esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Radio Access Network Slice Instantiation Process  
The user first defines the slice requirements on the GUI of the IBN tool and includes downlink, uplink, starting time, ending time, and slice SNSSAI information. After that, the intent manager of the IBN system interacts with the resource repository and policy store for the creation of the VNFFG type policy graph. The created policy graph contains the information of requested core and RAN resources and links between them. This policy graph is sent to policy configurators for further processing. These IBN policy configurators are a very important part of the IBN system. As discussed above, there are two types of policy configurators in our IBN system; one is the OSM policy configurator and the other is the RAN slicing configurator.  
Figure 8: Test and training accuracy of the GAN model.  
Figure 9: Network slice instantiation process.  
The OSM policy configurator's job is to take this policy graph and translate it into a JSON string format. In contrast, the RAN policy configurator converts the policy graph information into TOSCA and then into the JSON file. Moreover, the OSM policy configurator transfers these configurations to OSM NFVO for the deployment of core EPC resources. On the other hand, the RAN configurator sends the JSON file to the FlexRAN controller and the controller deploys the slice QoS to eNodeB. It also contains dedicated MME information and slice SNSSAI Which connects the RAN slice with the core EPC. The core EPC VNFs are deployed with the help of OSM NFVO and VIM (OpenStack) using the JSON string format slice template. After the slice creation, the user is notified with the help of IBN. This is how a network slice is created, which does not require any manual work or expertise. Hence, users can define slice QoS requirements in a higher-level language through drag and drop from GUI of IBN, and the system automatically translates these QoS requirements in the form of a slice template and deploys resources accordingly. The slice instantiation procedure is presented in Figure 9.
## 4 Results and Analysis
### Ice Topographic Feature Extraction for Completing Lake Ice  
During our time-series ice displacement surveying of Lake Khovsgol, a large number of ice ridges or ice mounds with elevated structures on the lake ice were visually observed in Sentinel-2 images, some of which were isolated and some of which were combined to form tandem images (\"mountain ranges\"). In a medium to large lake, ice cover becomes movable and moderate an uplift at the base of ice cover may be observed as ice thickness substantially decreases or wind speed increases. For example, in a study of the lake ice of Estonian/Russian Lake Peipsi (59\({}^{\circ}\) N, 27\({}^{\circ}\) E) on 15 and 19 March 2002, the authors of [64] observed that the southern part of the lake's lake ice broke up under the influence of strong winds, while the northern part of the lake experienced uplift in a pattern similar to the ice mounds observed in Lake Khovsgol. Figure 10 shows the textural characteristics of the lake ice in Lake Khovsgol at different periods. Most of these large ice ridges spanned the lake and were a combination of multiple ice ridges that formed a mountain range-like structure. Most of these ice ridges across the lake were observed in the northern and central parts of the lake, though the southernmost part of the lake was found to produce a ridge along the north-south direction (Figure 10p-t). By comparing Sentinel-2 images from multiple time stages, we found that the ice ridges and ice mounds on the ice surface did not significantly change in form and position between 8 January and 4 March 2020. However, new fractures were created or old fractures were deepened and became visible around the ice ridges over time (Figure 10c,e,h,m,r; illustrated with red line), especially after 7 February. These cracks on the ice cover usually extended for tens to hundreds of meters or more, forming a complex network that made navigation on the ice more difficult. The width of the cracks was generally 0.5-2 m, ranging from 1-2 cm to over 4 m and experiencing changes during the day [19]. During the day, melting snow and water filled up internal cracks in the lake ice, which froze at night when the temperature was negative [19].  
Figure 10: (**a–t**) represent the ice ridges and crevasses on the ice surface as observed through band 8 of Sentinel-2 optical remote sensing images. Red lines represent emerging or deepening crevasses.
## 2 Topology and Control Principles of TVSG  
The main circuit topology and control architecture of the grid-connected TVSG are illustrated in Figure 1. \(L_{\text{f}}\) and \(R_{\text{f}}\) denote the filter inductance and the parasitic resistance of the inductance, respectively; \(i_{\text{abc}}\) and \(u_{\text{abc}}\) denote the three-phase voltage and current post-VSG filtering, respectively, while \(L_{\text{g}}\) is the line inductance.  
The TVSG emulates the operating characteristics of the SG to derive its rotor equation of motion, which can be expressed as follows:  
\[P_{\text{m}}-P_{\text{e}}=J_{\omega}\omega_{0}\frac{d\omega}{dt}+D_{\omega} \omega_{0}(\omega-\omega_{0}) \tag{1}\]  
Figure 1: TVSG topology and its control methods.  
where \(J_{\omega}\) is the virtual inertia parameter; \(D_{\omega}\) is the virtual damping coefficient; \(\omega\) is the virtual angular frequency; \(\omega_{0}\) is the rated angular frequency; \(P_{\rm{m}}\) and \(P_{\rm{e}}\) denote the virtual mechanical power and output active power of the TVSG, respectively.  
The PFR and primary voltage regulation (PVR) equations for TVSG can be formulated as follows:  
\[P_{\rm{m}}=P_{\rm{ref}}+K_{\omega}(\omega_{0}-\omega) \tag{2}\]  
\[E=E_{0}+K_{\rm{q}}(Q_{\rm{ref}}-Q_{\rm{e}}) \tag{3}\]  
where \(K_{\omega}\) is the PFR coefficient; \(E_{0}\) is the no-load voltage of the VSG; \(K_{\rm{q}}\) is the PVR coefficient; \(Q_{\rm{ref}}\) is the reactive power adjustment command; and \(Q_{\rm{e}}\) is the instantaneous reactive power output of the TVSG.  
This paper primarily focuses on optimizing the transient response performance of active power in TVSG. Given that TVSG's grid-connected active and reactive power can achieve decoupled operation when the line impedance is predominantly inductive, the subsequent sections will exclude discussions on reactive power control and voltage-current dual-loop control [11].
# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 2 Background and Motivation
This challenge is particularly pronounced in vehicular edge computing, where most machine learning models require large-weight configurations to function optimally. Thus, models deployed on edge devices must be meticulously optimized for both power and speed, a balance that can be difficult to achieve without compromising accuracy or overall performance. Another challenge is the lack of standardization in hardware and software platforms, which complicates the development and deployment of machine learning models that maintain consistency across AVs and their associated edge devices. Furthermore, deploying machine learning models on edge devices raises concerns about data privacy and security, as sensitive vehicle data may be processed and stored locally. Addressing these challenges will necessitate advancements in hardware and software optimization, as well as improvements in standardization and data privacy measures [14; 37; 38; 39; 40]. Traditional improvements in machine learning and neural networks often focus on overall latency per frame and optimizing accuracy through loss function adjustments, typically centering on CPU and GPU impacts while neglecting memory considerations. Although targeted methods like the Pyramid Attention Network [41] and the Region Proposal Network [6] have led to many real-time detection algorithms, these are generally developed without the constraints of edge resources in mind. Common strategies in the literature for addressing edge memory limitations often involve trade-offs or sacrifices. For example, the authors in [42; 43] reduce the full YOLO architecture to decrease computational demands. Similarly, ref. [44] proposes distributing workloads through a pipeline to mitigate hardware constraints like memory contention. Traditional methods, such as [45], consider and attempt to eliminate memory contention for individual tasks. The contemporary literature addressing memory contention issues for DNNs on edge devices also encounters limitations. Among the various memory-aware middleware approaches, works like MASA [46], along with others such as Deepeye [47], NestDNN [48], and DART [49], tackle CPU and memory issues for low-end edge devices from a middleware perspective, considering the impacts of individual layers on CPU and memory usage. However, even in the case of MASA, the comprehensive effects of memory are not fully explored. Additionally, due to the methodologies and hardware platforms employed in MASA, the analysis is limited to two-layer types, neglecting single-stage and two-stage foundations. Furthermore, these works do not model memory characteristics as a variable impacting performance; instead, they facilitate DNN processes. Lastly, most modern DNNs leverage PyTorch [50] as their backbone, which is absent from these motivating studies.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 5 Future Opportunities
### High-Energy XPCS  
The coherent flux and coherence lengths at diffraction-limited storage rings will particularly be larger at higher X-ray intensities compared to third-generation synchrotron radiation sources. This will allow XPCS and XSVS experiments at high intensities for different applications. For instance, dynamics of samples in dedicated sample environments for experiments at extreme condition, such as diamond anvil cells for high-pressure research, will become accessible due to the lower absorption of X-rays at higher energies. Recent experiments demonstrated already XPCS on biopolymers at liquid-liquid interfaces [194] and the diffusion of colloidal particles on confined geometries [195; 196] using an X-ray energy of 21 keV. Furthermore, growth processes at interfaces on atomic length scales have been recently demonstrated [197; 198]. As these take place at high temperatures--in  
Figure 14: (a) \(q\)-dependence of relaxation time \(\tau\) as a function of \(q\) for nanoparticles of 1 nm, 10 nm, and 100 nm radius undergoing Brownian motion in water. The solid black line marks the relaxation time \(\tau_{0}=\tau(q_{0})\) at the intrinsic length scale \(q_{0}=\frac{2\pi}{R}\), the dashed line marks the repetition rate of three detectors listed in Table 1. (b) Geometrical speckle contrast \(\beta_{\text{gom}}\) as function of X-ray energy for some \(q\)-values. The parameters used for the calculation are: 75 \(\upmu\)m detector pixel size, 5 m sample-detector distance, 10 \(\upmu\)m beamsize, 1 mm sample thickness, and a bandwidth of a Si(111) monochromator. Contrasts are highlighted for \(q=0.01\) nm\({}^{-1}\) for 8 keV, 24 keV, and 40 keV.  
this case up to above 950 K--high-energy XPCS provides more flexibility for the sample environment for such experiments.  
Figure 14b shows the speckle contrast factor due to geometrical parameters (i.e., scattering geometry and detector pixel size) as a function of X-ray energy at some \(q\)-values. The speckle contrast drops significantly at large energies, basically because of the smaller speckle size (see Section 2.4). This will make experiments at energies beyond 30 keV challenging. Nevertheless, experiments in the range of 20 to 30 keV will become feasible, which is sufficient for studying buried interfaces or penetrating high-pressure chambers.
## 5 Simulation Results
### Simulation Results of DALM2  
The simulation results of DALM2 are shown in Figures 7-9. We show the relationship between DAL and DA for different values of IECs, AAS and DE. We investigate the effect of AAS on DAL, which is considered as a new parameter. We consider the AAS and DE as constant parameters and we change the IECs value from 0.1 to 0.9, and the DA value from 20 to 90.  
We can see in Figure 6(a) that when the DA value is 40, IEC is 0.1, AAS is 0.1 and DE is 0.1, the DAL is 0.7. Comparing the same point with Figure 6, the DAL is decreased 10%. In Figure 6(b), we changed the DE from 0.1 to 0.5. It can be seen that the DAL is decreased compared with Figure 6(a). Also, in Figure 6(c), the values of DAL have decreased much more.  
In Figures 8 and 9, we changed the AAS values from 0.1 to 0.5 and 0.9. We see that for the same point when the DA value is 40, IECs is 0.1 and DE is 0.1, the DAL is 0.8 and 0.92, respectively. Comparing with the same point of Figure 6(a), the DAL is increased by 10% and 22%, respectively.  
Thus, when the AAS value is increased, the DAL in DALM2 increases much more compared with DALM1. This shows that the AAS has a great effect on the driver's anxiety level.
# Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maximly Sinus Surgery
## 2 Surgical Robot Modeling
### Fuzzy Auto Regressive with Exogenous Input (ARX) Laguerre System Modeling
\right.. \tag{13}\]  
In this research, to modify the accuracy of the ARX-Laguerre method, a fuzzy logic technique is recommended. Based on the bounds of the uncertainties and faults, the primary objective is to present a systematic fuzzy algorithm for modeling the multi-input-multi-output (MIMO) systems in the presence of unknown parameters. Based on the fuzzy algorithm, this technique consists of the following parts: fuzzification, an IF-THEN rule, a reasoning mechanism, and defuzzification. The primary challenge of system modeling based on the fuzzy algorithm is improving the performance of the knowledge base and inference parts. The fuzzy ARX-Laguerre system estimation is defined based on the following equation:  
\[W_{fuzzy}(k)=f_{fuzzy}[W(k-1),W(k-2),\ldots,W(k-n_{W}),H(k-1),H(k-2),\ldots,H(k-n_ {H})], \tag{14}\]  
where \(f_{fuzzy}\) is a fuzzy function for system estimation. The fuzzy IF-THEN rule is defined as follows [28]:  
\[Rule\left(N\right):\text{ If }e_{m}\text{ is }H_{e}^{m}\text{ and }\dot{e}_{m}\text{ is }H_{e}^{m}\text{ then }f_{fuzzy}\text{ is }H_{fuzzy}^{m}, \tag{15}\]  
where \(e_{m},(H_{e}^{m},H_{e}^{m},H_{fuzzy}^{m})\), and \(\dot{e}_{m}\) are the estimation error, the fuzzy sets, and the estimation change of error, respectively. The fuzzy membership functions for \(e_{m}\) in the interval of \([-0.1,0.1]\) are Gaussian,  
Figure 7: Auto regressive with exogenous input (ARX) Laguerre method for modeling the surgical robot for the sinus manipulator. and the fuzzy sets are defined as negative big (NB), negative small (NS), zero (Z), positive small (PS), and positive big (PB). The fuzzy membership functions for \(\dot{e}_{m}\) in the interval of \([-1,1]\) are Gaussian, and the fuzzy sets are defined as NB, NS, Z, PS, and PB. The fuzzy membership functions for \(f_{fuzzy}\) in the interval of \([-10,10]\) are Gaussian, and the fuzzy sets are defined as NB, NS, Z, PS, and PB. Thus, the state-space fuzzy ARX-Laguerre estimation for the surgical robot for the sinus is represented by:  
\[\left\{ \begin{array}{l}X_{q_{F}\tau}(k+1)=[\alpha_{X}X_{q_{F}\tau}+\alpha_{ U}U_{F\tau}(k)+\alpha_{Y}Y_{q}(k)+\alpha_{F}f_{fuzzy}]+\delta(k)\\ Y_{q}(k+1)=(\kappa)^{T}X_{q_{F}\tau}\end{array} \right.. \tag{16}\]  
where \((\alpha_{X},\alpha_{U},\alpha_{Y},\alpha_{F})\) are coefficients. The rule table of the fuzzy ARX-Laguerre algorithm is shown in Table 3.  
Figures 8 and 9 illustrate the estimation accuracy and errors for the normal and abnormal conditions based on the ARX method, the ARX-Laguerre technique, and the fuzzy ARX-Laguerre algorithm. According to these figures, the estimation accuracy of the fuzzy ARX-Laguerre technique is higher than those of the ARX and ARX-Laguerre techniques, and the error rate in the fuzzy ARX-Laguerre estimation technique is close to zero under normal and abnormal conditions.
## 4 Measurements
### Narrowband Channel--2.45 GHz  
In this paragraph the results for the 2.45 GHz narrowband channel is investigated. The link channel measured in the five campaigns of measurements as a function of time is shown in Figure 9a. For the first 10 s, the variation in the channel is small (between \(-\)59 dB and \(-\)50 dB). When the subject starts walking the S\({}_{21}\) oscillation increases, but remaining between \(-\)67.8 dB and \(-\)48 dB. Finally the subject is standing in front of a wall, and the S\({}_{21}\) is almost constant for each set of measurements. The CDF of the five measurements is shown in Figure 9b. The minimum and the maximum values of the five measurements are summarized in Table 3. Considering all the measurements, the maximum range is equal to 19 dB. The CDF is compared with the MLE for the corresponding Rician Distribution, assuming that there is a main ray between the foot and the wrist.  
The K-factor of the Rician distribution is summarized in Table 3. All the measurements are characterized by a K-factor always greater than 7.5 dB, evidencing the presence of a main component of the received signal greater than the scattered components. The range of K-factors spans from 7.5 dB to 10.5 dB. The path loss is always smaller than 67 dB.
## 4 Results and Discussion  
### Storage temperatures  
In experimental crops, as constant storage temperature increased from 1\({}^{\circ}\)C to 20\({}^{\circ}\)C, the TSL of living lettuce decreased exponentially from an average of 18 days to 5 days (Fig. 1). This was similar to our previous observations of TSL of 20, 14, 10 and 5 days under constant 1\({}^{\circ}\)C, 5\({}^{\circ}\)C, 10\({}^{\circ}\)C and 20\({}^{\circ}\)C, respectively, in one study [8] and 19, 10 or 6 days under constant 1\({}^{\circ}\)C, 10\({}^{\circ}\)C, and 20\({}^{\circ}\)C, respectively, in another [6]. There are two implications seen with storage at constant temperatures. First, even under constant storage of 1\({}^{\circ}\)C or 5\({}^{\circ}\)C, the observed TSL varied substantially (Fig. 1). This may be attributed to pre-harvest factors such as the seasonal effects observed in this study. Secondly, in commercial practice, postharvest temperatures always vary throughout the distribution chain, and modeling on the basis of constant storage temperatures may not be suitable to commercial reality. This set of data indicated the need for a new approach to modeling shelf life of greenhouse lettuce which will include variable temperature scenarios. Among various approaches, neural networks were selected for this study. The use of neural networks has been suggested for modeling apple fruit quality in variable temperature scenarios [15].  
Figure 1: Total shelf life (y-axis) of living lettuce was affected by constant storage temperatures of 1\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\) or 20\({}^{\circ}\)C (x-axis). The exponential equation was based on Proc NLIN of SAS software package (SAS 9.1.3, 2002-2003 release; SAS Institute Inc., Cary, NC).
## 5 Testing and Development of Hybrid Connections (PRESSS Research Program 1989)
### Dynamic Properties  
The strain compatibility equations do not apply to hybrid connections due to combined nonlinear behavior of unbonded post-tensioning steel and bonded bars. Numerical analysis to predict connection behavior is a complex process. Hence, the design of precast frames with a hybrid connection involves the need for precast structural engineers with in-depth knowledge. According to code, all connections were tested to displacement-controlled cyclic loading. The actual earthquake scenario is of a dynamic nature. Hence, the dynamic behavior of the connection should be studied. This can be done either by numerical nonlinear dynamic analysis or by using shake tables. Several attempts have been made to study the dynamic behavior or hybrid system.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results
### Experimental Datasets
#### 3.1.1 Publicly Available Residential Dataset  
The ERGAN dataset, introduced as part of the ensemble recurrent GAN (ERGAN) framework [25], consists of high-resolution synthetic residential electricity load profiles designed to closely replicate real-world consumption behavior [41]. The dataset is developed from the original Pecan Street Database and consists of electrical hourly measurements of 417 households from 2017 [42]. In the original work of the authors, the dataset is processed using an ensemble of Bi-LSTM-based GANs, each trained on a distinct cluster of load patterns, identified using K-means clustering and the Davies-Bouldin score for optimal segmentation. Each cluster captures a specific category of household consumption behavior, such as high daytime usage, evening peaks, or flat weekend patterns, enabling more granular control over the diversity and realism of the generated data. For benchmarking purposes, one of the available clusters is used. This allows for focused evaluation on a well-defined and behaviorally consistent subset of residential profiles, making it suitable for assessing the performance of generative models under specific consumption conditions. The data include hourly power measurements and maintains realistic temporal dependencies, statistical properties, and intra-day variability, making it a strong proxy for real residential load data in both qualitative and quantitative evaluations. The dataset used in this study has approximately 110,000 samples, which classifies it as a case study on a large dataset.  
The heatmap of the ERGAN dataset is shown in Figure 5. One distinct pattern in the load profiles can be observed, as follows: an increase in the electrical consumption every day around the same period.
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design
### Constraints for the Control Design  
In the case of autonomous vehicles, speed limits must also be specified for safety reasons while crossing the intersection. The safe speed of autonomous vehicles is given by simplified vehicle dynamics: \(v_{l}=\sqrt{R_{l}g\mu}\) for left turn, \(v_{r}=\sqrt{R_{r}g\mu}\) for right turn and towards the straight line \(v_{s}=v_{lim}\) is calculated, where \(R_{l}\) and \(R_{r}\) are the radius of left and right turns at the intersection, \(g\) is the gravitational constant, \(\mu\) is the coefficient of adhesion between the tire and the road, which is assumed to be described by estimation methods, see e.g., [23; 24; 25], \(v_{lim}\) is the speed limit. For a typical intersection with four directions and a lane width of 5 m, taking into account a tire-road adhesion factor of \(\mu=0.8\), the safe right-turn speed is \(v_{r}\approx 16\) km/h, the safe left-turn speed is \(v_{l}\approx 28\) km/h. Thus, as vehicles turning left follow a trajectory defined with a much bigger cornering radius then right turning vehicles taking a sharp turn, their maximally allowed velocities in the intersection is almost the double of right turning vehicles. Note, that vehicles heading straight do not need to meet any dynamic constraint, hence the speed limit is posed as maximally allowed velocity for them. Maximum and minimum accelerations are also set for the autonomous vehicles to ensure passenger comfort, maintain traction and prevent wheel slip. Here, the thresholds \(a_{max}=5\) m/s\({}^{2}\) and \(a_{min}=-5\) m/s\({}^{2}\) are chosen based on [26].  
For collision-avoidance, some other constraints must also be built in the control design. The base for the collision-free passage of the autonomous vehicles at the intersection is to set the condition that only one vehicle can be located in the conflict zone of the intersection at the same time. Consequently, the oncoming vehicle (independently of its predefined trajectory) can enter the conflict zone of the intersection only at the exit time of the previous vehicle.