esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Operation Cost  
A final target of the control is the minimization of the Operation Cost (\(OC\)). In Figure 13, we observe the dry weather condition in the initial phase (second column of Table 2). The RL agent is shown to be the worst \(OC\) for the most of tops, but also the best for the most of the bottoms (on many occasions, in combination with the A configuration). The excellent behavior of the RL agent on weekends, when it can save a lot of \(OC\), is also relevant. If we look at Figure 11, dry weather, the RL agent minimally increases the \(S_{NH}\) without incurring high fines in order to save energy.  
Figure 12: \(S_{NH}\) concentration for the **final** phase.  
In Figure 13, dry weather, the control configurations are ordered from better to worse attending to the \(OC\) as follows: A, B1 and the set of B2, C and D. This order is the same if configurations are sorted from less to greater attending \(S_{NH}\) concentration (see Figure 11). The RL agent has not been included into this list because its behavior is different. RL agent behavior changes depending on tops and bottoms, weekday or weekend, etc.  
Figure 13, rainy weather, shows better RL behavior in \(OC\) than we first expected just looking at the \(S_{NH}\) concentration (Figure 11). Although this fact is the most extreme for the rainy weather condition, it also happens for the rest of them. Thus, in the RL configuration, \(OC\) tops are higher than in the rest of the configurations and bottoms are always lower. What is more, the RL agent excels on weekends for every weather condition for the final phase (see Figure 14).  
Figure 13: Operation Cost \(OC\) for the **initial** phase.
## 5 Open Challenges and Research Perspectives
### Line-Speed Executors  
Deploying line-speed executors, i.e., systems capable of performing real-time analysis and decision making at the speed of network traffic, is still a significant technical challenge and only a few works so far have practically evaluated their performance [70].  
A preliminary architectural description of an NDN-enabled software router integrating in-network computing capabilities is presented in [71]. Services are abstracted as distinct virtual machines (VMs) and managed by a local monitor that oversees resource utilization and service deployment. The proposed architecture includes mechanisms to dynamically optimize VM placement based on service demands and resource availability. However, the paper provides only simulation results as a proof-of-concept, using a simple line topology, with evaluations focused on traffic processing efficiency and service invocation delay. Practical implementation is left for future work.  
Currently, programmable switches and smart NICs are designed primarily for high-speed packet forwarding, not for complex computations: they have limited memory, computational power, and processing stages. For example, a high-performance switch like Intel Tofino offers 12-20 stages in its pipeline [72], and these must be shared between essential network functions (e.g., routing, ACLs) and in-network computations. Also, depending on their position, network devices must handle from hundreds to millions of packets per second, making problematic the parallel execution of different services. Although technologies like Intel Tofino or Smart NICs are making line-speed executors feasible for specific tasks, scaling these solutions to support diverse, complex workloads remains a challenge requiring careful optimization and continued innovation.  
The integration of ICN service names in the forwarding fabric introduces additional considerations for line-speed execution, due to the semantic information about the service or data being requested. This content-awareness can facilitate policy-driven and context-aware service execution, enabling SmartNICs to act as efficient intermediaries that prioritize traffic based on service-level requirements. However, the processing of ICN service names, which often requires name matching, hierarchical resolution, or context-specific decisions, can strain the limited processing resources of SmartNICs and switches. Advancements in both hardware and software technologies are required to fully exploit the potential of content-aware and policy-driven service execution.
# Taurodontism--Prevalence, Extent, and Clinical Challenge in Ashkelon, Israel--A Retrospective Study
###### Abstract  
Taurodontism, a known morpho-anatomic variation in the shape of teeth, has already attracted substantial attention. The aim of this study is to contribute to this by discussing its direct impact on the dental practitioner via an evaluation of the prevalence of taurodontism in molars in Ashkelon, Israel. This retrospective study analyzed panoramic radiographs of 624 patients (330 males, 294 females), aged of 9-29 years--2849; first and second permanent molars were screened. Objective measurements and variables were used and analyzed using statistical SPSS version 27 (IBM, Chicago, IL, USA). Taurodontism was observed in 33.6% of the patients and was more prevalent among females (53% vs. 47%) and in maxillary molars compared to mandibular molars (57% vs. 43%). The overall prevalence of hypotaurodontism, mesotaurodontism, and hypertaurodontism was 10.8%, 0.5%, and 0.2%, respectively. No statistical difference between right and left sides was discovered, however, taurodont teeth in the upper left side exhibited the highest frequency, while the lower left side had the least. The highest occurrence of hypotaurodontism was on the upper right second molar followed by upper left and second molars. The clinical challenge posed in endodontic, orthodontic, and restorative dentistry is discussed, and suitable alternative approaches are proposed for dental practitioners.  
tarodontism; permanent molars; pulp chamber; dental morphology; prevalence; treatment Article
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 4 Experimental Results and Discussion
### Ablation Study  
In this section, an ablation study was performed to evaluate the effectiveness of SubvectorS in the geolocation task. We named the model with SubvectorS removed and the full model separately as follows. SubvectorS_Geo is the complete model framework. SubvectorS_Geo_A is the model framework for removing SubvectorS. The SubvectorS_Geo_A model replaces SubvectorS with the cosine distance [37]. Furthermore, to increase the effectiveness of SubvectorS in comparison with other similarity measure evaluation methods, the SubvectorS_Geo_B model and the SubvectorS_Geo_C model were added. SubvectorS_Geo_B is a model framework that uses the edit distance on the real sequence (EDR) [38] instead of SubvectorS. SubvectorS_Geo_C is a model framework using the edit distance on real sequence (LCSS) [39] instead of SubvectorS.  
As shown in Figure 6, SubvectorS presents a stronger effect on the final IP geolocation accuracy than other similarity metric algorithms. Among them, the cosine similarity has the least impact on IP geolocation accuracy, which is mainly due to the delay vector dimension's nonuniformity of the route hot coding in this paper. Although the EDR and LCSS are less affected by the nonuniformity of the delay vector dimension, the sensitivity of the EDR to noise points leads to a less valid similarity measure than SubvectorS. The LCSS is more consistent with the delay vector of our model, but its minimum distance threshold e is more difficult to define. There is a possibility that the LCSS has the error of returning dissimilar paths as similar results.
## 3 Measurement Results  
### The UWOC System in Different Water Conditions  
In this experiment, a 450 nm blue-light laser beam was transmitted in different mediums, such as free space, tap water, and seawater. The different refractive indexes caused the scintillation of the focused laser beam, which caused signal fading and beam wandering in the transmission channel. This experiment was measured at a 1.5 m distance. The distance measurement of 1.5 m was used to compare measurements of the UWOC system in tap water, seawater, and free space with no additional loss from the mirror. The threshold current was around 15 mA using OSRAM PL-450B. As shown in Figure 3, when the blue LD had a typical drive current of 100 mA, the optical power of tap water decreased by about 21.6% compared to the free space medium. In 45.56 g/L of saline water, the laser optical power was reduced by approximately 58.62% compared to the tap water. The optical power decreased by about 80.22% in total because of the different mediums of seawater. Some of the factors involved in attenuating laser optical power were the transmission distance, the wavelength used in the UWOC system, and the different refractive indexes in the air and water.  
In Figure 4a, we measured the bit error rate (BER) curve of the tap water and seawater at a distance of 1.5 m. Know that, the value of hyphen (-) in the x axis defined as a minus sign from the Sigmaplot software. The explanation is the same for some of the following figures. The back-to-back measurement was measured directly from the LD into the APD without the medium of water. This experiment aimed to find the optical power difference between the tap and seawater to reach the maximum BER. Scattering and absorption in the seawater were significant reasons for the signal attenuation. The forward error correction (FEC) value for underwater communication was around 10\({}^{-3}\). In the FEC limit, the BER value recorded for the back-to-back system was 1.022 \(\times\) 10\({}^{-3}\) at 21.83 dBm (R1Q4). The BER value of tap water at a \(-\)16.96 dBm receiving power was 2.97 \(\times\) 10\({}^{-3}\). The BER value of saline water was 3.72 \(\times\) 10\({}^{-3}\) at a \(-\)9.18 dBm receiving power. The difference in the receiving power between tap water and sea water at 10\({}^{-3}\) was 7.78 dB (R1Q1). The BER measurement of the UWOC system in the seawater needed more optical power than in tap water. However, the BER curve in seawater had a nearly vertical graph, indicating  
Figure 3: Laser attenuation in a different medium of transmission.  
that BER could drift to the worst by only minor changes in optical power. Figure 4b shows the BER comparison between the Optisystem 13 simulation and experimental saline water-based UWOC system measurement. The difference in the receiving power between the experimental and simulation study was 5.65 dB at BER of 3 \(\times\) 10\({}^{-6}\) (R1Q1).
## 1 Introduction
### Establishment of Damage Model Considering Microstructure Weakening
Therefore, in the process of building the cable model, the wire model is divided into two parts. One part is the undamaged area, and this part of the material adopts the linear elastic constitutive relation. The other part is the damaged area, and this part of the material adopts the constitutive relation with damage variables. The local damage evolution process of steel wire is reflected through the change of damage parameters in the process of analysis. In the no-damage zone, the wire material adopts the linear elastic constitutive relation, and the uniaxial constitutive equation can be expressed as Formula (4) [43]:  
\[\sigma=E\varepsilon \tag{4}\]  
where \(E\) is the elastic modulus of the steel wire. Generally speaking, steel wire fracture caused by corrosion fatigue belongs to brittle fracture [30]. Therefore, the corrosion fatigue damage of steel wire can be regarded as elastic damage. According to the continuum damage mechanics theory, the weakening of material microstructure can be reflected by introducing damage variables into the elastic coefficient of the damage zone element. That is, the uniaxial elastic damage constitutive equation of the damage zone can be expressed as Formula (5) [44]:  
\[\sigma=(1-D_{i})E\varepsilon \tag{5}\]  
where \(D_{i}\) is damage variable of the wire \(i\) in the cord, \(\sigma\) is the stress value, \(\varepsilon\) is the strain value. The corrosion fatigue damage evolution model of steel wire was used to describe the fatigue damage evolution behavior of steel wire in cable body. The damage evolution rate of the steel wire in the cable was expressed as Formula (6) [45]:  
\[\frac{dD}{dN}= \begin{cases}\frac{\phi}{3R_{0}^{2}}f^{-\frac{1}{3}}\cdot\left( \frac{3 ML}{2\pi\phi^{2}mF\rho}\right)^{\frac{2}{3}}N^{\frac{1}{3}}&t\leq t_{0} \\ \left(1-\frac{S_{pit}}{S}\right)\cdot\left(\frac{3\sqrt{3}Kd_{0}^{2}}{2f}+\frac {2d_{0}PD}{f}-\frac{2D}{N}\right)&t>t_{0}\end{cases}  \tag{6}\]where \(D\) is the damage variable, \(N\) is the cycle loading times, \(\phi\) is the corrosion constant, \(m\) is metal ion charge, \(\rho\) is the density of the metal, \(t\) is time, \(R_{w}\) is the radius of the strand, \(S\) is the strand cross-sectional area, \(M\) is the relative atomic mass, \(d_{0}\) is the grain size, \(l_{c}\) is the intensity of pitting pit corrosion current, \(f\) is the loading frequency.
## 3 Signal Processing
### Unsupervised Classification  
In this section, the unsupervised classification method is presented. We will focus on a simplified separation in a cross-range direction by reducing the scenario to a single-target environment with only two antennas (a1 and a2) and two filters (8.5 GHz and 11.5 GHz). The simplification thus leads to a distinction between the following cases:  
1. Location of the target in the main beam of antenna a1 (notch at 8.5 GHz)
2. Location of the target in the main beam of antenna a2 (notch at 11.5 GHz)
3. Location of the target in the overlap of antenna a1 and a2 (notches at 8.5 GHz and 11.5 GHz).  
In order to analyze the corresponding features of interest, the feature space is spanned over the frequency components in the region of the resonant frequencies (see Figure 4a-c).  
Figure 3: Signal flow diagram of the feature extraction and classification [20].  
The absolute values of the power levels of the features A and B are then converted into a probability of occurrence of the respective frequency points. Thus, a notch in the feature space is determined by the distribution of its cumulative quantity. As can be seen in Figure 4d-f, the distribution of the data in the feature space is a direct measure of the  
Figure 4: (**a–c**) Extracted time sequence and (**d–f**) associated feature space  
probability of occurrence of a notch. In a \(n\)-dimensional feature space the respective features can thus be determined as a random variable \(\vec{x_{n}}\) by the random vector \(\mathbf{X}\).  
\[\mathbf{X}=\left(\begin{array}{c}\vec{x}_{1}\\ \vec{x}_{2}\\ \vdots\\ \vec{x}_{n}\end{array}\right) \tag{12}\]  
Since the distribution of \(\mathbf{X}\) is given by the covariance matrix, the notches can be analyzed via  
\[\mathbf{\Sigma_{X}}=\left(\begin{array}{cccc}\sigma_{1}^{2}&\sigma_{12}&\cdots& \sigma_{1n}\\ \sigma_{21}&\ddots&&\vdots\\ \vdots&&\ddots&\vdots\\ \sigma_{n1}&\cdots&\cdots&\sigma_{n}^{2}\end{array}\right). \tag{13}\]  
Here \(\sigma^{2}\) indicates the variance along the \(n\)-dimensional feature space. To investigate the correlation of the features \(\vec{x_{n}}\), the eigenvalues of the covariance matrix \(\mathbf{\Sigma_{X}}\) have to be taken into account. The eigenvalues \(\mathbf{\lambda}\) are calculated by  
\[\det(\mathbf{\Sigma_{X}}-\mathbf{I}\mathbf{\lambda})=0, \tag{14}\]  
This leads to a characteristic polynomial, whose zeros thus lead to the desired eigenvalues \(\mathbf{\lambda}\). A low eigenvalue thus indicates the occurrence of a notch. If a uniform distribution of \(\mathbf{\Sigma_{X}}\) between two features occurs, both features contain a notch [21; 22].
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
###### Abstract  
Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of [PERSON] and [PERSON] with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively.  
albedo; solar radiation; reflectance; satellites; remote sensing +
Footnote †: journal: Applied sciences
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Conventional Short-Term Frequency Support Method of a DFIG
#### 4.1.1 Power Reference in the Deceleration Period of Short-Term Frequency Support Method  
In the deceleration period, \(P_{n\!f}\) for STFS method, \(P_{STFS}\), is determined as:  
\[P_{\text{STFS}}=P_{MPT}+\Delta P_{\text{con}} \tag{12}\]  
where \(\Delta P_{\text{con}}\) is the incremental power of the conventional method and is proportional to the rotor speed, as explained in [12].  
To heighten the system frequency nadir, \(\Delta P_{\text{con}}\) is added to \(P_{MPT}\), as shown by the locus from Point A to Point B (see in Figure 3). Afterward, \(P_{STFS}\) reduces with the rotor speed, as shown by the locus from Point B toward Point C; thereafter, \(P_{STFS}\) meets the \(P_{m}\) curve, and then \(\omega_{\text{r}}\) converges at Point C so that this method can prevent the stall of the DFIG. Nevertheless, during this period, the difference between \(P_{m}\) and \(P_{e}\) becomes small as the rotor speed decreases, especially when the rotor speed is closed to \(\omega_{c}\), which is the rotor speed at Point C in Figure 3. As a result, this method delays the rotor speed convergence, thereby releasing more rotating kinetic energy to the grid after supporting the system frequency.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 3 Adaptive UAV-BS Detector Based on the HMP Rule
### Message Passing Scheduling  
The message passing scheme was summarized in the proposed GAMP-BG-MC algorithm, as shown in Algorithm 2. Specifically, the _Part I_ messages are first calculated in parallel at each time slot, as shown in Section 3.1.1, which contains Algorithm 1 and the message going into the _Part II_. Then, the MC messages can be calculated in sequence in lines 4-7. In _Part III_, the parameters of \(p_{k}^{10},p_{k}^{01}\) can be updated in lines 8-10. After that, the accuracy of the outgoing messages in _Part IV_ can be improved by updating the MC part messages, again based on the updated hyperparameters \(p_{k}^{10},p_{k}^{01}\). In the final step, the parameters of variance and the calculated output estimated data \(\hat{b}_{k,j},\forall k,j\) are updated with lines 12-15 in _Part IV_.  
```
Input: Received signal \(Y\), channel matrix \(H\), iteration number \(T\). Output: Decoding results \(\hat{b}_{kj,j},\forall k,j\). Initialize: Prior parameters: \(\epsilon_{kj,j},\eta_{kj,j}\), \(\forall k,j\in\mathbb{R}^{1}\), \(\epsilon_{kj,j},\epsilon_{kj,j},\epsilon_{kj,j},\epsilon_{kj,j},\forall k\). \(\epsilon_{kj,j},\forall k,j\in\mathbb{R}^{1}\)
1 whileconvergence = FALSE OR iteration number \(<T\)do
2 If Part I-GAMP part messages
3 Run Algorithm 2 and output \(v_{\oplus,j},\hat{\mu}_{kj,j},\forall k,j\)
4\(\forall k,j\): update \(\overrightarrow{\eta}_{kj,j}\) by (13)
5 ifPart II-MC part messages
6\(\forall k\): update \(\xi_{kj,j}^{1}\) by (16)
7\(\forall k,j\in[2:J]\): update \(\xi_{kj,j}^{1}\) by (16)
8\(\forall k,j\): update \(\xi_{kj,j}^{1}\) and \(\xi_{kj,j}^{1}\) by (20)
9\(\forall k,j\in[J-1:1]\): update \(\xi_{kj,j}^{1}\) and \(\xi_{kj,j}^{1}\) by (20)
10 IfPartII-Transition probability update part
11\(\forall k,j\): update \(B_{kj,j},B_{kj,j}^{01},B_{kj,j}^{01},B_{kj,j}^{01},\rho_{kj}\) by (40) in [15]
12\(\forall k\): update \(\hat{\epsilon}_{kj,j}\) and \(\xi_{kj,j}\) by (26)
13 perform lines 4-7 again
14//Part IV-Variance update part
15\(\forall k,j\): update \(\overrightarrow{\eta}_{kj,j}\) by (28)
16\(\forall k,j\): update \(B_{kj,j}\phi_{k,j}\) and \(\hat{\mu}_{kj,j},\theta_{kj}\) by (30)
17\(\forall k,j\): update \(\hat{\mu}_{kj,j}\) by (32)
18\(\forall k,j\): update \(B_{kj,j}\) and \(\hat{\mu}_{kj,j},\theta_{kj}\) by (30) again based on new updated line 13
19\(\forall k,j\): update \(B_{kj,j}\) by (33)
```  
**Algorithm 2**GAMP-BG-MC algorithm
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
###### Abstract  
The hybrid laser inertial navigation system (INS) is increasingly vital for high precision under high-dynamic, long-duration conditions, especially in complex aircraft environments. Key components like the base, platform, and inner/outer frames significantly impact system accuracy and stability through thseir static and dynamic characteristics. This study focuses on minimizing deviations in the INS body coordinate system caused by elastic deformation under high overload by developing a mechanical simulation model of a rotational modulation structure and a structural model of the outer frame assembly. A reliability analysis model is established, considering both functional and structural strength failures. To address uncertainties from manufacturing, technical conditions, material selection, and assembly errors, a global sensitivity analysis based on Bayesian inference evaluates parameter contributions to functional failure probability, using a sample size of N1 = \(5\times 10^{5}\). Additionally, uncertainty analysis via Sobol sequence sampling (N2 = 10,000) examines the impact of mean design parameter variations on failure probability for ZL107 and SiCp/Al aluminum matrix composite frames. Experimental verification concludes the study. The results indicate that the SiCp/Al composite material demonstrates superior mechanical performance compared to traditional materials such as the ZL107 aluminum alloy. The uncertainties in the inner frame thickness, inner frame material strength, and outer frame thickness have the most significant impact on the probability of functional failure in the hybrid INS, with sensitivity indices of \(\delta_{6}^{P[F]}\) = 0.01657, \(\delta_{2}^{P[F]}\) = 0.00873, and \(\delta_{4}^{P[F]}\) = 0.00818, respectively. The mechanical properties of the outer frame structure made from SiCp/Al are significantly enhanced, with failure probabilities across three failure modes markedly lower than those of the ZL107 frame, indicating high reliability. In an impact test conducted on the product, the laser gyroscope worked normally, the hybrid laser system function was normal, and the platform angular velocity change corresponding to each impact direction was less than 12 \({}^{\prime\prime}\)/s. The maximum angle change of the inner and outer frames was 0.107\({}^{\circ}\), indicating that the system structure can withstand large overloads and multiple levels of mechanical environments and has good environmental adaptability and reliability. This analytical approach provides a valuable method for reliability evaluation and design of new hybrid INS structures. More importantly, it provides insights into the influence of design parameter uncertainties on navigation accuracy, offering critical support for the advancement of inertial technology.  
keywords:hybrid laser inertial navigation; high maneuverability and overload; multiple failure modes; dual random uncertainty; reliability analysis; test verification