esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Continuous Measurements  
For the experiment the voltage, temperature and inner resistance of each 12 V mono-block were measured with a LEM Sentinel 2 sensor. Each monitoring device was connected via clamps to the poles of the battery. These clamps offered a separate connection for the power and sense cables. Such a 4-point measurement reduces the interference of the current signal within the voltage measurement. The manufacturer states the following measurement ranges and accuracies [21]:  
voltage: +4.8 to +15.0 V (\(\pm\)0.5%) temperature: \(-\)10 to +70 \({}^{\circ}\)C (\(\pm\)2 C) impedance: 0.05 to 250 m\(\Omega\) (\(\pm\)2%)  
The LEM Sentinel 2 measurements were steered through an own LabView program, which utilizes the communication protocol given by the manufacturer. Voltages are measured every 10 s, temperatures every 100 s and the impedance of one mono-block is measured every 14 h. Additionally, voltage and temperature were logged before and after each impedance measurement event.  
For the impedance measurement the LEM Sentinel 2 generates a half wave signal with a discharge pulse of 1.8 A, which is kept constant for 6.86 ms and repeated for 4.2 s at a frequency of 69.58 Hz. This behavior was measured via a Hameg HMO724 oscilloscope and also documented by [4]. Via a high-pass filter the measured voltage response is aggregated to a root mean square (RMS) value. With its constant current and the voltage response the modulus of the impedance is registered [4; 6]. The LEM Sentinel 2 is designed to measure the impedance during float charge conditions. Nevertheless, for the comparison with the check-up measurements the influence of the superimposed charge current was evaluated and compared to a measurement under OCV conditions. The measurement was performed at 24 \({}^{\circ}\)C. The deviation between a measurement with current flow, which was here smaller than 12.5 mA, and the subsequently conducted measurement without current and after voltage relaxation is shown in Table 2. The measurable deviation is within the range of accuracy. Thus, it can be concluded that an influence of the small superimposed DC charge current will not be detectable in the Sentinel measurements over time. The current measurement for each string was performed with a shunt 1 m\(\Omega\) by [PERSON] (50 mV per 50 A with an accuracy of 0.5%). The voltage signal of the shunt was logged by a Gantner Q.blox A104 module with a registration rate of 10 Hz.  
Figure 1: Deviation between the weekly maximum and minimum average mono-block voltage \(\overline{V}_{i}\) for both strings over time. The measurement period was divided into two phases as soon as string 2 reaches a delta of 0.5 V.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 3 Methods
### Test Stimuli  
The acoustic pointer consisted of a one-second pink noise burst encoded into 5 th-order Ambisonics, pertinent to the spatial resolution of the Ambisonic reproduction systems used in the experiment. The low-bitrate compressed scenes presented during the simple scene evaluation consisted of one-second pink noise bursts placed in six static target directions: above, behind, and on the sides of the listener. The coordinates of investigated directions are listed in Table 1. These directions were chosen to match the context of the experiment--360-degree video streaming, where spatial audio is often used to direct user's attention in the virtual space.  
The complex-scene stimuli consisted of the reference pink noise bursts and a modified Ambisonic soundscape [40] recorded with a 4 th-order Ambisonic microphone ([[https://mhacoustics.com/products](https://mhacoustics.com/products)]([https://mhacoustics.com/products](https://mhacoustics.com/products))). The HOA microphone signal was used to provide different input signal conditions for the Opus codec in comparison to the simple scene material and to mimic the typical audio content  
Figure 3: Physical controller designed for the auditory localization test.  
of 360 videos. The used excerpt of the forest soundscape did not include any prominent spatially defined sounds which could influence the perception of the target sound direction. The level of the soundscape was empirically adjusted to prevent the participants from being significantly distracted from the task. Because the Ambisonic soundscape spatial resolution was limited to the 4 th order, additional 5 th-order background noise was added. This 5 th-order noise signal was synthesized using 36 uniformly distributed virtual loudspeakers fed with decorrelated Brownian noise samples. Brownian noise was chosen because its power spectrum differs from the pink noise used as the virtual sound sources in this experiment. The target sound sources consisting of pink noise bursts (4 times repeated sequence of 2 s burst with 250 ms rise and fall times followed by 500 ms of silence) were pinned at the specified directions shown in Table 1.  
The required 1 st and 3 rd-order test stimuli were extracted from the 5 th-order simple and complex scenes and subsequently compressed using Opus encoder at different bitrates and with Channel Mapping Family 3 enabled. The resulting test stimuli set consisted of 60 scenes for both simple and complex-scene tests (a multiplication of six target sound source directions and ten system conditions). The investigated system conditions are shown in Table 2. The uncompressed 5 th-order Ambisonic scenes were used as the reference condition.
# Life Cycle Cost Analysis and Deterioration Patterns of Limestone Paving
## 3 Aims of the Current Research  
The primary objective of this research is to develop a structured analytical-empirical methodology for assessing the durability, deterioration patterns, and life cycle costs of natural stone pedestrian pavements under both standard and intensive service conditions. This study aims to provide tools for evaluating the impact of inadequate construction practices on the service life and life cycle costs of stone pedestrian pavements.  
The research methodology encompasses six primary phases: (1) a state-of-the-art review, (2) a diagnostic field survey, (3) core laboratory tests, (4) the diagnosis of defects, (5) the characterization of deterioration patterns, and (6) life cycle cost assessments. Figure 1 depicts a graphical abstract of the methodology. The methodology proposes core criteria for the field survey, five laboratory tests, the development of deterioration patterns of stone pavement under failure conditions, and life cycle costs models derived from the deterioration patterns.  
The novelty of this research lies in developing an integrated methodology that uniquely combines the following:  
* Diagnostic field surveys;
* Core laboratory tests;
* The characterization of deterioration patterns;
* Life cycle cost (LCC) analysis.  
This approach bridges material science, engineering practice, and economic assessment for natural stone pavements. It comprehensively evaluates how defective construction methods impact durability, deterioration, service life, and costs. By providing a reproducible framework that translates material properties and field observations into practical economic implications, this research advances sustainable infrastructure development, particularly for pavements under intensive service regimes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Typical Values for Limestone [1,48]** & **Requirements for Building Stones [50,51]** \\ \hline Water absorption & n/a & \textless{}0.5 \\ coefficient [kg/(m\({}^{2}\cdot\)hour\({}^{1/2}\))] & & \textless{}0.5 \\ \hline Compressive strength [MPa] & 20–100 & Sample \textgreater{} 56; Average \textgreater{} 60 \\ \hline Flexural strength [MPa] & 5–25 & \textgreater{}5 \\ \hline \multicolumn{3}{c}{Capon (Wide Wheel) abrasion resistance:} \\ \multicolumn{3}{c}{Abrasion characteristic [mm]} & For intensive environment & \multicolumn{1}{c}{Amsler abrasion wear:} \\ \multicolumn{3}{c}{\(<\) 23} & For public places—Sample \textless{} 2.3; Average \textless{} 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Graphical abstract.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 4 Compressed Imaging Using Random Phase Modulation  
The combination of random phase modulation and compressed sensing has been proposed and applied on many occasions, such as in various types of single-exposure imaging. Using them in imaging can not only overcome the problem of low resolution but also restore the possibility for the reconstruction of complex data or moving targets.  
In this section, we briefly review four compressed imaging strategies using random phase modulation: (i) compressed imaging with spatial-domain single random phase mask (CI-SSRPM). (ii) Compressed imaging with double random phase mask (CI-DRPM). (iii) Compressed imaging with Fourier-domain single random phase mask (CI-FSRPM). (iv) Compressed imaging with double random phase encoding (CI-DRPE).
## 3 Geometric Error Measurement Algorithm
### Set-Up Error Identification
Thus, Equation (5), which is related to the least squares method, can be expressed as follows:  
\[Q(E_{t})=\frac{1}{3i}\sum_{s=1}^{3i}\left(dP^{m}_{k,i}-AE\right)^{2}=\frac{1}{ 3i}\sum_{s=1}^{3i}R_{s}^{2}. \tag{5}\]  
In order to identify the optimal set-up errors of the reflector standard fixture, the average of the squared residuals are minimized and expressed in Equation (6):\[\min_{E_{t}}\sum_{s=1}^{3i}R_{s}^{2}=\min\left\|dP_{k,i}^{m}-AE\right\|^{2}=\min_{E_ {t}}\left\| \begin{bmatrix}dP_{x,1}^{1}\\ \vdots\\ dP_{z,i}^{m}\end{bmatrix} - \begin{bmatrix}A_{1\times 1}&\cdots&A_{1\times 4}\\ \vdots&\ddots&\vdots\\ A_{i\times 1}&\cdots&A_{i\times 4}\end{bmatrix}\begin{bmatrix}O_{ya}\\ O_{za}\\ S_{ya}\\ S_{za}\end{bmatrix} \right\|^{2}. \tag{6}\]
# Multi-Term Attention Networks for Skeleton-Based Action Recognition
## 5 Experiments and Analysis
### Analysis of Results  
In order to evaluate the proposed MTANs in detail, the UCLA dataset is taken as an example. Figure 5 shows the confusion matrix obtained from a random test in the experiment. We can find that although the dataset contains actions with different time scales, and many participants completed the same action at different times, almost all actions can be correctly classified. The classification of actions such as \"walk around\", \"sit down\", \"stand up\" can achieve 100% accuracy. The accuracy of the two actions \"donning\" and \"doffing\" are both 95%, far exceeding the 75% and 84% in MAN [9] (this accuracy is obtained by our reproduction of MAN). This shows that for actions with different time scales, our method can extract high-level temporal and spatial features and achieve excellent results. At the same time, these results demonstrate that the action features of different time scales are considered in our model after weighted fusion processing, which helps to improve the accuracy of action recognition.  
Figure 5: Comparison Confusion matrices of the MTANs on the Northwestern-UCLA dataset. The row of the confusion matrix denotes the true action label, while the column denotes the predicted action classes. The value in each column is the probability that the real data is predicted to be that category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Acc. (\%)** \\ \hline Lie group (CVPR 2014) [21] & 74.20 \\ Actionlet ensemble (IEEE TPAMI 2014) [44] & 76.00 \\ HBRNN-L (CVPR 2015) [5] & 78.52 \\ Enhanced skeleton visualization (PR 2017) [45] & 86.09 \\ Ensemble TS-LSTM (ICCV 2017) [8] & 89.22 \\ \hline MTANs & 93.73 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison on the Northwestern-UCLA dataset in accuracyFor the actions that were classified less accurately, one reason is using a two-side view as the training set, self-shielding of the human body is easy to occur. For example, \"pick up with two hands\" is easy to be confused with the action \"pick up with one hand\". The other reason is that some actions are overlapped quite a lot in the skeleton sequence. For example, the action \"drop trash\" and the action \"throw\" are easy to confuse, even for human perception. The ST-CNN in the proposed MTANs can extract richer spatio-temporal features compared to the method containing only RNNs. Therefore, the MTANs can carry out the correct classification for these similar actions to some extent.
## 1 Introduction
In mission-critical applications, such as disaster response, search and rescue, and surveillance, minimizing the end-to-end latency and ensuring system resilience are essential. The aforementioned methods for distributed CNN inference may fail to perform their mission due to the lack of mechanisms for ensuring system resilience. Therefore, the proposed system aims to enhance mission continuity by prioritizing latency minimization and system resilience. In this paper, a DRL-based resilient layer distribution (DRL-RLD) system is proposed to enable resilient distributed inference in UAV swarm. The proposed system is designed to minimize end-to-end latency through distributed inference, considering the UAV's position, available resources, and operability. The proposed system also incorporates both UAV-to-UAV and UAV-to-ground communication links, ensuring that final outputs reach ground devices with minimal latency. The proposed system is designed to ensure network resilience by dynamically reallocating CNN layers in real time when UAV malfunctions occur. This mechanism can ensure mission continuity. In addition, the robustness and scalability of the proposed system are demonstrated by evaluating its performance across various environmental conditions and swarm sizes. The main contributions of this paper are summarized as follows:  
* The proposed system minimizes the end-to-end latency of distributed CNN inference by considering both U2U and U2G communication links. Therefore, the proposed system can optimize the entire process from image acquisition to transmitting the final prediction to the ground device. * The DRL-RLD system is proposed to optimize resilient distributed inference in resource-constrained UAV swarm. The resilience of the UAV network can be enhanced through dynamic reallocation of layers even when UAVs become inoperable. This dynamic layer reallocation ensures the continuous operation of the network and enables adaptive real-time mission execution. * Extensive simulations have been conducted with various CNN models, available resources, and UAV configurations to evaluate the performance of the proposed system. In particular, the proposed system has been simulated with a larger number of UAVs than the settings in the previous research, with practical conditions of UAV malfunction. Therefore, it has the potential to offer greater scalability and generalization performance compared to conventional methods. The remainder of the paper is structured as follows: The system model is described in Section 2. The optimization problem is formulated in Section 3. The proposed deep reinforcement learning model for layer distribution is detailed in Section 4. The simulation results are presented in a variety of scenarios in Section 5. The conclusions and future directions are summarized in Section 6.
# Electochemically Engineered Mesoporous Sn-Oxide Rods for Anode Materials in Lithium-Ion Batteries
## 3 Results and Discussion
### Electrochemical Properties of Core (Sn Rod)-Shell (Mesoporous Sn-Oxide Layer) Anodes
Severe capacity degradation in the Sn rod anode made it difficult to assess their rate performance accurately. The core-shell anodes anodized at 6, 8, and 10 V, which exhibited reduced capacity fade, achieved discharge capacities of 356, 449, and 469 mAh/g, respectively, at a high discharge rate of 800 mA/g, corresponding to capacity retention of 60%, 69%, and 68%. If capacity degradation from cycling is excluded--focusing solely on kinetic performance--higher capacity retention can be expected. This excellent rate performance is most likely attributed to the open structure and large surface area of the mesoporous Sn-oxide, which facilitates efficient Li\({}^{+}\) transport and rapid surface reactions during discharge. Figure 11 shows the surface morphology of the Sn rod anode and the core-shell anode anodized at 10 V, both before and after cycling. For the Sn rod anode, distinct rod structures were clearly observed on the as-prepared anode surface (Figure 11a). However, after cycling, the rod-like morphology completely disappeared, and the surface was dominated by small or agglomerated particles (Figure 11b). This structural deterioration is ascribed to rod crushing, detachment, and shape change due to severe volume changes during charge-discharge cycles. These findings suggest that the relatively low cycle stability of Sn rod anodes is primarily due to physical damage of the active material. In contrast, the core-shell anode anodized at 10 V retained its initial anode structure, with rods still observed after 20 cycles (Figure 11c,d). The porous Sn-oxide layer on the rod surface also remained well-preserved (inset figures). This indicates that the superior cycling performance of the anodized Sn rod is due to the porous Sn-oxide layer, which effectively accommodates volume changes, thereby minimizing physical damage and enhancing structural stability throughout charge-discharge cycles. Figure 10: (a) Variations in discharge capacity retention and Coulombic efficiency of the samples with charge-discharge cycle at a rate of 100 mA/g, following the initial three cycles and (b) their rate capability.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 5 Performance Evaluation
### Test on Generality and Robustness  
To substantiate the impact of various features at different stages on the performance of _RoadInTCP_ proposed in this paper, an ablation study has been conducted. The corresponding topological relationship features, additional thematic information, and multi-scale temporal features are separately removed to demonstrate their effects. Additionally, the influence of the data training iteration method proposed in this paper on the model is validated.  
In Table 5, RoadInTCP\({}_{tr}\) denotes the output obtained after the removal of the topological relationship features extracted in Phase I, followed by processing in Phase II. RoadInTCP\({}_{ATI}\) represents the result obtained after the elimination of additional thematic information in Phase II in _RoadInTCP_. RoadInTCP\({}_{iter}\) signifies the result achieved when the iterative data training method is not employed for the training of Phase II. RoadInTCP\({}_{temporal}\) indicates the result derived in Phase II of _RoadInTCP_ without considering multi-scale temporal information, and based on existing works, only the temporal information of the preceding 12 h is taken into account. As shown in Table 5, we observe that the omission of any component or features from the _RoadInTCP_ model leads to a decline in its performance, thereby demonstrating the individual contributions of each structural element and characteristic to the model's efficacy. The _RoadInTCP_ model, by integrating a tri-Phase architecture, not only captures the discriminative latent features of intersections by utilizing the topological information of the road network (tr-features) and various relationships between neighboring intersections but also explores additional thematic information related to the road network intersections, as well as the multi-scale sequential information and their temporal variations. This concerted operation of the three phases synergistically optimizes the model's performance, capitalizing on the comprehensive exploration of spatial, contextual, and temporal features inherent to road traffic crashes.
## 4 Results and Discussion
### Future Research Directions
Communication-efficiency challenge: It is crucial to take into account the storage and capability limitations of the clients' devices when developing an approach for efficient communication. Some techniques overlook these constraints and only focus on the network communication cost. However, some of these techniques can result in the loss of critical information that affects the model convergence. For that, to ensure the effectiveness of FL, it is necessary to adopt an approach that facilitates efficient communication without placing an extra burden on the clients. Local model challenge: Researchers face the choice of selecting the appropriate learning model when evaluating their proposed work, which may impact the model's performance. It is worth noting that training the model in the FL environment takes place on client devices and necessitates sharing the trained model with a central server over multiple rounds, which can be challenging given the limited communication and computation resources of the client's devices. It would be beneficial to investigate the effects of various learning models on FL performance, particularly those that can be applied to the same tasks and have different model sizes, such as CNN, ResNet, and VGG. Real-world deployment and evaluation: It is important to evaluate the FL approach in real-world scenarios with actual datasets. Many studies focus on proposing solutions to overcome challenges in FL, but they do not always consider the limitations of real-world scenarios. For example, using a deep learning neural network to evaluate FL performance could improve the model's performance, but it may not be feasible due to limited resources on client devices and networks. Therefore, there is a need to carry out research studies and evaluate the performance of FL using a real-world deployment which will help guide this field in the future.
## 1 Introduction
### Contributions  
This document presents planning models with deterministic and stochastic optimization approaches, for the planning of urban community microgrids. The models combine the investment decisions (sizing and type of technology) and operational optimization. The contributions of this paper can be summarized as follows:  
1. Microgrid optimization planning models which consider: (i) the formation of different groups of randomly selected residential customers and (ii) the Economies of Scale (EoS) associated with investment costs of microgrid infrastructure, were successfully implemented. These models show an important cost reduction due to the aggregation of customers, highlighting the value of community association for microgrid development. Around 20% of the reduction is due to the diversity of energy consumption, and up to 15% of additional reduction in investment costs is due to EoS (up to 35% of total reduction).
2. The two-stage stochastic optimization approach, which can be used to plan microgrids capable of tackling different fault scenarios in the power grid. An important finding related to how to approach community microgrid planning has to do with solving the worst of the deterministic fault scenarios. Around 90% of the deterministic study cases addressed (considering reliability requirements) yielded results with an error less than or equal to 5% with respect to the solutions obtained with the stochastic planning approach.
3. A planning tool for designing microgrids, with information for the mathematical modeling of DER, operational constraints and the main grid, considering aspects such as: investment and operational costs, annual demand profiles for different numbers of residential clients, annual profiles of solar generation, purchase and sale of energy to the power grid, reliability indicators, EoS and identification of scenarios for stochastic simulations.  
Since hundreds of designs (i.e., optimization problems) were made for different groups of residential clients (different number of consumers/households associated with each microgrid), and also for different clients within the same group (different consumption patterns per household), the conclusions obtained in this research can be properly generalized.