esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 2 Literature Review
### Overview of UAVs and Benefits  
Over the past years, automation and information techniques are changing the construction industry and improving processes involved at different construction stages. Surveyors are using mostly robotic total station (RTS) and GNSS for surveying purposes. However, surveying with such methods takes time and these are labor-intensive approaches, while UAVs are considered to be a reliable and accurate approach for surveying applications. Researchers are showing their interest in UAVs because of their capabilities in many domains like volume calculation of stockpiles [11], managing disaster and emergency [12], forestry and agriculture [13], etc. They are also used by many researchers in civil engineering for infrastructure monitoring and inspection for bridges and roads [14; 15; 16], monitoring soil erosion [17], construction of 3D building [18] and surveying of earthwork project [4], etc. UAV has become a rapidly developing method for surveying. Photogrammetry is used to get features of the images and geometries are created with accuracy. It seems that UAV should be used also for earthwork projects in the design review to create a surface for measuring volume. Incorrect information in terrain for construction projects creates problems and has a negative impact on productivity. However, the implementation of UAV in the design review process will increase the accuracy of the project and reduce the problem associated with actual terrain elevations that affect estimation.
## 2 Related Work
### Noise Reduction Methods for Anomaly Detection
#### 2.2.4 Naive Bayes Classifier  
The Naive Bayes model is a supervised learning method that performs classification based on Bayesian estimation \(p(\theta|X)=\frac{p(\theta,X)}{p(X)}\) for feature \(X\) and label \(\theta\). Since this method performs classification based on conditional probabilities between features, it has the advantage of classifying large and complex datasets quickly. This advantage is suitable for analyzing network datasets. However, since the performance of this method is affected by the independence between features, the preprocessing technique for the features must be performed precisely [26].
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
## 4 Proposed Scheme
### Release of the Resource Reserved for the Cooperative Communication  
If the relay node does not receive data or response from the destination node for a certain period, it performs reservation resource release procedure. If cooperation communication is no longer needed, the destination node also performs a resource reservation release procedure. To release the reserved resource, the destination node sends a Coop Release message to the relay node. Figure 12 shows the format of the proposed Coop Release message.  
To terminate the relay communication, the relay node sets the Type field to Release and includes the information of the routing path and resources used for the cooperative communication to the Coop Release message. When the relay node receives the Coop Release message from the destination node, it forwards the Coop Release message to the RSU. After receiving the Coop Release message, the RSU broadcasts the WSA message except for the corresponding information at the next Sync Interval. The relay node that receives the WSA message no longer participates in the cooperative communication, and the destination node that receives the Coop Release message also no longer performs the cooperative communication. Figure 13 shows the timing diagram for the exchange of the control message for the resource release.  
Figure 11: An example of data exchange in service channel (SCH) interval.  
Figure 12: The format of the proposed Coop Release message.
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 2 Virtual Power Plant (VPP)
### Commercial Virtual Power Plant (CVPP)  
CVPPs allow DERs to gain visibility in energy markets, which would be very difficult, if not impossible, if the same DERs were operated as single entities. Moreover, aggregation allows them to participate in wholesale markets as if they were conventional generators [18]. Aggregation can also be facilitated through long-term contracts, potentially lowering transaction costs and reducing price uncertainties.  
CVPPs are considered to be commercial entities in that they report the price and amount of energy they can provide through production and consumption forecasting, based on weather forecasting and demand profiles, thereby optimizing the economic use of the VPP portfolio and schedule. Aggregate units of DERs for the electricity market are based on anticipated needs and cannot be limited to a defined electrical area, meaning VPPs are not limited geographically [19]. Trading in the wholesale energy market is one of the main services provided by CVPPs.  
Figure 2: Illustration of the Virtual Power Plant.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 1 Introduction
Transparency: Blockchain preserves an unchangeable and time-ordered history of all transactions connected to images. Every transaction, including the addition, retrieval, or modification of an image, is documented on the blockchain, ensuring a clear and verifiable path that simplifies the process of monitoring transaction records. This study presents several noteworthy contributions to the field. The key findings and innovations of our research are as follows:  
* In this study, a comparative analysis is proposed for systems with and without EDFAs. * This study also evaluates the FSOs model using Q-Factor and BER (Bit Error Rate) measurements. * This study focuses on the integration of sensor networks, FSOs, and blockchain technology. * To evaluate the blockchain network, three scenarios are considered: default, optimized, and congested network conditions. * The evaluation is based on parameters, including throughput, time, and bandwidth.
## 3 Candidate Selection and Localization
### Robust Features
Therefore, the weighting coefficients at the specific radius are defined as follows:  
\[w_{\sigma,l}(r)=(1-T_{min,l})\frac{H}{\text{log}(g)} \tag{10}\]  
\[w_{m,l}(r)=1-w_{\sigma,l}(r) \tag{11}\]  
where the range of variance-projection weighting coefficient is between 0 and \(1,1-T_{min,l}\) is designed for keeping the robustness against the noise in the image (\(T_{\text{min},l}\) is the similarity threshold on image pyramid level \(l\)), the weighting coefficient of ring-projection transformation is based on Equation (10), and in this study it is designed to get a symmetrical weighting coefficient. As mentioned in Equations (7) and (8), the weighting coefficients are separately determined at the specific radius within the circular region on each template image pyramid level. Using the hybrid-projection transformation in Figure 7a-d and normalizing the hybrid values to a range between 0 and 1, yield the results shown in Figure 6:0 All features of the texture-less searching images, _i.e._, Figure 7a-d, are clearly identified by this new descriptor. It means the features of the template image and searching image can be correctly recognized by evaluating the similarity based on the described features in the candidate selection and localization process. For this new descriptor, the weighting coefficients \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\) represent the weighting for the ring- and variance-projection values at the specific radius in the image pyramid level. Using the proposed method, the hybrid-projection transformation gives unique and more robust rotation-invariant features. Figure 6:1 Variance projection features of texture-less searching images.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 2 Background and Related Work
### Limitations of Current Methods  
1. ImageNet is a natural image dataset in which each image contains three colors, although the coronal slices taken from 3D sMRI images are only gray-scale. Therefore, using sMRI images directly as the input of the ViT model implies delivering the same images into all three channels, which is a total waste of computational resources. In addition, the gray-scale image deviates significantly from the original RGB color image in each channel.  
2. The majority of the current methods demand strict procedures for sMRI image preparation. Skull stripping is one of the crucial components. However, there are still some issues with employing SPM12 for skull stripping, such as partial stripping or loss of edge information due to over-processing. Therefore, the data must be visually checked and manually selected afterward. While eliminating the background (black region) of sMRI images has also become a critical step of preprocessing, the capacity of the deep-learning models is hampered by the growing complexity of the preprocessing process.  
3. The ViT model can only extract features at the same scale because its computational complexity is proportional to the square of the image size. In addition, it can only extract features on one scale at a time. However, the damaged brain areas of AD patients are typically subtle, making it possible to overlook some regionally specific details.
# Magnetic Vortex and Hyperthermia Suppression in Multigrain Iron Oxide Nanorings
## 4 Conclusions  
Highly crystalline, multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings were synthesized using a simple hydrothermal method. Systematic magnetic measurements indicated that the multigrain magnetite nanorings of an average outer diameter of \(\sim\)110 nm and an inner to outer diameter ratio of \(\sim\)0.5 showed typical characteristics of a ferromagnetic system at room temperature, unlike the predicated vortex-domain structure. Our experiments revealed that unlike the single-crystal Fe\({}_{3}\)O\({}_{4}\) nanorings, the multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings did not show a shape-induced vortex-domain structure, despite having similar dimensions and morphology. The corroborative results of both calorimetric and AC loop measurements of the multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings showed moderate inductive heating efficiency. Interestingly, alignment of the nanorings with the applied field allowed them to maximize their SAR values. Our results demonstrated the possibility of using Fe\({}_{3}\)O\({}_{4}\) nanorings for dual-purpose applications in localized magnetic hyperthermia therapy and controlled drug delivery because of their highly effective anisotropy and hollow morphology.  
Figure 5: (**a,b**) SAR vs. AC magnetic field curves as obtained from (**c,d**) the corresponding dynamic hysteresis loops for multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings after 8 h and 12 h of reaction, respectively.  
**Author Contributions:** Conceptualization, R.D. and M.-H.P.; methodology, R.D., C.W., and I.R.; investigation, R.D., C.W., Z.N., V.K., and I.R.; formal analysis, R.D., J.A., I.R., and M.-H.P.; resources, M.-H.P., H.S., J.A.G., and E.G.; writing--original draft preparation, R.D. and M.-H.P.; writing--review and editing, R.D., C.W., Z.N., V.K., J.A., I.R., J.A.G., E.G., V.D.L., A.-T.L., M.-H.P., and H.S.; supervision, R.D., M.-H.P., and H.S. All authors have read and agreed to the published version of the manuscript.  
Research at the University of South Florida was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, under Award No. DE-FG02-07 ER46438. This research was also supported by Vietnam Ministry of Science and Technology through the national-level project DTDLCN.17/19.  
The authors declare no conflict of interest.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 3 Method for Bridge Damage Detection
### Assessing Bridge Conditions with the Vehicle Response  
An instrumented vehicle travels across the bridge at a constant velocity and the corresponding acceleration response of the vehicle is measured. Then, the signal is applied to the wavelet transform for calculating the wavelet coefficient energy and the wavelet pseudo frequency of the VBI system. A peak wavelet energy represents a dominant frequency in the vehicle's acceleration and the change of this peak energy can be used to assess the bridge conditions.
## 2 Materials and Methods
### Sequence Encoding  
One-Hot encoding encodes the original DNA sequence directly, thus retaining the most primitive and simplest sequence information. It mainly uses the N-bit state register to encode N states. Each state has its independent register bit, and only one bit is valid at any time. So, the 4 nucleotides and 16 dinucleotides in DNA can be encoded by 0 and 1, and thus, A, T, C, G, AA, AT,... GC, GG can be converted to (1, 0, 0, 0), (0, 1, 0, 0) (0, 0, 1, 0) (0, 0, 0, 1), (0, 0,... 0, 1), (0, 0,... 1, 0),... (0, 1,... 0, 0), (1, 0,... 0, 0), respectively.  
In this study, we adopted One-Hot encoding to represent the DNA sequences. To obtain more abundant DNA sequence information, we first carried out One-Hot encoding for the adjacent \(k\)-nucleotide. Then, One-Hot encoding was also performed for the \(k\)-nucleotide between spaced \(gap_{k}\) nucleotide residues to obtain richer DNA sequence information to represent the original DNA sequence, where \(gap_{k}\) means the number of the spacing. It can be formulated as follows.  
For biological sequence analysis tasks, given a DNA sequence \(S\) with \(L\) residues, it can be formulated as:  
\[S=R_{1}R_{2}R_{3}R_{4}R_{5}R_{6}\ldots R_{L-1}R_{L} \tag{1}\]  
where \(R_{1}\) represents the nucleotide residue at the sequence position 1, \(R_{2}\) the nucleotide residue at position 2, and so forth.  
Then, each DNA sequence can be represented as \(f_{k,~{}gap_{k}}(S)\):  
\[f_{k,~{}gap_{k}}(S)=\left[\begin{matrix}\varphi_{k,~{}gap_{k}}^{1}&\varphi_{k,~{}gap_{k}}^{2}&\varphi_{k,~{}gap_{k}}^{3}&\varphi_{k,~{}gap_{k}}^{3}\ldots &\varphi_{k,~{}gap_{k}}^{i}&\ldots&\varphi_{k,~{}gap_{k}}^{L-(k+gap_{k})+1} \\ \end{matrix}\right]^{T} \tag{2}\]  
where \(\varphi_{k,~{}gap_{k}}^{i}\) is the \(i\)-th feature of the \(k\)-nucleotide between spaced \(gap_{k}\) nucleotide residues for DNA sequences. It can be formulated as Equation (3).  
\[\varphi_{k,~{}gap_{k}}^{i}=\begin{cases}R_{i},&k=1,~{}gap_{k}=0,1,2\ldots\\ R_{i}R_{i+(gap_{k}+1)},&k=2,gap_{k}=0,1,2\ldots\\ R_{i}R_{i+(gap_{k}+1)}R_{i+2(gap_{k}+1)},&k=3,~{}gap_{k}=0,1,2\ldots\\ \ldots&\ldots\\ R_{i}R_{i+(gap_{k}+1)}R_{i+2(gap_{k}+1)}\ldots R_{i+(m-1)(gap_{k}+1)},&k=m,~{} gap_{k}=0,1,2\ldots\end{cases} \tag{3}\]  
We constructed a preliminary experimental screening in advance, and the experiment finally determined that when \(k=1\), \(gap_{k}=0\); \(k=2\), \(gap_{k}=0\); \(k=2\), \(gap_{k}=1\); \(k=2\), \(gap_{k}=2\); and \(k=2\), \(gap_{k}=3\) were combined, the feature effect was the best. Hence, the sequence of DNA can be represented as \((4*L+16*(L-1)+16*(L-2)+16*(L-3)+16*(L-4))\)-dimensional vectors. In the six datasets, the sequence length is 41 bp, so each DNA sequence eventually contains vectors of 2628 dimensions.
# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment
### Model Training
In the example experiment, cross entropy is used as the loss function of model training, and accuracy is used as the evaluation index of model performance. The prediction performance of models with different lengths is as follows: without masking operations, modeling was carried out for three different sequence lengths (20, 40, 60), and the final prediction accuracy of the models is shown in Table 3.  
The model achieved the highest accuracy (58.51%) on the test set with a sequence length of 40, which suggests that sequences of medium length may contain just enough information to capture the key features of travel patterns. Sequences that are too short may fail to provide sufficient contextual information to capture complex travel patterns, thus limiting the model's predictive power. On the other hand, sequences that are too long may contain a substantial amount of redundant information, causing the model to overfit on unimportant details.
###### Abstract  
The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2) and lower Mean square error (MSE).  
rock excavation; soft computing; cutter life index; rock strength; brittleness +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)  
ASTER is one of five instruments (i.e., CERES, MISR, MODIS and MOPITT) aboard the Terra platform, launched in December 1999. Until April 2008, ASTER has collected images in 14 spectral bands (an additional backward-looking band is used for stereographic observations) in the VNIR (visible and near infrared), SWIR and TIR regions (Table 1). Since April 2008, ASTER has provided data only in the VNIR and TIR bands, owing to the failure of SWIR instrument. Each ASTER scene covers an area of 60 \(\times\) 60 km; the spatial resolution ranges from 15 m (VNIR) to 90 m (TIR). Due to several constraints (see [22]), ASTER data acquisition is not everywhere guaranteed every 16 days, as its acquisitions follow a pre-defined and/or an \"on-demand\" scheduling.  
Since November 2016, all ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance Product (AST_L1T) have been made freely available in Google Earth Engine. Data stored in AST_L1T imagery are calibrated at-sensor radiance, and they have been geometrically corrected and rotated to a north-up in UTM projection (see [23] for more details).
## 3 Routing Protocol: RFAODV
### Overview of RFAODV  
In this protocol, we redefined the policy of selecting the forwarding area and forwarding node. Instead of only using the position information as a forwarding node reference, RFAODV also takes the energy, the link communication status and communication time into account.  
RFAODV protocol contains two basic stages: route discovery and route maintenance.
## 3 Approach
### Update of DDPG-DMP  
Consistent with DDPG, the proposed methods exploration noises to the generated trajectory \([\mathbf{a}_{t},\mathbf{a}_{t+1},...,\mathbf{a}_{t+K-1}]\) and interacts with the external environment using this trajectory. The corresponding samples \(\{\mathbf{s}_{t+k},\mathbf{a}_{t+k},\mathcal{R}^{\mathbf{a}_{t+k}}_{s_{t+k}\
eq s_{t+k}^{ \prime}},\mathbf{s}_{t+k}^{\prime}\}_{k=0}^{K-1}\) within \(K=5\) steps are sequentially obtained and stored to the memory buffer \(\mathcal{D}\). Define the size of a mini-batch as \(N\); DDPG-DMP randomly samples \(N\) complete trajectories (each one has \(K\) steps) during its update. Denote \(\{\mathbf{s}_{t+k},\mathbf{a}_{t+k},\mathcal{R}^{\mathbf{a}_{t+k}}_{s_{t+k}\
eq s_{t+k}^{ \prime}},\mathbf{s}_{t+k}^{\prime}\}_{k=0}^{K-1}\) as one sampled trajectory; we only select the first step state \(\mathbf{s}_{t}\) and the first step of the output control trajectory \(\mathbf{a}_{t}\) as the input of critic networks \(Q_{\mathbf{\theta}}(\cdot)\) in order to satisfy the update formulas of the original DDPG. Therefore, we only use the first step's state \(\mathbf{s}_{t}\), the next step's state, \(\mathbf{s}_{t}^{\prime}\) and action \(\mathbf{a}_{t}\) for each sampled trajectory. To address this operation's neglect of information related to subsequent actions in the sampled trajectory and their corresponding states, we aggregate the reward functions in all \(K\) steps as follows:  
\[\mathcal{R}^{\text{traj}}_{t}=\sum_{k=0}^{K-1}\mathcal{R}^{\mathbf{a}_{t+k}}_{s_{t +k}\
eq s_{t+k}^{\prime}}. \tag{19}\]  
The processed sample \([\mathbf{s}_{t},\mathbf{a}_{t},\mathcal{R}^{\text{traj}}_{t},\mathbf{s}_{t}^{\prime}]\) is finally used to update both actor and critic networks in DDPG-DMP. The learn and update processes of the proposed method are summarized in Algorithm 1. Please note that there is one significant limitation of the proposed DDPG-DMP: beyond the first step, the remaining control actions and their corresponding states in the \(K\)-step trajectory generated by the actor networks are almost invisible to the agent during the update process. They only influence the accumulated reward \(\mathcal{R}^{\text{traj}}_{t}\). This characteristic substantially restricts the learning efficiency of DDPG-DMP. Addressing this limitation will be a crucial focus of our future work. Figure 2: Principle and workflow of the proposed method DDPG-DMP.