esa-sceva/satcom-chunk-collection huggingface dataset:
# PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation
## 2 Related Work
### Heterogeneous Multi-Robot Cooperation Planning  
The multi-robot system has the advantage that it can perform complex tasks that cannot be accomplished by one single powerful robot with many capabilities through cooperation [20]. For example, a large building can be cleaned with one robot, but it is time-consuming and unrealistic. Thus, a multi-robot system that dispatches the overall mission into smaller sub-problems to individual robots is necessary. [PERSON] et al. [1] proposed a cooperative control scheme for a heterogeneous ground-air robot team. [PERSON] et al. [21] integrated a temporal planning approach with a PDDL planner for heterogeneous teams of robots. [PERSON] et al. [22] solved the decision-making issues of aerial robots using an integrated decision-making framework. [PERSON] et al. [23] represented the environment in a scalar field and created a time-optimized mission plan for UGVs using a cascaded heuristic optimization algorithm. However, most studies of heterogeneous multi-robot systems focus on achieving shared goals effectively, with minimum time and cost, through algorithms rather than acquiring knowledge of the environment using multiple robots.  
Some researchers have attempted to solve the environmental knowledge acquisition problem through data sharing among the robots. [PERSON] et al. [24] used an adaptive transmission method for efficient distributed information sharing. [PERSON] and [PERSON] [25] proposed a shared information integration method for cooperative environmental data gathering. [PERSON] et al. [26] introduced two approaches that could learn how information may be shared: reinforced inter-agent learning and differentiable inter-agent learning. These studies shared raw sensor information that could hardly infer semantic meanings without algorithms. They had to be designed suitably for the individual robots in a heterogeneous multi-robot team. Unlike conventional studies, sharing information embedded with semantic meaning in the form of natural language can enable the heterogeneous robots to easily understand and communicate with each other. Moreover, we can decrease the quality of service  
Figure 1: Outline of our approach: PDDL mission planner is utilized to generate the sequence of actions using the PDDL domain and environment information for heterogeneous robots. The surrounding environment is represented as a scene graph. If a robot fails the mission, it generates a natural language description.  
problem, which is often observed in field robotics, by transmitting a compact representation of the environmental information. We introduce a method that acquires environmental knowledge in the form of natural language and applies it to multi-robot cooperation planning.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## Abbreviations  
The following abbreviations are used in this manuscript:  
BLSTM Bidirectional Long Short-Term Memory DFT Discrete Fourier Transform GMM Gaussian Mixture Models HMM Hidden Markov Models LSTM Long Short-Term Memory MFCC Mel Frequency Cepstral Coefficients PCM Pulse Coded Modulation SVM Support Vector Machine VAD Voice Activity Detector
## 2 Materials and Methods  
Polycrystalline PMN-PT is a transparent relaxor ferroelectric ceramic [5,15,19]. Unlike single crystals, ceramics are easier, cheaper, and faster to produce by several well-matured techniques like hot-pressing [5]. Ceramics are available in larger sizes compared to single crystals so that they can address a larger aperture. Polycrystalline PMN-PT, like other ceramics, is made of randomly arranged grains or crystals [5,19]. Although each grain may have different orientations, the ceramic's properties are defined by the sum of many grain orientations, making the ceramic isotropic. Therefore, the optical properties of polycrystalline PMN-PT are the same in all directions, even under its Curie temperature. Applying an external electric field makes its isotropic and stable structure distorted and converted to a uniaxial medium in which the external electric field defines the optical axis [1,2,7]. Therefore, polycrystalline PMN-PT can be used as an EO beam steerer, and its EO properties can be defined by measuring and simulating the deflection angles. Since an EO steerer's deflection angles also depend on the medium's refractive index, the refractive index of polycrystalline PMN-PT, its dielectric constant, and its transmission should also be determined.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 4 Experiment
### Performance of the Defect Detection Algorithm under Different Settings
#### 4.2.3 Influence of Different Resolutions on Performance  
We compared the influence of different resolutions on the detection performance. The results are shown in Table 7 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defect Categories** & **Damage** & **Corrosion** & **Grind Lacking** & **Scatch** \\ \hline AP (\%) & 81.92 & 84.23 & 84.75 & 85.02 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AP of each defect category.  
Figure 10: Detection results under different \(\alpha\): (**a**) \(\alpha=0.8\); (**b**) \(\alpha=0.9\); (**c**) \(\alpha=1.05\); and (**d**) \(\alpha=1.2\).  
It can be seen from the results that increasing the resolution had a significant impact on the mAP and detection time. As the resolution increased, the mAP increased but the detection time decreased. That was because the increase in resolution lead to an increase in computation. Therefore, it is necessary to select an appropriate resolution according to actual needs.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 5 Results
feature can be seen in the western basin of Lake Erie shown with a red solid line ellipsoid in Figure 6a (region 1), as explained earlier. Therefore, the proposed approach is not able to overcome this problem in both Lakes. All models provided reasonable accuracy similar to other research in this field; however, we noticed nominal differences among different models' performance. This initial finding motivated us to seek deeper insight by comparing models' performances within lakes in each zonal bathymetry class. As this classification can better represent lakes' ecology compared to sub-basin boundaries, zonal bathymetries were chosen as a classification scheme for comparing the scores. Results show that in Lake Erie, using aleatoric loss and including bathymetry in shallow near-shore zone (zone 2) improves the ice score by \(\sim\)2 percent in average. This is an interesting finding as it shows the CNN with aleatoric loss and bathymetry can partially overcome the above-mentioned challenges in detecting dark ice in the western basin. In Lake Ontario, in the deepest zone, profundal off-shore (zone 5), using aleatoric loss and including bathymetry improves open water score by \(\sim\)6 percent in average. We have also observed that water pixels in shallower zones leads to much lower scores compared to deep zones in both Lake Erie and Lake Ontario. Thus, we can use baseline CNN in deeper zones with more confidence, but for shallow zones (Zones 1 and 2), relying on the baseline CNN decreases the accuracy of mapping water pixels dramatically. Here, we have shown the predictions for only one date per lake. However, we have observed the same patterns on different dates, and our interpretations are transferable to those dates.
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Separation-Based Technologies
#### 4.1.5 Capacitive Deionization
Capacitive deionization is an energetically efficient and commercially viable technology for the desalination of water with moderate salt content [96]. The main limitations of CDI applied to nitrate removal are that the electrodes must be periodically regenerated, and the low selectivity of the electrodes towards nitrate ions, which means that in the presence of ions of similar characteristics (size and charge), interferences will occur and the performance of the process will be lower. On the other hand, it is a novel technology whose equipment has a relatively high market price [84]. Innovative approaches of this technology (MCDI, CCDI) are able to increase its efficiency in specific situations.
# Changes in Quality Features of Pork Burgers Prepared with Chokeberry Pomace During Storage
## 1 Introduction
However, the large-scale implementation of these methods will likely require additional investments, which could result in increased costs [12; 19]. Therefore, it seemed justified to attempt to use black chokeberry pomace in a minimally processed form as an ingredient of minced meat products. The literature provides limited information on the potential use of raw, shredded black chokeberry pomace in meat products. Previous studies examined its application in ground meat products, such as pork burgers [27]. Findings revealed that incorporating chokeberry pomace in the form of shredded paste was feasible. However, the quantity of added plant material significantly impacted the quality characteristics of pork burgers, including organoleptic attributes. It is important to note that this study was preliminary, with no storage tests or microbiological quality assessments. The promising results led to further research. Thus, the aim of this study was to assess selected physical, chemical, and microbiological quality characteristics of pork burgers containing black chokeberry (_Aronia melanocarpa_) pomace. Pomace was added to the meat batter in shredded form in amounts ranging from 2.0% to 5.0% relative to the weight of meat and fat raw materials. After thermal treatment, the burgers were vacuum-packed and stored under refrigeration for 14 days.
## 3 Approximate Entropy  
In the field of fault diagnosis, many symptom parameters (SPs) have been defined to reflect the features of vibration signals measured for condition diagnosis, such as root mean square (RMS), skewness, and kurtosis,. In this study, the approximate entropy (\(ApEn\)) calculated from four IMF components is used to extract the vibration signal feature of each bearing state. \(ApEn\) is a physical quantity that measures the probability of generating a new mode in a signal to reflect the complexity of a time series. It only requires a short series of data, has superior anti-noise capability, and is applicable to both random and deterministic signals [23].  
Assume that the original data are \(\{u(i),i=0,1\ldots N\}\), and denote the mode dimension and similarity tolerance as \(m\) and \(r\), respectively. Normally, the \(ApEn\) is calculated via the following procedure:  
1. Based on the series \(\{u(i)\}\), the dimension is expanded in sequence to an \(m\)-dimensional vector \(X(i)\): \[X(i)=[u(i),\ u(i+1)\ \ldots u(i+m-1)],i=1\ \sim N-m+1\] (13)
2. The distance between each \(X(i)\) and other vectors \(X(j)\) is calculated: \[d[X(i),X(j)]=\max_{k=0\sim m-1}\lvert u(i+k)-u(j+k)\rvert\] (14)
3. Assume that the threshold is \(r(r>0)\). For each, the number of \(d[X(i),X(j)]<r\) is counted, and the ratio of this number to the total number of vectors \(N-m+1\) is calculated as follows: \[C_{m}^{i}(r)=\left\{d[X(i),X(j)]<r\right\}/(N-m+1)\] (15)
4. The logarithm of \(C_{m}^{i}(r)\) is calculated, and the average of all \(i\) is denoted as \(\Phi^{m}(r)\): \[\Phi^{m}(r)=\frac{1}{N-m+1}\sum_{i=1}^{N-m+1}\ln C_{i}^{m}(r)\] (16)
5. The above procedure is repeated to obtain \(\Phi^{m+1}(r)\). Then, theoretically, the \(ApEn\) is computed as follows: \[ApEn(m,r)=\lim_{N\rightarrow\infty}\left[\Phi^{m}(r)-\Phi^{m+1}(r)\right]\] (17)  
The above final value is normally represented as having probability 1. However, \(N\) cannot approach \(\infty\). Therefore, the result calculated via the above procedure is actually an estimate of the \(ApEn\) for a series with length \(N\), denoted as  
\[ApEn(m,r,N)=\Phi^{m}(r)-\Phi^{m+1}(r) \tag{18}\]  
The above expression shows that \(ApEn\) is related to \(m\) and \(r\). Normally, when \(m=2\), \(r=0.1\sim 0.25 SD\) (\(SD\) is the standard deviation of the series \(\{u(i),\ i=0,\ 1\ldots N\}\)), the statistical characteristics of \(ApEn\) are more reasonable.
# Routing Protocols for Delay Tolerant Networks:
## 2 Reference Architecture  
In this section, we present a reference architecture for DTN routing protocols. The basic components of the architecture are presented in the following section. Then, we examine a selected number of characteristic DTN routing protocols and discuss how they can be incorporated
## 4 The Proposed Method
### DGNDP (Synthetic Directed Graph Based on Node Differential Privacy) Algorithm
#### 4.2.3. GGM (Generating Synthetic Graph Based on Graph Modification)  
To generate a synthetic directed graph by using a noised degree sequence, the graph modification method is adopted to present the GGM algorithm which consists of three steps. In this algorithm, step 1 compares two out-degree sequences \(NS_{dout}\) and \(S_{dout}\) as well as two in-degree sequences \(NS_{din}\) and \(S_{din}\), and records the difference between them. In step 2, some edges are added into a sub-graph \(Sn_{Gi}\) as the value of the out and in the degree of a node increases. To reduce the perturbation caused by adding edges, the nodes with increased out-degree and the nodes with increased in-degree are paired to add edges between them. In the last step, an out or in-degree sequence is selected to delete edges between these nodes in it and their neighborhood nodes. In particular, the relationships between nodes are considered when edges are added or deleted, which can preserve the original structure as much as possible. In the end, the GGM algorithm generates a differential private synthesis directed graph, which can effectively preserve the original directed graph. The detail of the Algorithm 4 is demonstrated as follows. In line 2 to line 3, after the \(NS_{dout}\) and \(S_{dout}\) are compared, \(Do_{1}\) and \(Do_{2}\), which record nodes and their increased/decreased out-degree values are obtained. Then, \(Di_{1}\) and \(Di_{2}\), which record nodes and their increased/decreased in-degree values are obtained. Starting on line 4, some edges are added into the graph \(Sn_{Gi}\) as few as possible. For each node, \(i\) in sorted \(Do_{1}\), \(k\) nodes in \(Di_{1}\), which are closer to node \(i\) than other nodes, are selected and edges are added between them from line 5 to 8. After that, the value in \(Di_{1}\) is modified from line 9 to line 11. In order to delete edges the least amount possible, \(Do_{2}\) and \(Di_{2}\) are compared in line 12. If \(Do_{2}\) is selected, as for each node \(i\) in \(Do_{2}\), a set of nodes \(Ina\), which is the intersection of \(N_{a}\) and \(Di_{2}\), is gained. Then, if the number of \(IN_{a}\) is more than zero, we delete min \(\{\)k\(,\)\(|IN_{a}|\)\(\}\) edges from \(Sn_{Gi}\); otherwise, the min \(\{\)k\(,\)\(|N_{a}|\)\(\}\)edges are removed from \(Sn_{Gi}\). If the \(Di_{2}\) is chosen, some edges are deleted in the same way as from line 22 to line 29. Therefore, through the graph modification method, a synthetic directed graph is generated.
## Appendix A The Flamant-Boussinesq Approximation
### Limitations  
In [PERSON]'s problem, a hypothetical concentrated load P acts at the origin of coordinates and is perpendicular to the plane surface of a semi-infinite solid. For our application, we consider the target to be a straight line buried at a specific depth below the surface.  
The Flamant-Boussinesq approximation relies on several assumptions, the most important of which are as follows:  
* Elastic, Homogeneous, Isotropic Medium: the soil is considered an elastic, homogeneous, isotropic, and semi-infinite medium.
* Linear Elasticity: the medium obeys Hooke's law.
* No Self-Weight: the self-weight of the soil is ignored.
* Initial Stress: the soil is initially unstressed.
* Volume Change Neglect: the change in soil volume upon loading is neglected.
* Surface Conditions: the top surface is free of shear stress and subjected only to point loads.
* Stress Continuity: stress is continuous throughout the medium.  
The application of the [PERSON]'s approximation applies easily on far-field domains and less so when describing the static deformation of a point loads located near the ground displacement, where the assumptions cannot be followed.  
However, there are many cases in which the approximation is applied successfully on relatively low distances (see [6; 9; 17; 20]), though most of the time with certain deep fibers (2 m [21]). According to [PERSON] ([22]), the behavior of the solutions near the point of application of the loads, where pathological singularities and discontinuities exist, is of particular importance. Studying these singularities and performing appropriate sensitivity analysis (see Figures 15 and 16) reveals that for very shallow cables (\(z<0.5\) m), the result is a homogeneous down-up wavelet shape that attenuates with distance. However, for larger depths and shorter distances, there may be inversion changes in the shape (from up-down to down-up shapes).  
For this approximation, the obtained waveforms will be on a continuous zone if a maximum shallow depth (\(z<0.5\) m) is maintained, or alternatively, if a minimum relative distance is ensured such that the depth component \(z\) has a value greater than or equal to the perpendicular separation component \(y+1.5\). In either of these two cases, adjusting the scale and sign will produce signals compatible with the observations.
## 2 Proposed Algorithm  
In this section, we present the main guidelines of the proposed clustering algorithm. Once the nodes and the BS are deployed, the BS sends the startup message to all the nodes. Nodes use this message to calculate the distance to the BS based on the Received Signal Strength Indicator (RSSI). The network is scheduled to work in a round basis. For each round, nodes run the Interval Type-2 Fuzzy System to evaluate whether they promote themselves as CH or not. Then, those nodes that select themselves as CH send the advice message to the whole network. Non-CH nodes send their data message to the closest CH, which aggregates all the data and relay it to the BS.  
In the next sections, first, we explain the underlying Type-2 Fuzzy System that rules the CH selection. Then, we detail the CH selection algorithm that schedules how the network behaves in order to accomplish its application. This description shows the distributed nature of the algorithm, which is occasionally guided by the BS.
## 3 Results
### Environmental Factors and GPP Spatial Characteristics  
#### 3.2.1 Spatial Characteristics of Environmental Factors  
In the study area (Figure 6), PAR has the most significant spatial attribute values in the Gurbantunggut Desert in the Junggar Basin and the Gobi Desert in eastern Xinjiang. In contrast, the spatial attribute values in the middle of the TM and the northernmost Altai Mountains are generally low, with an annual average of PAR of approximately 332.41 umol/m\({}^{2}\)/s. The overall distribution of VPD is consistent with that of PAR. The Gurbantunggut Desert shows the highest VPD value, while the value in the TM is the lowest. The annual average of VPD in the research area is approximately 0.80 kPa. The spatial presentation patterns of high and low values of Tair and Tooil are consistent with those of PAR and VPD. However, the high-value areas of Tair have significantly higher attribute values than those of Tooil. The annual average of Tair is approximately 13.9 \({}^{\circ}\)C and the annual average of Tooil is approximately 28.9 \({}^{\circ}\)C.  
Figure 6: Annual spatial characteristics of five environmental factors (PAR, VPD, SWC, Tair, and Tooil). The satellite products representing the spatial characteristics of environmental factors are derived from the comprehensive research findings of other scholars. Their applicability is evaluated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**GPP Change Time and Magnitude**} & \multicolumn{1}{c}{**Farmland Areas**} & \multicolumn{1}{c}{**Grassland Areas**} & \multicolumn{1}{c}{**Desert Vegetation Areas**} \\ \hline \multirow{3}{*}{Valley value} & Month & January & December & January \\  & Daily Average Minimum (gC/m\({}^{2}\)/day) & 0.00002 & 0.00003 & 0.00001 \\  & Daily Cumulative & 0.0003 & 0.0005 & 0.0001 \\  & Minimum (gC/m\({}^{2}\)/day) & 0.0003 & 0.0005 & 0.0001 \\ \hline \multicolumn{2}{c}{First Inflection Point} & Late March & Mid-to-Late March & Late March \\ \hline \multirow{3}{*}{Peak value} & Month & July & July & July \\  & Daily Average Maximum (gC/m\({}^{2}\)/day) & 0.94 & 0.31 & 0.11 \\  & Daily Cumulative & 11.25 & 3.43 & 1.29 \\ \multicolumn{2}{c}{Maximum(gC/m\({}^{2}\)/day)} & \multicolumn{1}{c}{} & \\ \hline \multicolumn{2}{c}{Second Inflection Point} & Late October & Early November & Mid-to-Late October \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall intra-annual variation characteristics of GPP in time and magnitude (valley and peak values) at each station.  
The spatial inter-annual variation in SWC is opposite to that for PAR, VPD, Tair, and Tsoil. The average yearly spatial value of SWC in the Gurbantunggut Desert shows a low value. In contrast, the SWC in TM and the Altai Mountains shows a significant spatial feature. The annual average of SWC in the region is approximately 0.15 m\({}^{3}\)/m\({}^{3}\). This is because the higher the temperature, the greater the evapotranspiration and the lower the SWC.
## 4 Experimental Results and Analysis  
### Dataset Overview  
The dataset used in this study was collected from the bearing experimental platform at Case Western Reserve University (CWRU). Figure 14 shows the rolling bearing experimental platform at CWRU, with the motor on the left, the torque sensor in the middle, and the force meter on the right.  
This study uses fault data collected at a sampling frequency of 12 kHz from a 6205-2 RS JEM SKF deep groove ball bearing. The data collected by the test platform include normal condition (NA), inner ring fault (IF), rolling body fault (BF), and outer ring fault (OF), each with three damage levels: 0.007, 0.014, and 0.021 inches. This results in nine fault states and one health state, for a total of 10 states. Thus, the fault categories can be divided into 10 subcategories (considering fault size) or four categories (ignoring fault size). The collected data consist of vibration signals in a time series tested under different load conditions and four distinct operating conditions (0, 1, 2, and 3 horsepower). The experimental working conditions at CWRU are shown in Table 2.  
This study considers the four working conditions as four distinct tasks (A, B, C, and D). Task 1 represents the transfer from the source domain of condition A to the target domain of condition B. In total, 12 transfer learning tasks were created. The division of the transfer learning tasks is provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working** & **Load/hp** & \
\begin{tabular}{c} **Rotational** \\ **Speed (r/min)** \\ \end{tabular} &
\begin{tabular}{c} **Fault Type** \\ **Diameter (in)** \\ \end{tabular} \\ \hline A & 0 & 1797 & NA/IF/BF/OF & 0.007/0.014/0.021 \\ B & 1 & 1772 & NA/IF/BF/OF & 0.007/0.014/0.021 \\ C & 2 & 1750 & NA/IF/BF/OF & 0.007/0.014/0.021 \\ D & 3 & 1730 & NA/IF/BF/OF & 0.007/0.014/0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CWRU migration experiment condition division.  
Figure 14: Case Western Reserve University rolling bearing experimental platform. 1. Fan end bearing; 2. Motor; 3. Drive end bearing; 4. Torque sensor encoder; 5. Dynamometer.