esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design for Transition Phase
### Control Law Design
#### 3.2.4 Lateral Directional Channel Control  
Because the design of the transition phase mainly focuses on the longitudinal channel and does not require changes in the command of the lateral channel, the lateral design can use the original channel for steady-level flight. This section only provides a brief introduction to the principle of lateral channel control.  
The lateral channel implements roll angle rate control. The roll angle adopts implicit NDI control, and the principle is:  
\[p_{c}=k_{\phi}(\phi_{c}-\phi)-\tan\theta(q\sin\phi+r\cos\phi) \tag{18}\]  
where \(\phi_{c}\) is the roll angle command and \(k_{\phi}\) is the roll angle control gain.  
The roll angle rate is controlled using by INDI, and its control principle is similar to that of the pitch channel, which is not explained further here.  
In the fixed-wing mode of the directional channel, yaw damping control is applied, while in the rotary-wing mode, yaw rate control is used to keep the yaw rate command at 0 to ensure directional stability. The yaw rate is controlled using INDI, and its control principle is similar to that of the pitch channel.
## 1 Introduction
However, the study utilized a batch reactor configuration, which has inherent limitations in terms of scalability and process efficiency. Continuous systems, in contrast, are better suited for industrial applications due to their ability to improve operational throughput and maintain consistent reaction conditions. In this study, a novel lithium-impregnated aluminosilicate catalyst (LiSA) was developed using a 3D-printed mold. The catalyst features a circular structure with strategically designed holes to optimize fluid dynamics and maximize reactant interaction. This catalyst was evaluated in a fixed-bed reactor for the continuous transesterification of non-edible oils. Additionally, diethyl ether (DEE) was employed as a cosolvent, allowing the reaction to proceed efficiently at room temperature, thus reducing the energy demands of the process. The use of DEE is particularly advantageous due to its lower tendency to form peroxides compared to other common cosolvents, such as tetrahydrofuran (THF), and its lower boiling point (34.6 \({}^{\circ}\)C), which facilitates easier separation and purification during downstream processing. These characteristics make DEE a safer and more energy-efficient alternative for biodiesel production. The novelty of this study lies not only in the development of a lithium-impregnated aluminosilicate catalyst using 3D-printed molds but also in the combination of this innovative catalyst design with the use of DEE as a cosolvent to enable efficient transesterification at low temperatures. This dual innovation--both the 3D-printed catalyst and the cosolvent strategy--has not been extensively explored in the field, particularly in combination. The 3D printing approach allows for precise control over the catalyst's geometric structure, optimizing fluid dynamics and minimizing mass transfer limitations, which are often challenges in traditional methods. The use of DEE further improves the solubility of methanol in the oil phase, addressing a common issue in biodiesel production.
## 3 Results and Discussion  
### Effect of Contact Time on the Reduction of Ammonium and Sorption Capacity of Soils  
Contact time is one of the most effective factors in batch adsorption process [23; 53]. The results showed that ammonium sorption attained an equilibrium within 48 h (Figure 4), which means that no significant sorption and/or desorption of this compound were recorded after this time [8]. It was observed that with increasing contact time, reduction of the ammonium ions increased, reaching the maximum value of 19.4%, 18.5%, and 14.4% for silt loam, loam, and sand, respectively (Table 4).  
The sorption capacity of the tested soils determined at equilibrium time (48 h) were found to be as follows (Figure 4): \(q_{t}=0.1011\) mg/g, \(q_{t}=0.0958\) mg/g, and \(q_{t}=0.0752\) mg/g for silt loam, loam, and sand, respectively, which indicates the great impact of the clay and organic matter contents on the sorption capacity of the soils. Similar variations in the sorption capacities, dependent on the soil type, were presented by [PERSON] et al. [27].
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### High-Definition Mapping
#### 3.2.2 Telco Requirements  
Since autonomous vehicles require the high-definition map to be always available, and always include the latest updates, it is streamed to the car, where it is stored in a local cache. This requires ubiquitous and seamless connectivity. An additional challenge arises from the requirement that the availability of autonomous driving must not end at country or operator borders. Thus, seamless connectivity is required even when the car is traversing a national border and/or switching between different network operators. In addition, sensor data gathered at both sides of the border must  
Figure 2: Schematic overview of the high-definition map use case.  
be available to the respective other side in a fast, efficient, and uninterrupted way. If the map service backend uses the network operator's MEC instead of third-party servers, communication between potentially different MEC architectures on both sides of the border must be possible.  
Depending on the sensor type, the data to be transferred from the car to the map service backend can be quite heavy and require large bandwidth. In other cases, a low latency is required, so that changes can be made available to other cars as quickly as possible.  
Another requirement is the need for accurate prediction of periods of time lacking the required communication quality. In the basic approach, the map is downloaded to the vehicle as it moves, i.e., for the next few kilometers ahead. However, in some situations it might be more sensible to download a larger part of the map well in advance. For example, a network might be highly utilized in a city, but have a lot of free capacity in the rural areas around the city. A car moving towards that city should thus download the whole map of the city while it is still in the rural areas offering a high quality of service and be finished with the download before it enters the areas with the most congested network. This requires the network to accurately predict the quality of service the vehicle will encounter in the near future.  
Finally, in particular in densely populated areas, the network needs to support a high number of communication devices both in uplink and in downlink.
## 4 Experiments and Results
### Experiments Results  
Table 3 shows the mean values of each of the methods to measure the similarity between malware. The mean values are then compared with the threshold value of 0.5; the threshold was chosen for empirical results. In this case, if a value of similarity exceeds the threshold, the malware under investigation is considered as a variant and therefore malicious. The Minkowisk distance shows a good result as it combines two methods: Manhattan and Euclidian distance, we have used Manhattan as it has been proven to have the best performance within the malware domain [34; 35]. The measures of the similarity give a value range between 0 and 1, which means that the compared malware programs are similar if the value is 0, or dissimilar if the value is 1. Table 4 shows the result of a benign program without low or high values. However, in the case of Tables 5 and 6, we can see that there is a high similarity/low distance between malware variants. For more analysis, we have computed the mean of those distances. Furthermore, in the signature database, a value of each signature is computed and hence generates a report of similarity. Thereafter, the highest similarities are indexed, which indicated the most likely distinct malware variant or family. Finally, a decision is made as to whether the unknown executable is a variant or not.  
Deep analysis on the similarity of two malware families (Banload and DyFuCa) are shown in Tables 5 and 6. We have selected Banload malware variants because its infection channel is based on the files downloaded from the internet by users when visiting malicious sites. For DyFuCa, it is essentially an intrusive adware or spyware that is hiddenly downloaded and installed on a computer. The highlighted cells in Tables 4-6 reveal that a malware variant is defined as having a distance less than or equal to 0.5. As shown, the entire cell in Table 5 can be detected as a variant of the original malware Win32.Bonload. For more details about the features, we have provided an explainable table in Appendix A.  
From the above result, the malware variants within the same family present a high similarity while the experiments show that there is no similarity among the benign files, as is both expected and logical. Last but not least, it can be observed that there is a low similarity between the malware dataset and the benign dataset. For that reason, we can be assured that the proposed method is able to make a distinction between malware and benign datasets clearly. In conclusion, a similarity test can be applied to detect malware mutants and hence the use of such distance malware can be classified.
## 5 Satellite Stabilization Problem  
The problem of damping the rotational motion of the satellite by the engines installed on it is considered. The system describing the motion of a rigid body relative to the center of inertia after the transition to dimensionless variables has the form:  
\[\left\{\begin{array}{c}\dot{p}(t)=[u_{1}(t)/6],\\ \\ \dot{q}(t)=[u_{2}(t)-0.2r(t)\,p(t)],\\ \\ \dot{r}(t)=[0.2\,\left(u_{3}(t)+p(t)\,q(t)\right)],\end{array}\right.\]  
where \(p,q,r\) are the projections of the angular velocity onto the main central axes of inertia and \(t\in[0,1]\) and \(u_{1},u_{2},u_{3}\) are controls that characterize the thrust of the engines located on the satellite.  
The set of initial states is given by a uniform distribution law on the set \(\Omega=[23;25]\times[13;15]\times[13;15]\).  
At the final moment of the system functioning, the following conditions must be fulfilled: \(p(1)=q(1)=r(1)=0\), corresponding to the meaning of the satellite stabilization problem. The fulfillment of terminal conditions should be accompanied by minimization of the fuel used to turn the satellite.  
The functional (6):  
\[I=\int\limits_{0}^{1}\big{[}\,|u_{1}(t)|+|u_{2}(t)|+|u_{3}(t)|\big{]}\,dt+10^{ 3}\cdot[p^{2}(1)+q^{2}(1)+r^{2}(1)].\]  
Next, we will consider two examples: the joint estimations and control problem with incomplete information about the state vector and the optimal control problem with complete information about the state vector.
# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 2 Materials and Method
### The Deep Learning Architecture  
The model architecture chosen consists of a _Multilayer Perceptron_ (MLP), a Feed-Forward Artificial Neural Network composed of a fixed number of layers, which contains nodes (called _neurons_) connected to each other as in a direct graph between the input and the output layer [22].  
Once given a training pair \((x,y)\), where \(x\) represents the input and \(y\) the output that we aim to model, the goal of a Feed-Forward Neural Network is to infer a function \(f(x)\) such that \(f(x)\) approximates \(y\) as precisely as possible, for each training pair provided.  
The function \(f\) is defined through a set of parameters \(\Theta=\{W^{l},b_{l}\}_{l=1}^{L}\), where: \(L\) is the total number of layers; \(W^{l}\) denotes the weight for the connection from the neurons of the \(l-1\) layer to the neurons of the \(l\) layer; and \(b_{l}\) represents the biases of the \(l\) layer. Moreover, to introduce non-linearity into the network, also a non-linear activation function \(\phi\) must be introduced.  
Hence, the function \(f(x)\), at layer \(l\), can be represented as:  
\[f_{l}(x)=W_{l}\,\phi(f_{l-1}(x))+b_{l} \tag{1}\]  
Specifically, in this paper, we will consider a two-hidden layer MLP with \(tanh(x)\) as a nonlinear function. Moreover, after the output layer, we add a _Scaled Exponential Linear Unit function_ (SELU):  
\[\text{SELU}(x)=\lambda\begin{cases}x&x>0\\ ae^{x}-\alpha&x\leq 0\end{cases}\]  
where \(\alpha\) and \(\gamma\) are two fixed constants. The introduction of the SELU non-linear function improved the performance significantly, as it automatically regularizes network parameters and makes learning robust due to its normalizing properties [23]. For the training, the backpropagation algorithm is utilized and the weights and biases of the model are updated during every epoch [24]. We also aim to provide a confidence interval together with the model's prediction, specifically by exploiting _deep ensemble network_ properties [19]. Thus, ten different topologies (i.e., different numbers of neurons distributed among layers) of MLP are introduced and trained. The final output of the model consists of the average of the ten results, while the uncertainty is computed on the basis of the difference between these predictions. Further details will be provided in Section 2.5.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Dataset Description
#### 3.1.1 Indian Pines  
The first dataset, named \"Indian Pines\" (IP), was collected over Northwestern Indiana, USA, with the airborne AVIRIS sensor; it includes a total of 220 bands covering wavelengths from 0.4 to 2.5 \(\upmu\)m. About 70% of the imaged area is agricultural land, while the remaining portions are forests. Due to the comparatively low spatial resolution (20 m/pixel) of the sensor, this dataset is challenging as it contains highly mixed pixels. The number of samples obtained per class is also unbalanced, which further complicates classification. The size of the scene is \(145\times 145\) pixels, and its Ground Truth (GT) data defines 16 different classes. The pseudo-color image, the GT map, and the class names for the dataset are all included in Figure 5.  
Figure 4: Possible Region of Interest (ROI) around the superpixel segment. \((r,u)\) denotes the location of pixel \(i\) in superpixels. \(r_{min}\), \(r_{max}\) represents the minimum and maximum row index, and \(u_{min}\), \(u_{max}\) are the corresponding \(min\) and \(max\) column indices.
## 3 Numerical Simulations and Discussion  
### Parameter Values  
In this section, we presented parameter values used for simulations, time series of the model dynamics simulations for plant and livestock populations, sensitivity analysis, and simulations for the spatial distribution of possible plant and livestock production.  
The plant's growth rate, \(r(t)\), is determined by multiple factors, including the quantity of rainfall and soil properties such as texture, structure, and type. Rainfall data, denoted as \(R(t)\), exhibits \(\omega\)-periodicity, and the time series data were retrieved from satellite observations. It's essential to note that the estimation is derived from a single point assumed to be representative of the entire grid cell within a given region.  
Despite various scientific methods used for soil classification [44; 45], scientists commonly employ terms such as sandy, clay, and loam to characterize soil, primarily focusing on its texture. Soil texture is determined by the sizes of mineral particles it contains. The proportion of these particle sizes influences the volume of pore space within the soil, affecting its capacity to retain air and water. Finer soil particles tend to compact more when moist, resulting in the stickiness often associated with clay soils. On the contrary, sandy soils, composed of larger particles, tend to drain water rapidly, hindering healthy plant  
Figure 4: Figure showing stable limit cycle of both plants and livestock population when parameter values remain constant.  
growth and typically containing fewer nutrients. However, they are easier to cultivate. To address these distinctions, we aim to select a value for the variable \(\beta\) that approximates nearly 0 for clay soils while approaching 1 for loam soils.  
The assumptions we took for the rest of the parameters are summarized in Table 1.
## 2 Materials and Methods
### Formation of Contaminants  
Silicon contaminants from dicing are similar in principle to residual water stains, as they both interfere with the function of the wafer [5,6]. The major difference involves the reactions that may occur at the interfaces between two substances. For example, reactions may take place between Si powder and water, as described in Equations (3) and (4).  
Water stains are the most common type of contamination seen on a blank wafer. The formation has been described as follows in the literature [6]. When water droplets are on the wafer surface, the following two reactions will occur between the air and the water droplets, as well as between the water droplets and the wafer. First, O\({}_{2}\) molecules in the air will dissolve into the water droplets, and together, they react with the silicon atoms on the surface of the wafer, as illustrated in Equations (1) and (2). The reactions are as follows:  
\[2\mathrm{H}_{2}\mathrm{O}+\mathrm{Si}\rightleftharpoons\mathrm{SiO}_{2}+4 \mathrm{H}^{+}+4\mathrm{e}^{-} \tag{1}\]  
\[\mathrm{Si}+\mathrm{O}_{2}\rightleftharpoons\mathrm{SiO}_{2} \tag{2}\]  
The product, SiO\({}_{2}\), will continue to react with water to produce silicic acid (\(\mathrm{H}_{2}\mathrm{SiO}_{3}\)) [6, 7, 8, 9, 10, 11, 12], as shown in Equations (3) and (4):  
\[\mathrm{SiO}_{2}+\mathrm{H}_{2}\mathrm{O}\rightleftharpoons\mathrm{H}_{2} \mathrm{SiO}_{3} \tag{3}\]  
\[\mathrm{H}_{2}\mathrm{SiO}_{3}\rightleftharpoons\mathrm{H}^{+}+\mathrm{H} \mathrm{SiO}_{3}^{-} \tag{4}\]  
As the water evaporates, the monomers of silicic acid in the water will gradually polymerize into dimer or trimer complexes to form insoluble polymers of polysilicic acid [6, 7, 8, 9, 10, 11, 12] that are deposited on the surface of the wafer. Polysilicic acid and silicon dioxide are the residual stains [13,14]. Similarly to the formation of water stains in the literature [6,15], the formation and characteristics of silicon contaminants from dicing are as detailed in Figure 3.  
Although silicon contaminants are similar to the water stains on the wafer surface, the aforementioned reaction of silicic acids will occur on both the water and contaminant surfaces, leaving residues of silicic acids on both sides. Given the presence of silicic acids on both surfaces as described by this mechanism, only an agent that dissolves silicic acid can remove the silicon contaminants (Table 3, test 4 and 7) [7, 8, 9, 10, 11].
## 5 Conclusions  
The effect of BMC in combination with osteochondral scaffold (Ti-PLA-collagen scaffold and HA-collagen scaffold) on the bone regeneration and bone microstructure was evaluated in the ovine stiffe condyle model. The retrieved tissues were examined by using micro-CT. The microstructural property of the bone surrounding the scaffolds were further analysed using Bone] software with respect to subchondral trabecular thickness, bone volume to total volume ratio, and degree of anisotropy. The study revealed that both scaffold groups regenerated bone. When observing all samples, the regenerated bone had an average trabecular thickness of 0.29 mm, bone volume ratio of 36.3%, and degree of anisotropy of 0.3; the only statistically significant result was produced when comparing all control and all Ti-PLA-collagen scaffolds with their sub-set regions. The Ti-PLA-collagen scaffolds did not consistently produce a higher quantity of bone in this study; however, they did produce a higher quality of subchondral bone, suggesting that the scaffold composition and addition of BMC enhance subchondral bone homogeneity and biomechanical durability.  
Conceptualization: T.F., M.T. and C.L.; Methodology: T.F., M.T. and C.L.; Software: T.F. and M.T.; Validation: T.F. and M.T.; Formal analysis: T.F.; Investigation: T.F., M.T. and C.L.; Resources: T.F., M.T. and C.L.; Data curation: T.F. and M.T.; Writing--original draft preparation: T.F.; Writing--review and editing: M.T.; Visualization: T.F. and M.T.; Supervision: M.T. and C.L.; Project administration: M.T. and C.L.; Funding acquisition: M.T. and C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Versus Arthritis (grant number 21160); and Rosetree Trust (grant number A1184) European Commission via H2020 MSCA RISE programme (BAMOS, grant number 734156); and Innovate UK via Newton Fund (grant number 102872). **Engineering and Physical Science Research Council (EPSRC) (Grant No: EP/T517793/1). Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Ethics Committee of the Royal Veterinary College (date of approval 08 August 2014). Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank [PERSON] for access to micro-CT facilities at UCL and NTL Biologica for providing the bone marrow concentration kits.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Class** & **F1-Score** & **Precision** & **Recall** & **AUC** \\ \hline
1 & Nihatbey & 0.826 & 0.809 & 0.844 & 0.9854 \\
2 & Ciftci & 0.857 & 0.848 & 0.867 & 0.9903 \\
3 & Sar98 & 0.924 & 0.942 & 0.906 & 0.9936 \\
4 & Inci & 0.986 & 0.983 & 0.989 & 0.9974 \\
5 & Hisar & 0.997 & 1.000 & 0.994 & 0.9997 \\
6 & Aslanbey & 0.983 & 0.978 & 0.989 & 0.9986 \\
7 & Akca & 0.983 & 0.989 & 0.978 & 0.9975 \\
8 & Aksu & 0.969 & 0.983 & 0.956 & 0.9965 \\ \hline \end{tabular}
\end{table} 
Table 4: TL+LDA results. Figure 4: ROC curves and AUC values of all models: (**a**) training phase and (**b**) test phase.