esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 6 Emerging Human-AI Collaboration Problems in RSA
### Emerging Problem 2: Localizing Users under Poor Networks  
Although cellular bandwidth has increased over the years, the bad cellular connection is still a major problem in RSA services, especially in indoor navigation [43], as listed in problem G4.(1) in Table 1. The common consequences include large delays or breakdowns of video transmissions [13; 94; 111]. Suppose that the poor network only allows transmitting limited amount of data and cannot support live camera feed, it is almost impossible for agents to localize the user and give correct navigational instructions. Based on this observation, we identify an emerging research problem of localizing users under poor networks that can be addressed by human-AI collaboration.  
With regard to AI-based methods, one possible solution is to use interactive 3D maps, constructed with ARKit [63] using an iPhone with a LiDAR scanner, as shown in Figure 1. During an RSA session under a poor network, the user's camera can relocalize them in the 3D maps. If their location and camera pose is transmitted to agents, agents can simulate their surroundings on the reloaded offline 3D maps. Considering the camera pose can be represented by a \(4\times 4\) homogeneous matrix, the transmitted data size is negligible. With voice chat and the camera pose displayed on the 3D maps (e.g., top-down and egocentric views in Figure 1), the agent can learn enough information about the user's surroundings and localize the user under a poor network momentarily.  
In terms of human-AI collaboration, to the best of our knowledge, there is no work for RSA on localizing users under poor networks. Without live camera feed, it would be a more interesting human-AI collaboration problem. To localize the user in such a situation, the communication between the agent and the user would be greatly different. We can imagine some basic communication patterns. First, the agent can ask the user to make certain motions (e.g., turn right, go forward) to verify the correctness of the camera pose display. In turn, the user can actively ask the agent to confirm the existence of an O&M cue (e.g., a wall) from the 3D maps. It is worth noting that the offline 3D map could be different from the user's current surroundings. When exploring the map, they also need to work together to eliminate the distraction of dynamic objects (e.g., moving obstacles), which do not exist on the 3D map. The specific problems have never been studied in detail, for example, how to detect localization errors and maintain effective RSA services with low data transmission rates.
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 2 MTJ Fundamental and Physical Modeling
### Stt-Mtj
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Memory** & **SRAM** & **DRAM** & **NOR-Flash** & **NAND-flash** & **STT-MRAM** & **PCRAM** & **ReRAM** & **FeRAM** 1  \\ \hline Cell area & \textgreater{}100 \(\mathrm{F^{2}}\) & 6 \(\mathrm{F^{2}}\) & 10 \(\mathrm{F^{2}}\) & 4 \(\mathrm{F^{2}}\) (3D) & 6\(\mathrm{\sim}\)50 \(\mathrm{F^{2}}\) & 4\(\mathrm{\sim}\)30 \(\mathrm{F^{2}}\) & 4\(\mathrm{\sim}\)12 \(\mathrm{F^{2}}\) & 15\(\mathrm{\sim}\)35 \(\mathrm{F^{2}}\) \\ Multi bit & 1 & 1 & 2 & 3 & 1 & 2 & 2 & 1 \\ Supply & \textless{}1 V & \textless{}1 V & \textgreater{}10 V & \textgreater{}10 V & \textless{}1.5 V & \textless{}3 V & \textless{}3 V & \textless{}1.8 V \\ Read duration & \textgreater{}1 ns & \textgreater{}10 ns & \textgreater{}50 ns & \textgreater{}10 μs & \textless{}10 ns & \textless{}10 ns & \textless{}10 ns & \textless{}10 ns & \textless{}10 ns \\ Write latency & \textgreater{}1 ns & \textgreater{}10 ns & \textgreater{}10 μs & \textgreater{}1 ms & \textgreater{}100 μs & \textless{}1 ms & \textless{}10 ns & \textless{}50 ns & \textless{}5 ns \\ Retention & N/A & \textgreater{}6 ms & \textgreater{}10 V & \textgreater{}10 V & \textgreater{}10 V & \textgreater{}10 V & \textgreater{}10 V & \textgreater{}10 V & \textgreater{}10 V \\ Endurance & \textgreater{}10\(\mathrm{{}^{16}}\) & \textgreater{}10\(\mathrm{{}^{16}}\) & \textgreater{}10\(\mathrm{{}^{5}}\) & \textgreater{}10\(\mathrm{{}^{4}}\) & \textgreater{}10\(\mathrm{{}^{15}}\) & \textgreater{}10\(\mathrm{{}^{9}}\) & \textgreater{}10\(\mathrm{{}^{6}}\)\(\mathrm{\sim}\)10\(\mathrm{{}^{12}}\) & 10\(\mathrm{{}^{13}}\) \\ Write energy & \textgreater{}f/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}100 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit & \textgreater{}10 \(\mathrm{\SIUnitSymbolMicro}\)/bit \\ \hline \end{tabular}
\end{table} 
Table 1: Mainstream and emerging memory candidates which drive most of research [12; 13; 14].
# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 1 Introduction  
With the increasing popularity of imaging devices as well as the rapid spread of social media and multimedia sharing websites, digital images and videos have become an essential part of daily life, especially in everyday communication. Consequently, there is a growing need for effective systems that are able to monitor the quality of visual signals. Obviously, the most reliable way of assessing image quality is to perform subjective user studies, which involves the gathering of individual quality scores. However, the compilation and evaluation of a subjective user study are very slow and laborious processes. Furthermore, their application in a real-time system is impossible. In contrast, objective image quality assessment (IQA) involves the development of quantitative measures and algorithms for estimating image quality.  
Objective IQA is classified based on the availability of the reference image. Full-reference image quality assessment (FR-IQA) methods have full access to the reference image, whereas no-reference image quality assessment (NR-IQA) algorithms possess only the distorted digital image. In contrast, reduced-reference image quality assessment (RR-IQA) methods have partial information about the reference image; for example, as a set of extracted features. Objective IQA algorithms are evaluated on benchmark databases containing the distorted images and their corresponding mean opinion scores (MOSs), which were collected during subjective user studies. The MOS is a real number, typically in the range 1.0-5.0, where 1.0 represents the lowest quality and 5.0 denotes the best quality. Furthermore, the MOS of an image is its arithmetic mean over all collected individual quality ratings. As already mentioned, publicly available IQA databases help researchers to devise and evaluate IQA algorithmsand metrics. Existing IQA datasets can be grouped into two categories with respect to the introduced image distortion types. The first category contains images with artificial distortions, while the images of the second category are taken from sources with \"natural\" degradation without any additional artificial distortions.  
The rest of this section is organized as follows. In Section 1.1, we review related work in NR-IQA with a special attention on deep learning based methods. Section 1.2 introduces the contributions made in this study.
## 2 Materials and Methods
### Flexibility Measurements and Grouping  
Three flexibility measurements were employed: TTT, SRT and MST. For TTT measurement, in line with the method of the previous studies [19; 20], participants kept their knees fully extended and tried to touch or overreach the floor baseline with their fingertips (Figure 1a). For SRT measurement, in line with the method of [PERSON] and [PERSON] [22], participants sat with their heels firmly against a fixed wooden box and kept their legs straight, placed one hand on top of the other, and then reached forward as far as they could by moving their fingers along the measuring ruler. How far beyond the toes each participant was able to reach was set as the SRT score (Figure 1b). For MST measurement, in line with the method of [PERSON] et al. [26], participants stood erect while the lumbosacral junction was marked as indicated by the dimples of Venus. A mark 5 cm below and 10 cm above the junction was then made. Participants were then requested to perform their maximum trunk flexion, and the stretched distance between these two points was measured as the MST value (Figure 1c). Each measurement for all participants was repeated twice for the reliability assessment, and the mean values were then included in data analyses. For flexibility grouping, high and low flexibility groups for the TTT were set to be 3 cm below and 3 cm above the floor baseline (i.e., 0 cm), respectively [3]. Besides the TTT, there was no clear-cut point of high and low flexibility for both SRT and MST measurements. The flexibility levels (i.e., high, middle and low) for the three measurements in the study were thus determined based on the average flexibility values, as suggested by the previous studies [3; 26; 27; 28], as well as the sample size and the flexibility allocation.
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 3 Materials and Methods
### Dimensionality Reduction and Classification  
In the process of our crowd anomaly detection analysis, we adopted dimensionality reduction techniques to handle the gathered descriptors. These techniques aim to reduce the complexity of the feature set while retaining essential information. We explored two distinct approaches for dimensionality reduction: principal component analysis (PCA) [39] and autoencoders [40].  
The first method employed was principal component analysis (PCA). PCA seeks to transform the original features into a new set of uncorrelated variables, known as principal components, while retaining as much variance as possible. By projecting the data onto these components, we effectively compress the information into a lower-dimensional space.  
The second approach involves utilising autoencoders, a type of neural network designed to learn efficient representations of the input data. Autoencoders consist of an encoder network that compresses the input into a latent space representation and a decoder network that reconstructs the original input from this representation. By training the autoencoder to minimise the reconstruction error, it learns to capture the most salient features of the data in the latent space.  
After applying dimensionality reduction using PCA and autoencoders, we move on to the classification stage. In this step, we leverage the reduced-dimensional feature set to train neural network classifiers. Specifically, we utilise neural networks like the multi-layer perceptron (MLP) [41]. Neural networks are well suited to handle non-linear problems and extract intricate patterns from the input data.  
The versatility of neural networks is particularly beneficial in detecting anomalies in crowd behaviour. They excel in uncovering nuanced relationships within data and identifying unusual crowd dynamics that might remain unnoticed by traditional methods. Due to their hierarchical architectures, neural networks can capture both intricate details and higher-level representations, providing a comprehensive understanding of crowd behaviour.  
Throughout our study, we conducted evaluations using neural networks with varying numbers of hidden layers to determine the optimal configuration for our analysis. Through systematic testing and performance comparisons, we identified the most effective approach for crowd anomaly detection. This thorough approach ensured that our methodology was robust and capable of addressing the complexities of detecting anomalies within crowd dynamics.
## 2 Preliminaries of State Estimation Methods Using Kalman Filter
### Extended Kalman Filter (EKF)  
As a well-known nonlinear filter, the EKF is quite simple to implement. The EKF uses Linear Taylor expansion, and when the observation and state equations become continuous and linear, the estimated value converges to the actual value [43]. The performance relies on process and measurement noise matrices, which may cause divergence in filter estimation if these matrices are not estimated accurately enough [44].  
Let us begin with a nonlinear system as  
\[x_{k}=f(x_{k-1},w_{k-1}), \tag{11}\]  
\[y_{k}=h(x_{k},v_{k}). \tag{12}\]  
where \(x_{k}\) is a state vector of the nth dimension and \(y_{k}\) is an observation vector of m-dimensions. The state transition and observation functions \(f\) and \(h\) are found to be nonlinear. The formulated prediction equations are given by (13) and (14):  
\[A=\frac{df}{dx}\bigg{|}_{x}=\hat{x}_{k-1},\ \ x_{k}^{-}=f(\hat{x}_{k-1}), \tag{13}\]  
\[P_{k}=(I-K_{k}H)P_{k}^{-}. \tag{14}\]  
where A is the state matrix (13). Similarly, the update equations are  
\[H=\frac{dh}{dx}\bigg{|}_{x}=\hat{x}_{k-1}^{-}, \tag{15}\]  
\[K_{k}=\frac{P_{k}^{-}H^{T}}{HP_{k}^{-}H^{T}+R}, \tag{16}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}(y_{k}-h(\hat{x}_{k}^{-})), \tag{17}\]  
where \(H\) is the measurement Equation (15), \(K\) is the Kalman gain matrix (16), (17), and \(P_{k}\) is the process covariance matrix (14). A case of conflict may arise because the EKF considers the system process and measurement noise to be non-Gaussian in nature, but this may not be the case practically. As a result, this could lead to a change in the process noise and measurement noise experiencing first-order Taylor series expansion; furthermore, the EKF may make assumptions regarding inconsistent noise. Another drawback of the EKF is that it has to recalculate the Jacobian matrices at each operational point, which may amount to a complicated and time-consuming EKF process.
## 3 Position Tracking Controller Design
### Control Gain Selection Using Optimal Control  
For the gain tuning guide, the LQR method is applied to the state feedback controller (14). Because the tracking error dynamics (13) is controllable, the LQR method can be applied to it. The cost function is defined as  
\[J_{c}=\int_{0}^{t}\Big{(}e^{T}Q_{c}e+u^{T}R_{c}u\Big{)}d\tau. \tag{15}\]  
where \(Q_{c}\) and \(R_{c}\) are positive definite. To obtain the control gain for optimal control, the Riccati equation is used as follows:  
\[\begin{split}& A_{e}^{T}P_{c}+P_{c}A_{e}-P_{c}B_{e}B_{e}^{T}P_{c }+Q_{c}=0,\\ & K_{e}=-R_{c}^{-1}B_{e}^{T}P_{c}.\end{split} \tag{16}\]  
Generally, \(Q_{c}\) and \(R_{c}\) are chosen to be diagonal for easy gain tuning. Then, \(Q_{c}(1,1)\) in \(Q_{c}\) is chosen to be relatively large compared to others for the position control. When the maximum velocity is slow in the reference position, \(R_{c}\) is chosen to be relatively small compared to the \(Q_{c}\) for the improvement of the position tracking performance. On the other hand, when the maximum velocity is fast in the reference position, \(R_{c}\) is chosen to be relatively large compared to the \(Q_{c}\) for the avoidance of the input saturation.
## 5 Validation Results and Discussion
### Dynamic Model  
The assessment of the dynamic model was focused on the daily temperature variations, because this is the timescale at which the transient behavior of buildings determines the thermal comfort and  
Figure 8: Steady-state condition of heat flow rate to indoor chamber of (**a**) hollow block; (**b**) block brick; and (**c**) wood frame and brick veneer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Measured Data [40]**} & \multicolumn{2}{c}{**Calculated Data**} & \multirow{2}{*}{\(\textbf{U}_{EXP}/\textbf{U}_{SIM}\)} \\ \cline{2-2} \cline{4-6}  & \(\textbf{R}\) **(k m\({}^{2}\)/W)** & \(\textbf{U}\) **(W/m\({}^{2}\) K)** & \(\textbf{R}\) **(k m\({}^{2}\)/W)** & \(\textbf{U}\) **(W/m\({}^{2}\) K)** \\ \hline Hollow block uninsulated & 0.49 & 2.04 & 0.4 & 2.501 & 0.82 \\ Hollow block insulated & 0.79 & 1.27 & 0.84 & 1.19 & 1.06 \\ Block brick cavity uninsulated & 0.63 & 1.59 & 0.599 & 1.667 & 0.95 \\ Block brick cavity insulated & 1.5 & 0.67 & 1.499 & 0.667 & 1.00 \\ Wood frame & 2.61 & 0.38 & 2.285 & 0.438 & 0.87 \\ Wood frame brick veneer & 2.78 & 0.36 & 2.448 & 0.408 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulink and test value of resistant and transmittance with six different types of walls.  
the energy consumption. For this reason, the model was used to simulate the condition of a two-day experiment, whose results were available in literature.  
Figure 9 shows the total energy response for 24 h as a function of the measured average thermal conductance of the walls. The results show that the amplitude of periodic heat flux was influenced by the thermal conductance of the wall. In addition, the maximum and minimum response were related to block and veneer, respectively.  
The model again showed very good agreement with experimental data: As shown in Figure 10, for this kind of dynamic simulation the fraction of data predicted within 10% was equal to 1, while _MAPE_ = 3.1%.  
Figure 10: Total energy input: Calculations and measurements comparison.  
Figure 9: Total energy vs. thermal conductance for 24 h (measured data from [40]).  
Finally, a simulation was conducted in order to evaluate the heat flow rate vs. time during a day. Using, as the input, the same diurnal sol-air temperature cycle of [40], the variation of the heat flow rate during 24 h was calculated in terms of heat flow rate to indoor chamber with \(t_{1}-t_{2}\
eq 0\), called \(Q_{t}\), and heat flow rate to indoor chamber with \(t_{1}-t_{2}=0\), called \(Q_{0}\), where \(t_{1}\) and \(t_{2}\) are, respectively, the average temperatures of inside and outside wall surfaces.  
At a general glance, Figure 11 shows that there was a very good agreement between measurements and calculations: For this dynamic simulation, the _MAPE_ was equal to 2.04% and the fraction of data, predicted to within \(\pm\)10%, was equal to 0.95 (Figure 12).  
Figure 11: Heat flow rate vs. time (measured data from [40]).  
Figure 12: Heat flow rate: comparison between calculations and measurements.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 5 Experimental and Performance Evaluation
### Evolution Metrics  
The TensorFlow library was used to implement and analyze the performance of the proposed stacked multi-model for SED detailed in Section 4. TensorFlow is an open-source and free end-to-end software library with Keras as the backend used for multiple machine and deep learning tasks. It provides high-level APIs built in Python for easily training and building deep learning models in deep neural networks. All three models are available in the library, and were modified and trained according to the available DCASE dataset. The evolution metrics used throughout the experiments were the F1-score and ER. The ideal F1-score is 100, which indicates that the system is providing the highest precision and recall values. Conversely, the worst F1-score is 0, which indicates that the system is providing the lowest precision and recall values. The segment-wise F1-score is modelled as formulated below:  
\[\text{F1}=\frac{2\cdot\sum_{k=1}^{K}\text{TP}(k)}{2\cdot\sum_{k=1}^{K}\text{ TP}(k)+\sum_{k=1}^{K}\text{FP}(k)+\sum_{k=1}^{K}\text{FN}(k)} \tag{9}\]  
where \(k\) presents the number of sound event labels active in both the predictions and ground truth and TP represents true or positive for each segment of one second. The number of sound event labels that are active in predictions and inactive in the ground truth is known as FP, denoting false positive. Similarly, the number of sound event labels that are active in the ground truth and inactive in the predictions is known as FN, meaning false negatives. The second metric, the error rate (ER), is measured using the following Equation (10):  
\[\text{ER}=\frac{\sum_{k=1}^{K}S(k)+\sum_{k=1}^{K}D(k)+\sum_{k=1}^{K}I(k)}{\sum _{k=1}^{K}N(k)} \tag{10}\]  
where \(N(k)\) represents the total number of sound event labels that are currently active in the ground truth of a segment \(k\). The following equations are used to calculate the substitutions \(S(k)\), deletions \(D(k)\), and insertions \(l(k)\) for each of the \(K\) one-second segments in Equations (11)-(13), respectively:  
\[S(k)=min(\text{FN}(k),\text{FP}(k)) \tag{11}\]  
\[D(k)=max(0,\text{FN}(k)-\text{FP}(k)) \tag{12}\]  
\[I(k)=max(0,\text{FP}(k)-\text{FN}(k)) \tag{13}\]where \(D(k)\) represents the number of reference events that were not correctly identified (false negatives after substitutions \(S(k)\) are accounted for) and \(I(k)\) represents the number of events in system output that are not correct (false positives after substitutions are accounted for).
## 4 Methodology
### Spectral Indices  
Sentinel-2A images have 13 different spectral bands with various spatial resolutions. VNIR bands have 10 m spatial resolution. Many studies have shown that using indices as features in the classification can increase accuracy, especially according to the related classes. In this study, spectral indices and band ratios were used in CNN models with two combinations. These indices are normalized difference vegetation index (NDVI), differential vegetation index (DVI), and simple ratio (SR) (Table 2). These indices are beneficial for distinguishing green leaf objects generally. Besides, in this study, the water bodies class was distinguished from others well. Thus, there was no need to use indices for the water bodies class. These three selected indices helped to differentiate artificial surfaces from the forest and seminatural areas class. The effect of indices on classification was also investigated in this study. In the experiment, the five-band dataset includes VNIR bands and NDVI index, and the seven-band dataset includes VNIR bands and NDVI, DVI, and SR indices as layer stacking.