esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Method
#### 2.3.1 Sentinel-2 Image Parcel Extraction  
* Cropland mask  
SAM has three versions of pre-trained models, namely vit-b, vit-l, and vit-h. In this study, the lightest vit-b model was employed for the extraction of the targets. This extraction process indiscriminately extracts and labels all identifiable objects within the imagery, which includes urban areas, water bodies, forests, and other land covers. These are not relevant to our study, which focuses on farmland parcels. To address this, we have introduced a cropland mask to filter SAM's prediction outputs. This masking process is instrumental in excluding non-cropland areas from the segmented results, isolating the agricultural parcels of interest. The effectiveness of this technique is evident in Figure 3a, where it is shown that the application of the cropland mask successfully eliminates regions outside the scope of cropland. Consequently, this selective exclusion significantly enhances the precision of our parcel extraction, as the focus is narrowed down solely to the farmland areas.
* Overlap prediction  
Satellite images, unlike those from natural scenes, are typically of large sizes. Therefore, it is common practice to slice these images before feeding them into the model and then stitch together the predictions. As shown in Figure 3b, when images are cut and predicted without any overlap, errors tend to occur at the junction of two adjacent cut images during the result merging process. Moreover, during prediction, SAM assigns a value to each segmented object. Consequently, if the same parcel is divided into two images, it will yield two values for the same parcel, leading to errors. To tackle these challenges, this study employs an overlap prediction method.  
Specifically, the Sentinel-2 images (with 10 m spatial resolution) of the study areas were set to have a 50% overlap and were cut into 1024 \(\times\) 1024 pixel tiles (corresponding to 10.24 \(\times\) 10.24 km) for prediction. During the prediction process, incomplete parcels located  
Figure 2: Overall experimental flow chart.  
at the edges of the image were removed. It is important to note that the prediction results of SAM may exhibit subtle pixel-level differences for the same image. To address this, we merged the overlapping areas by identifying differences and removing small areas. By setting the appropriate overlap degree for cutting, predicting, and merging, we ensured the integrity of the parcels and enhanced the accuracy of the prediction results (refer to Figure 3c). Finally, the results of the 10 m parcels were obtained.
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 5 Simulation Testbed
### Comparison with State of the Art Approaches  
The frontier heuristic proposed in this work (referenced as octomap heuristic) is compared with two other heuristics from the state of the art. One is the classical nearest neighbour heuristic, originally introduced in [28]. The other is the heuristic presented in [24], where priority is given to occupied space. In [24], only frontiers with occupied neighbours are considered for sampling, and those frontiers are referenced as surface frontiers. As the work in [24] relies on a combination of sensors that allows an assumption of omnidirectional sensing and in this work the sensor is 2D, the utility equation was adapted from 3D to 2D for a fair comparison. The result is Equation (1), where the safe flying distance from the structure is \(d_{s}\), and \(t_{m}\) is a maximum desired measurements per cell. Given the cell height \(h\), the distance from the sensor to the cell \(r\), and the number of points per scan \(N\), the utility of a view for observing a single surface frontier is  
Figure 6: The architecture applied to the case of an AUV (Autonomous Underwater Vehicle). The changes are highlighted in orange and are primarily at the hardware level.  
\[f(r)=\begin{cases}0,&\text{if }r<d_{s}\\ t_{m},&\text{if }r\leqslant r<d_{m}\\ \dfrac{hN}{2\pi r},&\text{if }d_{m}\leqslant r\end{cases} \tag{1}\]  
where \(d_{m}=\dfrac{hN}{2\pi r}\). The domain of Equation (1) is equivalent to the utility function used in [24]. The function is plotted in Figure 7 using the reference vehicle's sensors parameters and thresholds.  
The redundancy in the flyby manoeuvre (flying forwards and backwards) and allowing the frontier to be at any point in the map is considered part of the proposed approach. Consequently, when employing the surface frontier and the nearest neighbour heuristics, the flyby manoeuvre is done only in one direction and the frontiers must be at a distance shorter than 45 m, the distance the Lazy Theta* implementation is designed to reach in the allotted time.
## 4 Prediction based on LSTM
### Prediction Combining with Single-Wave Data  
Combined with the ship roll data and the wave elevation data, the roll motion is predicted for a different advance time. The advance prediction steps \(n\) based on the wave elevation input at 1 m from the ship's side are 5, 10, 15, 20, 30, 40, 50, 60 and 70. The number of advance prediction steps based on the wave elevation input at 3 m from the ship's side increases to 120. The number of advance prediction steps based on the wave elevation input at 5 m from the ship's side increases to 160. The errors of the prediction results with different input schemes are summarized in Table 4. The results solely based on the ship's  
Figure 5: Time series of roll prediction solely using data of roll input.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Advance Prediction Step**} & \multirow{2}{*}{**Model Scale**} & \multirow{2}{*}{**Full Scale**} & \multicolumn{2}{c}{**Prediction Error (deg)**} \\ \cline{3-5}  & & & **RMSE** & **MAE** \\ \hline
5 & 0.2 s & 1.4 s & 0.43 & 1.41 \\
10 & 0.4 s & 2.9 s & 0.54 & 1.53 \\
15 & 0.6 s & 4.3 s & 0.57 & 1.58 \\
20 & 0.8 s & 5.7 s & 0.81 & 3.12 \\
30 & 1.2 s & 8.6 s & 1.31 & 3.48 \\
40 & 1.6 s & 11.4 s & 1.36 & 4.29 \\
50 & 2.0 s & 14.3 s & 1.51 & 4.35 \\
60 & 2.4 s & 17.1 s & 1.57 & 4.48 \\
70 & 2.8 s & 20.0 s & 2.22 & 7.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Roll prediction solely using data of roll input.  
Compared with the prediction results solely based on roll input, when the wave elevation data at 1 m from the ship's side are also taken as the input feature, the overall prediction accuracy is greatly improved. Because the wave is the excitation of the ship rolling, the wave and roll data are related. When roll data and wave elevation data are used as input features for training, the LSTM can mine more effective features for roll prediction. By comparing the prediction results of three wave data inputs, the longer the input wave measurement point is from the ship, the longer will the effective advance prediction time of the ship roll take to gradually increase, although the corresponding prediction accuracy still gradually declines. On the one hand, because of the memory effect of the waves, with the increase in the distance between waves and ships, the time of propagation in space needs to be longer, which can effectively improve the prediction duration. On the other hand, the response relationship between the wave and ship motions will weaken with the increase in distance, resulting in the decline of the prediction accuracy. Combined with the wave data at 5 m from the ship, the number of advance steps for ship roll prediction can reach 120.
## 3 Proposed Model
### Attribute Embedding Layer  
The performance of machine learning algorithms relies heavily on the consistency of interpreting input vectors. Poor data interpretation can lead to suboptimal results. Therefore, feature extraction is crucial in machine learning algorithms [44]. Attribute values are often in the form of variable-length text, and to obtain dense feature representations, an encoder is required to encode the text into fixed-length vector representations. The bag-of-words (BOW) encoder is a commonly used method for feature representation in natural language processing (NLP) due to its simplicity and ability to capture word frequency information. However, it disregards word order and semantic information, making it unable to handle contextual relationships and word sense ambiguity. Hence, this paper adopts LSTM as the encoder. Deep learning techniques, on the other hand, facilitate faster information extraction, enabling scientists to extract discriminative features without relying solely on limited domain knowledge and manual effort [44]. The attribute value is denoted as \(v=w_{1},w_{2},...w_{n}\), where \(w_{i}\) denotes the position of \(i\) word, and the output of the last hidden state of LSTM is taken as the attribute vector \(\mathbf{v}\). Figure 3 shows the detailed process of the attribute embedding layer.  
\[\mathbf{v}=f_{lstm}(\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{n}), \tag{1}\]  
where \(\mathbf{w}_{i}\in\mathbb{R}^{d}\) denotes the word embedding vector and \(f_{lstm}\) denotes the LSTM network.
## 3 Proposed Methodology
### Image Segmentation  
Mammogram images suffer from several unwanted regions, including background, pectoral muscle, and different image noises and artifacts, which cause poor image quality. The undesirable presence of unwanted regions, noises, and artifacts can adversely affect textural features. Moreover, mammogram images do not provide optimal results in the case of extracting texture features and classification. Thus, the segmentation task is important because it limits abnormalities to the relevant breast region without interference from the background of the image. The cancerous region or ROI should be extracted from the image to obtain better classification results.  
In this study, two phases were performed for the extraction of ROI from mammogram images. We used a threshold technique to estimate the background of the image, including artifacts [49]. The threshold method used various features, including entropy, mean, and median, which are calculated based on subregions for each pixel according to the local information. We obtained the binary image from the grayscale image using this technique. Masking was used to retrieve original pixel values of the breast region and help select the cancer area, as well as the pervasion out of the cancer cells.  
The multi-segmentation stage is used to extract the whole ROI and pectoral muscle. The first stage depends on the threshold method to extract the whole ROI. However, a remaining area, called the pectoral muscle, can affect the next stage of CAD processing. This stage is difficult due to the overlap between the ROI and the unwanted ROI (pectoral muscles). Then, the second stage involves a trainable model based on histogram of oriented gradients (HOG) texture features and the NN classifier used to isolate and extract the ROI. In this stage, we have built the trainable model by splitting the data into testing and training. The single split testing strategy has been used by randomly selecting 35% from each class (for each dataset) to form a testing sample, whilst the remaining 65% of each class form a training sample. In the training stage, we have selected blocks from the pectoral muscle out of the pectoral muscle. The HOG features were extracted for each training sample and used as input vector with the labels (pectoral muscle or not) in the NN classifier. In the testing stage, each region extracted from the initial segmentation has been divided into blocks, and the HOG features extracted from each block have been used to classify it as pectoral muscle or not. The cropping technique is then applied to datasets with failed segmentation to derive the ROI for further processing. The cropping technique is dependent on the center and radius values, which are determined by the expert radiologists in the dataset. ROI extraction in abnormal cases is performed depending on the values of the center and radius, whereas in normal cases it is performed in randomly selected locations [50; 51].  
Figure 1: Proposed automated breast cancer recognition model.
## 3 Results and Discussion
Figure 5: Nusselt number results for CFD data, GRG and HFO-1 algorithm based on M1 ((a): square, (b): semicircular, (c): triangular). Performance improvement of HFO-1 over GRG based on models M1 and M2 (%) are given in Table 5. The results of the metric calculations conducted on all geometries in both models (M1 and M2) demonstrate that the HFO-1 algorithm outperforms the GRG in terms of success rate. The mean absolute error (MAE) metric in M1 and M2 indicates that the HFO-1 algorithm exhibited an average improvement of approximately 50%. Furthermore, the mean absolute percentage error (MAPE) metric reveals that this improvement rate is approximately 99% on average. The improvement rates of the MSE and RMSE metrics are approximately 70% and 50%, respectively. Conversely, the degree of improvement is comparatively modest, largely due to the fact that the R\({}^{2}\) values of the methodologies employed in both models are relatively similar.
# Laser Synthesized Graphene and Its Applications
## 3 Applications of Laser Synthesized Graphene
### Environmental Applications  
The ability of graphene to destroy or prevent the proliferation of bacteria and other microorganisms has been proven since the beginning of the last decade [87]. Such function has also been demonstrated for LIG. [PERSON] et al. have shown that LIG can serve the double role of anti-fouling surface as well as an antibacterial surface when acting as an electrode [88]. Tested against _Pseudomonas aeruginosa_ under biofilm growth conditions, no biofilm growth was detected on LIG. The antimicrobial activity was demonstrated on both LIG surface and a powder obtained by scraping LIG off the original surface. The performance increased as a voltage up to 2.5 V was applied to LIG on the surface, acting as electrode, compared with the powder [88]. Such function was enhanced using the S-doped LIG [42].  
A similar ability was demonstrated by [PERSON] et al., who showed that LIG can be utilized as a self-sterilizing bacterial air filter [89]. Indeed, as airflow passes through an LIG porous structure, it is able to retain microorganisms (Figure 14a). By applying a voltage to the material, joule heating can reach around 300 \({}^{\circ}\)C, which can kill any microorganisms present on the filter, thus achieving sterilization (Figure 14b) [89].  
The adsorption of organic dye compounds by LIG powders was demonstrated by [PERSON] et al. [90]. Here, methylene blue and methyl orange were used as sample molecules for cationic and anionic organic water pollutants, respectively. The adsorption mechanism was attributed to \(\pi\)-\(\pi\) interactions and electrostatic interactions. By varying pH, the adsorption capacity for methylene blue held very high, between 88% and 100% between pH = 2 and pH = 10, while for methyl orange the adsorption capacity decreased by increasing pH, but was still >70% at pH = 10 [90]. This system showed superior capacity in comparison to graphene oxide, which suffered at high pH. Furthermore, such system did not suffer interference from anions usually present in water such as chloride, sulfate, nitrate, and bicarbonate [90].
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
## 4 Fountain-coded MF-TWRN
### Asymptotic Analysis
Therefore, the theoretical upper bound on the throughput of MF-TWRN in an information exchange is given by  
\[T\leq C_{\text{min}}(h_{\text{A,R}},h_{\text{B,R}}) \tag{60}\]  
where  
\[C_{\text{min}}(h_{\text{A,R}},h_{\text{B,R}})=\min(C_{\text{A}}(h_{\text{A,R} },h_{\text{B,R}}),C_{\text{B}}(h_{\text{A,R}},h_{\text{B,R}})) \tag{61}\]  
Consider Rayleigh distributed channel fading coefficients, the mean upper bound on throughput for MF-TWRN in the flat Rayleigh fading channel is represented by  
\[T_{\text{ub}}=\int_{0}^{+\infty}\int_{0}^{+\infty}C_{\text{min}}(h_{\text{A, R}},h_{\text{B,R}})p(g_{\text{A,R}})p(g_{\text{B,R}})dg_{\text{A,R}}dg_{ \text{B,R}} \tag{62}\]
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 3 Ground Target 2D Localization Scheme
### Detection Stage  
To carry out targets' detection, a Cell Average-Constant False Alarm Rate (CA-CFAR) based detector is selected. To manage the 3-dimensional matrix, \(\mathbf{S_{CAF}}[m,p,\phi_{D,i}]\), a windowing technique that defines one single Cell Under Test (CUT) for each \([m,p]\) pair is designed. The CUT is selected as the one with the maximum power value from \(s_{CAF}[m,p,\phi_{SLL,i}]\) in the \(\phi_{SLL,i}\) dimension. The reference window spreads along the range-Doppler plane  
Figure 4: Set of three orthogonal beams for a five elements array.  
around the selected CUT. Guard cells are defined to reduce the impact of oversampling and extended targets in the threshold estimation.  
The estimated location of targets in detection space is the centroid of positively detected points or the position of the maximum level. However, a more accurate estimation of the targets range can be obtained when the reference and surveillance signals are oversampled by taking advantage of the signal correlation characteristics [21]. An improved estimation of target range is obtained by approximating the GPS signal correlation with a parabola. In this paper, the bin with the maximum level and its four adjacent bins are used to calculate a second-order function that approximates the CAF in the range dimension. The target range is estimated as the range where the parabola has the maximum value.  
Figure 5 shows the range profile of a punctual target at 125 m considering an L1 GPS signal with a sampling frequency of 12.5 MHz. The approximation with a parabola using five points is also depicted, showing an estimated range value (124.1 m), which is close to the maximum in the CAF. The estimation accuracy depends on the echo quality as a function of its SNR, as shown in Expression (7).  
\[\sigma_{m}=\frac{A}{\sqrt{\text{SNR}}}\Delta m \tag{7}\]  
where \(\Delta m\) is the range resolution and \(A\) is a scalar coefficient, typically in the interval \((0.5,0.9)\).
# Application of Blockchain Technologies in Verification of Software Metrics
## 3 Methodology
### System Architecture  
As shown in Figure 4, the architecture of the developed system comprises three components: frontend, backend, and blockchain. To access the system, the user must first connect their digital wallet to the tool. The digital wallet is essential, as all actions--such as initiating transactions and viewing verification history--are linked to the user's wallet address.  
The backend component does not include a centralized database, as this would compromise decentralization and introduce the risk of software metric manipulation. In this system, the backend is responsible solely for computing software metrics. To facilitate communication between the frontend and backend, a REST API is used to upload software  
Figure 3: Data retrieval process.  
files on which the metric computation is performed. Once calculated, the metrics are returned to the frontend and displayed to the user.  
The third component is the blockchain, which includes a smart contract responsible for permanently storing the computed metric data. After receiving the calculated metrics, the user proceeds to the verification step. To ensure transparency, the user triggers the smart contract, which then executes a transaction. Each transaction submitted to the smart contract is verified by the distributed network of nodes, ensuring data integrity, transparency, and protection against manipulation.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Evaluation of the System without Optimization
#### 3.4.6 The Goal Effectiveness and its Dependence on PER, Interference and Goal Cost
Let us now focus on Figure 7d-f, in which we plot the goal effectiveness as a function of DO user data rate loss in (18), i.e., the goal cost (\(y\)-axis) and the PER (\(x\)-axis), for a fixed \(D_{\text{max}}=45\) ms, and different goal value (NREI) thresholds across figures, namely the same as the previous results. In addition, a contour plot representing the 80% goal effectiveness feasibility region is shown in all figures. These plots are obtained by also varying the DO user transmit power (within the range \([0,200]\) mW). From these results, we can appreciate the goal feasibility region, also as a function of the goal cost (i.e., the aim of this paper), and we can draw the following conclusions:  
* Again, the goal effectiveness feasibility region is a surface, i.e., there are multiple solutions guaranteeing the goal-effectiveness constraint. * While the above consideration holds, there exists a minimum goal cost solution, i.e., the minimum cost needed to achieve the target goal effectiveness. The latter is the lowest point of the contour plots representing the effectiveness thresholds and is represented by the black horizontal dashed lines in each plot. * By increasing the goal value threshold (i.e., across different figures), the feasibility region shrinks as before and, as an additional observable effect, the minimum goal cost increases (e.g., above 60% of DO user data rate loss in Figure 7e). In other words, the stricter the constraint in terms of goal value is, the higher the minimum goal cost able to guarantee effectiveness is. * In certain conditions (see Figure 7f), desired values of goal effectiveness are not attainable (e.g., goal effectiveness above 80%)  
Interestingly, these results relate the performance of a legacy (DO) and a GO communication system interfering with each other, showing non-trivial outcomes, which suggest that higher communication reliability does not necessarily imply higher goal effectiveness. Therefore, they represent the basis to formulate a goal-oriented optimization problem in the next section. Indeed, the aim of a goal-oriented resource orchestration framework is to move within the goal effectiveness feasibility region, possibly finding the lowest cost in such region. Then, from these results and their corresponding conclusions, it is straightforward to formulate a goal-oriented resource allocation problem involving the variables that mostly affect goal effectiveness and goal cost: _(i)_ the PER of GO communication and _(ii)_ the DO user transmit power, in this work. Other variables can be taken into account, which represents further research directions on this topic. Figure 7: _Cont._