esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
Figure 2 presents the technical workflow of the proposed method. Grounded in regional ecological challenges, the characteristics of restoration measures, and ecological objectives, this study first gathered comprehensive data on the ecological environment and socioeconomic conditions of the study area. Building on this foundation, monitoring points were strategically established to assess water quality, soil conditions, biodiversity, and carbon sequestration capacity. Ecological environment surveys were conducted both before and after the restoration project, collecting plot survey data alongside meteorological and hydrological conditions, topography, hydrogeology, soil properties, vegetation charac  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Data Source** & **Remote Sensing Monitoring Index for Ecological Restoration Units** \\ \hline \multirow{4}{*}{Sentinel-2 land use/land cover} & \multirow{4}{*}{ESRI} & Landscape stability (spread, patch density, total edge contrast), habitat quality index, habitat connectivity index, landscape fragmentation, habitat irreplaceability index, water cultivation, net primary productivity, autotrophic respiration, total primary productivity \\ \hline \multirow{2}{*}{Sentinel-2 reflectance data} & European Space Agency (ESA) & Leaf area index, biodiversity index, vegetation cover, water retention, soil conservation \\ \hline Species distribution data & GBIF & Habitat irreplaceability index \\ \hline Temperature data & ERA5-Land & Gross primary productivity, net primary productivity, autotrophic respiration \\ \hline Rainfall data & ERA5-Land & Water conservation, soil conservation, net primary productivity, autotrophic respiration \\ \hline Soil data & HWSD v1.2 & Soil conservation, gross primary productivity, net primary productivity, autotrophic respiration \\ \hline DEM elevation data & SRTM1 & Soil conservation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing monitoring data sources at the ecosystem restoration unit scale.  
teristics, and land use information. To evaluate the effectiveness of ecological restoration at the unit level, analytical tools such as the InVEST model and landscape pattern analysis were employed.
## 1 Introduction
This article aims to experimentally evaluate and compare the effectiveness of some of the most popular adaptive algorithms based on the LMS procedure. The experimental comparison, unlike computer simulations, makes it possible to observe the different behaviors and the limits in implementing the algorithms on a real-time target (timing, memory, latency,...) and the effectiveness of the ANC system with regard to the imperfections in the positioning of the loudspeakers and the error microphones. The analysis and comparison of the performances are carried out for a 1 \(\times\) 2 \(\times\) 2 multichannel configuration, where the first digit represents the number of reference sensors (one microphone), the second digit is the number of control sources (two loudspeakers), and the last one is the number of control sensors (two microphones). In particular, three algorithms are developed in feed-forward configuration: the typical filtered-X LMS (FXLMS), which implements finite impulse response (FIR) filters to model the adaptive coefficients; the algorithm FXNLMS, a variation of the standard FXLMS with a normalized step-size [18; 19; 20; 21; 22], in which the parameter for the coefficients updating varies with the power of the filtered reference signal; a recursive version of FXLMS, called filtered-U recursive LMS (FURLMS), which implements infinite impulse response (IIR) filters for the adaptive coefficients [23; 24]. It should be stated that the application of this last algorithm is still missing for a multichannel configuration in an enclosed space. Moreover, a feedback configuration of FXLMS is studied, in which two reference signals are internally modeled, starting from the error signals [25; 26]. The experimental tests were carried out in a small enclosed space in a hemi-anechoic chamber, with the target of attenuating the noise signal of a simulated source at the ears position of a dummy torso. The choice of a tractor cabin as a case study is related to two larger projects (BRIC 2019 ID-14 and BRIC 2022 ID-11), in which this study is inserted. This analysis represents a preliminary step to reach the final target of these projects: the development of an ANC system for the reduction of noise exposure of tractor drivers. However, the conclusions found in this experimental study in the hemi-anechoic chamber are intended to be of a general nature for a multichannel ANC system in any confined space. In Section 2, the schematic representation of the LMS-based algorithms and their analytical development are described. Section 3 illustrates the experimental setup adopted for the tests and the main frequency response functions. Finally, the results of the experimental comparison for several kinds of noise signals and the conclusions are reported in the last two sections respectively, assessing the algorithm's performance in terms of speed of convergence, attenuation, and stability.
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 4 Proposed TLFC-RD Method
### Preprocessing Using Superpixel Generation  
In the TLFC-RD method, the superpixel method groups the identical pixels of the neighborhood into superpixel blocks. The similarity among pixels is calculated to group the identical pixels. This preprocessing method is used to minimize the redundant image information as well as preserve the boundary data of the images. Moreover, this superpixel generation reduces the difficulty in the subsequent road detection process. Here, simple linear iterative clustering is considered for the location and color of pixels in the neighborhood. The feature vector of the color image is transformed into a five-dimensional feature vector, Labxy, that contains a Lab color and two-dimensional planar space. Generally, the Lab is the color model in the CIELAB color space. The brightness is represented as \(L\), two colors are represented as \(ab\) and the spatial location in the flat image is denoted as \(xy\). Here, the calculation of the similarity is accomplished by using the distance among the pixels. Therefore, the similarity among the pixels is computed, labels are assigned to the identical pixels and the overall algorithm is iterated until convergence.  
Equations (1) and (2) shows the color difference and spatial distance among the pixels.  
\[d_{lab}=\sqrt{\left(l_{k}-l_{i}\right)^{2}+\left(a_{k}-a_{i}\right)^{2}+\left( b_{k}-b_{i}\right)^{2}} \tag{1}\]  
\[d_{xy}=\sqrt{\left(x_{k}-x_{i}\right)^{2}+\left(y_{k}-y_{i}\right)^{2}} \tag{2}\]  
where \(d_{lab}\) and \(d_{xy}\) represent the color difference and spatial distance among the pixels, respectively. The distance \(D_{s}\) is calculated by using Equation (3).  
\[D_{s}=d_{lab}^{2}+\frac{m^{2}}{s^{2}}d_{xy}^{2} \tag{3}\]  
where the step size among the pixels is represented as \(s\) and the compactness parameter is denoted as \(m\). This compactness parameter is utilized to define the relative proportion of various color and location distances. Each pixel in the image is allocated with a certain amount of superpixels. A processing alteration from pixels to superpixels using pre-processing is used to minimize the complexity during the classification. After pre-processing, the image is processed under feature extraction to extract the optimal features. The example for the pre-processed image is shown in Figure 3.  
Figure 2: Sample image.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 3 The Proposed Secret Message Multiplexing Scheme  
In this section, we will introduce the proposed CDMA-based secret multiplexing scheme. The scheme is split into the secret embedding procedure and the extraction procedure. Besides, the analysis of embedding capacity and theoretical PSNR are also presented in this section.
## 4 A Review of Variants in Project Scheduling Problem
### Based on Benchmarks  
When research on the RCMPSP first began, there were no established instance sets available to assess the performance of the proposed algorithms, so most studies relied on problem examples for validation. This approach is still common in recent studies, primarily because of the numerous features introduced and the absence of instance sets for each specific problem variant. Furthermore, many studies evaluate their models and algorithms using instances they generate themselves for the particular RCMPSP variant they are examining. [PERSON] and [PERSON] [97] proposed a project scheduling problem library (PSPLIB) for study of the RCPSP. Subsequently, numerous studies on the RCMPSP utilized that benchmark in their numerical experiments by merging two or more single-project instances, incorporating the necessary elements to address the relevant features, and creating specific multi-project instances. Additionally, various studies were conducted using real-world instances, highlighting the significance of this problem in the industrial sector. The initial and most well-known set of instances is the multi-project scheduling problem library (MPSPLIB), presented by [PERSON] [98], which focuses on RCMPSP with local resources. MPSPLIB contains 80 generated instances from the combination of 2, 5, 10, or 20 projects from the PSPLIB, each consisting of 30, 90, or 120 activities. Each project is assigned an arrival time, and resources can be utilized both globally and locally. Later, this library was expanded by [PERSON] [99], adding 60 more instances that vary in terms of access to local resources. MPSPLIB supports the online evaluation of solutions and offers a range of objective functions. Another notable variant with a specific set of instances is the multi-mode RCMPSP. This set was developed for the MISTA-2013 challenge and is composed of project groups taken from the PSPLIB by adding multiple execution modes. Table 7 shows the research in the reviewed literature that uses each instance set.  
Figure 8 shows the percentages of works using the different instance sets described above. The generated sets represent the highest percentage of the most used benchmark  
Figure 7: Contribution of different algorithms applied to scheduling problems since 2013.  
(27%). Then, the second highest percentage is represented by the PSPLIB in the different works, at 23%. It is important to note that 20% of the research was conducted using real-world-based instances.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 6 Implementation and Results
### Implementation Details and Simulation Setup  
This section will describe the implementation of the proposed algorithm and discuss its performance based on a set of simulated experiments. The experiments are conducted for a car-like robot and a double integrator, governed by the kinodynamic model described in Section 6.1. For each experiment, the interaction time horizon \(\tau\) is set to 3.5 s. The weighted matrix \(M\) defined in Definition 04 is set to identity matrix. The following constraints are considered on robot states in the simulations:  
* _Car-like robot:_ Maximum velocity \(v_{m}=1.5\) unit/s, Maximum curvature \(k_{m}=1.5\) unit\({}^{-1}\).
* _Double integrator:_ Maximum velocity \(v_{m}=2\) unit/s, Maximum acceleration \(a_{m}=1\) unit\(/s^{2}\).  
The algorithm is implemented in C++, on a computer running Windows 10. The timing results are generated on Intel i5-3550 PC with 4-GB RAM. Although it is possible to build \(U_{safe}^{\tau}\) and \(UU^{\tau}\) using multiple cores, we use single-core to produce timing results.  
Figure 5 shows the trajectory undertaken by the car-like robot to reach the goal while avoiding a collision with a static passive agent for different selected \(\beta\). Figure 6 shows the same for the double integrator.  
Figure 5: It shows the paths taken by a car-like robot to reach the goal at (20,10) from its initial position at (5,10), for selected \(\beta\), while avoiding collision with a static passive agent at (12,9).  
Figure 6: It shows the paths taken by a double integrator to reach the goal at (20,10) from its initial position at (5,10), for selected \(\beta\), while avoiding collision with a static passive agent at (12,9). The control parameter \(\eta\) was set to 3.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### The Training Objective
#### 3.5.2 The Cycle Consistency Loss  
To enhance the geometric plausibility and reversibility of the predicted transformations further, we introduce an unsupervised cycle consistency loss. Let \(\mathcal{F}^{(l)}=\{\hat{f}_{i}^{(l)}\}_{i=1}^{|\mathcal{P}^{(l)}|}\) and \(\mathcal{G}^{(l)}=\{\hat{g}_{i}^{(l)}\}_{i=1}^{|\mathcal{Q}^{(l)}|}\) be the sets of predicted forward and backward displacement functions, respectively. Additionally, let \(\mathbf{T}_{\mathcal{F}}^{(l)}(\cdot)\) and \(\mathbf{T}_{\mathcal{G}}^{(l)}(\cdot)\) denote the transformation functions that deform a point cloud by applying the corresponding per-point displacements; for instance, \(\mathbf{T}_{\mathcal{F}}^{(l)}(\mathcal{P}^{(l)})=\sum_{i}f_{i}\Big{(} \mathbf{p}_{i}^{(l)}\Big{)}=\sum_{i}\Big{(}\mathbf{p}_{i}^{(l)}+\hat{\mathbf{ v}}_{i}^{(l)}\Big{)}\).  
The cycle consistency loss penalizes deviations after performing forward and backward transformations sequentially, promoting reversible mappings between point clouds:  
\[\mathcal{L}_{\text{cycle}}^{(l)}=\text{CD}\Big{(}\mathbf{T}_{\mathcal{G}}^{(l )}\circ\mathbf{T}_{\mathcal{F}}^{(l)}(\mathcal{P}^{(l)}),\mathcal{P}^{(l)} \Big{)}+\text{CD}\Big{(}\mathbf{T}_{\mathcal{F}}^{(l)}\circ\mathbf{T}_{ \mathcal{G}}^{(l)}(\mathcal{Q}^{(l)}),\mathcal{Q}^{(l)}\Big{)}, \tag{16}\]  
where the Chamfer Distance (CD) between two point clouds \(S_{1}\) and \(S_{2}\) is defined as  
\[\text{CD}(S_{1},S_{2})=\frac{1}{|S_{1}|}\sum_{x\in S_{1}}\min_{y\in S_{2}}\|x- y\|_{2}^{2}+\frac{1}{|S_{2}|}\sum_{y\in S_{2}}\min_{x\in S_{1}}\|x-y\|_{2}^{2}. \tag{17}\]This unsupervised regularization term ensures geometric coherence and encourages mutually consistent bi-directional flows.
## 4 Pricing Algorithm
### Exact Algorithm
For any \(v\in\triangle M\), because \(v\) is nonminimal, there exists at least one minimal provenance \(v^{\prime}\in M^{\prime}(Q,I)\) that makes \(v^{\prime}\subset v\). Through Proposition 2, we can obtain \(\|v\|_{p}\geq\|v^{\prime}\|_{p}\) (when the price of the result tuples in \(v-v^{\prime}\) is zero, the equality holds). The price of the least expensive provenance in \(M^{\prime}(Q,I)\) is denoted by \(w^{min}\), that is \(\|v^{\prime}\|_{p}\geq w^{min}\). Thus, it is true that \(\|v\|_{p}\geq w^{min}\). From what has been discussed above, the existence of the non-minimal provenances does not affect the price of the least expensive provenance. Thus, \(min_{T\in M(Q,I)}\|T\|_{p}=min_{T\in M^{\prime}(Q,I)}\|T\|_{p}\). Given a database instance, the exact price of a query \(Q\) is computed using Algorithm 1, and the correctness is guaranteed by Theorem 4 and Lemma 2. ```
Input:\(I\), \(Q\) Output:\(p_{I}(Q)\)
1 for\(\forall\ t_{i}\in Q(I)\)do
2 obtain \(W^{i}\) defined in [11];
3
4 end for
5\(M(Q,I)=\{\bigcup_{i}w^{i}_{k_{i}}|w^{i}_{k_{i}}\in W^{i}\}\);
6 for\(\forall\ T\in M(Q,I)\)do
7 calculate \(\|T\|_{p}\);
8
9 end for
10\(p_{I}(Q)=min_{T\in M(Q,I)}\|T\|_{p}\);
```  
**Algorithm 1**Exact pricing. Suppose that the query result contains \(n\) result tuples, each of which contains \(m\) why-provenances. First, \(W^{i}\) is calculated in the first through third lines of the algorithm. Then, \(M(Q,I)\) is constructed with the fourth line of the algorithm, for which the complexity is \(O(n^{m})\). Finally, \(\|T\|_{p}\) is calculated, and \(p_{I}(Q)\) is selected on Lines 5 through 8 of the algorithm, for which the complexity is \(O(n^{m})\). The total complexity of calculating the exact price of the query is \(O(n^{m})\).
## 3 Methodology
### Evaluation Measures  
Measuring and evaluating the performance of a trained ML model is crucial to assess the predicted results. This is important to establish the validity and reliability of the ML models. Thus, we incorporated four measure evaluation measures known as accuracy, precision, recall, and F1-score. Following are the equations (i.e., 1 to 4) that represent the mathematical formulation of mentioned measures. Where TP is the number of True Positives--the number of positive RUW tweets that were correctly predicted in the right emotion class, TN is the number of True Negatives--the number of negative RUW tweets that were correctly predicted in the right emotion class. FP is the number of False Positives--the number of negative RUW tweets that were incorrectly predicted as positive RUW emotion class. FN is the number of False Negatives, i.e., the number of positive RUW tweets that are incorrectly predicted as negative RUW tweets. Overall, accuracy is responsible for assessing the ML model's performance. Precision is responsible for measuring the relevancy of the generated prediction results. A recall is responsible for measuring the correctly classified RUW tweets and the F1-Score is a weighted combination of recall and precision scores [73]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{F1 score}=\frac{2\left(\text{precision}\;\times\;\text{recall}\right)}{ \text{precision}+\text{recall}} \tag{3}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{4}\]
# DentalArch: AI-Based Arch Shape Detection in Orthodontics
## 3 Results
### Evaluating Performance of Machine Learning Techniques across Dental Arches  
In this study, we evaluate the efficiency of various machine learning models applied to the analysis of dental arches, specifically DentalArch v1 and v2. A diverse set of models was employed, the performance of each being gauged in terms of accuracy. Table 3 encapsulates the average accuracy achieved by each model.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Models** & **Dt,Low** & **Dt,Low** & **Dt,Lup** & **Dt,Lup** & **Dt,Lup** & **Are** \\ \hline \multirow{3}{*}{Decision trees} & 1 Fine tree & 867 & 783 & 797 & 803 & 797 \\ \cline{2-7}  & 2 Medium tree & 807 & 77.7 & 80 & 80.7 & 79.7 \\ \cline{2-7}  & 3 Coarse tree & 81 & 87 & 79.7 & 82 & 81.1 \\ \cline{2-7}  & 41 Lower discriminant & 82.7 & 76.7 & 77.3 & 77 & 78.4 \\ \cline{2-7}  & 5 Quadratic discriminant & Failure & 78.7 & Failure & 82.3 & 80.5 \\ \cline{2-7}  & 6 Gaussian naive Bayes & 72.7 & 68 & 64.7 & 68.3 & 67.6 \\ \cline{2-7}  & 7 Kernel naive Bayes & 78.7 & 73 & 73.7 & 78.7 & 75.6 \\ \hline \multirow{4}{*}{Support vector machines} & 3 Linear SVM & 81 & 78 & 76.3 & 76 & 77.3 \\ \cline{2-7}  & 9 Quadratic SVM & 90.3 & 82 & 87.7 & 85.3 & 86.3 \\ \cline{2-7}  & 10 Cubic SVM & 90.7 & 82.7 & 87.3 & 85 & 86.4 \\ \cline{2-7}  & 11 Fine Gaussian SVM & 78.7 & 86.3 & 78.3 & 91 & 83.5 \\ \cline{2-7}  & 12 Medium Canadian SVM & 86.3 & 79.3 & 80.7 & 82.3 & 81.9 \\ \cline{2-7}  & 13 Coarse Gaussian SVM & 76 & 76 & 76 & 76 & 76 \\ \hline \multirow{4}{*}{Nonnet neighbor classifiers} & 14 Fine KNN & 79.3 & 93 & 90.7 & 92.3 & 91.6 \\ \cline{2-7}  & 15 Medium KNN & 79.7 & 77 & 78.3 & 84 & 79.7 \\ \cline{2-7}  & 16 Counter KNN & 76 & 76 & 76 & 76 & 76 \\ \cline{2-7}  & 17 Cubic KNN & 82.3 & 74.7 & 75 & 83 & 78.3 \\ \cline{2-7}  & 18 Cubic KNN & 79.3 & 77 & 78.3 & 83.7 & 79.5 \\ \cline{2-7}  & 19 Weighted KNN & 88.7 & 88.7 & 84 & 92.7 & 88.5 \\ \cline{2-7}  & 23 Bounded trees & 82 & 86.3 & 82 & 80.3 & 83.4 \\ \cline{2-7}  & 21 Regular trees & 87.7 & 67 & 84.3 & 87.3 & 86.5 \\ \cline{2-7}  & 22 Subspace discriminant & 79.3 & 76 & 76 & 75.3 & 76.5 \\ \cline{2-7}  & 23 Subspace KNN & 94.7 & 86 & 93 & 87.7 & 90.3 \\ \cline{2-7}  & 24 RUSBoost trees & 76 & 69.3 & 56.3 & 72.7 & 68.5 \\ \cline{2-7}  & 25 Narrow neural network & 88.3 & 86 & 84 & 83.3 & 85.4 \\ \cline{2-7}  & 26 Medium neural network & 86.3 & 86 & 84.7 & 86.7 & 86.1 \\ \cline{2-7}  & 27 Wide neural network & 88.3 & 85 & 87.7 & 88.7 & 87.4 \\ \cline{2-7}  & 28 Bi Biforest neural network & 89.7 & 88 & 83.7 & 88.3 & 86.6 \\ \cline{2-7}  & 29 Biviewed neural network & 84 & 82.7 & 77.3 & 86.3 & 82.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of machine learning techniques for DentalArch.
## 2 Materials and Methods  
### Blood Collection and Serum miR Analysis in KPC Mice  
The experiment with genetically engineered _LSL-Kras_\({}^{\rm G12D/+}\); _LSL-Trp5_3R172H/+; _P48-Cre_ (KPC) mice [23] in this study was approved by the Georgetown University Institutional Animal Care and Use Committee (IACUC). Twelve KPC mice were euthanized at the age of 5 months before \(\sim\)1 mL blood was collected via cardiac puncture in Serum Z-Gel tubes with clotting activator (Sarstedt). The serum tubes were inverted 5 times and centrifuged at 10,000 \(\times\) g for 15 min. The serum was stored in aliquots at -80 \({}^{\circ}\)C until further analyses. In addition, pancreas, liver and lungs were collected and processed by formalin fixation and paraffin embedding (FFPE). Tissues were stained with hematoxylin and eosin (H&E) and the slides were examined by a clinical pathologist to evaluate pancreatic neoplasia and to determine the tumor stages. Two sections of the entire pancreas were randomly selected. Multiple tissue sections from the liver and lungs were evaluated to detect micrometastatic lesions. The animals were then divided into two groups based on disease progression: one group with PanIN-3 lesions as the worst disease stage (\(n=3\)), and a second group of mice with PDAC as well as lymph node, liver, and lung metastases (\(n=3\)). The remaining animals had local PDAC only (\(n=6\)), and were not included in our miR study.  
Equal volumes of serum samples from the mice belonging to the same group were pooled together, followed by miR isolation of the two samples, using the miRCURY RNA Isolation Kit for Biofluids (Exiqon). The murine miRs were reverse transcribed to complementary DNA (cDNA) using the miRCURY LNA(tm) Universal RT microRNA PCR, Polyadenylation and cDNA synthesis kit II (Exiqon). Expression of 179 miRs was analyzed with the qPCR-based Serum/Plasma Focus microRNA PCR Panel (Exiqon) using the ExiLENT SYBR(tm) Green master mix (Exiqon). MiRs with raw threshold cycle (Ct) values higher than 30 cycles were excluded. The median miR expression value in each pooled serum sample was used to normalize for miR expression. The fold differential expression for each miR was calculated (2\({}^{\sim}\)-\(\Delta\Delta\)Ct) and plotted with Prism Graphpad 5.01, using \(\geq\)3-fold differences as the cutoff.