and monitor aesthetic, physicochemical, and nutritional quality. This paper proposes a cost-effective temperature and humidity traceability system that utilises wireless sensor networks (WSN) and Internet of Things (IoT) services to monitor perishable products within the agrifood supply chain ecosystem. It also provides an overview of recent innovations in sensor technologies, along with food quality indicators relevant to real-time monitoring of food quality. The proposed research examines the available sensor technologies and methodologies that enable continuous monitoring of agrifood supply chains. Moreover, the paper presents a pilot full-scale project from both functional and technological perspectives.  
real-time monitoring: IoT sensors; agrifood supply chain; last-mile delivery; LoRaWAN
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 5 Mass Storage
### Postprocessing and Archiving
#### 5.3.2 Weather and Climate  
Today, prediction of weather and climate is one major use of supercomputing facilities, with a tremendous amount of data to be stored (In 2017, ECMWF's data archive grew by about 233 TB per day [[https://www.ecmwf.int/en/computing/our-facilities/data-handling-system](https://www.ecmwf.int/en/computing/our-facilities/data-handling-system)]([https://www.ecmwf.int/en/computing/our-facilities/data-handling-system](https://www.ecmwf.int/en/computing/our-facilities/data-handling-system)) [97]). Thus, using compression on the whole I/O system (main memory, communication, storage) can significantly affect performance [98]. Typically, ensemble simulations are used, allowing to exploit correlations.  
\begin{table}
\begin{tabular}{c c c c} \hline \(n\) & number of checkpoints & \(T_{\text{C}}\) & time for actual computation [s] \\ \(N\) & number of compute cores & \(T_{\text{CP}}\) & time to write/read a checkpoint [s] \\ \(p_{\text{RS}}\) & probability of failure & \(T_{\text{DS}}\) & time to recover data structures [s] \\  & per unit time and core [1/s] & \(T_{\text{R}}\) & recovery time \(=T_{\text{CP}}+T_{\text{DS}}\) [s] \\ \(N_{\text{RS}}\) & number of restarts & \(T_{\text{RS}}\) & time for restart [s] \\ \(T\) & overall runtime (wall clock) [s] & \(b\) & \(:=1-T_{\text{R}}p_{\text{RS}}N\) \\ \hline \end{tabular}
\end{table}
Table 2: Notation used for optimal checkpointing. Where applicable, units are given in brackets.  
Figure 11: Influence of \(T_{\text{CP}}\), comparing the nominal scenario (green) to scenarios with \(T_{\text{CP}}\) reduced (blue) and increased (red) by a factor 4. _Left:_ overall runtime vs. number of checkpoints for \(N=4\) and \(T_{\text{C}}=100{,}000\) s. _Right:_ overall runtime vs. actual computation time for \(N=100\). In both cases \(p_{\text{RS}}=7.74\times 10^{-7}\), \(T_{\text{CP}}=245.583\) s and \(T_{R}=545.583\) s were used. While the runtimes were measured for the parallel-in-time solution of a 3D heat equation on the HLRN-III Cray XC30/XC40 supercomputer (www.hlrn.de), the probability of failure was determined from HLRN-III logfiles.  
Three different approaches are investigated in [97]. The most successful one takes forecast uncertainties into account, such that higher precision is provided for less uncertain variables. Naturally, applying data compression should not introduce artifacts, or change, e.g., statistics of the outputs of weather and climate models. The impact of lossy compression on several postprocessing tasks is investigated in [42; 43], e.g., whether artifacts due to lossy compression can be distinguished from the inherent variability of climate and weather simulation data. Here, avoiding smoothing of the data due to compression via transform coding can be important, favouring quantize-then-transform methods or simpler truncation approaches like fpzip [9].
## 4 Results and Discussion
### Effect of Laser Pulse Frequency
The Ra parameter decreased to 2.154 \(\upmu\)m. Figure 17c shows that when the laser pulse frequency is 900 Hz, the coating on the surface of the sample has a good removal effect, and no residual coating can be observed. The surface finish of the metal substrate is high, which is close to the original substrate morphology. The Ra parameter of roughness reaches the minimum value  
Figure 15: EDS diagram of the surface after water jet-guided laser cleaning with different laser frequencies. (**a**) Region A; (**b**) Region B; (**c**) Region C; (**d**) Region D.  
(0.832 \(\upmu\)m). Figure 17d shows that when the laser pulse frequency continues to increase, the energy density of the water jet-guided laser is low, and mottled craters are formed on the coating. The stereoscopic effect of the coating surface is obvious, and the Ra parameter of roughness increases to 3.638 \(\upmu\)m.  
In conclusion, a water jet-guided quasi-continuous laser cleaning coating is a complex result of the coupling of multiple mechanisms. It includes not only the thermal ablation  
Figure 16: Effect of laser pulse frequency on the surface roughness of the samples. Figure 17: Variation of sample surface roughness with laser pulse frequency. (**a**) Stage 1; (**b**) Stage 2; (**c**) Stage 3; (**d**) Stage 4.  
effect of energy absorption but also the thermal stress vibration effect of energy conversion and the laser plasma impact effect. At the same time, it is also affected by the erosion effect of the high-pressure water jet.
## 2 Background
### Power Line Histograms
#### 2.2.1 Time Synchronization  
The cyclic histogram is the average per-cycle behavior over the sampling period. It is processed relative to a sine function (i.e., sample \(x[n]\) is zero, sample \(x[n-1]\) is negative, and sample \(x[n+1]\) is positive). Due to the DFR clock drift and source voltage frequency drift, each cycle must be detected and time-synchronized before creating the histograms. This synchronization begins by detecting a recorded cycle. A recorded cycle's start is determined to be two consecutive samples: the first is negative, and the second is positive (i.e., a negative-to-positive zero crossing). The cyclic histogram-generating process then skips forward three-quarters of the number of Samples per Cycle (SPC). For example, if the SPC is 16, the function skips forward 12 samples. The end of the cycle is determined to be the next negative-to-positive zero crossing. The power signal histogram generates code checks for the end of a cycle of up to the number of samples equaling one-fourth of the sampling rate, past the expected position of the cycle's end. For example, at an SPC of 16, the power signal histogram generates code checks up to four samples after the expected location of the cycle's end. Suppose the cycle's end is not detected within the given window. In that case, the function drops the detected cycle's start location and resumes parsing the recorded power signal to detect a new cycle's starting sample. Because the actual sampled value is never zero, the function estimates the time at which the sampled cycle is zero by calculating the slope between the negative and positive samples around the zero crossing,  
\[m_{1}=\frac{x[n+1]-x[n]}{\text{d}t}, \tag{2}\]  
where \(x\) is the sampled waveform, \(n\) is the negative sample index before the positive sample index \(n+1\), and \(\text{d}t\) is the time between sample points. The initial zero point is  
\[\Delta t_{1}=\frac{m_{1}}{x[n]}. \tag{3}\]  
The second time offset \(\Delta t_{2}\) is calculated using the estimated end of the cycle. Next, \(\Delta T\) is created with \(\Delta t_{1}\) and \(\Delta t_{2}\) as its start and end points, with steps of \(\text{d}t\). The detected cycle is interpolated into the time-synchronized cycle using \(\Delta T\) and stored for cyclic histogram creation. The end of cycle \(i\) is the start of cycle \(i+1\). This process is repeated until the cyclic histogram-generating process reaches the waveform's end.
## 3 Numerical Simulation of the Seepage of Dislocated Fracture  
### Seepage Theoretical Model  
The Navier-Stokes equation is generally used to describe fluid motion [38; 39]. The steady flow of incompressible fluids can be expressed as Equation (6). The N-S and continuity Equations (6) and (7) are widely used to analyze single fracture flow characteristics [42].  
\[\rho(\mathrm{u}\cdot\
abla)\mathrm{u}=-\
abla p+\mu\
abla^{2}\mathrm{u} \tag{6}\]  
\[\
abla\cdot\mathrm{u}=0 \tag{7}\]  
where \(\mathrm{u}\) represents the velocity vector in \(\mathrm{m}/\mathrm{s}\), \(p\) is the pressure in \(\mathrm{Pa}\), \(\rho\) is the fluid density in \(\mathrm{kg}/\mathrm{m}^{3}\), and \(\mu\) is the fluid's dynamic viscosity in \(\mathrm{Pa}\cdot\mathrm{s}\).  
With the increase in shear dislocation, the distribution of voids in the fracture space continuously changes, and the fluid in the permeability process is more likely to enter a nonlinear flow state locally. The calculation of nonlinear fluid flow is commonly based on the Forchheimer empirical equation, as shown in Equation (8) [43; 44].  
\[-\
abla P=\mathrm{A}Q+\mathrm{B}Q^{2} \tag{8}\]  
where \(\bigtriangledown P\) is the pressure gradient, \(\mathrm{A}\) is the linear term coefficient or viscous term coefficient, and \(\mathrm{B}\) is the nonlinear term coefficient or inertia coefficient. When the influence of fluid inertia can be neglected at low flow rates, the nonlinear term in the second part (\(\mathrm{B}Q^{2}\)) disappears, and the Forchheimer law reduces to [PERSON]'s law. The coefficients \(\mathrm{A}\) and \(\mathrm{B}\) are mainly related to the geometric characteristics of the fracture surface tomography [45]. Combining [PERSON]'s law, the permeability of the fracture is calculated through Equation (9) [46]:  
\[K=-\frac{Q\mu}{S\
abla P} \tag{9}\]  
where \(K\) represents the permeability of the fracture, \(\mathrm{m}^{2}\); \(S\) is the cross-sectional area of the fracture, \(\mathrm{m}^{2}\).  
The dimensionless Reynolds number (\(Re\)) is commonly used as an indicator for determining fluid flow regimes, defined as the ratio of inertial forces to viscous forces. For fluid within fractures, \(Re\) can be calculated using Equation (10) [22]:  
\[Re=\frac{\rho vb_{h}}{\mu}=\frac{\rho Q}{w\mu} \tag{10}\]  
In the equation, \(b_{h}\) represents the hydraulic aperture, and \(w\) is the width of the fracture along the direction of the pressure gradient.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
###### Abstract
However, this work lacks latency and power models, as well as comprehensive beamforming modeling at the BS/MS. Moreover, the work in [17] presents a beam alignment technique with partial beams using neural networks for multi-user mmWave massive MIMO system in efforts to improve the spectral efficiency at reduced training overhead, as compared to hierarchical search and compressed sensing methods. Offline training is conducted on the channel model, after which online prediction is achieved for beam distribution vector using partial beams. Here the obtained dominant indices from the beam distribution vector are used to align the beams for the multi-user. The work in [18] combines machine learning and situational awareness to learn the power and optimal beam index, during which the angles of arrival (AoAs) are first estimated based on the location and then this information is used as input to the neural network for beam selection. However, the requirement for user location information (prior knowledge) here for training weakens the proposed algorithm and adds to the system complexity. A joint beamforming approach between distributed BSs is developed in [19] that deploys machine learning to simultaneously serve a mobile MS. The latter transmits a single uplink training sequence to the participating BSs using omni or quasi-omni beam patterns to develop a pattern for the location signatures. The signatures are then deployed at the deep learning stage to estimate the beamforming vectors at these BSs, thus reducing the training overhead. The limitation of this work is the use of wide beams (omni or quasi-omni), which makes it inefficient in blockage scenarios during the user mobility. These wide beams also yield low channel gains and throughput levels. Out-of-band information is likewise used in [20] for deep learning beam prediction scheme to minimize the training complexity. Namely, a dual-band (sub-6 GHz and mmWave links) approach is implemented, where the optimal beam in mmWave band is estimated from sub-6 GHz channel state information (CSI). The work focusses on testing the network accuracy without investigating the beamforming and channel models. One limitation here is the assumption of similar spatial features between the channels of the two bands, which is an inaccurate assumption. Overall, existing deep-learning-based beam access schemes (summarized in Table 1) still lack key operating assumptions that are in conflict with the objectives of the FR2 NR of 5G systems. First and foremost, some models assume omni-directional mode at the MS, where the beam discovery is limited to the BS. Others are dependent on the multiple BSs, the MS location and sub-6 GHz bands, and thus fail to operate mmWave as a standalone network. Other limitations include indoor implementation and marginal enhancement to existing conventional methods (e.g., beam sweeping and exhaustive searches). These models overall lack time delay and power consumption models in the control plane and hence work is needed to investigate the delay in standalone beamforming-based mmWave networks.
## 3 Research Analysis
### System Types  
A user normally needs to inspect an object from different elevations or angles in \(360\times 360\) degrees. Virtual objects must be tracked in order to be displayed in a correct orientation. When a virtual object is superimposed in a physical world, correct relative location should be maintained without drifting after a user moves. The system actually tracks the configuration of the real environment to ensure that the virtual object is placed accurately or in a correct relative translation to the former location. In contrast, a GPS or a compass can hardly achieve this in a precise manner.  
Marker-based AR and markerless AR constitute two major categories of AR [27]. Markers are 2D patterns or symbols that can be recognized by a computer system [28]. Marker-based AR needs to identify corresponding markers, so that presupposed information can be presented. The marker system requires an image or 2D barcode to achieve registration and tracking. A difference exists between the simulated scenario and the real one. An image-based marker also separates students from learning a real scenario, since a printed marker, which has to be placed appropriately for registration, does not necessarily appear in a real site. Pasting and tearing markers on the surface, which may cause damage to an artifact, are prohibited by law while working with items with a national heritage designation. Although a real scene can be used as a marker in a markerless approach, difficulty may be encountered upon deviations made to the preset view or insufficient lighting conditions.  
Markerless AR does not require marker registration, and tracking is more robust in enabling the realization of mobile AR. This approach is made by matching a model with the geometries of a subject or an environment to be registered for correct alignment. 3D attributes of the geometry, which can be categorized as a type of marker, can fit in a real physical scenario to reduce the difference from reality for easier student adaption in the future.  
Figure 6 shows the distribution of the types adopted in each study. Fifteen papers adopted marker-based AR [24; 39] and three papers used markerless AR [35; 40]. Three papers did not develop or specify systems [25; 38].  
Figure 5: The number of adopted development tools in AR studies for ACE education.  
Figure 6: The number of studies in each applied AR system.