esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Development of the Methodology
Previous works [20] have shown that the GRACE signal is correlated with precipitation at various lags prior to the GRACE overpass, but also comprises non-climatic factors which are related to human activities, e.g., abstractions for irrigation and domestic water supply. The non-climatic factors cannot be easily incorporated into the model, but the residual correction approach may help to account for those components of the GRACE signal as well. Evaluation of the downscaled variable was performed at two Water Districts in Greece, i.e., in Thrace (NE Greece) and Thessaly (Central Greece) regions, where modeled results for TWSA from the Soil and Water Assessment Tool (SWAT) [56] from 2005 to 2015 were available from previous research [12]. In the Thrace Water District, the SWAT model refers to two adjacent basins of ~800 km\({}^{2}\), whereas in Thessaly, the model is applied in a basin of ~11,000 km\({}^{2}\). TWS from SWAT was computed by summing the individual components of the water budget, i.e., surface runoff, soil water and groundwater recharge at the GRACE time steps. Snow water storage is considered negligible in the examined areas (see provided snow water equivalent from ERA5-Land Monthly Averaged--ECMWF Climate Reanalysis for the two test basins in the Supplementary Material Table S1). The SWAT-based TWSAs were evaluated by subtracting the 2005-2009 TWS mean from each record in the time series to match the GRACE baseline temporal average. In an analogous manner, the GRACE TWSAs were computed for the same baseline period, i.e., 2005 to 2009, following the technique described in [44]. Details of the SWAT model development and application can be found in [12], along with the model parameterization, calibration and verification process. Additionally, the downscaled results were compared to TWSAs estimated from the ERA5-Land Monthly Averaged--ECMWF Climate Reanalysis [57]. ERA5-Land is a global dataset and provides land variables combining model data with observations for several decades at ~11 km spatial resolution. ERA5 Land TWSAs were estimated by adding the individual water components in the two test sites (i.e., surface and subsurface runoff, soil moisture, snow depth, water equivalent) and subtracting the mean TWS for the reference period. Cross-correlation analysis has been conducted at the basin scale, and the GPM time lags with the highest correlation were used in the downscaling computations. The regression equation was then computed for the 2005-2015 period for the two predefined areas, estimating the pixel-wise regression parameters along with the residuals from Equations (1)-(4).
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 2 Distinctive Characteristics of Tumor Microenvironment and Heterogeneity of EPR
### Cell Communication Within Tumor Microenvironment  
Intercellular communication is essential for the proper function and movement of cells, as well as for their response to external stimuli. Furthermore, it plays an important role in the dynamic and reciprocal exchange of information between cancer and normal cells, which is often mediated by the extracellular matrix (ECM) [44,52].  
Intercellular communication is a complex system in which cells transmit signals either through direct contact with other cells, via electrical coupling, gap junctions (GJs), receptor-ligand interactions, or nanotubes (e.g., membrane nanotubes, MNTs), or indirectly through endocrine or paracrine signaling. Non-cancerous stromal cells communicate through the transport of healthy mitochondria and other organelles via tunnel or horizontal transport nanotubes. Soluble factors such as cytokines, chemokines, and growth factors, as well as membrane vesicles, which do not require specific receptors, facilitate indirect signaling between neighboring or distant cells [44].
# Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving
## 2 Methods
### Self-Tuning Lam  
We now introduce the new Self-Tuning Lam by deriving our approach to learning the hyperparameters that we identified in Section 2.2.  
In the subsections that follow, we utilize the constants itemized in Table 1. The \(\text{LamRate}(i)\) refers to Equation (1), and \(N\) is the run length in number of SA iterations. Although they appear to vary with \(N\), the \(N\) cancels the \(N\) in the definition of \(\text{LamRate}(i)\). Thus, these are truly constants, and the value of \(\lambda_{0.001}\) is independent of \(N\), and likewise for the others. The purpose and rationale for these is explained as they are used.  
We have decomposed the formal presentation of the Self-Tuning Lam into several subsections. In Section 2.3.1, we show how to determine the length of the tuning phase. In Section 2.3.2, we explain how we tune the hyperparameters that control the EMA of the acceptance rate throughout the run. Those parameters, \(\text{AcceptRate}(0)\) and \(\alpha\), only depend upon the run length \(N\) in a rather straightforward manner. We derive the initial temperature \(T_{0}\) in Section 2.3.3, and the rate of temperature change \(\beta\) in Section 2.3.4. This is where most of the tuning occurs. Tuning the temperature related parameters depends upon the run length, as well as the scale of cost differences between neighboring solutions, the latter of which involves sampling the solution space. Finally, Section 2.3.5 puts it all together with pseudocode of the Self-Tuning Lam.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constant** & **Definition** & **Value** \\ \hline \(\lambda_{0.001}\) & \(\text{LamRate}(0.001N)\) & 0.976867078879564 \\ \(\lambda_{0.002}\) & \(\text{LamRate}(0.002N)\) & 0.9546897506857566 \\ \(\lambda_{0.01}\) & \(\text{LamRate}(0.01N)\) & 0.8072615745900611 \\ \(\lambda_{0.02}\) & \(\text{LamRate}(0.02N)\) & 0.6808590431613767 \\ \(\zeta_{0.001}\) & \(-1/\ln\left(\frac{0.001}{1.001-\lambda_{0.001}}\right)\) & 0.3141120890121576 \\ \(\zeta_{0.002}\) & \(-1/\ln\left(\frac{0.001}{1.001-\lambda_{0.002}}\right)\) & 0.260731492877931 \\ \(\zeta_{0.01}\) & \(-1/\ln\left(\frac{0.001}{1.001-\lambda_{0.001}}\right)\) & 0.1898791047222955 \\ \(\zeta_{0.02}\) & \(-1/\ln\left(\frac{0.001}{1.001-\lambda_{0.002}}\right)\) & 0.17334743675123146 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constants used in the specification of the Self-Tuning Lam, including the constant, its mathematical definition, and its double-precision floating point value.
## 3 Materials and Methods
### Supervoxel Model Construction  
Supervoxelization: Original MLS point clouds collected using onboard LiDAR systems are often noisy and unorganized. The amount of data and the density of the point cloud data are reduced by reorganizing the original MLS point cloud into supervoxels, thereby reducing computational time and storage costs while improving processing efficiency. Supervoxels represent a collection of points designed to segment a point cloud into 3D models with a certain level of connectivity and semantic consistency [41]. In this study, we used the boundary-enhanced supervoxel segmentation algorithm proposed by [PERSON] et al. [42] to process the original MLS point cloud. This algorithm considers local information and accurately reflects the boundary characteristics of objective objects during the transformation of a point cloud dataset into supervoxel sets. The supervoxelization results for the MLS point data are shown in Figure 3. Supervoxelization converts 3D discrete point information into clustered information with defined geometric and spatial features, providing the foundation for accurate and comprehensive road surface and road boundary extraction in subsequent steps.  
Figure 2: Statistical filter denoising: (**a**) Before point cloud denoising (**b**) After point cloud denoising.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Range Measurement  
According to the range measurement, we divide the localization algorithms into two classes, namely, range-based scheme and range-free scheme. Generally speaking, the ranged-based scheme estimates the distance and then converts it into the position information. The algorithm contains the TOA, Time Difference of Arrival (TDOA), AOA and Received Signal Strength Indicator (RSSI). In [38], the authors describe the range-based scheme as three steps, namely, range measure, location estimation, and calibration. Two cases of range measurements are discussed, namely, reference node within transmission range of the ordinary and reference node outside the transmission range of the ordinary. It is suggested that RSSI, TDOA, and TOA are suitable for ordinary nodes to estimate their distances to the reference nodes, while, for the second case, the Euclidean distance propagation method is more suitable. In the phase of location estimation, at least d + 1 range measures are needed to estimate the location of the d-dimensional space. The aim of calibration is to refine the location estimation. The range-free scheme does not need the range measurement and bearing information, and employs the local topology and locations of the surrounding anchor nodes to estimate the location. This scheme can only obtain a coarser position, that is to say that the precision is lower than the range-based scheme. Then, we continue to classify range-free or a range-based algorithm into two subcategories as synchronization and asynchronization schemes. As we know, the time synchronization is an important factor to pay attention. In addition, in many cases, the assumption is made that sensor nodes are synchronized with each other. Because of the difficulty of the precise time synchronization, some localization algorithms are proposed without the synchronization requirement.
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
However, the former requires complex post-processing, does not improve detection speed, and may result in decreased recognition accuracy. Although the latter has improved recognition accuracy, the model parameters are too large, leading to longer computation time. Existing algorithms fail to achieve a balance between container code recognition speed and accuracy, and they also have high requirements for hardware facilities, making it difficult to deploy them at port entrances. To address these issues, this paper presents a two-stage automatic container number recognition method based on deep learning algorithms. This method offers high recognition accuracy, fast speed, and easy deployment on devices with limited computational power. The first stage utilizes YOLOv4 for container number region localization, while the second stage employs Deeplabv3+ for container number character recognition on the localized region images. Additionally, improvements are made to the network structures of YOLOv4 and Deeplabv3+ to enhance the speed and accuracy of both models in the CCL and CCR tasks, resulting in the modified algorithms named C-YOLOv4 and C-Deeplabv3+. The method is applied to the data input segment of the port Internet of Things system. The main contributions of this paper are as follows:  
1. A dataset of container number images is constructed, primarily collected from container terminal gates. The dataset includes various complex scenarios such as tilted containers and rusty or contaminated container number characters;
2. The paper proposes the CCL algorithm based on C-YOLOv4. By compressing the backbone feature-extraction network, redesigning the multi-scale recognition module,and improving the loss function of YOLOv4, the algorithm achieves higher accuracy and speed in container number region localization in complex scenarios;
3. The paper introduces the CCR algorithm based on C-Deeplabv3+. A decoding structure combining sub-pixel convolutional upsampling and transpose convolutional upsampling is designed and applied to the DeepLabv3+ network. Additionally, a new dilated convolution branch is introduced to preserve more character details in the image without increasing the parameter count. The remaining parts of this paper are organized as follows: Section 2 provides a brief review of existing methods for CCL and CCR. Section 3 describes the framework of the container number recognition method based on C-YOLOv4 and C-Deeplabv3+. In Section 4, experiments on container number region localization and character recognition are conducted using a self-made dataset, followed by an analysis of the experimental results. Section 5 discusses some conclusions.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 2 Preliminaries
### Stochastic Decoding Algorithmic Description  
A binary \((N,K)\) LDPC code in 5G is characterized by the null space of parity check matrix (PCM) \(H\) with dimension \(M\times N\) over GF(2). The PCM is also visualized graphically as a bipartite Tanner graph [10; 11]. A set of \(M\) parity or check nodes of the Tanner graph represent the rows of \(H\), and a set of \(N\) bit or variable nodes of the Tanner graph represent the columns of \(H\). In stochastic decoding of LDPC code, the stochastic bit sequence of the channel probability values corresponds to their respective channel LLRs exchanged iteratively bitwise between the SCNs and the SVNs until the desirable codeword is found or the maximum iteration limit is reached [25]. To illustrate the simplification of the decoding process, a degree-3 SCN and a degree-3 SVN are adopted. Both SCN and SVN elements perform the bitwise modulo-2 arithmetic operations in SD. The steps are shown in the following:  
(1) Initialization: Once the channel probability values are reached at SVNs, compare the W-bit fractional binary equivalent sequence of \(P_{ch,v_{5}}\) with W-bit PRN \(U\) and initialize generated stochastic bit sequence to its corresponding SVNs \(v_{tj}\).  
(2) SCN update: As shown in Figure 3, \(SCN_{2}\) is connected to three SVNs: \(SVN_{2}\), \(SVN_{3}\), and \(SVN_{4}\). The arriving SCN-to-SVN single-bit extrinsic message for node \(SVN_{4}\) is computed as  
\[F_{c_{2}\to v_{4}}=a.(1-b)+b.(1-a) \tag{3}\]  
Here, \(a\) is the single-bit of stochastic bit sequence \(\{a[k]\}\), \(k=0,\ldots,f-1\), belonging to its \(P_{ch,v_{2}}\), which has been received as a message from \(SVN_{2}\) to \(SCN_{2}\) in the previous clock cycle, and \(b\) is the single-bit of stochastic sequence \(\{b[k]\}\) belonging to its \(P_{ch,v_{5}}\), which has been received as a message from \(SVN_{3}\) to \(SCN_{2}\) in a previous clock cycle.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Clock Cycle (\(k\))** & \(U\) & \(u\) (\(Real\)\(Value\)) & **Comparator Output (\(P>U\))** \\ \hline
0 & 1101 & 0.8125 & 0 \\ \hline
1 & 0111 & 0.4375 & 0 \\ \hline
2 & 0011 & 0.1875 & 1 \\ \hline
3 & 0110 & 0.375 & 0 \\ \hline
4 & 1001 & 0.5625 & 0 \\ \hline
5 & 0010 & 0.125 & 1 \\ \hline
6 & 1100 & 0.75 & 0 \\ \hline
7 & 0100 & 0(3) SVN update: In Figure 4, \(SVN_{4}\) is connected to three SCNs: \(SCN_{1}\), \(SCN_{2}\), and \(SCN_{4}\). The arriving SVN-to-SCN single-bit extrinsic message for node \(SCN_{2}\) is computed as  
\[F_{c_{4}\to c_{2}}=\frac{e.d}{e.d+(1-e).(1-d)} \tag{4}\]  
Here, \(e\) is the single-bit of stochastic bit sequence \(\{e[k]\}\) belonging to the received message from \(SCN_{1}\) to \(SVN_{4}\) in the previous clock cycle, and \(d\) is the single-bit of stochastic bit sequence \(\{d[k]\}\) belonging to the received message from \(SCN_{4}\) to \(SVN_{4}\) in a previous clock cycle.  
(4) Termination: This iterative process will stop if it reaches the maximum decoding cycle (DC) limit or all parity check equations are satisfied.
## 5 Advanced MPPT Techniques
### Metaheuristic MPPT Techniques
#### 5.2.1 Optimization-Based Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Tracking** & **Sensed** & **Circuity** & **Tracking Speed** & **Implementation Complexity** & **True MPPT** & **Accuracy** & **Cost** \\ \hline PSO & V, I & D & Fast & Medium & Yes & High & Medium \\ GWO & V, I & D & Very Fast & Medium & Yes & High & Medium \\ ACO & V, I & D & Fast & Complex & Yes & Medium & High \\ ABC & V, I & D & Fast & Complex & Yes & Medium & High \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of optimization-based MPPT algorithms [53; 82]. Figure 15: Flowchart of the ABC MPPT.
## 6 Conclusions  
This research addressed the problem of maximizing the DASR in the MMIMO with FDD systems considering URLLC scenarios, i.e., with LCT. To this end, a low-complexity power allocation optimization method was proposed for DASR maximization. In addition, this paper developed an analytical expression for the SINR and the DASR of the RZFP in FDD systems using the random matrix method. Different scenarios were examined to determine the effect of power optimization on the DASR of MMIMO with the FDD mode and LCT. The results demonstrated a precise alignment between the theoretical analysis (lines) and the simulated (markers) results, thereby substantiating the primary contributions of this paper. The results also indicated that a considerable enhancement in the DASR was obtained using the proposed power allocation method in comparison with the conventional uniform power allocation method. These findings allow for the implementation of FDD systems with high frequencies, especially in the scenarios of LCT. Future wireless communication systems, e.g., 6G, might adopt cell-free networks. Therefore, it is worthwhile to investigate a viable CSI estimation mechanism for the FDD systems with cell-free networks in the future. In addition, investigating the power allocation strategy for the aforementioned scenario may also be worth considering in the future.  
Conceptualization, M.A., M.I.S. and M.A.N.; methodology, M.A.; software, M.A. and M.I.S.; validation, M.A.N. and M.I.S.; formal analysis, M.A.; investigation, M.A.N.; resources, M.I.S.; writing--original draft preparation, M.A.N.; writing--review and editing, M.A. and M.I.S.; visualization, M.I.S.; supervision, M.A. and M.I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The code used to generate the results in this paper is available upon request.  
The authors want to make it clear that there are no conflicts of interest in this research.
## 3 Results
### Attribute Importance Assessment  
To assess feature contributions, we systematically evaluated each descriptor's impact on model performance using two complementary approaches: permutation importance and drop-column importance analysis. Applied to our test set, these methods quantified how feature alterations affected the accuracy of our top-performing models (see Supplementary  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Algorithm** & **Sensitivity** & **Specificity** & **Accuracy** & **AROC** & **MCC** \\ \hline \(k\)NN & 0.73 & 0.46 & 0.60 & 0.60 & 0.20 \\ LDA & 0.71 & 0.53 & 0.62 & 0.63 & 0.22 \\ QDA & 0.67 & 0.81 & 0.74 & 0.83 & 0.49 \\ SVM & 0.80 & 0.66 & 0.73 & 0.80 & 0.46 \\ RF & 0.71 & 0.79 & 0.75 & 0.82 & 0.51 \\ XGBoost & 0.75 & 0.75 & 0.75 & 0.81 & 0.50 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the performance of the ML models on the training set (10-fold cross-validation). AROC—area under the ROC curve (sensitivity vs. 1-specificity); MCC—[PERSON]’s correlation coefficient.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Algorithm** & **Sensitivity** & **Specificity** & **Accuracy** & **AROC** & **MCC** \\ \hline Proteins & & & & & \\ QDA & 0.63 & 0.81 & 0.72 & 0.82 & 0.44 \\ RF & 0.71 & 0.79 & 0.75 & 0.81 & 0.50 \\ XGBoost & 0.74 & 0.77 & 0.75 & 0.80 & 0.51 \\ \hline Peptides & & & & & \\ SVM & 0.79 & 0.86 & 0.82 & 0.83 & 0.64 \\ RF & 0.76 & 0.93 & 0.85 & 0.80 & 0.70 \\ XGBoost & 0.79 & 0.76 & 0.77 & 0.83 & 0.55 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of the best protein and the best peptide models on their respective test sets. AROC—area under the ROC curve (sensitivity vs. 1-specificity); MCC—[PERSON]’s correlation coefficient.  
\begin{table}
\begin{tabular}{l l l} \hline
**Algorithm** & **Accuracy** & \\ \hline Proteins & & & \\ QDA & 0.4986 & \\ RF & 0.4906 & \\ XGBoost & 0.4975 & \\ \hline Peptides & & & \\ SVM & 0.5075 & \\ RF & 0.5182 & \\ XGBoost & 0.4987 & \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the Y-scrambling analysis on the selected protein and peptide models on their respective test sets.  
File S1, Tables S1.1-S1.3). The importance score for each feature was calculated as the difference between baseline accuracy and the modified accuracy after either (1) randomly permuting feature values or (2) completely removing the feature. Positive importance scores indicate features whose perturbation degrades performance, signifying predictive relevance. Near-zero/negative scores suggest features with minimal or counterproductive effects on model accuracy.  
Table 4 delineates the top 10 most important features of each model. The ACC features are represented as follows: the first numerical index represents the E-descriptor of the first amino acid, the second index represents the E-descriptor of the second amino acid, and the third index represents the lag-value.
## 3 Results and Discussion
### High-Risk Zones Created by Cruise Ships' Routes
#### 3.1.6 Susac-Lastovo Passage  
The passage between Susac island and Lastovo island is part of a transversal route that cruise ships use on northerly and southerly courses. Cruise ships on southerly courses proceed through the Susac-Lastovo passage to the western Adriatic coast or they join the longitudinal corridor on the way to Dubrovnik, Kotor or the Oranto strait. Meanwhile, cruise ships on northerly courses proceed through the Susac-Lastovo passage from western Adriatic shores or from the longitudinal corridor from Dubrovnik or Kotor on the way to the central and north Adriatic ports.  
During the research period, 58 cruise ships passed through the Susac-Lastovo passage (Table 16). Traffic monitoring showed that two cruise ships met in the passage within a 3 M distance from the shore six times in a 90 min period. On one occasion, four cruise ships met inside the passage within 3 M from the shore in a 90 min period.
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion  
### NDVI Results  
The pattern of NDVI time series has been closely analyzed in order to demonstrate its sensitivity in vegetation growth dynamics and its relation with LST and SAR data. As it is known, NDVI values vary from \(-1\) to \(+1\), where values smaller than 0 are classified as non-vegetated areas such as man-made objects, or water areas. Since in the study area no man-made objects were present, values lower than 0 were considered to be water areas. Values higher than 0 can be classified into three classes; values from 0-0.2 are considered to be bare lands, values from 0.2-0.4 are considered to be a mixture of land and small vegetation or unhealthy vegetation, while values higher from 0.4 are considered to be healthy vegetated areas. In Figures 2-4 are presented the NDVI time series results. Since the results of the same season months were similar, in Figure 3, with respect to the date, the seasonal NDVI values of each class are compared. The full NDVI results are presented in Figure 11.  
higher NDVI values in the leaf-on season. Since there is the presence of small open water bodies, the NDVI values vary from slightly below zero to 0.3. The last class, Sediment Bog, is similar to the Bog class with the main difference of the land structure. While the land structure of Bog is formed of land, in the Sediment Bog the land is formed out of white sediment deposits formed over the years.  
Figure 3: NDVI time series results.  
Figure 2: Normalized Difference Vegetation Index (NDVI) time series average values.