esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 3 DM-CPDP Approach
### Dissimilarity Space Construction
#### 3.1.2 Dissimilarity Transformation  
The novelty of this paper is to use a kernel function to interpret the dissimilarity between samples in dissimilarity spaces. The kernel tools can represent a certain relationship between two objects. When relaxing the requirement for Mercer kernels, there are more powerful dissimilarity measures can be defined in the domain [24]. With the goal of better representing the dissimilarity between vectors, the arc-cosine kernel is used to measure the dissimilarity between samples. According to the analysis of the arc-cosine kernel, we use \(k_{0}(x,y)\) to measure the pair-wise dissimilarity, short in \(k(x,y)\), and shown as:  
\[k(x,y)=1-\frac{1}{\pi}\cos^{-1}\frac{x\cdot y}{||x||\cdot||y||} \tag{5}\]  
In the transformation process, we first use the arc-cosine kernel function to measure the dissimilarity between each original sample and the prototype set, and these values are used as row vectors, and then the low-dimensional dissimilarity space is constructed. The process is shown in Figure 3 and Equation (6). The  
Each sample in the dissimilarity space is represented as:  
\[x_{i}^{\prime}=k(x_{i},R)=\left\{k(x_{i},p_{1}),k(x_{i},p_{2}),\ldots,k(x_{i},p_{ r})\right\} \tag{7}\]  
The above methods are used to map each source domain and target dataset into the dissimilarity space. The dissimilarity space construction method is shown in Algorithm 2. Step 3-9 is the process of mapping a source domain data set into the subspace. In this stage, \(x_{i}^{\prime}\) is the dissimilarity representation of the sample \(x_{i}\). It is obtained by calculating the dissimilarity between each source data sample and the prototype set. Then we add \(x_{i}^{\prime}\) to the subspace \(D(X_{Su},R)\). Step 13-17 is the process of mapping target set into dissimilarity, and the process is similar to the source domain dataset. Finally, the dissimilarity space \(D\) is obtained by combining these subspaces.  
Figure 3: The process of data mapping.
## 4 Results
### Case Study: Cinca River--A Transect Around 50 m  
This subsection examines the challenges and insights gained from analyzing the Cinca River using FFSAR data. The river, which is approximately 50 m wide at the location where it intersects with S6's track 111, poses difficulties due to its narrowness, abrupt changes in bathymetry, and the presence of nearby puddles. Figure 14 (top panel) illustrates a 1 km section of the river characterized by three distinct WL\({}_{t}\) jumps marked with yellow rectangles. Additionally, the transect is nearly perpendicular to the river flow, precluding analysis of the river's slope on each pass.  
While acquiring WL\({}_{a}\) time series for complex river systems remains challenging, FFSAR data offer valuable insights into elevation changes within river sections. As depicted in the bottom panel of Figure 14, focusing our analysis on WL\({}_{i}\)s corresponding to waveform peak powers above 150 aW revealed two distinct clusters of WL\({}_{i}\)s with a noticeable jump occurring at a longitude of 0.302\({}^{\circ}\). By averaging the WL\({}_{i}\)s within each cluster and calculating the difference, we estimated a WL jump of 1.17 m. To qualitatively validate this result, we compared it with a Digital Terrain Model (DEM) derived from Light Detection and Ranging (LIDAR) data, covering both the shoreline and river centerline of the same section (Figure 14, bottom panel). The DEM, obtained from the Centro Nacional de Informacion Geografica (CNIG), had a mesh step of 5 m and a vertical resolution of 25 to 50 cm per pixel above land. It is important to note that LIDAR's response over water surfaces can be unpredictable, leading to sparse data and irregular waterbody representations [38]; thus, the validation remains qualitative. However, despite noise present in the river section (Figure 14, bottom panel), both DEM values along the shoreline and centerline exhibited  
Figure 13: Scatterplot representing the correlation between FFSAR water level errors for each pass obtained in Mequinensa-North and Mequinensa-South. Three outliers with errors larger than 30 cm were removed out of seventy-three measurements (Figure 7). The resulting Pearson correlation coefficient is 0.58 [37].  
an elevation jump around 0.302\({}^{\circ}\) longitude, corroborating the WL jump identified using FFSAR-derived data.
## 4 Results and Discussions  
The city of Hanzhong in Shaanxi province was chosen to be a representative of a hot summer and cold winter region in China. The temperature of Hanzhong during July 2017 and January 2018 is presented in Figure 2. The average maximum temperature and minimum temperature for summertime in Hanzhong is 33.97 and 24.13 respectively while it is 6.26 and \(-\)0.67 respectively for winter time. During the summertime, the average solar radiation intensity in the daytime is \(I_{ave}=755.40\)\(W\) and the sunrise time is 6:00 a.m. while the sunset time is 20:00 p.m. During the winter time, the average solar radiation intensity in the daytime is \(I_{ave}=646.75\), and the sunrise time is 8:00 a.m. while the sunset time is 17:00 p.m. The environmental temperature, \(T_{e}\), is expressed by using a sinusoidal function as follows [35]:  
\[T_{e}=-sin\left[\frac{2\pi(t+7200)}{86400}\right]\left(\frac{T_{max}-T_{min}}{ 2}\right)+\left(\frac{T_{max}+T_{min}}{2}\right) \tag{34}\]  
where \(T_{max}\) is the maximum temperature during a day, \(T_{min}\) is the minimum temperature during a day, and \(t\) is the time. Similarly, the solar radiation intensity \(I\) during daytime is given as:  
\[I=\sqrt{2}I_{ave}sin\left[\frac{2\pi(t-21600)}{100800}\right]\ (\text{summertime}) \tag{35}\]  
\[I=\sqrt{2}I_{ave}sin\left[\frac{2\pi(t-28800)}{64800}\right]\ (\text{ winter time}). \tag{36}\]  
The current research investigated the energy saving characteristics of a PCM roof under the influence of solar radiation with intermittent air conditioner (AC) working conditions. During the AC working time, the internal room temperature, \(T_{in}\), was set to be 26 for the summer cooling season while it was 18 for the winter heating season. During the AC off time, the windows of the building were open so that the internal room temperature, \(T_{in}\), was equal to the environmental temperature \(T_{e}\). In order to eliminate the effect of the initial roof temperature on the energy consumption results, the computation was iterated for 10 days (240 h) until the temperature of the roof exhibited a periodic variation as shown in Figure 3.
## 4 Experimental Results and Discussion  
The proposed approach is implemented using Keras with Tensorflow backend using GitHub repository [30] as a base algorithm. The experiments were performed on Google CoLab, utilizing Tesla T4 GPU with 16 GB memory and 12 GB RAM. The proposed network was evaluated from six different aspects: mAP, precision, recall, false positives, log-average miss rate, and inference time.  
The network training process can be divided into two steps; first, the Darknet53 network was fixed and the rest of the network was trained for ten epochs with a batch size of thirty-two keeping the learning rate to \(1\times 10^{-3}\). Then after obtaining a stable loss, the complete network was trained for fifty epochs with a batch size of eight, and the learning rate was reduced to \(1\times 10^{-4}\). The Adam algorithm is used for loss functions optimization with a learning rate decay of 0.1 per three epochs for constant validation data set loss. Training and testing images were given as patches to the network as discussed in Section 3.2.
## 1 Introduction
The traditional method of SAFT is called delay and sum (DAS) [29; 30]. The wave packets of the defect in different signals can be superposed in the image with the distances between the defect and the transducer locations in the scanning. This method is theoretically applicable for arbitrary scanning paths of the transducer. However, the method is carried out in the time domain with poor computational efficiency, and residual trailing exists in the image. Another method of SAFT is proposed based on sound field migration in the frequency domain, providing better computational efficiency as well as imaging quality compared with the DAS method [31; 32; 33]. However, the sound field migration of this method is deduced in the Cartesian coordinate system, which can only manage signals acquired by the transducer moving along a straight path. The paths of the transducer for the scanning of the tank floors are circular, making this method inapplicable. Efficient methods of SAFT for guided wave-based circumferential scanning have not been seen yet. In this paper, a method of SAFT for circumferential scanning (CSAFT) is proposed. In Section 2, the shear horizontal mode in the plates, named SH0, is selected for inspection, and the sound field of the magnetostrictive sandwich transducer (MST) used in this study is calculated; the imaging method is developed based on sound field migration in the polar coordinate systems. In Section 3, the proposed method is verified by numerical simulations and compared with other two methods, the B-scan and DAS methods, for different affecting factors. Experimental verifications are carried out in an iron plate in Section 4 and conclusions follow in Section 5.
## 3 Result and Discussion
### Simulation Results of Precipitation Distribution  
Snow is the material condition for the occurrence of drifting snow. Sufficient snow sources will increase the probability of drifting snow, and the amount of snowfall will also affect the intensity and duration of drifting snow. Figure 10 illustrates the monthly cumulative precipitation around the railway in winter. In region 1, precipitation is mainly concentrated in the Ya'an area east of Kangding and the area south of Kangding station. Precipitation is affected by temperature, and the eastern area has higher temperatures, so it has more precipitation than other areas. Notably, January exhibits the lowest average temperature and, consequently, the lowest level of precipitation. In February 2017, there was more precipitation in Kangding area, and the cumulative precipitation reached 500 mm in the southern part of Kangding. Conversely, the precipitation in region 2 is significantly lower than that in region 1. During the simulation period, only a few places have weak precipitation, and the areas with higher precipitation are far away from the railway. Moreover, the precipitation was highest in February 2017, which is similar to the result of region 1.
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 2 Materials and Methods
### Experimental Procedure  
The design of the device used was similar to those previously reported by [PERSON] et al. and [PERSON] et al. (2017, 2018). It consisted of a glass vessel with two openings with a screw thread on each. The bottom opening (1.8 cm diameter) was used to expose the membrane to the feed solution; the membrane was fixed using a rubber ring and a screw cap. The top opening (0.9 cm diameter) was used to fill the device with the receiving solution (5 mL of 0.01 M HNO\({}_{3}\)) and it was also closed with a screw cap. The PIM device was contacted with 100 mL of feed solution (1 cm immersed) which was continuously stirred (see Figure 1) and at the end of the experiment, both receiving and feed solutions were taken for analysis.  
Using this setup, different studies were performed using the appropriate feed solutions for the effect of metal concentration, competitive studies, and the effect of ligands. Moreover, a river water sample was also analyzed and used as received (no filtration or pH adjustment).  
All experiments were at least performed in triplicate and the temperature was kept within the range of 22 \(\pm\) 1 \({}^{\circ}\)C.  
For Zn/Cu competitive studies, the accumulation efficiency was calculated by means of Equation (1)  
\[\text{Accumulation efficiency}\left(\%\right)=\frac{V_{r}}{V_{f}}\cdot\frac{ \left[M\right]_{r,t}}{\left[M\right]_{f,0}}\times 100 \tag{1}\]  
where \(V_{r}\) denotes the volume of the receiving solution, \(V_{f}\) the volume of the feed solution, \(\left[M\right]_{r,t}\) the metal concentration of the receiving solution at a certain time, and \(\left[M\right]_{f,0}\) the metal concentration of the initial feed solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Membrane** & **Polymer (CTA)** & **Carrier (D2 EHPA)** & **Plasticizer (NPOE)** \\ \hline M1 & 50\% (0.20) & 50\% (0.20) & - \\ M2 & 50\% (0.20) & 40\% (0.16) & 10\% (0.04) \\ M3 & 50\% (0.20) & 30\% (0.12) & 20\% (0.08) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the PIMs studied in this work expressed in % mass. The amount of each component (g) is indicated in parentheses.  
Figure 1: Scheme of the experimental setup.  
To evaluate the results of the real water sample, the metal accumulated in the receiving solution was correlated with the free metal concentration by means of an experimental calibration curve.
## 3 Linear Streaking Measurements at Free-Electron Lasers
### Terahertz Streaking with Hard X-rays  
**Issues at high photon energies**  
THz streaking with hard X-rays should, theoretically, be easier to achieve than streaking with soft X-rays or HHG sources for a given ponderomotive potential \(U_{p}=e^{2}E_{0}^{2}/4m_{e}\omega_{L}^{2}\), where \(\omega_{L}=\frac{2\pi c}{\lambda_{L}}\) is the angular frequency of the streaking field. The energy shift term of Equation (4) predicting the maximum THz streaking effect, \(\sqrt{8W_{i}U_{p}}\), shows that the energy shift increases as the square root of the electron kinetic energy after photoionization \(W_{\mathrm{i}}\). Thus, the streaking effect is indeed stronger and more pronounced, but the difficulties in streaking with hard X-rays come from different sources.  
Hard X-rays, especially those over 5 keV photon energy, tend to be fairly non-interactive with most materials. The photoionization cross sections for most elements and molecules drop by orders of magnitude when compared to soft X-rays [108], and make the observation of any kind of photoelectron spectrum on a single-shot basis difficult. Since most FELs that can generate high-energy X-ray radiation tend to have shot-to-shot temporal jitter, and THz streaking is used as a diagnostic to measure that jitter, this difficulty has to be overcome, and there must be enough signal generated in a single shot to measure the temporal properties of the FEL beam. Simply ramping up the gas pressure in the interaction region to get more signal is also not an option, since the TOF spectrometers used for THz streaking measurements have MCPs as their detectors, and they tend to begin malfunctioning if the background pressure of the vacuum chamber becomes too high. Last, but not least, the choice of observable photoionization lines is very limited for hard X-rays, and the large streaking effect heavily influences the settings on the TOF spectrometers themselves. Yet, the experience with the Photon Arrival and Length Monitor (PALM) at the beamtimes performed at the SACLA FEL [12] have conclusively shown that these challenges can be overcome.  
Figure 21: Streaking measurement made with THz fields generated in DAST at the LCLS. (**a**) In the panel, the single-shot measurements are displayed in the order that they were collected. (**b**) The single-shot measurements are sorted based on the X-ray pulse arrival time measured by spectral encoding. (**c**) The panel shows the sorted and binned spectrogram collected with the opposite sign THz field, providing access to the spectral phase.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 3 Network Model, Q-Learning, and Fuzzy Logic
### Q-Learning  
Q-learning is a reinforcement learning technique that is widely used in making a decision. The main idea is to achieve a specific goal based on experience learning from the past. The standard Q-learning framework consists of four components: an environment, one or more agents, a state space, and an action space, as shown in Figure 2. The Q-value represents the approximate goodness of the action concerning the agent's goal. An agent chooses actions according to the policy and the Q-value. After performing an action, the agent modifies its policy to attain its goal. The Q-value is updated using the Bellman equation as follows:  
\[Q(S_{t},A_{t})\leftarrow(1-\alpha)Q(S_{t},A_{t})+\alpha[R_{t}+\gamma\underset{ a}{max}Q(S_{t+1},a)], \tag{1}\]  
where \(Q(S_{t},A_{t})\) is the Q-value of action \(A_{t}\) at a given s \(S_{t}\). \(R_{t}\) is the reward obtained if performing action \(A_{t}\) in the state \(S_{t}\). Moreover, \(\underset{a}{max}Q(S_{t+1},a)\) is the maximum possible Q-value in the next state \(S_{t+1}\) for all possible actions \(a\). \(\alpha\) and \(\gamma\) are the learning rate and the future reward discount factor. Their values are set between 0 and 1.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Datesets and Settings
#### 4.1.2 Evaluation Metrics  
The study benchmarks the proposed method using several standard evaluation metrics, including Multiple Object Tracking Accuracy (MOTA) [27], IDF1 Score [28], Mostly tracked targets (MT), Mostly lost targets (ML), the number of False Positives (FP), the number of False Negatives (FN), and the number of Identity Switch (IDs) [29].
## 1 Introduction
### Structure and Objectives of This Review  
In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. Due to the increasing impact of climate change and EWEs on temperate forests over the past decade [35; 38; 41] and the launch of high-potential Earth system observers, the Sentinel satellites [75], 2014 was identified as an appropriate starting year. The overall structure of the review is outlined below:  
* The introduction in Section 1 presents the relevance of the potential of remote sensing to monitor temperate forests in the context of a changing climate and an increasing number and intensity of EWEs.
* The literature selection process is explained in Section 2 by providing an overview of the literature databases used and the filters applied. By focusing on the primary abiotic disturbances caused by climate change and EWEs, these filters include the distinction from biotic forest disturbances such as bark beetle infestations.
* Section 3 presents the results of the review process. It aims to identify the potential of Earth observation to determine the impacts of climate change and EWEs on temperate forests. First, the evolution of the research field over time is described. This is followed by the identification of hotspots of study areas and author affiliations. The sensors used and the temporal and spatial scales are presented in the next subsection. The Section 3 concludes with a detailed analysis of the research foci. The studies are classified according to climate change or different EWEs and their remotely sensed impacts on temperate forests, as well as an in-depth analysis of the forest differentiations used in the studies to identify relevant research gaps.
* The discussion of the results, the limitations of the review, and the urgent need for dense forest monitoring is presented in Section 4.
* Section 5 highlights the main findings, and concludes with the potential of remote sensing to detect the impacts of climate change and EWEs on temperate forests.
## 4 Experiment
### Ablation Experiments
#### 4.4.1 The Function of Individual Modules
By incorporating gradient changes, adaptive weight computation, and multi-head self-attention, RAA offers an effective enhancement strategy for medical image segmentation tasks, addressing challenges such as variable cell morphology and ambiguous boundaries. Experimental results show that after integrating the RAA method into the network, when the proportion of labeled data \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Split** & **Method** & **F1-Score** & **Precision (P)** & **Recall (R)** & **Recall (MIOU)** \\ \hline \multirow{4}{*}{\(1/4\)} & Base & 0.6717 & 0.6915 & 0.6586 & 0.5375 \\  & Base+CAS & 0.6789 & 0.6998 & 0.6649 & 0.5393 \\  & Base+SWA & 0.6805 & 0.7014 & 0.6670 & 0.5404 \\  & Base+RAA & 0.6828 & 0.7038 & 0.6693 & 0.5412 \\ \hline \multirow{4}{*}{\(1/8\)} & Base & 0.6551 & 0.6871 & 0.6304 & 0.5198 \\  & Base+CAS & 0.6610 & 0.6935 & 0.6369 & 0.5233 \\  & Base+SWA & 0.6632 & 0.6960 & 0.6395 & 0.5241 \\  & Base+RAA & 0.6655 & 0.6984 & 0.6419 & 0.5247 \\ \hline \multirow{4}{*}{\(1/16\)} & Base & 0.5829 & 0.6596 & 0.5541 & 0.4645 \\  & Base+CAS & 0.5900 & 0.6675 & 0.5610 & 0.4719 \\  & Base+SWA & 0.5932 & 0.6705 & 0.5642 & 0.4752 \\  & Base+RAA & 0.5970 & 0.6740 & 0.5680 & 0.4788 \\ \hline \multirow{4}{*}{\(1/32\)} & Base & 0.6006 & 0.6379 & 0.5909 & 0.4668 \\  & Base+CAS & 0.6081 & 0.6458 & 0.5982 & 0.4691 \\ \cline{1-1}  & Base+SWA & 0.6105 & 0.6484 & 0.6007 & 0.4713 \\ \cline{1-1}  & Base+RAA & 0.6130 & 0.6511 & 0.6033 & 0.4726 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results of individual modules on the PanNuke dataset. was 1/4, 1/8, 1/16, and 1/32, the F1-score improved by 1.65%, 1.59%, 2.42%, and 2.06%, respectively, and the MIOU scores increased by 0.69%, 0.94%, 3.08%, and 1.24%, respectively. As shown in Table 4, the regional-adaptive attention mechanism demonstrated the highest effectiveness among the three strategies, with its improvement nearly equivalent to the combined effects of the other two. This suggests that in instance segmentation tasks involving complex backgrounds, the adoption of the regional adaptive attention mechanism enables the model to dynamically adjust attention based on regional complexity, directing greater focus toward intricate areas and consequently improving the accuracy of cell segmentation.
## 3 Methodology
### Post-Processing  
The post-processing phase commenced after identifying PS points meeting the AS-Index > 0.75 criterion within each of the 13 sub-stacks. To construct the comprehensive dataset for VUG analysis, all PS points satisfying the >0.75 threshold in any individual sub-stack were initially merged based on their geocoded coordinates. This merged dataset, containing points identified across different temporal segments, forms the foundation for subsequent analysis. The following steps detail how this consolidated dataset was cleaned, aligned temporally, and processed to derive the final height change information.