esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ash Analysis
#### 3.5.3 Phase Analysis of the Biomass Ash Samples
This observation is in line with the results from the FTIR analyses where bands of calcite were dominant. [PERSON] et al. [127] also reported on the formation of calcite on -OH surfaces in high Ca\({}^{2+}\) concentration. A recent study of [PERSON] et al. [50] on rice husk and straw ashes showed that once the temperature exceeds 550 towards 700 \({}^{\circ}\)C, there is a high tendency to form thermally non-stable compounds such as arcanite, calcite, and sylvite, which were also observed in this study. The result of this study only showed the presence of quartz instead of the other stable polymorphs forms of silica, i.e., cristobalite and tridymite [83; 93; 123]. The formation of cristobalite and tridymite phases in ashes is reported to occur at temperatures above 800 \({}^{\circ}\)C, lower than the temperature at which the selected biomass fuels in this study were combusted. [PERSON] et al. [128] also reported the formation of cristobalite in rice husk ashes produced at higher temperatures (900 \({}^{\circ}\)C) as the main crystalline silica phase. The absence of cristobalite and tridymite in our ash samples affirms the appropriateness of the selected temperature regime for the combustion of the biomass fuels. This is because a high percentage of cristobalite and tridymite in ashes could negatively impact the textural properties and pose other health risks due to the carcinogenic attribute of crystalline silica [50; 129]. Figure 9 also shows the improvement of silica content by employing higher citric acid concentration during the pre-treatment stage. As observed, the O-Si-O bands at CorH-A-5% are of higher intensity than those of CorH-A-1%. More importantly, they are comparable to the bands of commercial reference silica (CWK Kostropur\({}^{\circledown}\) 021012) on the market, paving the way for the possibility of a wide variety of applications. The literature also reported silica purity improvement by pre-treatment of other silica-rich biomass fuels [20; 125]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **Ashes** & & \\ \cline{2-5}
**Phase** & **CasP-A-1\%** & **YamP-A-1\%** & **CocH-A-1\%** & **CorC-A-1\%** & **CorH-A-1\%** \\ \hline Anhydrite [Ca(SO\({}_{4}\))] & x & x & & & x \\ Quartz (SiO\({}_{2}\)) & x & x & x & x & x \\ Arcanite (K\({}_{2}\)SO\({}_{4}\)) & x & & x & & \\ Sylvite (KCl) & & & x & & \\ Calcite (CaCO\({}_{3}\)) & x & x & x & & \\ Albite [Na(AlSi\({}_{3}\)O\({}_{8}\))] & & x & & & \\ Sanidine (KAlSi\({}_{3}\)O\({}_{8}\)) & x & x & x & & \\ \hline \hline \end{tabular} \({}^{\star}\)x\({}^{\star}\) denoting the presence of such a phase in the ashes.  
\end{table} 
Table 5: Crystalline phases identified with XRD in the ash samples after the combustion of \(1\)\(w\)/\(\%\) acid-treated biomass fuels at 600 \({}^{\circ}\)C.
## 1 Introduction
Both novel approaches in both studies have been integrated into computational algorithms designed to read DICOM images on a voxel basis and generate a 'G-code' that contains adjustments to the flow rate or print speed, calibrated to achieve reference voxel-based densities, i.e., HU values. However, these algorithms are not accessible, and creating similar algorithms could be a challenging task for the majority of researchers. Therefore, the need for practical and accessible methods to mimic low-density tissues such as the lungs still stands. The recent introduction of special filaments has paved a new way for printing low-density parts. One of these filaments is Light Weight PLA (LW-PLA), which was introduced by colorFabb. Differing from other PLA-based regular filaments, LW-PLA has the unique feature of utilizing active foaming technology triggered by the temperature. The expansion resulting from foaming allows users to achieve similar line widths with reduced flow rates at higher temperatures. Overall, this makes it possible to decrease the density of a 3D-printed object without compromising either the infill rate or the line width, thus maintaining a much higher level of homogeneity. Taking into account the developments of 3D printer technologies and new filament materials, this study aims to investigate the impact of the printing temperature, flow rate, print speed, and print size on the physical density and corresponding HU values of 3D-printed LW-PLA samples.
## 2 Adam-Bayesian-GPR-Based MC Reliability Calculation Procedure Framework
### Gaussian Process Regression  
The Gaussian process (GP) [27] is a type of general supervised learning algorithm that can be used to solve both regression and classification problems. Gaussian process regression (GPR) can implement training on input data to predict the value of unknown data. It can obtain the probability distribution of prediction values, different from the deterministic machine learning algorithm. The empirical confidence interval can be calculated accordingly, which can take into better account the influence of system uncertainty. The GPR steps are presented as follows:  
Step 1: Calculate the kernel function and mean function of GP. The mean function is often set to 0 or the average value of training data, and the kernel function is selected based on the actual situation, as denoted in Formula (2). The hyperparameters of the GPR model can be determined by the optimization algorithms, such as the Adam algorithm [28] and the maximum likelihood estimation (MLE) method.  
\[\mathbf{K}=\left[\begin{array}{cccc}k(x_{1},x_{1})&k(x_{1},x_{2})&\cdots&k(x_{1},x_{ n})\\ k(x_{2},x_{1})&k(x_{2},x_{2})&\cdots&k(x_{2},x_{n})\\ \vdots&\vdots&\ddots&\vdots\\ k(x_{n},x_{1})&k(x_{n},x_{2})&\cdots&k(x_{n},x_{n})\end{array}\right] \tag{2}\]  
Step 2: According to the properties of Gaussian distribution, for the to-be-predicted \((x_{*},y_{*})\), Formula (3) is obtained by Gaussian distribution.  
\[\left[\begin{array}{c}y\\ y_{*}\end{array}\right]\sim\mathcal{N}\Big{(}0\,,\begin{bmatrix}\mathbf{K}&\mathbf{K} _{*}^{T}\\ \mathbf{K}_{*}&\mathbf{K}_{**}\end{bmatrix}\Big{)} \tag{3}\]  
where \(\mathbf{K}_{*}=[k(x_{*},x_{1})k(x_{*},x_{2})\ \cdots k(x_{*},x_{n})]\); \(K_{**}=k(x_{*},x_{*})\); \(y_{j}(j=1,2,\ldots,M)\) is the response of \(x_{i}(i=1,2,\ldots,n)\).  
Step 3: The posterior probability is calculated using the Bayesian inference theory, and the relationship between the predicted value and actual value \(\mathbf{y}\) is denoted by Formula (4).  
\[y_{*}\big{|}\mathbf{y}\sim\mathcal{N}\Big{(}\mathbf{K}_{*}\mathbf{K}^{-1}\mathbf{y},K_{**}-\bm {K}_{*}\mathbf{K}^{-1}\mathbf{K}_{*}^{T}\Big{)} \tag{4}\]  
Therefore, the mean \(\overline{y}_{*}=\mathbf{K}_{*}\mathbf{K}^{-1}\mathbf{y}\), and the variance \(\text{var}(y_{*})=K_{**}-\mathbf{K}_{*}\mathbf{K}^{-1}\mathbf{K}_{*}^{T}\).  
Step 4: Substitute the to-be-predicted data and calculate the predicted value using Formula (4).
## 4 Discussion  
Detecting the fruit flower clusters is a very difficult task. This is mainly due to the sheer number of clusters as well as the size of the clusters. Observing the results shown in the previous section in Table 3, the average error calculated was \(-14\%\). This means that on average, the fruit flower cluster detection model is predicting \(14\%\) less than what is actually there. As previously explained in Section 1.3, there are many challenges to this problem. The biggest challenge is the issue of occlusion due to the fruit flower cluster being in very dense clumps. This makes it extremely difficult for the model to differentiate between separate clusters as well as what fruit flower belongs to which cluster. This issue is not only a problem for the model but while preparing the dataset, it was very quickly realized that manually annotating the bounding boxes around the training set was very confusing and difficult.  
Slightly incorrectly annotating the training dataset most likely played a part in the error of the model being \(-14.00\%\) off from the hand-counted values. Compared to annotating the trees, due to occlusion and many other factors, the process was very time consuming and difficult, whereas with hand-counting the fruit flower clusters was just very time-consuming, since being physically there allowed the ability to move the branches or clusters to see what was behind them.  
The maximum difference shown in the same table can be observed as a big problem with the model or it may be an issue with the hand counting for that specific tree. Though on average, the model had a difference in prediction of 21 clusters with the average number of clusters counted per tree being 114. This means if a tree had 114 clusters, the model would only predict that there were 93 clusters present. Accounting for occlusion, annotation error, cluster to apple accuracy, etc., 21 clusters off the actual would still give a researcher or farmer sufficient information to understand the number of expected apples from each apple tree.  
The results of the object detection model are very promising as the F1-Score being 80% is similar to that of previously research papers, with an F1-Score of ranging between 77.3% to 84.1%. The only difference is that YOLOv4 is significantly faster in terms of computational speed than the other methods. Additional information on this project may be found in [37].
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 5 An Overview of Attack-Resilient Control and Communication Techniques
### Resilient Communication Techniques  
Besides the event-based communication approaches discussed in Section 5.1.1, there are a few other communication techniques specifically developed for achieving resiliency in multi-agent consensus as well as in networked state estimation problems. In what follows, we provide an overview of those protocols.
## 3 Preliminaries and Motivations
### Practical Research Motivation  
From the perspective of practice, a new hybrid-MOP with higher performance is valuable to seek. For instance, some advanced metering infrastructure networks or industrial monitoring networks [20; 24] might coexist in the same or overlapped regions. Each application network makes up a RPL instance. These networks may be managed by different organizations. If different MOPs are chosen, they cannot perform interoperation with each other or be organized into a single RPL instance, even if an interoperation is needed to improve network performance and robustness or to meet some application requirements in the future. Therefore, achieving the better solution of hybrid-MOPs in downward routing becomes a practically meaningful issue.
## 3 Materials
### GIS Products and Ground Data  
In this EO service, other datasets were also considered.  
First, the _digital terrain model (DTM)_ from the Aosta Valley autonomous region with a 2 m GSD was resampled in SAGA GIS with a nearest neighbor algorithm in order to perfectly overlay the Sentinel imagery. It is worth noting that the DTM was acquired with flight lidar sensors in 2008.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Absolute Orbit Number** & **Polarization** & **Frame** & **Path** & **Flight Direction** \\ \hline
24,789 & VV+VH & 146 & 88 & ASCENDING \\
24,417 & VV+VH & 441 & 66 & DESENDING \\ \hline \hline \end{tabular}
\end{table}
Table 2: SAR stacks parameters criteria.  
Second, the _training set_ was defined as different ESRI shapefile polygons per each class in order to train the classifier. This dataset was defined from one side by object-based segmentation (OBIA) using red, green, blue, near-infrared, red edge and shortwave bands and considering the spectral signatures and the photo-interpretation analysis by adopting the ground truth data polygon (GTDP).  
Third, the _validation set_ was defined through the ESRI shapefile polygons to validate the classification. This dataset was obtained both through photo-interpretation and in-the-field GTDP. The validation was carried out in two phases: the first by calculating the confusion matrix by adopting the dataset obtained from S1 and S2 processing bands and finally by assessing the classification accuracy after merging each part.  
It is worth noting that a Garmin 64S and Lemon GPS smartphone application developed by the Italian GeneGIS company were also used to define the GTDP.  
As mentioned before, the collection of such data allowed us to populate both the training set and the validation set. In particular, a random GTDP selection was performed in SAGA GIS vers. 8.2.0. with an allocation of 70% of the GTDP to the training set and 30% of the GTDP to the validation set.  
Finally, the Italian AGEA (Agency for Disbursements in Agriculture) yearly _air flights imagery_ coupled with the Aosta Valley 2018 Orthophoto were used to perform deep learning on built-up areas and refine the final product with a minimum mapping unit of 100 m\({}^{2}\) in order to keep the product coeval with the Sentinel datasets.  
Generally, the tools adopted were the GEE [1], the SNAP vers. 8.0.0 to obtain and calibrate the data during the pre-processing phase, Orteo Toolbox vers 8.0.0 [39; 40], SAGA GIS vers.8.0.0 [41] to perform the classification during the processing phase and QGIS with GRASS and R v.3.0.1 [42; 43; 44] during the post-processing phase to prepare the final product.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 3 Physical Layer Security Technology Based on Coded FTN Signaling
### Symbol-Wise Coded FTN Signaling  
In this symbol-wise coded FTN signaling, each symbol has a different symbol interval. Let us consider the transmission frame with \(K\) symbols (i.e., \(K\) is the length of transmission frame). In this system, the specific \(\tau\) code can be written as follows:  
\[\tau_{\text{code}}=[\tau_{1}\ \tau_{2}\ \tau_{3}\cdots\ \ \tau_{K-1}\ \ \tau_{K}] \tag{3}\]  
where different values can be allocated to different \(\tau_{k}\) such as \(\tau_{k}=0.8,\tau_{k+1}=0.7\). The different \(\tau_{k}\) value is allocated to each symbol as  
\[z_{\text{code}}(t)=\sum_{k}w_{k}\cdot f\left(t-\sum_{m=0}^{k}\tau_{m}\cdot T \right). \tag{4}\]  
The transmission signal concept comparison between general FTN signaling and coded FTN signaling with \(\tau_{\text{code}}\) is described in Figure 2. Because general FTN signaling uses an identical \(\tau\) value for all the transmission symbols, the ISI value for all the symbols can be easily calculated based on the one \(\tau\). By using the calculated ISI value, the interference cancellation and signal detection processes are performed. However, in coded FTN signaling, since a different \(\tau\) value is used for each symbol, interference cancellation and signal detection can be accomplished when the receiver has the overall \(\tau_{\text{code}}\). Furthermore, according to the combinations of different \(\tau\) values, various \(\tau_{\text{code}}\) values can be generated and utilized as shown in Figure 2.  
Figure 1: Signal wave comparison between general communication and FTN signaling.  
The information about the \(\tau_{code}\) is released to the legitimate receivers, and the legitimate receivers then operate the interference cancellation and signal detection process based on the released \(\tau_{code}\).
## 2 Maglev Planar Motor and Coordinate System
### Coordinate Definitions and Transformation  
Three different coordinate systems are defined in Figures 2 and 3. The first one is the stator global coordinate system denoted with the superscript \(m\), which is fixed on the top face of the stator permanent magnet. It can be expressed as  
\[^{m}\mathbf{x}=\left[\begin{array}{cc}^{m}\mathbf{x}&^{m}\mathbf{y}&^{m}\mathbf{z}\\ \end{array}\right]^{\mathrm{T}} \tag{1}\]  
The second one is the mover local coordinate system denoted with the superscript \(c\), which is fixed on the mover bottom surface center. It can be expressed as  
\[^{c}\mathbf{x}=\left[\begin{array}{cc}^{c}\mathbf{x}&^{c}\mathbf{y}&^{c}\mathbf{z}\\ \end{array}\right]^{\mathrm{T}} \tag{2}\]  
The third one is the corner local coordinate system denoted with the superscript \(s\). It can be expressed as  
\[^{s}\mathbf{x}=\left[\begin{array}{cc}^{s}\mathbf{x}&^{s}\mathbf{y}&^{s}\mathbf{z}\\ \end{array}\right]^{\mathrm{T}} \tag{3}\]  
Figure 3: Analysis model of the Halbach magnet array.  
Figure 2: Top views and A-A view of the components of a maglev planar motor: a Halbach array and a concentric structure winding.  
The vector \(\mathbf{p}\) is the position vector of the mover local coordinate system in the global coordinate system. It can be expressed as  
\[\mathbf{p}=\left[\begin{array}{cc}p_{x}&p_{y}&p_{z}\end{array}\right]^{\rm T} \tag{4}\]  
The coordinate position of the \(j\)-th winding center in the mover local coordinate system can be expressed as  
\[{}^{c}\mathbf{x}_{j}=\left[\begin{array}{cc}{}^{c}\mathbf{x}_{j}&{}^{c}\mathbf{y}_{j}&{}^ {c}\mathbf{z}_{j}\end{array}\right]^{\rm T} \tag{5}\]  
The two coordinate systems can be converted into each other with the coordinate transformation. For example, the vector in the mover local coordinate system and that in the global coordinate system can be transformed into each other through the following equations  
\[{}^{m}\mathbf{x}={}^{m}\mathbf{R}_{c}{}^{c}\mathbf{x}+\mathbf{p} \tag{6}\]  
\[{}^{c}\mathbf{x}={}^{c}\mathbf{R}_{m}({}^{m}\mathbf{x}-\mathbf{p}) \tag{7}\]  
The orientation transformation matrix is defined as  
\[{}^{m}\mathbf{R}_{c}=\mathbf{Rot}({}^{c}\mathbf{x},\psi)\mathbf{Rot}({}^{c}\mathbf{y},\theta)\mathbf{ Rot}({}^{c}\mathbf{z},\phi) \tag{8}\]  
\[{}^{c}\mathbf{R}_{m}={}^{m}\mathbf{R}_{c}^{-1}={}^{m}\mathbf{R}_{c}^{\rm T} \tag{9}\]  
where  
\[\mathbf{Rot}({}^{c}\mathbf{x},\psi)=\left[\begin{array}{ccc}1&0&0\\ 0&\cos(\psi)&-\sin(\psi)\\ 0&\sin(\psi)&\cos(\psi)\end{array}\right] \tag{10}\]  
\[\mathbf{Rot}({}^{c}\mathbf{y},\theta)=\left[\begin{array}{ccc}\cos(\theta)&0&\sin( \theta)\\ 0&1&0\\ -\sin(\theta)&0&\cos(\theta)\end{array}\right] \tag{11}\]  
\[\mathbf{Rot}({}^{c}\mathbf{z},\phi)=\left[\begin{array}{ccc}\cos(\phi)&-\sin(\phi )&0\\ \sin(\phi)&\cos(\phi)&0\\ 0&0&1\end{array}\right] \tag{12}\]  
and in the above equations \(\psi,\theta\), and \(\phi\) are the rotation angles about the \({}^{c}\mathbf{x}\)-, \({}^{c}\mathbf{y}\)-, and \({}^{c}\mathbf{z}\)-axes, respectively. Thus, the position and orientation of the translator can be described in six degrees of freedom.
## 4 Results and Discussion
### Comparison of the Main Effects
#### 4.1.1 Main Effects of Factors on Throughput  
Figure 3 illustrates the main effects of factors on the AODV throughput (TH) from the FFM, PBM and TM, respectively. First, we notice that the values of normalized effects from the FFM are closer to those from the Taguchi method, unlike the PBM, which shows quite different values. Secondly, the trends of the main effects for factors ND, BKH and WMH are almost the same from the three methods. They all have a negative effect on the AODV throughput. This finding reveals that when the number of vehicles grows, and when the black hole and the wormhole attacks are initiated, the AODV throughput degrades. These results are logical since in the black hole attack, the attacker node acts by dropping the entire data packet forwarded to it, pretending to have the shortest path to the destination. Similarly, the two colluding nodes in the wormhole attack create a tunnel between them, claiming that they are next to each other. Hence, neighboring nodes believe that the shortest path passes through this tunnel and thus, they begin the transmission. Consequently, the majority of the data packets are absorbed; they are either dropped or replayed in the network. Therefore, the amount of successfully forwarded packets decreases, and the AODV routing performance degrades.  
Additionally, it is stated that the negative impact of the node density on throughput is important. This can be justified by the dynamic topology and the high mobility of vehicles. Indeed, the wireless link may fail in transmitting all the data packets and generate more route error packets (RERR).  
As far as the factor NC, we notice that it has a negligible effect from the full factorial method, as the slope of the line is almost horizontal. This could be due to the ranges defined for each factor. Moreover, the magnitude of the effect on the throughput is quite important in both TM and PBM. Indeed, the increment in the number of communicating pairs of vehicles lowers the successfully transmitted packets. This decrease may be due to many factors, such as collision and transmission errors, so packets fail in reaching their destination resulting in the degradation of AODV throughput.  
In summary, the graphical analysis of the results obtained from the three methods revealed three main influential factors (ND, BKH and WMH). This means that the three methods were able to efficiently estimate the effect of factors on the AODV throughput.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 2 Materials and Methods
### Model Construction
#### 2.2.1 Sperm Feature Extraction and Segmentation  
EdgeSAM achieves results similar to the original SAM while utilizing only 1.5% of the trainable parameters, significantly lowering the computational demands for both training and inference. As a result, we use EdgeSAM for feature extraction and segmentation. However, the pre-trained EdgeSAM mistakenly considers the sperm tail as part of the head, which leads to inaccurate segmentation results. To address this issue and enable mini-batch training on a single 24 GB GPU, we freeze the weights in the prompt encoder, re-train RepViT, and fine-tune the self-attention parameters Q, K, and V in the mask decoder using Low-Rank Adaptation (LoRA) technology [27].  
Clinical analysts often provide rough annotations by marking any point within the sperm head when collecting images, leading to deviations between the center points of the captured image and the sperm head. Therefore, during training, our prompt encoder utilizes a single random point within the sperm head as input, while during inference, it employs the center point of the image. This approach allows the network to achieve accurate segmentation with limited prompts.