esa-sceva/satcom-chunk-collection huggingface dataset:
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 4 Discussion
### Anchor Results under Tensile Loading
#### 4.1.3 Age Effect (Comparison between Two Tested Ages)  
Concrete aging is a well-known phenomenon and has been intensively investigated in the last few decades [4; 5; 38; 39]. Nevertheless, many questions remain due to the complex multi-scale behavior of concrete and the sensitivity to composition parameters and casting and curing conditions. The previously observed differences in the mean relative cone capacities (if data of all ages are combined, the COV increases to 15%) indicate the existence of other effects that are not fully captured by the design equation. Since the increased COV occurred when all different ages were combined, naturally a potential age-effect is checked by means of a tensile aging prediction ratio, defined as ratio of relative concrete cone capacities for two different ages according to Equation (3):  
\[\alpha_{t,age}=\frac{\
u_{t}(2 nd\;age)}{\
u_{t}(1 st\;age)} \tag{3}\]  
where \(\
u_{t}(1 st\;age)\) and \(\
u_{t}(2 nd\;age)\) are calculated according to Equation (2). If aging and curing effects were fully captured by the design model in terms of compressive strength, \(\alpha_{t}\) would be equal to 1.0. However, it is well known that different concrete material properties age at different rates [18]. Furthermore, concretes with exactly the same mix design can significantly differ in final mechanical properties depending on the curing and storage conditions [5; 40]. Figure 7 shows the aging prediction ratios \(\alpha_{age}\) for all concretes. On average, predictions at the second age are about 9% higher with a scatter band of approximately 15%. This is in line with the findings previously reported by [PERSON] et al. [26]. They reported an increase in conservatism of about 20% for the later age.  
In almost all cases, predictions at later ages are more conservative. However, this is not always the case. For two low strength concretes tested by CSTB, the opposite trend is observed. Considering the substantial increase in scatter when tests at different ages are combined, it is likely that aging effects are present. These may depend on intrinsic (e.g., mix design, cement type, aggregate type) and extrinsic (ambient temperature and relative humidity) and require further investigation. The surprisingly large age differences for the CDL concretes which are not observed in the CSTB investigation may be explained by differences in cement type in combination with the ambient storage conditions. CDL used a cement type (CEM II 42.5 N) with ordinary early strength development in combination with relatively low curing temperatures of approximately 15-20 degrees C (288.15-292.15 K), while CSTB used cement characterized by high early strength.
# ER Morphology in the Pathogenesis of Hereditary
## 2 Genetics of HSP  
The genetic diversity of HSP is considerable, with more than 80 HSP genes defined by genetic linkage analysis. The relative frequency of each HSP gene mutation varies substantially by geographic region. The three HSP genes most commonly found mutated in patients are Spastin, Atlastin-1, and REEP1 (Receptor expression-enhancing protein 1), accounting for about 50% of total cases, while other HSP genes have been found mutated in only a few consanguineous families. Although many HSP genes have been identified, a large set of them remains unknown [5].  
Almost all types of inheritance have been documented for HSP, the most common being autosomal dominant (AD, 75-80% of affected individuals). Autosomal recessive (AR, 25-30%), X-linked and mitochondrial inheritance (1-2% collectively) have been reported with lower frequencies.  
SPG proteins have been implicated in a number of cellular pathways, such as ER shaping, axonal transport, membrane trafficking, protein folding and ER-stress, lipid metabolism, and axon myelination [6; 7]. Remarkably, around half of AD-HSP patients carry mutations affecting proteins that have a direct role in ER shaping (e.g., Atlastin-1, Reticulum-2, or REEP1) or act on cellular pathways that indirectly have an impact on ER tubules shape or positioning (e.g., cytoskeleton maintenance, ER-phag, ER stress) (Figure 1 and Table 1).  
Strikingly, if one considers only pure HSP (i.e., those characterized by clinical symptoms likely arising from degeneration of the corticospinal tract), the vast majority of cases  
Figure 1: HSP proteins involved in ER-related functions fundamental for neuronal maintenance.  
are due to mutations in genes involved in the maintenance of ER shape/function [6; 8]. This suggests a tight relationship between ER shape/function and corticospinal axon maintenance, although the reason why only subpopulations of neurons are affected by the potential morphological changes caused by mutation of ER-shaping proteins is still unknown.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## 6 Simulation Results and Discussion
### Optimum Configuration without DSM Strategy  
As observed from Case 1 simulation results, the optimal system configuration obtained consists of 1196 kW, 2054 kW and 7150 kWh (with power converter capacity of 960 kW) component size of the PV, WT and the BESS, respectively. The minimum total investment costs for case 1 optimal system configuration is US $15,247,276.77 consisting of individual components costs for PV, WT and BESS as summarized in Table 2. Due to the variability of the renewable energy sources generation, the mismatch gap between the generation and load demand profiles tends to increase. With the incorporation of the BESS, an optimal configuration of PV-WT-BESS manages to meet the system load demand reliably. Figure 4 depicts the aggregated renewable power generation, BESS power and the load demand profiles without the consideration of the DSM strategy. As it can be observed, whenever the total generation surpasses the load demand, the surplus power is used to charge the BESS. In the event of deficit power generation, the BESS discharges to compensate the mismatch between the generation and load demand. Figure 5 and Figure 6 show the charging and discharging dynamics and the state of charge of the BESS, respectively, for case 1 optimal configuration. However, in this case, the optimal capacity of the BESS is relatively large, and thus, the total system costs are relatively expensive. Thus, the inclusion of the DSM strategy is essential.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Case 1** & **Case 2** \\ \hline Demand side management & None & FDR scheduling \\ Optimal PV size, \(S_{pv}\) (kW) & 1196 & 1401 \\ Optimal WT size, \(S_{wt}\) (kW) & 2054 & 1866 \\ Optimal BESS size, \(S_{b}\) (kWh) & 7150 & 5200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal components size and system configuration comparison without and with demand-side management (DSM) strategy consideration.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Case 1** & **Case 2** \\ \hline Demand side management & None & FDR scheduling \\ PV net present cost (US \$) & 2,453,969.95 & 2,874,591.89 \\ WT Net present cost (US \$) & 6,492,053.33 & 5,897,843.97 \\ BESS Net present cost (US \$) & 6,301,300.00 & 4,582,729.8 \\ Total investment costs (US \$) & 15,247,276.77 & 13,355,165.67 \\ \% cost saving of TIC & - & 12.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Investment costs per component and total system cost comparison for cases without and with DSM strategy consideration.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 1 Introduction
[24] produced a map showing the global extent (\(\pm 60^{\circ}\) latitude) of intertidal zones. Similarly, [PERSON] et al. [25] extracted the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. In regions with persistent cloud cover like the Norwegian coast, cloud- and light-independent SAR imagery from S1 provide the most temporally consistent dataset to be used. At high latitudes, e.g., Northern Norway, the two S1 satellites provide nearly daily coverage with acquisition times around 5:00 a.m. or 5:00 p.m. (\(\pm 1\) h). In a preliminary study, [PERSON] and [PERSON] [26] showed that S1 SAR satellite data are perfectly suited to map and monitor the ITZ in Norway. [PERSON] et al. [27] used S1 combined with Landsat-8 observations in a similar study in southern China to extract the highest and lowest tides. On a local scale, the atmospheric exposure is directly linked to the topography of the ITZ via the tidal range. The \"water-line\" method [28] has been used extensively to map the topography in the ITZ [29; 30]. It is based on extracting the water line in single satellite images and associating them with observed tidal gauge height measurements. Due to speckle noise in single SAR scenes, the detection of the water line can, however, be challenging, and methods such as edge-preserving filter technics [31], edge detection based on the Touzi algorithm [32], and wavelet-based algorithms with subsequent segmentation [29] have been used to detect the water-land separation line. Instead of mapping the topography using single SAR images and tidal heights, this study presents a method to map the percentage of atmospheric exposure on the basis of a statistical analysis of long S1 time series that consistently sample the full tidal range. In this paper, we first present our study site in Northern Norway, the Sentinel-1 and tidal reference data, and the methods that were developed. The results are then described and validated using field data collected in the Tromso Municipality before the discussion and conclusion.
# Express Construction for GANs from Latent Representation to Data Distribution
## 4 Materials and Methods
### Method
#### 4.2.3 Constructing the Transportation Map from Random Noise to Latent Representation  
In practice, we only have the empirical data distribution defined as \(\hat{v}_{gt}\), which is pushing forward to be the empirical latent distribution \(\hat{\mu}_{gt}\). From the empirical latent distribution, we construct the transportation map from the random noise \(\zeta\) to the empirical latent representation \(\hat{\mu}_{gt}\). Thus, the GAN model is trained to compute the transport map from \(\zeta\) to \(\hat{\mu}_{gt}\) on the manifold.  
\[(\zeta,\mathcal{Z})\xrightarrow{g_{\xi}}(\mu,\Omega) \tag{7}\]  
Figure 1: The proposed workflow of Express Construction, all components are parameterized by neural networks. An auto-encoder is trained to map the data manifold into the latent manifold, as shown with the blue dashed box. Then, an adversarial game between a discriminator and a generator with a small scale is used to produce a generated latent representation from random noise, as shown in the green dashed box. Finally, a GAN model with a large scale is trained to construct a transportation map from the result of the previous step to the data distribution, as the black dashed box shows.  
where \(g_{\varepsilon}\) is parameterized by neural networks and \(\mu\) is a distribution whose support has a similar topology to that of \(\mu_{gt}\). We adopt the vanilla GAN model to do this based on the Wasserstein distance. In particular, the Wasserstein distance performs better than Kullback-Leibler (KL), Jensen-Shannon (JS), and total variation (TV) divergence. It correlates with convergence and sample quality, which can serve as a useful metric over probability distribution [25; 26]. Following [27], the Wasserstein distance in the dual mode is introduced to evaluate the generated quality, which can be formulated as:  
\[\text{Wasserstein}(\mathbb{P}_{r},\mathbb{P}_{\theta})=\sup_{\|f\|_{L}\leqslant 1 }\mathbb{E}_{x\sim\mathbb{P}_{r}}[f(x)]-\mathbb{E}_{x\sim\mathbb{P}_{\theta}} [f(x)] \tag{8}\]  
The generator \(G_{1}\) is used to generate a new latent representation while the discriminator \(D_{1}\) is used to discriminate if the distribution of the generated samples is the same as that sampled from the empirical latent distribution \(\hat{\mu}_{gt}\). Both \(G_{1}\) and \(D_{1}\) are built with a small-scale architecture to speed the training up. The training process of is formalized to be a min-max optimization problem, in which the critic solves the following cost function:  
\[\mathcal{L}_{D_{1}}=\mathbb{E}_{z\sim\zeta}[D_{1}(G_{1}(z))]-\mathbb{E}_{x\sim \hat{\mu}_{gt}}[D_{1}(x)] \tag{9}\]  
The training cost of this procedure is very small, less than ten minutes. Compared to the AE-OT and its variations, the proposed method is lightweight to produce new distribution close to the empirical latent representation.
## 1 Introduction
The 3D model produced with the proposed low-cost mobile mapping system is compared with the TLS survey, which is used as reference dataset. The survey with the Pegasus backpack was conducted on 21 December 2016 (Figure 1b). It is worth noticing that the GNSS signal is available only before entering the bastion; hence, most of the survey was done just by relying on Pegasus' inertial navigation system (INS) [22; 32; 33] and on simultaneous localization and mapping (SLAM) technology [34; 35]. The Pegasus backpack and our low-cost mobile mapping system are compared with the TLS survey taking into consideration both absolute and relative positioning accuracy. Absolute positioning accuracy aims at comparing two georeferenced models, i.e., models whose coordinates are related to a global coordinate reference system. Relative positioning accuracy aims at evaluating differences in the reconstructed 3D models, in which the georeferencing component has been removed. As such, relative positioning involves point coordinates related to a local reference frame. In the latter case, error is due to differences in the shape of the reconstructed models, whereas in the first case, georeferencing error can have a larger impact than shape differences between the models. These two cases will be referred to as absolute and relative error. This paper extends a previous work presented in [36] providing a much more detailed analysis of both the Pegasus backpack and photogrammetric reconstruction errors. Furthermore, this work considers the use of a larger UWB network, which provided a better metric reconstruction (e.g., a better estimate of the photogrammetric reconstruction scale factor). The rest of the paper is organized as follows: Section 2 describes the TLS survey. Section 3 provides details on the Pegasus survey and investigates its performance on the considered case study with respect to the TLS 3D model. Section 4 describes the low-cost mobile mapping system proposed in this paper and validates its results. Finally, the discussion and conclusions are drawn in Sections 5 and 6, respectively.
## 6 Conclusions  
This paper presented DigiHortiRobot, a digital twin-based architecture that advances the state of the art by enabling closed-loop coordination between virtual models and robotic actions in hydroponic greenhouse horticulture. Unlike many prior efforts limited to simulation or monitoring, our system closes the loop between perception and action through predictive planning and real-world task execution. This methodological integration, bridging AI, robotics, and IoT, is central to advancing automation in structured agricultural domains.  
Beyond its initial deployment on aubergine and tomato crops in hydroponic greenhouses, DigiHortiRobot establishes foundational principles applicable to a broader range of agri-tech scenarios. These include automated nursery management, vertical farming control, and adaptive resource allocation in smart agriculture infrastructures. The system's modularity and compliance with semantic interoperability standards facilitate its adaptation across heterogeneous production environments.  
Future research will focus on multi-robot coordination, dynamic policy refinement through reinforcement learning, and edge-cloud architectures with adaptive synchronization strategies. In particular, we aim to pursue hypothesis-driven evaluations of how semantic feedback loops influence task efficiency, fault tolerance, and predictive reliability under biological uncertainty. These studies will also explore broader questions of economic viability and integration into farm-level decision support systems. Furthermore, we recognize the importance of promoting reproducibility across digital agriculture initiatives. As such, we intend to contribute to the development of standardized benchmarks, simulation testbeds, and structured evaluation protocols to support scientific transparency and comparability.  
Conceptualization, R.F.; methodology, R.F. and E.N.; validation, D.R.-N. and L.E.; formal analysis, E.N. and L.E.; investigation, D.R.-N., A.A.R.-G. and L.E.; data curation, D.R.-N. and A.A.R.-G.; writing--original draft preparation, R.F.; writing--review and editing, R.F. and E.N.; visualization, D.R.-N. and A.A.R.-G.; supervision, R.F.; principal investigator, R.F. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results was supported in part by the following: (i) the Grant TED2021-132710B-I00 funded by MICIU/AEI/10.13039/501100011033 and by the \"European Union NextGenerationEU/PRTR\"; (ii) the Grant PID2023-151493 OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU; (iii) Programas de Actividades de I+D con referencia TEC-2024/TEC-62 y acornimo iRoboCity2030-CM, concedido por la Comunidad de Madrid a traves de la Direccion General de Investigacion e Innovacion Tecnologica a traves de la Orden 5696/2024; and (iv) CSIC under Grant 202350E072, Proyecto Intramural IAMC-ROBI-II (Inteligencia Artificial y Mecantica Cognitiva para la Manipulacion Robotica Bimanual--2\({}^{\circ}\) Fase).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# On a Framework for Federated Cluster Analysis
## 2 Materials and Methods
### The Federated Fuzzy Clustering Framework
#### 2.2.1 Federated Fuzzy \(c\)-Means with \(k\)-Means Averaging
At the same time, sharing only the local cluster centers (as opposed to local centers and assignment vectors such as in [19; 20; 21]) increases the privacy of the data. We know that both the fuzzy \(c\)-means and \(k\)-means algorithm converge (even though possibly to local optima) when applied separately. Convergence means that the centers and assignment vectors are guaranteed to stabilize after finitely many iterations. Our federated algorithm converged if the global cluster centers stabilized. For that to happen, the local cluster centers must have stabilized. The local cluster center, in turn, stabilizes if the global centers do not change much between two iterations. We want to provide intuition on why we observe such a behavior in our experiments. Each clients starts with calculating local cluster centers in the first iteration and reports them back to the central server. The central server essentially groups together all centers that are close to each other, calculates the average of close centers, and reports those averages back to the clients as their new centers. Due to this averaging, it is likely that for any previous local center there is a new nearby global center (which is a function of that previous center). The client updates the global center with its local data. Since it is close to a previously reported center, the new local centers do not deviate much from the global center, and the update is small. If a new center has low cluster membership, the update is naturally small. With small updates, however, we know the \(k\)-means algorithm to converge. Usually, the updates can be quite big after the first global round, but are small thereafter, which is consistent with the behavior of \(k\)-means one-shot learning with \(k\)-means averaging [22]. Even though this is not a formal convergence proof, we hope to provide insights into how the algorithm is expected to behave.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Neural Network Architecture and Model Evaluation  
We started by preprocessing the data, applying a standard scaler to normalize the features, and guaranteeing that all features are on a similar scale to enhance the model's performance. We then implemented a Multi-Layer Perceptron (MLP) neural network due to its simpler architecture, which requires less computational power compared with other machine learning algorithms, making it ideal for high-speed network environments.  
As shown in Figure 8, our MLP model has an input layer, followed by three hidden layers. The input layer has two neurons (for time and amplitude/reflection) (indicated by blue), while the hidden layers have 8, 16, and 8 neurons, (indicated by green, red, and green), respectively. All layers use the ReLU activation function, except the output layer (indicated by blue), which has a single neuron and uses the sigmoid activation function for binary classification (e.g., fault or no-fault). In total, this model has 313 trainable parameters.  
To assess the model's performance and robustness, we utilized stratified K-fold cross-validation, where each fold maintains the same class distribution as the original dataset. The training was conducted over 40 epochs with a batch size of 256, using 20% of the  
Figure 8: The proposed MLP model for fiber fault diagnosis and localization.  
Figure 7: Simulation-based evaluation setup for generating faulty branch data using Optisystem.  
training data as a validation set to monitor for overfitting. Performance metrics such as accuracy, precision, recall, and F1-score were calculated for each fold. After completing all folds, we computed the average of these metrics to summarize the model's overall performance on unseen data. The model achieved an average accuracy of 0.8149, precision of 0.8433, average recall of 0.7818, and average F1-score of 0.8100. These results indicate that the model performs robustly in distinguishing between \"Normal\" and \"Fault\" classes. The high average precision suggests effective minimization of false positives, meaning the model reliably identifies true positives when making positive predictions. However, the slightly lower recall indicates that some fault instances may be missed, resulting in false negatives. The balanced average F1-score reflects a good trade-off between precision and recall, making the model suitable for applications where both types of errors are of concern.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
### Organization of This Article  
The remainder of this paper is organized as follows: In the next section, we discuss FANETs, their components, their dynamic time-varying topology, and suitable mobility models for FANETs. In Section 3, we briefly review the QL algorithm and its relationship with routing in FANETs. In Section 4, QL-based position-aware routing protocols are extensively reviewed, and their respective advantages and limitations are outlined. In Section 5, existing QL-based position-aware routing protocols are qualitatively compared. In Section 6, the open issues and research challenges associated with the respective potential research directions are discussed. Finally, the paper is concluded in Section 7.