esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Injection molded parts have a homogeneous structure, while the material pushed through the nozzle in FDM has fibers oriented along the direction of the extension during mechanical tests. When the sample is extended and starts to break, the rupture propagates uniformly in injection molded parts until the parts are damaged. However, for the FDM printed samples, there is no damage to the entire cross section but only to individual fibers in the structure [12]. After breaking the weakest fiber, the sample continues to withstand the stress until more fibers start to be damaged. The more fibers in the cross section of the test sample, the more durable it is. On the other hand, injection molded samples are more prone to plastic deformation after exceeding the maximum stresses, while in FDM printed parts the fibers are damaged and the sample breaks. The effect of the layer thickness applied in the \(Z\)-axis was also analyzed. As the thickness of the deposited material increases, the strength of the samples decreases rapidly [5]. In the case of a layer with a low height (0.05 mm), direct contact of the nozzle with the applied layer creates additional heat-spot influencing the printouts. This creates an additional factor thermally connecting subsequent layers together, giving the advantage of the mechanically strong element. However, the time for making a single type 5A fitting with a infill density of 70% increased up to 35 min. An increase in the layer height deteriorates \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & & **FDM 30\%** & **FDM 50\%** & **FDM 90\%** \\
**Perimeter Layers** & & & & \\ \hline \hline \multirow{2}{*}{3} & Tensile strength [MPa] & 19.01 & 24.36 & 25.41 \\  & In relation to injection molding & 47\% & 60\% & 62\% \\  & Tensile strength [MPa] & 24.61 & 25.53 & 31.69 \\
5 & In relation to injection molding & 60\% & 63\% & 78\% \\  & Tensile strength [MPa] & 28.54 & 30.58 & 32.38 \\
7 & In relation to injection molding & 70\% & 74\% & 79\% \\
9 & Tensile strength [MPa] & 33.12 & 35.02 & 35.12 \\  & In relation to injection molding & 81\% & 86\% & 86\% \\
11 & Tensile strength [MPa] & 38.03 & 38.34 & 39.63 \\  & In relation to injection molding & 93\% & 94\% & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the mechanical tests of the samples printed with different numbers of perimeter layers and different infill density. the connection between the applied layer and the already printed element, reducing the strength of the entire component. An additional disadvantage is the lower quality of details [6]. However, the print time is very short. With a layer height of 0.2 mm, the printing time was 10 min, while for 0.4 mm, the time was reduced to 5 min.
## 3 Results and Discussion
### Biological Activity of AmPS
#### 3.2.4 Anti-Cancer Potential  
The antiproliferative potential of AmPS against gastric adenocarcinoma (AGS), colorectal adenocarcinoma (HT-29), and lung carcinoma (A549) was determined. These types of cancer are recognized among the five most common cancers globally according to the data from 2020 presented by the World Health Organization [33]. Therefore, searching for potential natural agents preventing the incidence of stomach, colon, and lung cancer is desirable. It was found that the crude polysaccharides from _A. mellea_ significantly inhibited the viability of the AGS and HT-29 cell lines in a dose-dependent manner, while the viability  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **COX-1** & **COX-2** & **LOX** \\ \cline{2-4}  & & **(\% of Inhibition)** & \\ \hline AmPS (0.5 mg/mL) & 52.07 \(\pm\) 1.78 & n.d. & n.a. \\ AmPS (1 mg/mL) & 71.69 \(\pm\) 2.47 & n.d. & 38.83 \(\pm\) 0.47 \\ ASA (10 mM) & 32.95 \(\pm\) 0.64 & 98.64 \(\pm\) 0.93 & 93.97 \(\pm\) 0.76 \\ \hline \hline \end{tabular} Mean values from three replicate determinations with standard deviation.  
\end{table}
Table 4: Inhibition of cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), and lipoxygenase (LOX) by crude polysaccharides from _A. mellea_ (AmPS) expressed as % of inhibition. Abbreviations: ASA—acetylsalicylic acid; n.d.—not detected; n.a.—not analyzed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Antioxidant Assay** & **Result** \\ \hline ORAC (\(\mu\)M TE/g AmPS) & 414.3 \(\pm\) 2.74 \\ ABTS (\(\mu\)M TE/g AmPS) & 94.69 \(\pm\) 0.26 \\ \hline \hline \end{tabular} Mean values from three replicate determinations with standard deviation.  
\end{table}
Table 3: Antioxidant properties of AmPS. The results of the antiradical activity with ABTS radical cation and the oxygen radical absorbance capacity (ORAC) assay are expressed as \(\mu\)M of Trolox equivalents per g of AmPS.  
of the A549 cell line was slightly decreased (Figure 3A). The NRU assay was used to evaluate the cytotoxicity of AmPS in relation to different cell lines. The HT-29 cell line was found to be the most susceptible to the effects of AmPS. All the analyzed concentrations slightly reduced cell viability. Only the highest concentration of AmPS (400 \(\upmu\)g/mL) inhibited the viability of the AGS and A549 cell lines (Figure 3B). In an available study on the anticancer properties of polysaccharides from _A. mellea_, it was found that it exerted a potent anticancer effect on A549 cells via apoptosis-involving mechanisms [3]. Due to the fact that _A. mellea_ is considered an edible and safe mushroom species, it can be suggested that including it in the diet may help preventing cancers of the gastrointestinal tract.
## 4 Low Complexity Non-Linear Detection
### Two Approaches for Non-Linear Data Detection
#### 4.2.1 OSIC Detection  
The ordered successive interference cancellation (OSIC) algorithm is an effective method to carry out the cancellation of the ICI. This method is suitable to apply to the proposed model in Equation (26) combined with the QR decomposition described above, allowing the sub-optimal detection of the data with very low computational complexity. Substituting this decomposition in Equation (26), it results in:  
\[\mathbf{v}=\mathbf{Q}\mathbf{R}\mathbf{P}^{T}\chi+\mathbf{z}_{D}; \tag{37}\]  
pre-multiplying both sides of this equation by \(\mathbf{Q}^{H}\) leads to the following system of equations:  
\[\tilde{\mathbf{v}}=\mathbf{R}\mathbf{P}^{T}\chi+\mathbf{Q}^{H}\mathbf{z}_{D}, \tag{38}\]  
\[\tilde{\mathbf{v}}=\mathbf{R}\widetilde{\chi}+\mathbf{z}_{D}; \tag{39}\]  
where \(\tilde{\mathbf{v}}=\mathbf{Q}^{H}\mathbf{v}\), and \(\tilde{\chi}=\mathbf{P}^{T}\chi\) is the vector of the precoded data, ordered in decreasing order with respect to its contained energy. The \(\mathbf{z}_{D}\) noise vector maintains its statistics due to the fact that Q is unitary. Due to the triangular structure of \(\mathbf{R}\), the \(\widetilde{\mathbf{v}}\) vector elements can be expressed individually as:  
\[[\tilde{\mathbf{v}}]_{j}=[\mathbf{R}]_{j,j}[\chi]_{j}+\sum_{i=j+1}^{N_{D}}[ \mathbf{R}]_{j,i}[\chi]_{i}+[\mathbf{z}]_{j}; \tag{40}\]  
where the notations \([\cdot]_{a}\) and \([\cdot]_{a,b}\) indicate the \(a\)-th element of the vector and the \(b\)-th element of the \(a\)-th row of the matrix, respectively. This way, the detection of each of the data signal can be obtained iteratively using the following expression:  
\[[\tilde{\chi}]_{N_{D}}=\mathcal{Q}\Bigg{\{}\frac{[\widetilde{\mathbf{v}}]_{j} }{[\mathbf{R}]_{j,j}}\Bigg{\}}, \tag{41}\]  
\[[\tilde{\chi}]_{j}=\mathcal{Q}\Bigg{\{}\frac{[\widetilde{\mathbf{v}}]_{j}- \sum\limits_{i=j}^{N_{D}}[\mathbf{R}]_{j,i}[\tilde{\chi}]_{i}}{[\mathbf{R}]_ {j,j}}\Bigg{\}},j=\{N_{D}-1,\cdots,1\}; \tag{42}\]  
where the operator \(\mathcal{Q}\{\cdot\}\) is a decision operator that maps its arguments to the closest point in the constellation \(\Omega\) used by the transmitter. Assuming that at each iteration, the previous decisions are correct, then the interference of the previously detected symbols can be subtracted from the current symbol to be detected.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results
### Results of Semi-Physical Experiments  
In order to further verify the performance of the reinforcement learning algorithm model proposed in this paper, we built a semi-physical platform. In the semi-physical platform, real valves are used to execute the instructions given by the algorithm, and the engine pressure and thrust are calculated by the fine dynamics model. In order to increase the persuasiveness of the experimental results, we use complex mixed signals to test the algorithm model. The test results are listed in Figure 13, which shows the tracking effect of \(F_{y}\), \(F_{z}\), \(F_{pitch}\), \(F_{yaw}\), and \(F_{roll}\) on the given signal under complex mixed signals. It can be seen intuitively that the algorithm model proposed in this paper has achieved a good tracking effect.  
Figure 13: Test results of semi-physical simulation experiment: (**a**) \(y\)-axis thrust, (**b**) \(z\)-axis thrust, (**c**) _pitch-axis_ thrust, (**d**) _roll-axis_ thrust, (**e**) _yaw-axis_ thrust, and (**f**) pressure.
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 2 Omnidirectional Imaging and Global Appearance Descriptors
### Global Appearance Descriptors
#### 2.2.2 Two-dimensional Discrete Fourier Transform  
The two-dimensional Discrete Fourier Transform (2D-DFT) of an image \(im(x_{i},y_{i})\in\mathbb{R}^{M\times N}\) can be expressed as a new matrix \(IM(u,v)\in\mathbb{C}^{M\times N}\) that can be split into two matrices, one containing the magnitudes \(A(u,v)\in\mathbb{R}^{M\times N}\) (or power spectrum) and other with the arguments \(\Theta(u,v)\in\mathbb{R}^{M\times N}\). Since the most relevant information in the Fourier domain concentrates in the low frequency components and the high frequency information is usually more affected by noise, retaining only a number of low frequency components may lead to better results in localization with an improved computational cost. Taking this fact into account, the number of rows retained from the matrices \(A\) and \(\Theta\) will be \(k_{1}\leq M\) and the number of columns \(k_{2}\leq N\).  
Another interesting property when working with panoramic images is the rotational invariance, which is reflected in the shift theorem:  
\[\begin{split}&\mathcal{F}_{2}[im(x_{i}-x_{0},y_{i}-y_{0})]=IM(u,v) \cdot e^{-2\pi j\left(\frac{uv_{0}}{N}+\frac{uv_{0}}{M}\right)},\\ & u=0,\ldots,N-1,v=0,\ldots,M-1.\end{split} \tag{2}\]  
where \(IM(u,v)\) is the 2D-DFT of the original image \(im(x_{i},y_{i})\) and \(im(x_{i}-x_{0},y_{i}-y_{0})\) is a shifted version of this image. According to this theorem, the power spectrum of the shifted image remains the same of the original image and only a change in the argument of the components of the transformed image is produced, whose value depends on the shift along the \(x_{i}\)-axis (\(x_{0}\)) and the \(y_{i}\)-axis (\(y_{0}\)). Thanks to this property, this transform has previously been used to estimate the position and orientation of a robot when it moves on the ground plane [26]. In this case, if the robot captures two panoramic scenes \(im_{1}\) and \(im_{2}\) from the same position on the ground plane but with different orientations, the magnitudes matrices \(A_{1}\) and \(A_{2}\) are the same and only a shift along the \(x_{i}\)-axis is produced, which can be calculated from the theorem and used to estimate the relative orientation.  
Equation (2) also shows that the first row of the 2D-DFT, which corresponds with \(v=0\), is only affected by shifts along the \(x_{i}\)-axis of the image, whereas the first column of the transform, which corresponds with \(u=0\), is only affected by shifts along the \(y_{i}\)-axis.
# Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods
## 3 Proposed Model
### Model Neck  
The neck part of the whole detection network served in the role of convolution feature fusion. In the original YOLOv5s implementation, PANet is used as the neck, which adds a bottom-up pathway on top of the FPN. For the singularity of the terahertz image dataset, it is hard to obtain and fuse the significant features. As an extension of PANet, a bi-directional feature pyramid network (BiFPN) is adopted as our model's neck, as shown in Figure 6. It takes level 3-5 input features \(P_{i}=P_{3},P_{4},P_{5}\), where \(P_{i}\) represents a feature level with a resolution of 1/2i of the input image. For instance, our input terahertz image is transformed into 640 px by 640 px; \(P_{3}\) then represents level 3 with resolution 80 px by 80 px (640/23 = 80). A skip connection is applied after input \(P_{4}\) and \(P_{5}\) to enhance the feature representation. The different features in BiFPN are concatenated with the same size after upsampling or downsampling. The output features of BiFPN can be calculated as:  
\[\begin{split} O_{3}&=Conv(P_{3}\oplus Down(Conv(P_ {4}))\\ O_{4}&=Conv(Up(O_{3})\oplus P_{4}\oplus Conv(P_{4}) \oplus Down(Conv(P_{5}\oplus Conv(P_{4}))))\\ O_{5}&=Conv(Up(O_{4})\oplus P_{5}\oplus Conv(P_{5}) \oplus Up(Conv(P_{4})))\end{split} \tag{1}\]
## 2 Related Research
### Multi-Label Text Classification Task Based on Deep Learning Technology  
At present, there is relatively little research on multi-intention recognition, and the multi-intention recognition task is similar to the multi-label text classification task. For multi-label text classification tasks, with the rapid development of deep learning technology in recent years, using deep learning methods to solve multi-label classification tasks has become a research hotspot. Deep learning methods automatically extract features through neural network structures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which reduces labor costs and enhances feature expression ability [9]. Convolutional Neural Networks (CNN) [10] were initially applied in the field of computer vision. Later, [PERSON] [11] proposed the TextCNN model, which is suitable for text classification tasks and was obtained by [PERSON] through some modifications to the input layer of a CNN. It only has one layer of convolution and one layer of maximum pooling, so it has advantages such as a simple network structure and a fast training speed; it is widely used in multi-label text classification tasks [12; 13]. Its characteristic is that it can effectively extract the local features of sentences, but its disadvantage is that it requires the use of fixed windows. Therefore, when facing long texts, it is not suitable for capturing the long-distance dependencies of text sequences and is prone to losing key information. Recurrent Neural Networks (RNN) [14] can capture long-distance dependencies and are therefore applied to multi-label text classification tasks [15]. However, RNNs may experience gradient vanishing or exploding when processing text. To address this issue, the RNN variant BiLSTM [16] has emerged, which can effectively capture contextual information, extract the global features of sentences, and improve the classification accuracy of multi-label texts [17].  
In text classification tasks, in order to extract key information that is more effective for classification, [PERSON] et al. [18] proposed using the BiLSTM model to extract key features globally by introducing an attention mechanism to assign higher weights to key information, thereby improving the effectiveness of text classification. Some scholars have combined a CNN with BiLSTM for use. Some scholars have first obtained the local features of text through a CNN and then input them into LSTM for classification [19; 20], while others have been used to extract key information. The authors have proposed a model that combines attention and semantic slot filling [7; 8] to improve the accuracy of attention recognition and semantic slot filling [7; 8] to improve the performance of the multi-attention recognition task.
## 2 Related Work
In their work, they compared various microstrip patch antenna designs intended for energy harvesting, analyzing how geometric modifications achieved miniaturization, harmonic rejection, and frequency/polarization reconfigurability. They explained multiple patch variations (via slots, unconventional shapes, etc.) and ultimately comparatively evaluated several patch rectennas, highlighting methods to obtain more compact antennas with harmonic filtering and polarization/frequency selectivity. Although they qualitatively presented benefits of each design and suggested these strategies could improve RF harvesting performance, the study was limited to comparing theoretical designs without providing unified simulations or measurements. It did not include their own experiments or a quantitative statistical analysis, focusing instead on a narrative synthesis of existing proposals. This pioneering review demonstrated the antenna's importance in a rectenna and served as a promising research topic for future research.
# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 2 Background  
### Verticillium Wilt  
Verticillium wilt of olive tree is caused by the soil-borne fungus Verticillium dahliae Kleb. It is currently considered the main soilborne disease threatening olive production worldwide. This disease was first described in Italy in 1946 [1], followed by California [8] and Greece [9]. Descriptions of disease occurrence have been reported from 1970 and onwards in Turkey, France, Spain, Syria, Morocco, Jordan, Algeria, Israel, Iran, Malta, and Australia [10; 11; 12; 13; 14; 15; 16; 17], practically covering all olive production zones. This disease is one of the most significant diseases of olives, causing significant economic damages every year, not only in terms of yields, but also in terms of trees that die, thus permanently decreasing production potential [3]. One more factor affecting financial sustainability is the fact that fruits of V. dahliae-infected trees have poor organoleptic properties [18]. The fungus survives in soil by means of microsclerotia, which serve as primary inoculation means. Hyphae generated by microsclerotia germination penetrate the roots and grow toward the xylem vessels, producing mycelium and conidia [19]. The typical symptoms of infestation include early drop of asymptomatic, green leaves from individual twigs and branches that eventually end to complete defoliation. However there are cases that apoplexy is rapidly developing (acute form of the disease) [20; 21; 9]. The symptoms are more evident from late fall to late spring. The blocking of xylem by fungus mycelia reduces the water flow and leads to water stress [22; 23], affecting amongst others, plant transpiration rates. Cultivation techniques have contributed to fast dispersal of the disease worldwide. Infested plants that are transported to new areas are the means of new infestations; increased water levels in soil help microsclerotia migrate to new areas infecting new olive trees [2; 3]. V. dahlia biology includes specific traits that make control very difficult. The most important are the numerous alternative hosts and the asymptomatic appearance of infested olive trees at initiative stages of infection [4].
## 4 Empirical Evaluation
### Experimental Results and Analyses
To alleviate the impact of outliers on the accuracy of the FSA, we can choose larger _eps_ values, which help to treat outliers as part of a cluster. Therefore, on the premise of ensuring the accuracy of clustering results, it is helpful to choose as large an _eps_ value as possible to solve the problem of outliers in clustering. We obtained experimental statistics on the number of different method calls. When experimenting with the HashSet, Signature, Socket, StringTokenizer, StackAr, ToHTML-Stream, and SMTPProtocol classes, each class generated more than 100,000 method calls. In this case, the average F-measure value of DSM was 78.52%, and the average F-measure value of MCLSM was 92.03%. When experimenting with the ArrayList, LinkedList, HashMap, Hashtable, ZipOutputStream, and NFST classes, each class generated less than 100,000 method calls. In this case, the average F-measure value of DSM was 63.86% and the average F-measure value of MCLSM was 93.44%. These results indicate that MCLSM has a more significant effect than DSM, and MCLSM exhibits excellent performance on different data scales.
## 5 Quantitative Results
### Time  
One of the first factors that we identified in which participants were affected was in their time. The easiest way to identify how participants are affected in their time is by the direct relation of the distances traveled and the time spent. Our initial assumptions were that people who live in underserved regions would always take the shortest route, which would mean the shortest travel time. The collected data showed that participants often would end up taking a much longer route, both when walking or when using public transportation. For pedestrian mobility, the longer routes are mostly due to the geographic situation, so we will discuss it in the next subsection. In the case of vehicular mobility, most people use public transportation; this not only involves longer routes (as compared with driving), but also overhead by walking to the bus stop, waiting times, transfers between buses, and other factors that the participants could not control. In total, we calculated the expected travel time for 382 routes. 58.115% (222/382) of the routes that were provided by Google had a shorter travel time. In Figure 6, we can see a scatter plot of all the route travel times, where positive values belong to instances where the real route was shorter. In contrast, routes with negative  
Figure 5: Occurrences of individual divergences (**left**) and occurrences of divergences per participant (**right**), when considering both pedestrian and vehicular modes.  
values belong to instances where the route that was suggested by Google Maps was shorter. This distribution means that, although a shorter (faster) route existed, the participants would take a longer route more frequently.  
To mention a concrete example, participant 35 (Figure 7) took longer routes than expected and had to spend more time commuting between different city locations using public transportation. The time that was dedicated to commuting represented a considerable amount of the day. The participants reported having spent anywhere from two to four hours daily on public transport.