esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Scale Consistency in Depth Estimation  
Scale ambiguity is a fundamental challenge in monocular depth estimation. This is especially true in self-supervised approaches, where the depth scale cannot be directly observed. In static scenes, scale ambiguity is often resolved by enforcing geometric constraints, but in dynamic scenes, moving objects introduce additional scale inconsistencies. The authors proposed to address the scale ambiguity in dynamic regions by generating pseudo-depth labels for moving objects and using a scale-alignment module to align the depth predictions of dynamic objects with the static background [6]. This approach significantly improves depth estimation for dynamic regions but does not incorporate an iterative refinement strategy to further enhance depth accuracy [19]. Our DI-MDE framework extends this idea by combining scale alignment with an iterative depth refinement process. This ensures that depth predictions for dynamic and static regions are not only aligned in scale but also progressively refined for improved accuracy. The integration of both techniques allows for a more holistic solution to the challenges of monocular depth estimation in dynamic environments. While significant progress has been made in monocular depth estimation, existing methods still face challenges when dealing with dynamic scenes and scale ambiguity [20]. Self-supervised approaches have improved depth prediction in static scenes, but struggle in dynamic regions. Iterative refinement methods have demonstrated their ability to enhance depth accuracy, but often do not address the specific issues related to scale inconsistencies. Finally, approaches that attempt to align depth scales between static and dynamic regions offer promising results but lack the refinement process necessary for state-of-the-art performance. The proposed DI-MDE framework advances these developments by integrating iterative elastic bins for depth refinement. It also includes dynamic region scale alignment, creating a unified self-supervised framework. This combination allows for better handling of both dynamic and static regions while ensuring scale consistency and iterative accuracy improvements. By addressing the limitations of previous methods, DI-MDE represents a novel step forward in achieving state-of-the-art monocular depth estimation performance.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 3 Forward Process
### Hybrid Approach  
Although some limitations exist, DA can significantly reduce the computational burden compared with the RTE computation. Taking advantage of DA, hybrid methods of RTE and DA have been studied [84; 85; 86]. The RTE is used only in the spatial and temporal domains, wherein using DA can be invalid for hybrid approaches.  
[PERSON] et al. succeeded in image reconstruction by employing a forward process coupling the RTE and PDE computations in 2D numerical simulations of the FD measurement [87]. The measured circular object was divided into two circular subdomains. The RTE was used in one of the subdomains near the measured surface, whereas the PDE was used in the inner subdomain. The forward calculation coupling the RTE and PDE was implemented using FEM. The optimization problem with weighted Tikhonov regularization was solved using the Gauss-Newton method. It was demonstrated that the coupled RTE and DA models reconstructed low-scattering inclusions as well as the RTE model from the measurements simulated using the MC method, including Gaussian-distributed noise.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 4 Results
### Jason-3 Attitude Determination
Finally, the yaw angle \(\theta_{y}\) is between \(-180^{\circ}\) and \(180^{\circ}\) in all cases, as expected. It is worth noting that the satellite spacecraft performs recurrent yaw flip maneuvers (i.e., yaw transitions from \(\theta_{y}=0^{\circ}\) to \(\pm 180^{\circ}\) and conversely) approximately every 58 days. These maneuvers are carried out for sufficient power inflow, and their scheduling is based on a Sun-pointing algorithm. A spectral analysis of Jason-3 attitude angles over CDN1 and GVD1 transponders was performed to identify main periodic constituents. The amplitude spectra were calculated using the standard Fast Fourier transform algorithm and are provided in Figure 8. The results reveal various periodic constituents for Jason-3 roll, pitch and yaw angles. The periodicities of the most distinct spectral peaks are estimated after applying a Hann window to the time series of Figure 7 to reduce side lobe artifacts and are summarized in Table 2. Differences were observed in both peak amplitudes and peak periodicities among the different attitude angles and satellite passes. For example, the amplitude of the roll angle at 117 days period is larger for D018 compared to A109, whereas the opposite is true for the yaw angle amplitude at 58 days period. Additionally, there were periodic constituents present in D018 amplitude spectra but not in A109, and vice versa. For example, the periodic constituent of 88 days for the yaw angle spectrum is evident in D018 but does not exist in A109. Another case is the periodic constituent of 51 days for the pitch angle, which is only present in A109 (Table 2). Figure 7: Jason-3 roll, pitch and yaw angles at CDN1 and GVD1 calibration sites. For the majority of cases, the constituent with the strongest amplitude corresponds to the 117 days period, followed by the 39 days period for the roll and pitch angles and by the 58 days period for the yaw angle. Two notable exceptions are the pitch amplitude spectra for the CDN1 and GVD1 D018 passes, where the strongest amplitude is at the 39 days period with a marginally higher value than the 117 days period. Some differences in the amplitudes of the most prominent periodic constituents are also evident. For example, the D018 passes have a larger amplitude than A109 at the period of 117 days for the roll angle. The most pronounced periodic constituents (117, 58 and 39 days) of Jason-3 attitude angles are also evident in the time series of range bias, as derived from the CDN1 transponder [47]. The period of 117 days corresponds to Jason-3 draconic period (i.e., period of solar beta angle). Finally, the period of 58 days is the yaw flip-maneuver period of Jason-3 and exists only in the yaw angle spectrum, as expected.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 2 Food Quality Indicators
### Temperature  
Temperature is another important indicator related to food spoilage, with high values and significant fluctuations with a strong impact on the shelf-life of refrigerated food [14; 52; 53]. Indeed, temperature rising can easily trigger microbial proliferation and subsequent product deterioration. Specifically, since temperature fluctuations may occur at any point in the distribution chain, real-time monitoring is essential for certain food products, such as fish, for which the temperature broadly affects the microbial activity rate.
# A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification
## 3 Experiment
### Result and Analysis  
We performed experiment for AARCNN model and the five models in Section 4 with same training dataset and test dataset. Each model was trained and tested five times with the same training data in different input order. The results are shown in Table 2 as the average stats.  
In Table 2, we can see that LSTM gets the worst performance. Standard LSTM model only builds memory of the sentence and considers neither aspect information nor attention information. It fails to detect the sentiment for different aspects and only outputs an overall sentiment for the sentence. Therefore, standard LSTM network is not suitable for the ABSA task.  
TD-LSTM beats LSTM by over 1% on both Restaurants and Cameras datasets, which shows that the aspect information does good to the ABSA task. However, TD-LSTM only introduces aspect information and does not contain attention mechanism, so it cannot recognize whether a word is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Restaurants**} & \multicolumn{2}{c}{**Cameras**} \\ \cline{2-5}  & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline LSTM & 74.21 & 81.55 & 84.59 & 87.90 \\ TD-LSTM & 75.67 & 82.75 & 85.43 & 88.93 \\ AT-LSTM & 76.43 & 83.24 & 86.28 & 89.28 \\ AT-CNN & 76.08 & 83.11 & 85.97 & 89.23 \\ RCNN-LSTM & 78.59 & 83.58 & 86.32 & 89.41 \\ AARCNN & **79.04** & **84.00** & **86.75** & **89.80** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental data on Restaurants and Cameras dataset. Evaluation metrics are accuracy and F1 measure. Best scores are in bold.  
truly important to express the sentiment. Unimportant words may disturb the analysis, and may lead to mistakes.  
The two attention-based approaches, AT-LSTM and AT-CNN, outperform LSTM by about 2%. The two models only introduce attention mechanism. Although the models cannot analyze the aspect information, they still show better performance than TD-LSTM, which means attention mechanism alone is better to promote the accuracy of ABSA task than aspect information analysis alone. Additionally, AT-CNN gets a higher score than AT-LSTM, which means that CNN-based attention model is better than LSTM-based attention model.  
RCNN-LSTM gets the highest score in the baseline methods. Compared to AT-CNN, RCNN-LSTM adds LSTM network to model sentence representation, which assures the dependency information does not get lost. Compared to AT-LSTM, its independent attention module has better performance to extract the important parts in the sentence.  
Our model, AARCNN, gets the best score. Our model takes advantages of both attention mechanism and aspect information to analyze the sentiment of the comment sentence. The model has promotion on the accuracy and F1 value, but it costs about twice the time compared to single LSTM network.
## 5 Experiment and Evaluation  
### Simulation Setup  
Through pre-training with the expert demonstrations, the agent passes over the initial stage and its neural network is trained without trial-and-error cost. Furthermore, memory optimization helps the agent exploit the data more efficiently and further improves its performance. All of this means that the PM-DQfD-based scheme is able to achieve faster convergence speed, less memory occupancy, higher energy efficiency and lower routing latency, which is vital to effectiveness and reliability in wireless communication. Thus, the approach is especially suitable for energy-constrained and delay-limited applications in CRN.  
In this section, the performance of the PM-DQfD-based scheme is assessed. The results of the proposed scheme are compared with traditional schemes such as i) Deep Q-Network (DQN) [31]; ii) Prioritized Memories Deep Q-Network (PM-DQN) [20]; iii) Natural DQfD [29]; iv) Conjecture Based Multi-agent Q-learning Scheme (CBMQ) [32]; and v) Cognitive Radio Q-routing (CRQ-routing) [33] with respect to effectiveness, robustness and learning speed. The simulation framework was built using Python (3.5.1, Google, Mountain View, CA, USA). Specifically, the network environment and learning algorithm were coded on the basis of the Networkx (2.3, Los Alamos National Laboratory, Los Alamos, NM, USA) and Tensorflow (1.4.0, Google, Mountain View, CA, USA), respectively. It was assumed that the expert source node applies DQN.  
In addition, we consider a networking scenario comprising 30 SU nodes. The location of nodes is subject to a two-dimensional uniform distribution. It is assumed that the coordinate of a SU node is \((x,y)\), and its probability density function is given by:  
\[f(x,y)=\left\{\begin{array}{ll}\frac{1}{A},&(x,y)\in G\\ 0,&\text{otherwise}\end{array}\right. \tag{21}\]  
where \(G\) is a bounded domain on the plane (\(G\) is a square area of \(2000\times 2000\) m\({}^{2}\) in our simulation process), and \(A\) is the size of bounded domain \(G\). The network topology is shown in Figure 5. In Figure 5a, SU node \(n_{24}\) is the source node and it establishes route to the destination node \(n_{10}\) through DQN. In Figure 5b, SU node \(n_{22}\) becomes a newly joined source node, and it routes messages by learning the routing protocol from the expert source node \(n_{24}\). Simultaneously, the relay node \(n_{0}\) and \(n_{6}\) have moved, and some changes take place in the network topology. Furthermore, there are 10 licensed channels available for SUs, and the set of transmit power comprises 40 levels: \(\{100,\ 120,\ 140,\ \ldots,\ 1000\ mV\}\). The remaining parameters are demonstrated in Table 1.  
Figure 5: Network topology consisting of 30 SUs: (**a**) Route establishment by self-learning, and (**b**) route establishment by apprenticeship learning.
## 5 Quantitative Evaluation  
### Evaluation Method  
The methods of evaluating topic models mainly include perplexity and topic consistency. The perplexity has its own merits, as it can evaluate probability-based topic models well, whereas in non-probability-based topic models, these methods do not capture semantic consistency between words [51]. Topic consistency can be used to measure whether words within a topic are coherent, i.e., if a group of terms are consistent with each other, then these terms are coherent. For a specific topic, the semantic similarity between words in the topic determines the degree of coherence of the topic, so topic consistency can be measured by the semantic similarity between words in the topic [52; 53; 54; 55]. The greater the consistency value of the topic is, the more coherent the words of each topic will be. To evaluate the non-probabilistic topic model proposed in this paper, the following two consistency measures were used: (1) University of Massachusetts (_U_Mass_) [56], (2) Normalized Pointwise Mutual Information (_NPMI_) [57].  
_U_Mass_ is defined as:  
\[U\_Mass=\frac{2}{N\times(N-1)}\sum_{i=1}^{N-1}\sum_{j=i+1}^{N}\log\frac{P(w_{ i},w_{j})+\epsilon}{P(w_{j})} \tag{5}\]  
where \(P(w_{i},w_{j})\) is the joint probability of two words \(w_{i}\) and \(w_{j}\). A small value for \(\epsilon\) is chosen to avoid calculating the logarithm of 0.  
_NPMI_ is defined as:  
\[NPMI=\frac{1}{K}\sum_{K}\frac{2}{T(T-1)}\sum_{1\leq i\leq j\leq T}\frac{\log_{ 2}(\frac{p\left(w_{i},w_{j}\right)}{p(w_{i})p(w_{j})})}{-\log_{2}p\left(w_{i}, w_{j}\right)} \tag{6}\]  
where \(K\) is the number of topics, and each topic consists of the \(T\) most relevant word. \(p\big{(}w_{i},w_{j}\big{)}\) is the probability that the word pair \(\big{(}w_{i},w_{j}\big{)}\) co-occurs in a document, and \(p(w_{i})\) is the probability that the word \(w_{i}\) appears in the document.
# Forest Resistance and Resilience to 2002 Drought in Northern China
## 2 Materials and Methods
### Data
#### 2.2.2 Satellite-Derived Vegetation Growth Data  
Remote sensing data could reflect long time series and large-scale spatial information of vegetation. To compare with the response generated from MTE-GPP, the satellite-derived MODIS 500 m 16-day GPP product (MOD17A2H collection 6) was used, hereafter referred to as 'GPP' directly. Another type of satellite-derived vegetation index (enhanced vegetation index, EVI) has been widely used in previous researches about the responses of forests to droughts. The EVI describes the growth condition of canopies, and has been suggested as having improved sensitivity over high biomass regions, e.g., forests [22], compared to the other popular vegetation indices such as normalized difference vegetation index (NDVI). The responses of EVI to climate factor might be different from GPP, while only a few studies compared their performances on the responses of forests to drought [10; 11]. To explore the differences between GPP and EVI, the MODIS 500 m 16-day vegetation index product (MOD13A1 Version 6) was chosen as EVI data source in our study, hereafter referred to as 'EVI' directly. It contains the global EVI values since 2001 to present. Both of the two datasets in our study area were mosaiced and extracted via Google Earth Engine (GEE). After that, we regenerated monthly EVI and GPP from these 16-day data, following an official procedure from MODIS team which considered days as weights [23]. The regeneration was performed in MATLAB.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood In Situ Sensors

Table 2: Advantage and disadvantage analysis of flood in situ sensors. These sensors are very important components of the flood early warning for flood level monitoring and detection. There are different methods for flood prediction based on the precipitation data, but they totally depend on the spatial resolution of the dataset [63]. The measurement intervals can be improved in the first place by using automated sensors, such as automated ultrasonic water level sensors [64] and automated pressure sensors. Pressure sensors are more economical, but they require additional barometric measurements to obtain more accurate values. Non-contact radar measurements at times of high water levels are regarded as one of the most accurate and reliable methods, significantly improving temporal and spatial resolution. In addition, fiber optic sensors [65] and capacitive sensors are also used for flood detection. Infrared spectroscopy (IR) sensors are useful for water measurement; however, they have different issues during the measurement process due to the limitations of the principle of operation, the measurement distance, the cost of maintenance, and the environmental disturbances [66]. There are a few urban flood-sensing installations that use IR sensors for the feeder task of remote temperature sensing [67]. Ultrasonic sensors, due to their ability to penetrate various media and lower long-term maintenance costs, are more suitable than IR sensors for harsh outdoor environments, making them a more mature and common choice for water level measurement in flood early warning systems [68]. Most of the effective flood monitoring systems or flood wireless sensor networks use ultrasonic sensors as carriers [69; 70; 71] using Internet of Things (IoT) technology, wireless sensors, and a new type of battery-less wireless sensor based on radio frequency identification (RFID) [72]. The passive nature of these sensors preserves the RFID range, enabling more accurate hydrological data extraction in flood monitoring. Advances in water level monitoring have led to the development of wireless sensor networks (WSNs) [73], a promising technology that offers near-real-time, automated flood monitoring with less energy consumption, extended data transmission distances, and convenient wireless formats [74]. This is now considered to be one of the main promising solutions for flood monitoring [75]. The interconnection of multiple sensors enables low-cost remote monitoring and real-time data sharing of water conditions [76]. Current flood monitoring relies on data from river-based sensors, including direct water level measurements and indirect indicators like temperature, humidity, rainfall, wind speed, and flow rate, which can calibrate hydrodynamic models. Artificial intelligence algorithms can not only combine data from multiple sensors to make
## 2 System Model
\right]_{N\times N} \tag{4}\]  
where \(\tau\) is interference coefficient with \(\tau\in[0,0.5)\). Correspondingly, the error performance for the traditional WDM-VLC system utilizing multi-color LED can be significantly improved if the color crosstalk problem is considered. At the receiver side, equipped with the specific optical filters, the receiver PDs are used to detect the received signals with defined wavelengths. Accordingly, the received signal is given as  
\[\mathbf{r}=\mathbf{THs}+\mathbf{n}, \tag{5}\]  
where the channel gain matrix \(\mathbf{H}\) can be expressed as \(\mathbf{H}=diag([h_{11},h_{22},\cdots,h_{NN}])\) because the interval for multi-color LED chips is negligible compared with the distance between the transceivers Ref. [19], and \(diag(\cdot)\) denotes the diagonal matrix. Based on [20], \(\mathbf{n}\) can be modeled as signal-independent additive white Gaussian noise (AWGN) with zero mean and noise variance \(\sigma^{2}\). Combining Equations (1) and (5), the received signal \(\mathbf{r}\) can be rewritten by  
\[\mathbf{r}=\mathbf{TH}\boldsymbol{\gamma}\circ\mathbf{d}+\mathbf{TH}_{DC}+ \mathbf{n}. \tag{6}\]  
After removing the DC component \(\mathbf{TH}_{DC}\), the received signal \(\bar{\mathbf{r}}\) is expressed as  
\[\bar{\mathbf{r}}=\mathbf{TH}\boldsymbol{\gamma}\circ\mathbf{d}+\mathbf{n}. \tag{7}\]  
Figure 2: Spectral model for the quadrichromatic LED (QLED) with red/amber/green/blue (RAGB) colors. It should be noted that the \(i\)th PD is interested in the data information from the \(i\)th color chip while the data from other color chips can be regarded as interference. Thus, the received signal from the \(i\)th receiver is given by  
\[\tilde{r}_{i}=t_{ii}h_{ii}\gamma_{i}a_{i}+\sum_{j=1,j\
eq i}^{N}t_{ij}h_{jj} \gamma_{j}d_{j}+\sigma^{2}, \tag{8}\]  
where \(t_{ij}\) is the item from the \(i\)th row and \(j\)th column of color crosstalk matrix \(\mathbf{T}\).
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 3 Recommended Deep Learning for Inverter Sizing  
### System Cost Consideration  
This study recommends running the optimization process of PV array-to-inverter ratio with one platform approach using Deep Learning algorithms taking into account the PV system's whole cost, annual energy yield, and hyperparameters to control the learning process. Therefore, it is essential to identify some terms in this concern such as the net present value (NPV) [100; 101; 102], which is determinedby:  
\[\mathrm{NPV(i,N)}=\sum_{\mathrm{t=0}}^{\mathrm{N}}\frac{\mathrm{R_{t}}}{\left( 1+\mathrm{i}\right)^{\mathrm{t}}} \tag{7}\]where \(\rm R_{t}\) represents the net cash flow at time t. The net present value can be defined as the summation of a time series of cash flows brought into the present. The choice of the ideal PV-inverter ratio that maximizes NPV is a moving target, as represented in Figure 4, while the significant variables that affect NPV and how they interact is shown in Figure 5.
## 3 Empirical Results
### Robustness Test  
Considering the comparability of estimation results and the reliability of conclusion, this paper adopts three methods to conduct the robustness test, and the results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Stage Heterogeneity Analysis**} & \multicolumn{4}{c}{**Regional Heterogeneity Analysis**} \\ \cline{2-6}  & **2000–2014** & **2015–2020** & \multirow{2}{*}{**East-Middle**} & \multirow{2}{*}{**East-West**} & \multirow{2}{*}{**Middle-West**} \\  & **Early Stage** & **Systematize Stage** & & & \\ \hline L.Ingtfp & 0.1233 & 0.6049 *** & 0.3720 *** & 0.0826 & 0.1439 \\  & (1.17) & (2.87) & (3.02) & (0.75) & (0.12) \\ W*Iner & \(-\)0.0053 & \(-\)0.1274 *** & \(-\)0.0092 & \(-\)0.0441 & \(-\)0.0398 \\  & (\(-\)0.16) & (\(-\)2.19) & (\(-\)2.05) & (\(-\)1.18) & (\(-\)0.99) \\ W*Infiscal & 0.7801 ** & 3.3919 *** & 1.5725 *** & 1.0188 *** & 0.3726 \\  & (2.52) & (2.16) & (3.12) & (2.63) & (0.61) \\ W*Iner \(\times\) Infiscal & \(-\)0.0422 & \(-\)0.7482 * & \(-\)0.3354 ** & \(-\)0.0977 & 0.0139 \\  & (\(-\)0.58) & (\(-\)1.79) & (\(-\)2.31) & (\(-\)1.32) & (0.12) \\ Control variables & Control & Control & Control & Control & Control \\  & 0.1915 ** & 0.2438 * & 0.1647 ** & 0.0471 & 0.0923 \\ \(\rho\) & (2.75) & (1.85) & (2.36) & (0.49) & (1.20) \\ N & 434 & 155 & 380 & 460 & 400 \\ Log-L & \(-\)2039.9252 & 105.59 & \(-\)2364.6315 & \(-\)2025.4008 & \(-\)1073.0839 \\ R\({}^{2}\) & 0.0857 & 0.0511 & 0.1912 & 0.2878 & 0.6239 \\ \hline \hline \end{tabular} Note: Standard errors in parentheses, *** \(p\) < 0.01, ** \(p\) < 0.05, * \(p\) < 0.1.  
\end{table}
Table 6: Analysis of heterogeneity.