esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 5 Simulations and Experiments
### Experiment Results  
To verify the effectiveness of the proposed fault-tolerant gait in practical robots, the experiments were implemented in an authentic hexapod robot. The experimental platform of the hexapod robot is illustrated in Figure 23. For the experiments, six force sensors were installed at the bottom of the foot end to collect the normal contact force between the foot and the ground, and a gyroscope, mounted on the body center, collected the attitude data of the hexapod body.
## 3 Accuracy and Feasibility for Hardware Implementation
### Experimentally Registered Signatures  
To measure an aluminum cylinder in free space, three responses from different positions were obtained using two low-cost TSA600 Vivaldi antennas from RFSpace with a VNA in the frequency range from 1 MHz to 3 GHz. The setup is shown in Figure 15 alongside the reconstruction-wise behavior of each method in Figure 16.  
Figure 14: Comparison of the CNR poles calculation of the simulated thin wire in the complex plane.  
Figure 13: Thin wire time domain response for Prony’s based reconstruction, MPM, frequency domain response with Cauchy-based reconstruction, and VCM.  
Now, as a critical evaluation requires the backscattered response of buried objects, a sandbox experiment was performed using an ultrawide band (UWB) radar from 0.8 GHz to 5 GHz and Vivaldi antennas as well [22]. As before, the time and frequency responses for a metal can and a metal plate were obtained and processed with complex natural resonances extraction methods (CNREMs), and the reconstruction of the signal is shown in Figure 17.  
Figure 16: Metal cylinder in free space. Prony reconstruction, MPM-based reconstruction, frequency response Cauchy method based reconstruction, and VCM reconstruction.  
Figure 15: Experimental setup for free space experiments and the metal cylinder with two Vivaldi antennas.
## 3 Processing Method
### Pre-Processing  
After identifying the product IDs of tracks over proposed target areas, the CO-SHARPS processing boutique was employed for the purpose of clutter simulation. The clutter simulator available in the CO-SHARPS boutique is based on the JPL (Jet Propulsion Laboratory, Caltech) focussed processor and comprises products called QDA clutter-grams [53].  
The processing parameters of the CO-SHARPS boutique are decided as follows. The QDA products have a fixed aperture (frames) of 4096. The pre-sum value, which varies the bandwidth or frame interval, is set as 16. A Hann window is selected to reduce sidelobes in the azimuth compression. The QDA products in this study are simulated using MOLA DEM with the ellipsoid as the equipotential reference surface, i.e., aeroid. The multi-look bandwidth is fixed to 15 Hz for the QDA products. The simulator also offers the depth-corrected radargram if a dielectric constant is specified during QDA processing. It is worth noting that the dielectric constant in the clutter simulator is assumed to be uniform over the entire subsurface for simplified processing (\(\varepsilon=3.15\), in this study). The dielectric constant varies in both latitudinal and longitudinal direction according to the subsurface material properties and is an area of ongoing research [26, 27]. However, a dielectric constant value of 3.15 has been used as a suitable approximation in past studies of the Euripus Mons region [54, 55].  
It is optional to adopt either the depth-converted radargram or the time-domain radargram. The process of depth conversion results in under-sampling of the signals and, hence, would lead to loss of useful information, potentially indicative of desired clutter. In this study, the time-domain radargram is utilized as it does not suppress the original signal. The selection of dielectric constant does not affect the DL algorithm in this study, as the model has been trained on time-domain radargram, and the depth conversion has been employed only for 3D-subsurface reconstruction.  
Noise-reduction techniques are introduced to reduce the backscatter or background speckles in radargram images. It supports the identification of discontinuities in the data as the signal-to-noise ratio in the original radargram is not adequate. Several filtering methods such as log-Gabor filtering, block-matching, and wavelet shrinkage denoising, etc. [56] were applied for suppressing the backscatter noise. Finally, bilateral filtering was employed (refer Figure 4) as it provides a balanced outcome between the preservation of the original signals and the noise levels.  
Figure 3: Flow chart of employed algorithms (see Section 3.3 for the details of ML algorithms).
## 2 Materials and Methods
### Multi-Sensor Drought Indices
#### 2.1.2 Agricultural Dry Condition Index (ADCI)  
The ADCI is an agricultural drought index that takes into account the vegetation conditions, soil moisture, and LST of the affected region. First, the vegetation condition index (VCI) is applied for vegetation analysis. [PERSON] et al. [7] proposed the ADCI as a new agricultural drought index, which is a combination of the three indices mentioned above (SMSI, VCI, and TCI). The cause of the agricultural drought was developed based on the concept of reducing the vitality of vegetation due to the lack of soil moisture and overheating of the surface temperature caused by high temperatures, developing into agricultural drought as this phenomenon continues. The ADCI can be calculated by using the Equation (1) given below:  
\[\text{ADCI}=\ 0.6*SMSI+0.2*VCI+0.2*TCI. \tag{1}\]  
The VCI is a suitable index for agricultural drought monitoring, such as temporal and spatial vegetation changes and the onset and intensity of drought [29; 30]. [PERSON] [29] developed the VCI, which was standardized using the maximum and minimum values of the NDVI developed based on the notion that droughts do not provide normal water supply to plants (Equation (2)).  
\[\text{VCI}=\ \frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}}, \tag{2}\]  
where \(NDVI_{min}\) and \(NDVI_{max}\) represent the minimum and maximum values of NDVI for the entire period of the pixel. The following is the LST-related index, called the temperature condition index (TCI), which is an index developed by [PERSON] [29] based on the fact that LST affects the stress of vegetation and is one of the drought factors that affect soil moisture. The TCI is a standard LST that uses the maximum and minimum LST values and as shown in the following equation (Equation (3)).  
\[\text{TCI}=\ \frac{LST_{max}-LST}{LST_{max}-LST_{min}} \tag{3}\]  
Finally, soil moisture needs to be considered for determining the ADCI. The soil moisture saturation index (SMSI) assumes that soil moisture is directly proportional to thermal inertia (TI). One of TI's simple approximations is the apparent thermal inertia (ATI), which can be derived in Equation (4); note that we assume that the solar energy is uniform.  
\[\text{ATI}=\ \frac{(1-\alpha)}{LST_{day}-LST_{night}}, \tag{4}\]  
where \(\alpha\) is the land surface albedo and \(LST_{day}\) and \(LST_{night}\) are the surface temperatures during day and night, respectively. The SMSI can be calculated using the ATI, as shown in the following equation (Equation (5)).  
\[\text{SMSI}=\ \frac{ATI-ATI_{min}}{ATI_{max}-ATI_{min}} \tag{5}\]
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 4 Simulation
### Simulation Results
#### 4.2.3 Temporal Coherence  
This simulation investigated the relationship between the coherence of the clutter channel [26] and the detection probability of the proposed approach for TCR varying from \(-20\) dB to \(20\) dB. The false alarm probability \(P_{FA}\) was set to \(10^{-4}\) and the threshold was adjusted according to the coherence values. The results were obtained by \(1000\) simulations for each TCR according to coherence values \(\rho=0.05,0.22,0.49,0.79,1\), which are depicted in Figure 9. Note that \(\rho=1\) means that the clutter channel is time-invariant while it becomes more decorrelated as \(\rho\) goes to zero. As observed in the figure, for \(\rho=1\), the clutter signal is also enhanced by the time reversal; therefore, the detection probability degrades while for \(\rho=0.05\), indicating that the time-variance of the clutter is relatively high, the approach achieves a better performance.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 2 Telecommunication Systems in an Isolated and Constrained Environment  
Three solutions (Figure 1) stand out for setting up a telecommunication system in an isolated and constrained environment:  
* Telecommunication towers
* Optical fiber
* Flying systems
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### Random Forest  
The Random Forest (RF) algorithm was first introduced by [PERSON] [62; 63] and belongs to the category of ensemble machine learning methods. RF operates by generating a large number of classification trees and then aggregating their predictions to enhance the classification accuracy and reduce variance [62; 63]. It is a nonparametric model that is widely applied in landslide susceptibility assessment due to its robustness in handling complex datasets and nonlinear relationships [18,64]. The RF algorithm primarily depends on two key parameters: t, the number of decision trees that are constructed, and m, the number of input features that are considered when splitting each node of a tree [65]. A major advantage of RF over other machine learning models is its ability to prevent overfitting by constructing multiple trees while maintaining its generalization capability. Unlike many other models, RF does not require cross-validation to assess its classification accuracy, as it inherently provides out-of-bag (OOB) error estimates [66]. The OOB error serves as an unbiased estimate of the generalization error, which stabilizes as the number of trees (t) increases. Therefore, selecting a sufficiently high t-value is crucial for achieving model convergence and optimal performance.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 8 Trust Model Design Case Study: Factory 4.0 vs. Agriculture 4.0
### Trust Parameters to Measure  
As stated in Section 7, trust models should be designed with attacks in mind. The parameters selected to measure trust should be relevant to the attacks that are of concern to the network. Table 3 compares the two networks in the six attack areas discussed in Section 4 and identifies the key parameter/calculation used to combat each type.  
Factory 4.0 is more like the typical network structure seen in the reviewed works, meaning the \"typical\" means of calculating and measuring trust, as seen in the reviewed works, can be applied. Agric 4.0 comes with more complexities and challenges. As we have seen, there can be multiple types of networks operating within an Agric 4.0 network. As previously seen in this work, different network types require different methods of calculating and measuring trust; thus, it makes sense that a complete Agric 4.0 network can be broken into multiple subnets. In each subnet, trust can be calculated and managed in a way that suits those subnets requirements. The subnets are still part of a larger network; thus, a proposed solution for this is to treat each subnet as a cluster with a cluster head that reports to a base station. With a structure such as this, there will be inter- and intra-cluster trust (similar to that seen in [29]). This network structure type could be classed as a hierarchical heterogeneous cluster network (every cluster is different) and could provide a good solution to the challenges posed by Agriculture 4.0.  
Figure 2: Comparison of Agriculture and Factory 4.0 proposed network structures.
## 5 Related Works  
As the evaluation of our systems was carried out under the conditions of the IberSpeech-RTVE 2020 Challenge [31], we can now compare our work to other ASR systems participating in this challenge and thus also trained on this specific domain related to TV programs. As a first general comment, we can say that all the results for the developed ASR systems showed that end-to-end systems are still far from hybrid systems in the challenging conditions of RTVE 2020. [PERSON] et al. [40] developed an end-to-end system based on the wav2 letter architecture [7], which was not able to generalize very well on the acoustic conditions of the RTVE2020 database, reporting a WER of at least 13% higher than their best hybrid ASR system. [PERSON] et al. [41] presented a Quartznet-based [42] ASR implementation showing promising results due to the use of more than a hundred hours of speech data. Nevertheless, the results in terms of the WER from that system were 9% worse when compared to other hybrid systems they developed for the challenge.  
Furthermore, the acoustic conditions of databases provided in other international challenges, such as CHiME-6 [14], have also been responsible for the poor performance of end-to-end ASR systems. Different approaches based on RNNs and transformers, along with the RNN-T and CTC-Attention [15] have been evaluated on one of the CHiME partitions, concluding that speech-enhancement techniques contribute to the reduction of to the gap between end-to-end and hybrid systems. The difficulty of obtaining high  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Decoding** & **LM Rescoring** \\ \hline
20H\_dev1 & 0.033 & 0.115 \\ AP\_dev1 & 0.035 & 0.175 \\ CA\_dev1 & 0.225 & 1.976 \\ LM\_dev1 & 0.092 & 0.450 \\ Mill\_dev1 & 0.082 & 0.442 \\ LN24H\_dev1 & 0.383 & 0.148 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real-Time factor (RT) for the different stages carried out in the PyChain-based baseline according to the different datasets.  
quality labeled speech leads to emergent machine-learning-based paradigms, such as Self-Supervised Learning (SSL), whose goal for ASR is to learn powerful speech representations from unlabeled examples (e.g., wav2 vec 2.0 [43]). Other recent works have tried to mitigate the effect of limited training data [44] or noisy environment conditions [45]. In the case of [44], the use of CTC and end-to-end LF-MMI to fine-tune a wav2 vec 2.0 model showed similar performance even for out-of-domain and cross-lingual adaptation. Regarding [45], the authors integrated SSL with contrastive learning from original-noisy speech pairs to model representations with noise robustness.  
Along the same lines, we proposed the application of DAT in our baseline end-to-end ASR systems as an alternative to obtain robust features invariant to the domain, i.e., different acoustic conditions of TV shows. It is a fact that DAT is beneficial for building robust embeddings. In recent works, it has been even integrated with wav2 vec embeddings to have an accent-robust speech recognition [46]. The authors reported good results when no accent labels were available for training.
# Quadrupedal Robots Whole-Body Motion Control Based on Centroidal Momentum Dynamics
## 2 Methods
### Application to Quadrupedal Robot Dynamic Motion  
This section demonstrates the application of centroidal momentum control to the dynamic motion of a quadruped robot. Figure 3 shows the block diagram of our control framework. The set of future footprints are given from high level behaviour, and the reference walking pattern of the quadruped robot is generated based on the Capture Point dynamics in Section 2.3.1. The motion of the robot is projected according to the walking pattern. The according reference rate of change of the centroidal momentum is formulated as a feedback control task derived from the CoM motions of the quadrupedal robot in Section 2.3.2. We design a QP to solve an optimal control problem to track the reference rate of change of the centroidal momentum as closely as possible while satisfying the constraints in Section 2.3.3. The joint accelerations and the Ground Reaction Forces (GRFs) outputted from the quadratic programme solver are used to compute the reference joint control torques using an inverse dynamics algorithm. The application of the whole-body control system based on centroidal momentum to control a quadruped robot dynamic motion is shown in Section 2.3.4.  
Figure 3: The whole body control system based on centroidal dynamics. \(P_{d}\) is the desired footprint from a high level behaviour. \(v\) is the desired velocity of CoM. \(\dot{h}_{d}\) is the desired rate of change of the centroidal momentum. \(p,\dot{p},\dot{p}_{sing\_d}\) are the desired position, velocity and acceleration of the swing foot in Cartesian space, respectively.
# Multifunction RF Systems for Naval Platforms
## 3 MFRF System Design Considerations
### Practical Limitations and Trade-Offs
#### 3.2.1 Combined vs. Separate Transmit/Receive Arrays
Consequently, an electronic switch can be included in the Rx path between the LNA and circulator, and opened during radar pulse transmission to provide sufficient isolation for the LNA. In the case of fully duplexed communication systems, transmission and reception are conducted on different frequency bands, as indicated in Section 2. As a result, any residual HPA output power leaking from the Tx channel into the Rx channel through the circulator would be outside of the Rx band, and can consequently be attenuated by a bandpass filter inserted into the Rx channel between the LNA and circulator. Furthermore, radar and communication functions are assigned different frequency bands of operation, so that they would generally not interfere with each other in a MFRF system. It is only when the ES and EA functions are included in a MFRF system that isolation between Rx and Tx channels becomes critical, because the ES function must continuously monitor a large range of frequencies including those at which the other RF functions may be simultaneously transmitting, and the EA function may be required to transmit at frequencies over which the other RF functions are simultaneously receiving. This discussion suggests a possible MFRF system design compromise, in which radar and communication functions share a combined Tx/Rx array, while the EA and ES functions employ separate Tx and Rx arrays, respectively, to enable simultaneous transmission and reception.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 2 Mathematical Model
### Constraints
#### 2.3.4. Energy Storage Device  
Household energy storage devices are usually batteries (e.g., lead-acid, Li-ion). Batteries charge and discharge to match the output of the local photovoltaic generation. The operational limits during the charging and discharging process are as follows [26]:  
\[SOC_{i+1}=SOC_{i}+\frac{\eta_{ch}\cdot P_{ESD,i}^{ch}\cdot\Delta t}{Q_{r}}- \frac{P_{ESD,i}^{ch}\cdot\Delta t}{\eta_{ch}\cdot Q_{r}}-\varepsilon\cdot\Delta t \tag{18}\]  
\[0\leq P_{ESD,i}^{ch}\leq p_{\max}^{ch}\cdot x_{ESD,i}^{ch}\ \ \ \ \forall x_{ESD,i}^{ch}\in\{0,1\} \tag{19}\]  
\[0\leq P_{ESD,i}^{chch}\leq p_{\max}^{chch}\cdot x_{ESD,i}^{chch}\ \ \ \ \forall x_{ESD,i}^{chch}\in\{0,1\} \tag{20}\]  
\[x_{ESD,i}^{ch}+x_{ESD,i}^{chch}\leq 1 \tag{21}\]  
\[SOC_{\min}\leq SOC_{i+1}\leq SOC_{\max}\ \ \ \ \ \forall i\in\{1...N\} \tag{22}\]  
\[SOC_{N+1}\geq SOC_{ini} \tag{23}\]  
where \(P_{ESD,i}^{ch}\) and \(P_{ESD,i}^{chch}\) represent the charging and discharging power; \(x_{ESD,i}^{ch}\) and \(x_{ESD,i}^{chch}\) represent the charging and discharging status (\(x_{ESD,i}^{ch}=1\) and \(x_{ESD,i}^{dch}=0\) indicate that the energy storage device is charging; \(x_{ESD,i}^{ch}=0\) and \(x_{ESD,i}^{dch}=1\) indicate that the energy storage device is discharging); \(\eta_{ch}\) and \(\eta_{dch}\) are the efficiency of charging and discharging; \(p_{\max}^{ch}\) and \(p_{\max}^{dch}\) are maximum power of charging and discharging; \(\varepsilon\) represents self-discharge rate; Equations (18)-(22) constrain that the battery should not overcharge and overdischarge; and Equation (23) ensures that the battery capacity at the final time step of the day is no less than that of the initial time step at the beginning of the day.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Unpredictability and Reliability Issues  
The unpredictability and potential unreliability of LLMs, such as GPT-3 and GPT-4, raise significant challenges when used in decision support tools during health crises like pandemics. These models generate responses based on patterns from extensive datasets  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Capacity** & **Information Accuracy and Reliability** \\ \hline
1. & Unpredictability and Reliability Issues \\ \hline
2. & Dissemination of Outdated Information \\ \hline
3. & Amplification of Misinformation \\ \hline
4. & Technical Limitations in Communication \\ \hline
**Alignment** & **Ethical and Legal Concerns** \\ \hline
1. & Ethical and Accountability Concerns \\ \hline
2. & Privacy and Data Security Risks \\ \hline
3. & Ineffectiveness with Diverse Populations \\ \hline
4. & Resource Allocation Concerns \\ \hline
5. & Unpredictability and Reliability Issues \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of issues related to AI-based chatbots in public health emergencies.  
without true understanding, which can lead to \"hallucinations\", that is, plausible yet factually incorrect information [89]. In critical situations, such misinformation can mislead individuals, potentially worsening disease spread. While LLMs offer scalable, immediate responses, their risks can undermine public trust and hinder effective health interventions [103].  
Although improvements have been made to reduce inaccuracies [10], LLMs still struggle with maintaining factual consistency. Further research is needed to develop evaluation metrics and real-time verification systems to ensure reliability in high-stakes domains [104]. Enhancing training protocols by incorporating domain-specific data and real-time fact-checking algorithms are key technical measures to improve AI reliability [105]. Tools that promote AI explainability can also help identify and correct errors [106].  
Beyond technical solutions, socio-technical measures are essential. Human-in-the-loop systems allow experts to validate AI outputs, reducing the risk of misinformation [107]. Educating users on AI's limitations and establishing clear regulatory frameworks are essential for responsible AI deployment in healthcare [108].  
Research should focus on addressing AI hallucinations, enhancing real-time verification, and understanding how users interact with AI-generated health information [109; 110]. Regulatory frameworks must evolve to ensure the safe, ethical deployment of AI in health crises [111].