esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of Layer-1 BoD Services
### LCAS Operation Time  
We started the layer-1 BoD services in 2008 by setting the assigned and available bandwidths to the same requisite minimum value. We started to operate LCAS functions in mid 2009, after finding that users frequently used layer-1 paths and sometimes experienced resource contentions. Figure 8(b) plots the sample times for LCAS operation in the real network _versus_ decreases or increases in bandwidth, in multiples of 150 Mbps. The figure plots the times for bandwidth changes of 1v (150 Mbps), 15v (2.25 Gbps), and 34v (5.1 Gbps) between neighboring layer-1 switches. The LCAS operation time was defined as the time difference from when the BoD server sends a \"_changeSNCBandwidth_ REQ\" to when it receives the response \"_changeSNCBandwidth_ RESP\". To ensure there is no packet loss, the LCAS operation needs an initial negotiation period to change the bandwidth between layer-1 switches, so even a 1v change took around two minutes. However, the subsequent increases in bandwidth took only marginally longer. The LCAS operation times for a bandwidth decrease were slightly longer than those for a bandwidth increase
# Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis
## 2 Results
### Imaging Comparison--CI-MS Patients vs. CP-MS Patients  
#### 2.2.1 White Matter Diffusion Changes--MS Patients CI vs. CP  
Decreased FA values and increased MD and RD values were observed in a large number of WM tracts across the brain in CI compared with CP patients (Figure 1). FA was decreased in 44% of the studied WM, and almost all the areas exhibiting decreased FA overlapped with areas exhibiting increased MD and RD. Statistical analyses revealed that MD and RD values were increased by 51% and 55%, respectively, in the CI group compared with the CP group (Figure 1, red bars). However, the areas showing increased AD (30% of the investigated WM) were restricted to a small number of WM tracts, including the corpus callosum, corona radiata, external capsule, superior longitudinal fasciculus, anterior limb of internal capsule, middle cerebellar peduncle, fornix (cres)/stria terminalis. In addition, these areas overlapped with regions exhibiting reduced FA, increased MD and RD (\(p_{\text{FWE correction}}<0.05\)) (Figure 3 and Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Brain Region**} & \multirow{2}{*}{**Number of Voxels (mm\({}^{3}\))**} & \multicolumn{2}{c}{**Peak MNI Coordinate**} & \multicolumn{2}{c}{**Brodmann**} & \multirow{2}{*}{\(T\)**Value**} \\ \cline{3-4}  & & \(x\) & \(y\) & \(z\) & & **Area** \\ \hline Right insula & 75 & 45 & \(-9\) & 12 & 13 & 5.36 \\ Right inferior frontal gyrus & 205 & 22 & 15 & \(-24\) & 47 & 5.56 \\ Right middle frontal gyrus & 268 & 43 & 18 & 39 & 9 & 5.68 \\ Left cingulate gyrus & 195 & \(-7\) & \(-15\) & 43 & 24 & 5.52 \\ \hline \hline \end{tabular} Each cluster represents the extension where local maxima are located, along with the MNI coordinates, and the \(T\) values of the most significant voxel are reported. A threshold of \(p<0.05\) is set for the data, and the data are corrected using the false discovery rate (FDR). MNI = Montreal Neurological Institute; FWE = family-wise error.  
\end{table}
Table 2: The decreased volumes of gray matter (GM) in MS patients compared to HCs (\(p_{\text{FWE correction}}<0.05\)).  
Figure 2: Statistical parametric mapping (SPM) regions with decreased gray matter (GM) volume in patients compared with healthy controls (\(p<0.05\), family-wise error (FWE) corrected for multiple comparisons). SPM regions exhibiting significant GM loss: right insula (**A**); right inferior frontal gyrus (**B**); right middle frontal gyrus and left cingulate gyrus (**C**). R: right; L: left.
# Balance Control of Brushless Direct Current Motor Driven Two-Rotor UAV
###### Abstract  
In this study, the balance control of a Brushless Direct Current Motor (BLDCM) driven Two-Rotor UAV (2R-UAV) was carried out. First, a MATLAB/Simulink model of the balance system of the 2R-UAV was built. Afterwards, classical and 2-DOF PID, and proposed Adaptive Fuzzy (AF) 2-DOF PID control structures were created on the STM32F4 microprocessor for both balance angle of the system and speed control of the BLDCMs. Classical and 2-DOF PID controller parameters were determined via Particle Swarm Optimization (PSO), a technique that is commonly used in control applications. For the balance control of the 2R-UAV, a Co-Simulation structure was created using the STM32F4 microprocessor and MATLAB/Simulink, and the performances of classical and 2-DOF PID, and AF 2-DOF PID controllers were examined comparatively. Upon examining the comparison results, it was found that the classical and 2-DOF PID, and AF 2-DOF PID stably controlled the balance of the 2R-UAV. The AF 2-DOF PID controller, proposed in this research, performed better than the classical and 2-DOF PID, especially under variable operating conditions.  
Two-Rotor UAV; Adaptive Fuzzy 2-DOF PID; BLDC Motor; Co-Simulation Article Balance Control of Brushless Direct Current Motor Driven Two-Rotor UAV. 2024 14059. 10 May 2024
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation  
### Automated Service Discovery  
Information about the actual state of devices in a networked system can be obtained through network scanning. In this section, we explain M0-2, i.e., how our model actively probes and gathers network information _on the fly_ without requiring extensive training data. We automate service discovery by compiling the following commands in a _bash script_. First, we dynamically discover the network IP class; secondly, we scan for all active IPs in the network, followed by a fingerprint scan for the OS version. This _bash script_ enables us to extract a network topology _on the fly_ based on active IPs as input to our model. Hence, our experiment can be easily replicated elsewhere as per M0-2, as follows:  
IPs = $(ip addr show | grep 'inet' | awk '{print $2}' | cut -d/ -f1 | head -n 1)  
Then, obtain a list of all active IPs  
nmap -n -sn $IPs -oG - | awk '/Up$/print $2'  
Loop through each IP and obtain the OS version'  
nmap -O -T2 $Each_ip | grep \"Device\" | awk 'print $3' > scan.txt  
Additionally, through _Wireshark_[35], a commonly available _open-source_ network protocol analyzer, we execute remote packet capture to extract network traffic, applying \(TCP.flags()\) SYN, ACK, and FIN to filter all established TCP connections per device. The Wireshark output becomes an input network model to our visualization system set to update in real time, depending on network activities. We selected Wireshark capture on the basis of the knowledge that the identification of connectivity from such a network-level view, same as stated in [36], would provide a realistic network model for Algorithm 1, instead of ineptly considering all relevant network aspects (e.g., user behavior and size).  
```
0: All hosts \(S\), time-intervals \(t\)
0: Total Cyberattack Effect (TCE%)
1: Start Timer
2:for\(i\gets 1,t\)do
3:for\(j\gets 1,S\)do\(\triangleright\) hosts ordered by IP address
4:if\(\texttt{Timer}>t_{i}\)then\(\triangleright\) Check in intervals
5:break;
6:endif
7:if Solve (14),then
8: Solve (15)\({}_{j}\)
9:\(\forall j_{(TCP)}\leftarrow\) Solve (21) Color Red;
10:\(\forall j_{(TCP)}\leftarrow\) LineWidthSize Double
11:else
12:\(\forall j_{(TCP)}\leftarrow\) Solve (21) Color Blue;
13:\(\forall j_{(TCP)}\leftarrow\) LineWidthSize Single;
14:endif
15: Solve (16)
16: TCE[i] += Solve(17)
17:endfor
18:endfor
19:Stop Timer
20:return (TCE)
```  
**Algorithm 1** Visualization of TCE (\(S,t\))
# _remote sensing_
## 2 Materials and Methods
### Retrieval of Biophysical and Agoronomical Variables
#### 2.1.3 SAFYsw Model Calibration and Data Assimilation  
The model used for simulations was a modified version of SAFY [13]. The modification was originally introduced by [34] to equip SAFY with a water balance module based on the DSSAT approach on water uptake and water stress. The new version will be called hereinafter SAFYsw. The original version of SAFY is a relatively simple crop simulation model that leans on a radiation engine to simulate biomass accumulation, according to the widely used Monteith approach [35; 36]. Phenology development is dependent on the thermal sum concept [37]. The main concepts and equations of the SAFYsw model are reported in Appendix A. Calibration of the model was carried out by trial and error considering data from the 2017-2018 cropping season. For each of the fields cultivated with durum wheat, fitted LAI curves were used as a reference on which to overlap the simulated LAI trend, adjusting phenological parameters and _ELUE_ (Table 1). Calibration of yield was considered as the second step in the process, by adjusting mainly the _Py_ parameter. The model calibration was carried out to obtain a more realistic range of model parameters, more accurate than those obtained from the literature regarding our location and conditions. This range was subsequently used in the first step of the data assimilation that was performed independently for each field following the Ensemble Kalman Filter (EnKF) procedure, as detailed in [38; 39]. This provides an analytic solution to the state estimation problem, using two sources of information: simulations and observations. In this case, observations of the LAI state variable are assimilated into the SAFYsw model to reduce uncertainty and bias in model simulations. For non-linear and high dimensional models, to reduce the computational demand the EnKF is used, allowing for an easier approximate estimation of the error statistics through the Monte Carlo approach, replacing the covariance matrix by the sample covariance. The EnKF is basically a Monte Carlo implementation of the Bayesian update problem. The steps followed by data assimilation were: (1) initialization of the model ensemble, (2) forecast step, (3) observation error propagation, (4) update step, and (5) repetition of steps 2-4. Details on the procedures and formulas of these steps are provided in Appendix B. Concurrently, an \"open loop\" run using the average parameter values without the EnKF data assimilation was run. Both the model and the data assimilation were run in Matlab [40].
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Performance on Corel-2K image Dataset  
The optimal performance for the Corel-2K image dataset is obtained for a vocabulary size of 600. Table 4 provides a comparison of Corel-2K with the state-of-the-art image retrieval approaches. It is evident that the proposed approach yields the highest retrieval accuracy. The proposed approach provides 13.68% highest mean retrieval precision compared to the second best method. Figure 13 illustrates the average precision and recall values for the top 20 image retrievals. The experimental results validate the efficacy of the proposed approach for content-based image retrieval.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Performance/ Method** & **RGSIR** & \
\begin{tabular}{c} **Visual Words** \\ **Integration** \\ **SIFT-SURF** [8] \\ \end{tabular} &
\begin{tabular}{c} **MissSVM** [44] \\ **M-SVM** [45] \\ \end{tabular} \\ \hline
**MAP** & 79.09 & 65.41 & 65.20 & 54.60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the mean average precision using Corel-2K image benchmark.  
Figure 13: Average Precision and Recall of the proposed RGSIR for the top 20 retrievals using Corel-2K image benchmark.  
The image retrieval results for the semantic classes of Corel-2K image dataset are shown in Figures 14 and 15 (which reflect the reduction of the semantic gap). The image displayed in the first row is the query image and the remaining images are the results of the top 20 retrievals selected on the basis of the image classification score displayed at the top of each image.
## 6 Application of the Optimization Techniques: Routing Protocols
### Hierarchical Routing Protocols  
The main objective of hierarchical routing is to reduce energy consumption by classifying nodes into clusters. In each cluster, a node is selected as the leader or the cluster head. The different schemes for hierarchical routings mainly differ in how the cluster head is selected and how the nodes behave in the inter and intra-cluster domain.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Ground Station
#### 3.5.1 Software-Defined Radio RTL-SDR  
An SDR was the device we used for the Alpha ground station. We picked the SDR because it can do many different things with radio signals. Usually, radio receivers have hardware parts that are made for a certain kind of signal. With an SDR, the software does all the work on the signal. Things like mixers, filters, amplifiers, modulators/demodulators, and detectors are all performed by the software, not the hardware. For Alpha, we used a Realtek RTL2832U SDR (RTL-SDR) [38; 39]. The RTL-SDR is a cheap SDR that uses DVB-T TV tuners to adjust the signal and has an RTL2832U chip. It talks to a COMs channel through a USB port.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 2 Genetic Components of BAV
### Notch1  
NOTCH1 mutations have been extensively studied due to their significant role in aortic valve development. [PERSON] et al. (2005) identified mutations in NOTCH1 that cause a spectrum of developmental aortic valve anomalies and severe valve calcification [5]. [PERSON] et al. (2015) demonstrated that NOTCH1 affects calcium deposition through inhibition of Runx2 activation, with haploinsufficiency leading to calcification of endocardial cells [6]. [PERSON] et al. (2018) highlighted the sequential ligand-dependent NOTCH signaling activation that regulates valve primordium formation and development [4]. [PERSON] et al. (2016) reported that NOTCH1 mutations were highly penetrant (75%), most presenting as BAV in a cohort of 428 probands with left-sided congenital heart disease (LS-CHD) and their families [17]. [PERSON] et al. (2016) linked other members of the NOTCH1 pathway to BAV and left-ventricular outflow tract obstruction pathologies [18]. [PERSON] et al. (2018) found reduced expression of the NOTCH gene in BAV patients, affecting endothelial progenitor cells [19]. [PERSON] et al. (2019) indicated that familial studies show loss of function mutations in NOTCH1 responsible for less than 1% of non-syndromic BAV cases [20]. [PERSON] et al. (2021) and [PERSON] et al. (2020) further demonstrated heterozygous NOTCH1 deletion and novel loss of function mutations associated with a spectrum of congenital heart defects including BAV [21; 22]. Mutations in NOTCH1, which regulate calcium deposition, align with imaging findings of calcified valves observed through echocardiography and CMR. Future studies could explore how NOTCH1 mutations influence hemodynamic forces such as WSS and contribute to aortic dilation.
## 5 Comparison of the LSPMSM Parameters Identification Results Obtained by DE and NM
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Real Value** & \begin{tabular}{c} **NM Identified** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **NM RMS** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **NM-3 Identified** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **NM-3 RMS** \\ **Deviation** \\ \end{tabular} \\ \hline \(L_{d}\), H & 0.26 & 0.256 & 0.06 & 0.253 & 0.0093 \\ \(L_{p}\), H & 0.15 & 0.15 & 0.01 & 0.159 & 0.02 \\ \(L_{cd}\), H & 0.038 & 0.038 & 0.07 & 0.039 & 0.0023 \\ \(L_{eq}\), H & 0.041 & 0.041 & 0.03 & 0.043 & 0.0067 \\ \(r^{\prime}_{d}\), Ohm & 9.24 & 9.24 & 4.9 & 8.99 & 0.59 \\ \(r^{\prime}_{d}\), Ohm & 10.1 & 10.09 & 6 & 9.6 & 1.2 \\ \(\lambda^{\prime}_{0}\), Wb & 0.76 & 0.76 & 0.21 & 0.75 & 0.023 \\ \(J\), kg-m\({}^{2}\) & 0.003 & 0.003 & 0.00 & 0.003 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 7: Deviations of the LSPMSM parameters obtained using the NM identification algorithm with a random initial approximation. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Real Value** & \begin{tabular}{c} **DE Identified** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **DE RMS** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **DE-3 Identified** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **DE-3 RMS** \\ **Deviation** \\ \end{tabular} \\ \hline \(L_{d}\), H & 0.26 & 0.272 & 0.056 & 0.29 & 0.12 \\ \(L_{d}\), H & 0.15 & 0.143 & 0.017 & 0.147 & 0.012 \\ \(L_{vd}\), H & 0.038 & 0.042 & 0.0064 & 0.035 & 0.0011 \\ \(L_{eq}\), H & 0.041 & 0.035 & 0.0104 & 0.042 & 0.0005 \\ \(r^{\prime}_{d}\), Ohm & 9.24 & 10.03 & 0.98 & 9.1 & 0.055 \\ \(r^{\prime}_{d}\), Ohm & 10.1 & 11.1 & 1.2 & 10.03 & 0.97 \\ \(\lambda^{\prime}_{0}\), Wb & 0.76 & 0.76 & 0.014 & 0.77 & 0.012 \\ \(J\), kg-m\({}^{2}\) & 0.003 & 0.003 & 0.00 & 0.003 & 0.00 \\ \hline \end{tabular}
\end{table} 
Table 8: Deviations of the LSPMSM parameters obtained using the DE identification algorithm with a random initial approximation. parameters (\(L_{d},L_{q},L_{dq};L_{q\varphi};r^{\prime}_{d};r^{\prime}_{q},\Lambda^{\prime}_{0};J\)) are greater than zero; \(L_{d}>L_{q};L_{d}>>L_{dq};L_{q}>>L_{qq}\), which confirms the ability of NM to successfully solve the problem under consideration.
# Speeding up Statistical Tolerance Analysis to Real Time
## 6 Results and Discussion
### Current Drawbacks of the Method and Future Research Demands  
The tolerance optimization methodology using sampling methods with stochastic solution algorithms is approaching the limits of reasonable computation time according to [37]. With our approach to compute the Monte Carlo simulation on the GPU, we show a possible way to reduce the computation time that is expected to further increase in the future due to the growing complexity of future tolerance synthesis models [37]. However, several aspects require improvement and further research.  
* The mathematical model used does not yet take into account geometric deviations of the manufacturing components. The consideration of these however is warranted because a majority of companies consider geometric deviations in the specification of tolerances [8]. The implementation in this paper is not limited only to deviations in size. Vector-chains with geometric tolerances can also be analyzed.
* The vector-chain needs an extension to fully account for 3D effects. This is due to the assumption that most problems can be traced back to 1D or 2D is usually overly optimistic [37]. However, the demonstrated approach using a derivative free optimization algorithm already lays a foundation to implement this in the future, since the problem does not have to be oversimplified [37]. The prerequisite is that a closed vector-chain is available.  
Figure 10: Manufacturing costs vs. differential evolution steps with 100,000 samples.  
* The approach has been tested on an Nvidia GPU. However, the CuPy library also has experimental support for RadeonOpenCompute (ROCm) [16], the non-proprietary open-source alternative to CUDA from AMD. Further work could investigate how applicable this is.
* Finally, we see a need for research concerning the support of multiple GPUs. This would allow the statistical tolerance synthesis to be distributed across several graphics units analogous to the already existing CPU implementation and thus to be accelerated. For this purpose, the applied solver or alternative approaches could be investigated.
###### Abstract  
The growing demand for cost-effective, high-quality protein ingredients in the meat industry highlights the need for advanced processing methods capable of producing uniform, functional meat-bone pastes from poultry by-products. This study investigates the optimization of colloid milling parameters for the fine grinding of chicken meat-bone by-products, with a focus on improving particle size distribution, rheological properties, and processing efficiency. A modified rotor-stator system with teeth inclined at 20\({}^{\circ}\) and a reduced pitch (0.5 mm) was compared to a conventional configuration (45\({}^{\circ}\) inclination, 1.5 mm pitch). Experiments were conducted at rotor speeds ranging from 1000 to 4000 rpm, with a fixed clearance of 0.1 mm. The results showed that the modified design significantly enhanced grinding efficiency, reducing the proportion of bone fragments > 1 mm and yielding over 70% of particles under 0.1 mm at 3000 rpm. Viscosity and shear stress measurements indicated that grinding at 3000 rpm yielded a dynamic viscosity of 71,507 Pa-s and a shear stress of 43,531 mPa-s, values that were significantly lower (\(p<0.05\)) than those observed at other tested speeds, thereby producing a paste consistency with the most favorable balance of elasticity and flowability. At 4000 rpm, the temperature rise (up to 32 \({}^{\circ}\)C) led to partial denaturation of muscle proteins, accompanied by emulsion destabilization and disruption of the protein gel matrix, resulting in reductions in the viscosity and water-binding capacity of the paste. Comparative analysis confirmed that tool geometry and rotor speed have critical effects on grinding quality, energy use, and thermal load. The optimal operating parameters, 3000 rpm with modified rotor-stator teeth, achieve the finest, most homogeneous bone paste while preserving protein functionality and minimizing energy losses. These findings support the development of energy-efficient grinding equipment for the valorization of poultry by-products in emulsified meat formulations.  
colloid mill; chicken meat-bone paste; fine grinding; rotor-stator design; particle size distribution; rheological properties +
Footnote †: journal: Applied Sciences