esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 6 Discussion
### Allowing More Iterations for Using up Additional Time  
Reducing communication latency is of paramount importance for real-time applications; however, when there is additional time available, the PHY can use it for increasing the quality of the received data. An example is adjusting the number of allowed decoding iterations per MCS. Depicted in Figure 13(a), every MCS can allow the decoder to execute a given number of iterations before it becomes a bottleneck. For example, allowing it to conduct 20- instead of 10 iterations, during transmission at MCS 2.0, will take up approximately the same amount of time. Beyond that point, the RX can dynamically decide and allow the decoder to consume more time based on the momentary latency constraint. This is especially useful for MCSs with higher indexes, where the decoder becomes a bottleneck at a considerably lower number of iterations. For instance, beyond three iterations for all three of the highest MCS indexes (64 QAM). As noted in Section 5.2, making the decoder a bottleneck is not sustainable and is avoided in practice.  
Figure 13(b) illustrates several MCS and \(\frac{E_{b}}{N_{0}}\) combinations where increasing the number of allowed iterations has a considerable effect on the BER. The \(\frac{E_{b}}{N_{0}}\) values are derived from the BER curves in Figure 10(a), and they represent the points with the most negative slope. The benefit of executing additional iterations is most visible in those regions of the BER curves. Contrarily, improvements at \(\frac{E_{b}}{N_{0}}\) values with highly erroneous data or with a 0 BER are negligible. These would result in horizontal lines on Figure 13(b) and have been omitted in view of better visibility of the existing results.  
Allowing additional iterations for the MCS--\(\frac{E_{b}}{N_{0}}\) combinations causes a steep decline in BER from 1 to 5 iterations and a moderate improvement in data integrity from 5 to 20 iterations. The observations are further backed by Figure 13(c),d. These show a high improvement in terms of BER decrease between 1 and 10 iterations, and somewhat less profitable results when comparing BER values at 10 and 100 iterations. Therefore, selecting the highest possible MCS index that the channel conditions allow will reduce latency, while allowing between 3 and 20 decoding iterations can reduce the amount of erroneous data while sustaining a high enough throughput.  
Figure 13: Minimal achievable latency at different channel noise levels and maximum allowed number of LDPC decoding iterations. The colour represents the MCS at which minimal latency was achieved.
## 4 Discussion  
In this work, the following objectives were achieved:  
* Detection of ripe tomatoes: the system detected those ripe tomatoes located in the foreground of the image whose surfaces were not occluded by the plant or the fruit that surround it, or at least, not so much that they could not be collected. Specifically, it detected the \"candidate\" tomatoes to be collected, representing each of them by a single region (convex area) separated from the rest.
* Location of the ripe tomatoes in XY: once detected, the system located the ripe tomatoes in the XY plane of the image by calculating the position of their centers.
* Location of the tomato peduncle in XY: for each ripe tomato detected, the system indicated whether or not its peduncle was visible from the position where the image was captured. If the peduncle was visible, the system located it by providing its position in the image's XY plane and informed us that the tomato could be collected. If the peduncle was not visible, the system advises as such, and informs us that the tomato cannot be collected.
## 4 Outline of the Approach
### Integration and Consumption of Profile Information  
In order to integrate a ubiquitous user model gathering profile information from heterogeneous sources and share and reuse this information, two main processes are done: (i) integrate a profile supplier information to the ubiquitous user model (Algorithm 1) and (ii) consume profile information from the ubiquitous user model (Algorithm 2). ```
0: U2 MIO ontology
0: All the semantic mappings \(M^{X_{i}U}\), U2 MIO enhance recommendations and new instance in U2 MIO with profile information
1: Extract \(sd_{i}\) from profile information supplier
2: Save \(sd_{i}\) with a timestamp and source id in the Raw data repository
3:if source id is new to U2 MIO then
4: create a new concept scheme \(X_{i}\), identify \((C_{i},H_{i},V_{i}\) ) and insert an instance with \(V_{i}\) values in U2 MIO  comment: Element level matching
5: Extract \(C_{T}\) from the ubiquitous user model concept scheme \(U\)
6: Calculate the relation \(R_{0}\) of every triplet \(\left\{c_{s},c_{t},R_{0}\right\}\forall c_{s}\in C_{S},\forall c_{t}\in C_{T}\)
7: comment: Structure level matching
8: Select the set of neighbors \(N_{S},\forall c_{s}\in C_{s}\) and \(N_{T},\forall c_{tb}\in C_{T}\)
9: Calculate relation \(R_{I}\) of every triplet\((c^{\prime}_{s},c^{\prime}_{tb},R_{1})\)\(\forall c^{\prime}_{s}\in N_{S},\forall c^{\prime}_{tb}\in N_{t}\)
10: Apply IF THEN rules given \(R_{0}\) and \(R_{1}\forall c_{s}\in C_{s}\) and obtain \(M^{X_{i}U}=map(c_{s},c_{tb})\)\(\forall c_{s}\in C_{s},\forall c_{tb}\in C_{T}\) and U2 MIO
11: Add \(M^{X_{i}U}\) to U2 MIO and implement recommendations
12:else
12: Update \(X_{i}\) instance with \(V_{i}\) values in U2 MIO
13:endif
```  
**Algorithm 1**Integration of profile supplier information to ubiquitous user model. As a precondition of both algorithms, we require the U2 MIO ontology which contains the current ubiquitous user model concept scheme, the concept schemes and mappings of previously integrated sources and their instances. We defined Algorithm 1 for the integration of profile supplier information to the ubiquitous user model. Algorithm 2 describes how to reuse information from other sources. Both algorithms use the process of concept alignment when the supplier or consumer is not known to define all the semantic mappings \(M^{X_{i}U}\) between a interoperability stakeholder concept scheme \(X\) and the ubiquitous user model concept scheme \(U\). It is important to notice that an instance with only the most recent values extracted from each sources are stored. The concept scheme of the ubiquitous user model allows only the addition of new concepts, sub collections and collections from the process of concept alignment recommendations. Two statements from Algorithm 2 deserve further explanation. It is easy to deduce that it is likely that a required value can be populated from more than one source, so the query (statement 11) can get several requested value candidates.
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
## 2 Chaotic Systems and Chaotic Encryption
### Chaotic Encryption  
We can distinguish between two types of chaotic encryption. The first one is based on the synchronization phenomena [25], where a chaotic system is combined with the on-line data stream and it is received with another chaotic system that synchronizes and enables us to filter the original data stream. Although this is a secure scheme, it only works for continuous data stream (e.g., airplanes telemetry), and not for static data because we could lose data during the system synchronization.  
On the other hand, the second type where a chaotic signal is simulated and used to diffuse and confuse the plaintext information. In this paper, we focus on this second kind of encryption. Chaotic systems have been broadly used in image encryption, e.g., the logistic map is used in [26], 2D Arnolds Cat Map is compared to 3D logistic map for RGB image encryption [27], also 3D chaotic maps are used in general multimedia data [28].  
Using different chaotic maps could increase the information entropy as is evidenced in [29]. In [14], a survey of image encryption is offered, and in [15] a comparison of different chaotic maps is shown.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 5 Simulation Results
### Packets Received Per Round  
Figure 5 shows that, in the proposed scheme, the packets received per round at the sink are higher than all other routing protocols that are considered. This is due to the selection criteria of forwarding nodes. Packets received per round are also achieved in EEDBR at the cost of high residual energy. In SEEC, total packets received at the sink are smaller than that in the proposed routing protocol. This is because, in SEEC, the movement of mobile sinks is only based on sparse regions irrespective the adjacent sparse regions and transmission range of nodes. On the other hand, in the proposed scheme, mobile sinks are moved in alternative regions in every round. Consequently, the efficient movement of mobile sinks results in increased throughput.
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 2 Materials and Methods
### Multilayer Perceptron Neural Network  
The MLP is a ubiquitous feed-forward artificial neural network architecture extensively used in supervised learning tasks. In particular, the MLP adopts a layered structure consisting of input, hidden, and output layers. The interconnection between neurons within the latter layers is established through several weights [31]. First, the input data are received by neurons in the input layer. Then, hidden layer neurons process their inputs or outputs from the prior layer using non-linear activation functions, selected from the linear function (\(\varphi(x)=x\)), the sigmoid logistic function (\(\varphi(x)=1/(1+e^{-x})\)), hyperbolic tangent (\(\varphi(x)=tanh(x)\)), or rectified linear unit (\(\varphi(x)=\max(0,x)\)) [32].  
The backpropagation algorithm, the core of the learning process, computes the gradient of the loss function in the output layer [33]. In particular, the latter algorithm facilitates iterative adjustments to network weights, enabling the model to minimize the loss function progressively. Moreover, it must be noted that for a dataset with dimensions \(m\times n\), the input layer represents input features as a feature vector \(\{x_{i}|x_{1},\ldots,x_{n}\}\). Then, each hidden layer neuron performs a linear combination of the previous layer's outputs \([x_{1},x_{2},\ldots,x_{n}]^{T}\) with corresponding weights \([w_{1},w_{2},\ldots,w_{n}]^{T}\). The latter can be mathematically expressed as \(w_{1}x_{1}+w_{2}x_{2}+\ldots+w_{n}x_{n}\).  
The network's signals are then passed through the hidden-to-output activation function, and the outputs are calculated to the output layer, which generates predictions. Figure 4 illustrates the above-introduced architecture, for example, of an MLP composed of two hidden layers [34].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kernel** & **Mathematical Formulation**\(K(\mathbf{x}_{i},\mathbf{x}_{j})\) \\ \hline Linear & \(\mathbf{x}_{i}^{T}\mathbf{x}_{j}\) \\ Sigmoid & \(\tanh(\mathbf{x}_{i}^{T}\mathbf{x}_{j}+1)\) \\ RBF & \(\exp(-\gamma\|\mathbf{x}_{i}-\mathbf{x}_{j}\|^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kernel functions, where \(\gamma\) represents the kernel parameter. It must be noted that the choice of kernel function usually impacts the decision boundary learned by the SVM in a significant way.
# Multiellipsoidal Mapping Algorithm
###### Abstract  
The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e., a map that preserves main structures of the environment and object shapes) yet still has a low memory cost. Point clouds offer a highly descriptive and information-rich environmental representation; accordingly, many algorithms have been developed to approximate point clouds and lower the memory cost. In recent years, approaches using basic and \"simple\" (i.e., using only planes or spheres) geometric entities for approximating point clouds have been shown to provide accurate representations at low memory cost. However, a better approximation can be implemented if more complex geometric entities are used. In the present paper, a new object-mapping algorithm is introduced for approximating point clouds with multiple ellipsoids and other quadratic surfaces. We show that this algorithm creates maps that are rich in information yet low in memory cost and have features suitable for other robotics problems such as navigation and pose estimation.  
Keywords:object mapping; Geometric Algebra; Differential Evolution +
Footnote †: journal: Applied Soft 2018, 8, 1239; doi:10.3390/app8081239
# Quantifying the Training and Match-Play External and Internal Load of Elite Gaelic Football Players
###### Abstract  
The current investigation quantified the training and match-play load of elite Gaelic football players across a two-season period using global positioning system technology (GPS), rating of perceived exertion (RPE) and sessional rating of perceived exertion (sRPE). Total weekly workload variables were collected across GPS, RPE, and sRPE across thirty-six elite Gaelic footballers (mean \(\pm\) SD, age: 26 \(\pm\) 5 years; height: 177 \(\pm\) 8 cm; mass: 81 \(\pm\) 7 kg) from one elite squad during a two-season observational period. External training load variables included: Total distance (m), High speed running (m; \(\geq\) 17.1 km\(\cdot\)h\({}^{-1}\)), Spyint distance (m; 22 km\(\cdot\)h\({}^{-1}\)), Accelerations (n), Average metabolic power (W\(\cdot\)kg\({}^{-1}\)), High-power distance (m; \(\geq\) 25 W\(\cdot\)kg\({}^{-1}\)). Internal load variables included: sRPE and RPE. Repeated measures ANOVA were used to understand the differences in loading patterns across phases, position, and week types when significant main effects were observed a [PERSON]'s post hoc test was applied and standardized effect sizes were calculated to understand the practical meaning of these differences. When total weekly loading across phases was considered total load was significantly greater in club 1 and provincial 1 with these phases showing the highest loading for players when compared to all other phases (\(p\leq\) 0.001; ES: 2.95-7.22; _very large_). Furthermore, in-season 1 was greater for total loading when compared to in-season 2 and both championship phases (\(p\leq\) 0.05; ES: 0.47-0.54; _small_). Total distance in training was greater during preseason 1 when compared to all other phases of the season (\(p\leq\) 0.001; ES: 2.95-7.22; _very large_). During the in-season period, training based total distance was higher during provincial 1 when compared to other phases with similar trends across all measures (\(p\leq\) 0.005). Finally, a positional profile for load measures was observed, with weekly context (match or non-match) having an impact on the internal and external loading players experienced across phases. The current data provide useful information for practitioners on the training periodization currently present within the elite Gaelic football training process. Specifically, the data provide positional profiles of loading across weekly and segmented phased of an elite Gaelic football season. These data can increase understanding as to the periods of increased and decreased loading across different phases of an elite Gaelic football season, while providing a framework for future analysis concerning Gaelic football periodization.  
positional profile; periodization; load monitoring; team sports; GPS +
Footnote †: journal: Applied Sci. 2021, 11, 1756. [[https://doi.org/10.3390/app11041756](https://doi.org/10.3390/app11041756)]([https://doi.org/10.3390/app11041756](https://doi.org/10.3390/app11041756))
## Materials and Methods
### Prediction of Intestinal First-Pass Metabolism
#### 2.3.2 Hydrolysis Experiments  
Human intestinal Caco-2 cell homogenates were used to assess the stability of TU; Caco-2 cell homogenate was previously shown to be an equivalent alternative to human small intestinal homogenate for ester prodrug stability studies [30]. \(Cl_{\text{int}}\) (mL/min/mg protein) was calculated from the in vitro half-life (\(t_{\text{1/2 inv}}\)), which was measured from the first-order depletion constant of TU incubation in human intestinal Caco-2 cell homogenate. Hydrolysis experiments were performed as previously described [31]. Briefly, cell homogenates were spiked with TU to give a final concentration of 10 \(\upmu\)M. The reaction mixtures were incubated at 37 \({}^{\circ}\)C and shaken at 200 rpm for 1 h. Samples were withdrawn at 0 min, 15 min, 30 min, 45 min, and 60 min time points. The reaction was then immediately terminated by the addition of ice-cold acetonitrile. Samples were processed on the same day for HPLC analysis, as described below. Hydrolysis experiments were performed in quadruplets.  
\(Cl_{\text{int}}\) was then corrected by \(f_{\text{u,mic}}\) to find \(Cl_{\text{u,int}}\) (mL/min/mg protein) as described for the microsomal stability studies using Equations (2) and (3). The calculated value of \(Cl_{\text{u,inc}}\) was then scaled, based on human physiological factors for the total weight of enterocytes in the small intestine, to calculate gut intrinsic clearance (\(Cl_{\text{G,int}}\), L/h). It was previously determined that average regional weights of enterocytes in the duodenal, jejun, and ileal parts of the small intestine are 18.2 g, 65.8 g, and 38.3 g, respectively [32].  
The fraction of TU that could escape intestinal first-pass metabolism (\(F_{\text{G}}\)) was estimated using the \"Q gut\" model as detailed in Equation (6) [33]:  
\[F_{\text{G}}=\frac{Q_{\text{villi}}}{Q_{\text{villi}}+\,f_{\text{u,G}}\cdot Cl_{ \text{G, int}}\cdot(1+Q_{villi}/Cl_{\text{Perm}})} \tag{6}\]  
where \(Q_{\text{villi}}\) is the villous blood flow (18 L/h) [34], \(Cl_{\text{Perm}}\) is the permeability across enterocytes, and \(f_{\text{u,G}}\) is the fraction of drug unbound to enterocytes, which was assumed to be 1 as this value has been shown to give the most accurate prediction [33].  
\(Cl_{\text{perm}}\) was calculated from the effective intestinal permeability (\(P_{\text{eff}}\)) of TU and net cylindrical surface area of the small intestine (\(S\), 0.66 m\({}^{2}\)) using Equation (7) [33].  
\[Cl_{\text{perm}}=P_{\text{eff}}S \tag{7}\]
## 4 Performance Analysis
### Channel Model and Simulation Settings  
In this study, we employ the widely used acoustic channel model explained in [41; 42; 43] to simulate the dynamics of acoustic communication in UWSNs. In this model, the path loss is mainly caused by the geometrical spreading and signal attenuation associated with frequency dependent absorption is calculated as:  
\[10\log A\ (d,f)/A_{O}=k\times 10\log d+d\times 10\log a(f)^{d} \tag{35}\]  
The absorption coefficient \(a(f)\) using the [PERSON]'s formula [44] is given as:  
\[10\log a(f)=\frac{0.11\times f^{2}}{1+f^{2}}+\frac{44\times f^{2}}{4100+f^{2} }+2.75\times 10^{-4}f^{2}+0.003 \tag{36}\]  
in which, the factors transmission loss \(A(d,f)\) and noise level \(N(f)\) are functions of the distance \((d)\) and frequency. It is worth mentioning that noise decreases with frequency and turbulence, shipping activities, breaking waves and thermals are the primary sources of ambient noise. Therefore, the noise that affects the underwater acoustic channel is originated from ambient and site-specific sources which is modelled using four sources, including waves, shipping, thermal noise and turbulence, respectively. In addition, the widely used general energy consumption model discussed in [45; 46; 47] is employed in the proposed scheme. In fact, we evaluate the performance of QoSRP against well-known LRP [24], QERP [26] and MERP [10] routing protocols developed for UWSNs-based events monitoring applications in UWEs. The network simulation tools, namely NS2 and AquaSim 2.0 are used to implement the schemes in random network topologies. During simulations, a set of 250 ASNs in a region of size length (m) \(\times\) width (m) \(\times\) depth (m) is randomly deployed to simulate the continuous events monitoring using acoustic sensor network in UWEs. The sea surface buoys is deployed in the middle of the UASNs. The initial energy of each ASN and sea surface sink were set to 100 J and 100 kJ with average ocean depth is 1 mile (\(\approx\)1.6 km). The beam widths of each ASN is varied between 0 and 360 degrees in each set of experiments while the acoustic communication range of the sink was 200 m. The results of the simulations correspond to an average value of 53 runs. The settings of different parameters is given in Table 3.
# Dynamic Modeling and Simulation of a Cyclic Towing System for Underwater Vehicles
## 3 Model Validation
The simulation time is set equal to the operating time of the underwater vehicle. The solver employs the GSTIFF method, with an integration error and a time step set to 0.001 and 0.01 s, respectively. As shown in Figure 5b, the displacement of the underwater vehicle calculated using the method in this paper was 8.005 m, while the displacement simulated by the ADAMS software was 8.035 m, resulting in a relative error of 0.37%. From the speed curve of the underwater vehicle shown in the figure, it can be observed that the vehicle undergoes acceleration from 0 to 2 s, moves at a constant speed from 2 to 4 s, and decelerates from 4 to 6 s. The cyclic towing test system primarily focuses on the stability of the vehicle's speed during the constant speed phase, as this is crucial for conducting hydrodynamic experiments with the underwater vehicle. Therefore, the comparison here is mainly focused on the constant speed phase. The average speed of the underwater vehicle during the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Symbol** & **Unit** & **Value** \\ \hline Equivalent mass of towing wheel and tensioning device & m\({}_{1}\) & kg & 590 \\ Equivalent mass of guide wheel and tensioning device & m\({}_{2}\) & kg & 383 \\ Mass of the underwater vehicle & m\({}_{3}\) & kg & 1200 \\ Moment of inertia of the towing wheel & J\({}_{1}\) & kg\(\cdot\)m\({}^{2}\) & 39.2 \\ Moment of inertia of the guide wheel & J\({}_{2}\) & kg\(\cdot\)m\({}^{2}\) & 17.368 \\ Radius of the thewing pulley & r\({}_{1}\) & m & 0.4 \\ Radius of the guide pulley & r\({}_{2}\) & m & 0.35 \\ Young’s modulus of the cable & E & N/m\({}^{2}\) & 6 \(\times\) 10\({}^{9}\) \\ Diameter of the cable & r & m & 0.017 \\ Cross-sectional area of the cable & Ac & m\({}^{2}\) & 2.27 \(\times\) 10\({}^{-4}\) \\ Density of the cable & \(\rho_{c}\) & kg/m\({}^{3}\) & 970 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the cyclic towing system. constant speed phase, calculated by the method in this paper, was 2.0235 m/s, while the average speed during the constant speed phase in the ADAMS simulation was 2.0174 m/s, resulting in a relative error of 0.3%; the average acceleration of the underwater vehicle during the constant speed phase, calculated by the method in this paper, was 0.0155 m/s\({}^{2}\), while the average acceleration during the constant speed phase in the ADAMS software was 0.0166 m/s\({}^{2}\), resulting in a relative error of 6.63%. The average tension in the cable during the steady-state segment, calculated using the method in this study, was 5001 N, while the value obtained from the ADAMS simulation was 5125 N, resulting in a relative error of 4.7%. Figure 5f-h show the comparison of displacement, speed, and acceleration, respectively, for the upper end of the cable in the cyclic towing system, with the curves closely aligning. In summary, the modeling method adopted in this paper is feasible and can be used to simulate the motion process of the cyclic towing system. Figure 4: Calculation flowchart. Figure 5: _Cont_.