esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 3 The Proposed Method  
### Foundation  
The overview of the proposed MSBA is illustrated in Figure 2. As depicted in Figure 2a, MCF comprises two feature information streams. The \(\{C_{2}^{\prime},C_{3}^{\prime},C_{4}^{\prime},C_{5}^{\prime}\}\) indicate the features derived from the multi-resolution input image, processed through multiple convolutions to capture sufficient coarse-grained information. \(\{C_{2},C_{3},C_{4},C_{5}\}\) represent features from distinct stages of the single-resolution image undergone by the backbone network. In Figure 2b, to ensure consistent notation within the same module, we employ \(\{M_{2}^{\prime},M_{3}^{\prime},M_{4}^{\prime},M_{5}^{\prime}\}\) in BFI to denote features derived from MCF's output. SRT concentrates on enhancing the multi-scale semantic representation in the high-level feature, specifically targeting \(C_{5}\). Besides, Additionally, BFI encompasses pixel-level filter interaction (PLI) and channel-wise prompt interaction (CWI). The output of PLI is denoted as \(\{M_{2},M_{3},M_{4},M_{5}\}\), where \(M_{2}\) remains unchanged (\(M_{2}^{\prime}\)) without any further operations. Similarly, \(\{P_{2}^{\prime},P_{3}^{\prime},P_{4}^{\prime},P_{5}^{\prime}\}\) mirrors \(\{M_{2},M_{3},M_{4},M_{5}\}\) and represents features resulting from PLI's output. Additionally, \(\{P_{2},P_{3},P_{4},P_{5}\}\) signify features enriched with meticulous semantic prompt information, primed for predictions.  
The matching gate functions as a controller, aiming to mitigate inconsistencies and redundancy arising from rigorous interaction between two features. It dynamically modulates the fusion process in response to the present input. In detail, when provided with input features \(X,Y\in\mathbb{R}^{c\times h\times w}\) as input, the matching gate \(\mathcal{G}(\cdot)\) can be described as:  
\[\mathcal{G}(X,Y)=[F_{mut}(\alpha^{fine},X)+F_{mut}(1-\alpha^{fine},Y)], \tag{1}\]  
in which \(\alpha^{fine}\in\mathbb{R}^{c\times 1\times 1}\) represents the control matrix of \(X\) and \(F_{mut}\) means the Hadamard product. \(\alpha^{fine}\) can be obtained from the switch (\(\mathcal{S}\)) in the matching gate as:  
\[\alpha^{fine}=\mathcal{S}(X), \tag{2}\]  
\[\mathcal{S}(X)=\sigma[\mathcal{O}(\cdot),X], \tag{3}\]  
where \(\mathcal{O}(\cdot)\) represents the operations such as \(3\times 3\) convolution and pooling. \(\sigma(\cdot)\) signifies a nonlinear activation function, executed as _Tanh_ within our method. The matching gate adeptly fosters complementarity between the two features.
# Modelling and Study of the Effect of Geometrical Parameters of Piezoelectric Plate and Stack
## 2 Materials and Methods
After performing a series of measurements, the data were stored in a CSV file and exported to a computer using a USB stick. Two measurements were designed to assess the performance of a piezoelectric stack made up of two the same and different plate sizes. All the tests were performed with the actuation voltage set at 2 V on the source, which was amplified to 400 V. The measurements were calibrated for sinusoidal and triangular wave at different frequencies. To create the actuation voltage for both the tested piezoelectric plates, a waveform generator from Tabor Electronics was used. The study employed a model designated \"WW5064.\" It was capable of concurrently creating four distinct waveforms with a peak amplitude of 5 V and a maximum frequency of 20 Mhz. With respect to the experimental results, a simulation of the proposed model using parameters from the manufacturer data sheet was not practicable. The parameter considered in the model needed to be identified from the experimental findings in order to produce reliable results. It is important to account for piezoelectric physical properties such as strain, elasticity, and mass, as well as electrical properties, such as impedance or capacitance, in the case of the synthesized model. To take these properties into account, an analogous model was proposed in the mechanical domain. The system characteristics, as well as the physical setup for model-based control design, must match the simulation results. In this paper, the parameter of damping coefficient and the spring constant were estimated from the experimental results of different frequency actuated stack displacement output as shown in Table 1. A list of plate dimensions and constants from the manufacturer's data sheet is provided in Table 2, which will be utilized to plot the plate characteristics graph in the following step. For the estimation of the experimental data, the Simplex algorithm, which is commercially available in the MATLAB toolbox, was run for 100 iterations, and the approaches of parameter identification using the described algorithm were briefly presented in Figure 5. The bases for identifying damping coefficient and spring constants of the plate were from the time domain response of the displacement measurement of the experimental results. The data were collected at 0.000004 sampling time and different frequencies of voltage for each plate. \begin{table}
\begin{tabular}{l c c} \hline
**Symbol** & **Value** & **Unit** \\ \hline Mass of plate 1 (\(m_{1}\)) & 0.069300 & kg \\ Mass of plate 2 (\(m_{2}\)) & 0.0200 & kg \\ Stiffness plate 1 (\(k_{1}\)) & \(8.0269\times 10^{10}\) & N/m \\ Stiffness plate 2 (\(k_{2}\)) & \(7.96625\times 10^{10}\) & N/m \\ Damping coefficient plate 1 (\(b_{1}\)) & \(1.91999\times 10^{4}\) & Ns/m \\ Damping coefficient plate 2 (\(b_{2}\)) & \(7.0601085\times 10^{2}\) & Ns/m \\ \hline \end{tabular}
\end{table} 
Table 1: Identified parameter from experimental data.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 4 Communication, Navigation, and Observation
### Mars Observation  
Further Martian explorations and ventures towards human habitation will require increasingly autonomous systems that can operate independently without the need for interventions from Earth-based operators. The current generation of Mars rovers landed in easily accessible areas on Mars. However, large areas of Mars, such as the poles, remain unexplored due to difficult terrain and harsh weather conditions. This further motivates the need for a constellation of satellites observing Mars, predicting extreme weather events such as dust storms, identifying water and fuel sources, and facilitating surface operations.  
Global space agencies are positioning to include AI technologies within future missions, ranging from collaborative satellite networks to autonomous extraterrestrial landscape exploration. ESA Discovery's OPS-SAT program is focused on integrating AI advancements to improve spacecraft onboard operations, enhance remote-sensing quality, and augment inter-satellite and satellite-to-Earth communications [53]. NASA's Artificial Intelligence Group is also developing autonomous spacecraft capabilities, including through their \"Continuous Activity Scheduling Planning Execution and Replanning\", or CASPER, initiative [54]. A recurring hurdle encountered by these international space research teams in deploying AI in orbit, on the Moon, or other planets is this technology's reliance on high-performance compute infrastructures.  
Ensuring that the autonomous intelligence systems on Mars vehicles are optimized for inherent compute, memory, and power constraints is paramount to performing complex AI. Neural network (NN) compression, specifically context-aware NN pruning, reduces the size of NNs to the bare skeleton of what is required to correctly perform predictions of interest [55]. Neural architecture search (NAS) can also identify the most suitable NN for a specific task [56]. NAS coupled with advanced compression creates NNs that can perform under severely resource-limited conditions. Such research is being applied to onboard, near-sensor processing for future intelligent Earth observation satellite platforms, with obvious extensions to observations and spectral analyses of the Martian landscape [57].  
For satellites, intelligent sensor fusion can permit both active and passive imaging instrumentation to enrich observations through the sharing of a single algorithmic stack, thus minimizing hardware costs and power requirements. For ground agents that can be impacted by Martian dust storms, for example, automated switching between sensors can be performed when required, such as focusing on radar measurements when atmospheric obstructions prevent the use of visual cameras and LiDAR [58]. A machine learning model is trained to operate on a single, common sensing representation, thereby saving resources while analyzing streams from different sensors in parallel.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 3 Experimental Protocol
### Performance Metrics for Diarization and Speaker Attribution  
The original evaluation considers the Assignment Error Rate (AER) as the measure to evaluate the performance of the systems. This metric, conceived as a modification of the popular Diarization Error Rate (\(DER\)), reflects the proportion of misclassified audio.  
The original \(DER\) metric assumes errors to be caused by three different causes: missed audio (lost audio with speech), false alarm audio (audio falsely considered to contain speech) and speaker error (speech assigned to a wrong speaker). Thus, the formulation of the \(DER\) metric is:  
\[DER =\frac{L_{\text{WRONG}}}{L}=\frac{L_{\text{MISS}}+L_{\text{FA}}+ L_{\text{SPK}}}{L}\] \[=E_{\text{MISS}}+E_{\text{FA}}+E_{\text{SPK}} \tag{1}\]  
where \(L\), \(L_{\text{WRONG}}\), \(L_{\text{MISS}}\), \(L_{\text{FA}}\) and \(L_{\text{SPK}}\) stand for the total length of the audio, the length of audio wrongly assigned and the lengths of audio per error cause, respectively, miss, false alarm and speaker. Moreover, as shown in the formulation, \(DER\) can also be interpreted as a composition of three terms, \(E_{\text{MISS}}\), \(E_{\text{FA}}\) and \(E_{\text{SPK}}\), each one representing the specific error due to one of the previously described causes.  
AER, heavily inspired by \(DER\), follows a similar formulation that measures the proportion of misclassified audio. Furthermore, AER also differentiates among three causes of error: Miss, False Alarm and Speaker errors. However, differences with \(DER\) arise when defining these causes. Missed audio now reflects the lost audio exclusively coming from the enrollment speakers. The false alarm audio includes the audio falsely considered to contain an enrolled speaker. Finally, the speaker error is the audio attributed to an incorrect enrolled speaker.
## 4 Empirical Study
### Analysis and Results
#### 4.2.3 Phase Three: Domain-Adaptive Sentiment Analysis (DASA)
Accordingly, in the comparison of overall subjective experience levels, TC2 outperformed the others, with the most favorable positive sentiment, while TC1 lagged with the least favorable negative sentiment. 2. Variety in the visualization approaches yielded diverse insights. Continuing to filter the top-five subjective experience keywords-- \"design\", \"space\", \"interior\", \"exterior\", and \"rear seat\"--and presenting them visually, as depicted in Appendix C, Table A5, revealed the following insights: The appeal of design elements was consistently moderate, with minimal variance; yet, TC3 showed a lower proportion of extremely negative evaluations, indicating a comparable UX in design performance among the four companies. Spatial attributes displayed considerable sentiment variability, with notably negative perceptions for TC1, succeeded by TC3, and TC2 users exhibited strong positive evaluations. Exterior sentiment encompassed a range of evaluations, with TC2 receiving the highest and TC4 the lowest ratings. Interior and rear seat sentiments were largely negative, with the exception of a superior performance by TC2. 3. From the focused analysis of the \"interior\" keyword, Figure 3 indicates that the interior sentiment metric leaned toward zero, indicating a general negative perception. TC1, approaching 0, recorded the highest proportion of negative evaluations at 28.00%, implying that TC2's interior could be a benchmark for addressing product \"pain points\"; conversely, TC2, approaching 1, recorded the highest proportion of extremely positive evaluations at 11.02%, indicating that TC2's interior could be a benchmark for \"best practices\" in product development. Figure 2: Sentiment score histogram for the “subjective perception” theme in target competitors.
## 4 Discussion  
### Performance Analysis of GPP\({}_{\text{OLCI-FAPAR}}\) Using MODIS GPP Algorithm  
The significant relationship between the GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{EC}}\) across all sites indicates the potential of Sentinel-3 OLCI FAPAR in estimating GPP. However, the performance of the model varies at different sites. The US-WCr site obtained the best performance across all sites. This finding isconsistent with previous studies [5; 63]. The reasons can be summarized as follows. Firstly, the seasonal fractional vegetation cover and vegetation structure of DBF were obvious, which can be easily tracked by FAPAR [12]. Secondly, DBF grows in the middle and high latitudes with less cloud cover, and thus time series images with high quality can be obtained [30]. The performance of the US-Ton site was only worse than the US-WCr site. Many studies have reported that the savanna GPP estimation accuracy was lower than other vegetation types due to the misclassification of the savanna [64; 32; 65]. Unlike savanna, which has a forest canopy cover between 10%-30%, woody savanna's canopy cover is greater than 30%. Dense vegetation cover can minimize the impact from understory vegetation, which may improve the GPP estimation accuracy. The performance of two shrubland sites was relatively inferior than that of the US-Ton site in our study. Previous studies have indicated that the estimation accuracy of the GPP at shrubland sites varied with regions [66; 36]. Temperate regions aregenerally superior than tropical regions. The growth of shrubland is mainly controlled by water ability [32]. As shown in Figure 3c,d, f(VPD) plays a dominant role, especially during shrubland growing season. Moreover, due to the sparse vegetation at the shrubland region, the background soil conditions may have a great impact on GPP estimation [67]. The relationship between GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{EC}}\) was moderate at the US-Bi1 and US-Bi2 sites. Previous studies have demonstrated that GPP estimation accuracy varied with regions and crop types [68; 69; 70]. Our study also confirmed this conclusion. It is noteworthy that the RMSE was large at the two cropland sites (Table 3). There are mainly two reasons for this situation. Firstly, the rotation period is different for the two crop types. The US-Bi1 site is a single croppingsystem whereas the US-Bi1 site is a multiple cropping system. However, the threshold of the environmental variables in the MODIS GPP algorithm was set the same for different crop types. Secondly, the algorithm assumed one value of \(\varepsilon_{\text{max}}\) for all C3 and C4 crops, which led to the underestimation of GPP. The GPP estimation accuracy was lowest at the US-KFS site. This was not consistent with other studies, which obtained great performance for grassland [71; 72]. The seasonal changes of grassland is obvious and can be tracked well by satellite images [73]. Thus, the flux and climate data uncertainty may lead to the discrepancy between GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{EC}}\).
## 5 MPPT Algorithms
### Deep Q-Network (DQN)
#### 5.2.3 Training Results of DQN  
The training process of the DQN agent is depicted in Figure 14, where the light-blue curve represents the episode reward, the dark-blue line indicates the moving average of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time per episode & 0.5 s \\ Agent sample time & 0.01 s \\ Maximum number of episodes & 300 \\ Steps per episode & 50 \\ Discount factor & 0.9 \\ Mini-batch size & 64 \\ Experience buffer length & \(1\times 10^{6}\) \\ Initial exploration rate (\(\varepsilon\)) & 1.0 \\ Minimum exploration rate & 0.001 \\ Exploration decay rate & 0.0001 \\ Double DQN & Enabled \\ Critic optimizer & Adam \\ Critic learning rate & 0.0001 \\ Policy for saving agent & Episode reward \(\geq\) 150 \\ Stopping criterion & Average reward \(\geq\) 10,000 \\ Parallel training & Disabled \\ \hline \hline \end{tabular}
\end{table}
Table 10: DQN agent configuration parameters.  
the reward, and the yellow curve corresponds to the Episode Q0 metric. The Episode Q0 provides an estimation of the expected future reward based on the current policy and is a useful indicator of learning progress.  
It is evident that the training converged around episode 100 as the average reward stabilized and episode rewards consistently exceeded the defined performance threshold. The final episode reward and Q0 values confirmed the agent's ability to maximize the power extracted from the PV system.  
A summary of the training performance is presented in Table 11, with hardware specified in Table 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Mean reward per episode & 28.73 \\ Final episode reward & 11.74 \\ Episode Q0 & 3.89 \\ Average steps per episode & 50 \\ Total agent steps & 15,000 \\ Total training time & 2 h 3 min \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary of the training performance for the DQN agent.  
Figure 14: Training process of the DQN method over 300 episodes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Specification** \\ \hline PC Model & HP ProDesk 600 G4 Mini PC \\ Processor & Intel® CoreTM i7-7700T CPU @ 2.90 GHz \\ RAM & 16 GB DDR4 2400 MHz \\ Storage & 512 GB SSD \\ Operating System & Windows 10 Pro 64-bit \\ Integrated GPU & Intel® HD Graphics 630 \\ Parallel computing & Disabled \\ GPU acceleration & Disabled \\ \hline \hline \end{tabular}
\end{table}
Table 12: Specifications of the hardware used for simulations and training of ANN and DQN agents.
## 3 Fuzzy MPC Algorithm with Advanced Generation of Prediction
### Fast Generation of the Control Action--Analytical Approach  
Note that if in each iteration of the proposed approach the following optimization problem without constraints is solved:  
\[\arg\min_{\Delta\vec{u}}\left\{J_{MPC+}=(\overline{\vec{y}}-\widetilde{\vec{y}}- \vec{A}_{k}\cdot\Delta\vec{u})^{T}\cdot\boldsymbol{\kappa}\cdot(\overline{\vec {y}}-\widetilde{\vec{y}}-\vec{A}_{k}\cdot\Delta\vec{u})+\Delta\vec{u}^{T} \cdot\boldsymbol{\Lambda}\cdot\Delta\vec{u}\right\}\]  
then it has the following solution given by the analytical formula:  
\[\Delta\vec{u}=\left(\vec{A}_{k}^{T}\cdot\boldsymbol{\kappa}\cdot\vec{A}_{k}+ \boldsymbol{\Lambda}\right)^{-1}\cdot\left(\vec{A}_{k}^{T}\cdot\boldsymbol{ \kappa}\cdot(\overline{\vec{y}}-\widetilde{\vec{y}})+\Delta\overrightarrow{u }\right). \tag{47}\]  
where \(\Delta\overrightarrow{u}\) contains elements of future control increments trajectory, corrections of which are collected in the vector \(\Delta\vec{u}\).  
**Remark 2**: _If one uses the performance index (37), that is, the modified version:_  
\[J_{MPC+v2}=(\overline{\vec{y}}-\widetilde{\vec{y}}-\vec{A}_{k}\cdot\Delta \vec{u})^{T}\cdot\boldsymbol{\kappa}\cdot(\overline{\vec{y}}-\widetilde{\vec {y}}-\vec{A}_{k}\cdot\Delta\vec{u})+\Delta\vec{u}^{T}\cdot\boldsymbol{\Lambda }\cdot\Delta\vec{u}\, \tag{48}\]  
_then the following analytical formula should be used:_  
\[\Delta\vec{u}=\left(\vec{A}_{k}^{T}\cdot\boldsymbol{\kappa}\cdot\vec{A}_{k}+ \boldsymbol{\Lambda}\right)^{-1}\cdot\vec{A}_{k}^{T}\cdot\boldsymbol{\kappa} \cdot(\overline{\vec{y}}-\widetilde{\vec{y}}). \tag{49}\]  
_It should be once again emphasized, however, that this modification changes the meaning of the parameter matrix \(\boldsymbol{\Lambda}\) comparing to the classical formulation and algorithms with a modified form of the performance index generate faster responses than the algorithm using the control law (47), but influence of the \(\lambda\) parameter on system robustness becomes significantly limited._  
Due to the fact that the dynamic matrix \(\vec{A}_{k}\) changes, in general, in every iteration, then in each iteration of the algorithm the formulas (47) (or (49)) allowing to calculate the control action should be used. As a consequence, appropriate rows of the matrix \(\left(\vec{A}_{k}^{T}\cdot\boldsymbol{\kappa}\cdot\vec{A}_{k}+\boldsymbol{ \Lambda}\right)^{-1}\) (corresponding to the control increments for the current sampling instant \(\Delta\vec{u}^{m}_{k|k}\)) should be calculated in each iteration of the algorithm.
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 5 Influence of Intact Rock Strength and Rock Mass Quality on Level of Strain Burst  
The models realized have highlighted the influence of mitigation measures on low and medium strain burst hazards. However, as shown in Figure 8, when facing severe strain burst, only a 30% of the cases are mitigated. For the other 70% of the cases (10 events on 15 initial events without any mitigation measures), no mitigation measures have proven to be efficient. To understand these cases better, an analysis of the intact rock strength (UCS) and the rock mass quality (GSI) is realized. These two parameters are considered as it is recognized that they both have an impact on strain burst potential (lower GSI reduces the potential for bursting and higher UCS increases the potential of bursting) [3].  
Figure 10 presents the influence of the UCS (Figure 10a) and the GSI (Figure 10b) values on the number of events and the occurrence of the strain burst, considering the various mitigation measures studied. It shows that, regardless of the GSI values, the number and occurrence of the events do not vary with the mitigation measures proposed. Conversely, when increasing the UCS value, it can be observed that the mitigation measures allow the number and occurrence of events to be decreased. More specifically, for a UCS of 220 MPa, only the combination of a support system and destress blasting have an impact on the events. For a UCS of 270 MPa, the combination of a support system and destress blasting, and the support system alone, have an impact on the events. Finally, for a UCS of 320 MPa, each time that a mitigation measure is implemented, the occurrence of an event can be reduced to none.  
The lack of influence of the GSI on the number and occurrence of events can be questionable as it is assumed that it should have an impact on the hazard level (burst potential) and therefore the occurrence of the events. However, due to the formulation of the DISL approach considered in the numerical models, the GSI is not comprehensively integrated in the models, and this could explain these results. This theory should be explored in a further study, for example, by integrating directly in the model family of discontinuity.  
Figure 9: Influence of mitigation measures on the strain burst occurrence.