esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
In order to further analyze the abovementioned results, Principal Component Analysis (PCA) was used. PCA is a statistical procedure used to summarize the information content in large data tables via a set of new orthogonal variables called principal components [59]. As can be observed from the PCA score plot (Figure 3b), the OD-pretreated mushroom pieces could be divided in six major groups, numbered from 1 to 6. Group 1 contains the samples treated with 50% glycerol at all three temperatures, Group 2 refers to the samples treated with 40% glycerol at all three temperatures, and Group 3 includes the samples treated with 30% glycerol at all three temperatures. Groups 4, 5, and 6 contain the untreated samples (t\({}_{0}\) of OD process). Thus, the factor which affected the process the most was the glycerol concentration. These results are in agreement with the findings of [45; 58]. Group 1 has the highest values of \({}^{\circ}\)Brix of osmotic solution and SG and the lowest of a\({}_{w}\) and %MC. On the contrary, Group 3 has the lowest values of \({}^{\circ}\)Brix and SG and the highest values of a\({}_{w}\) and %MC. Furthermore, based on Figure 3a and Table 3, the first principal component was positively correlated with %MC and water activity, while it was negatively correlated  
Figure 2: 3D response surface graphs of the effect of OD time and temperature at constant glycerol concentration (40%) on (**a**) % moisture content (%MC, wb), (**b**) water activity (a\({}_{w}\)), (**c**) water loss (WL), and (**d**) solid gain (SG).  
with SG and salt intake. The second principal component was only positively correlated with \({}^{\circ}\)Brix of the osmotic solution.
## 3 Results and Discussion
### Wettability and Surface Energy  
The average contact angle values \(\theta_{\text{av}}\) with distilled water and diiodomethane and the determined average surface energy values \(\gamma\)S are shown in Figure 9.  
A comparison of example contact angle values for printed and from the bar after plastic working are presented in Figure 10.  
Figure 8: (**a**) Results of roughness measurements according to ISO 25178—parameter Sa. (**b**) Results of roughness measurements according to ISO 21920—parameter Ra.  
Figure 9: Mean values of contact angle \(\theta_{\text{av}}\) and surface energy \(\gamma\)S for bar after plastic working samples and 3D-printing samples.  
From the results obtained, it was found that all the samples tested showed hydrophilic properties, indicating good wettability (Figure 9). No significant effect of steam sterilization and surface energy on the contact angle values for the samples for plastic working was observed. However, a significant change in contact angle after steam sterilization was observed for the DMLS samples (1, 2, 4), which is due to chemical and physical changes that occur on the material surface compared to the samples in the initial condition (\(p<0.05\)).
## 2 Related Work
### Uncertainty Measurement for Next Best View Selection  
A next best view (NBV) selection is a robot vision problem in which the robot chooses the optimal viewpoint to minimize uncertainty in an unseen environment or object model. To solve the NBV selection problem efficiently, a high-quality uncertainty measurement is crucial.  
Recently, using neural implicit representation for uncertainty quantification in NBV has been attracting attention. Refs. [36, 37, 38] have proposed a stochastic NeRF incorporating Variational Inference [39] into the NeRF learning pipeline. Refs. [40, 41] have designed additional MLPs within the NeRF model to directly quantify uncertainty. Ref. [42] utilizes a deep ensemble approach [43] to quantify NeRF uncertainty through the variance of ensemble members.  
Although prior works have shown successful results, these approaches require modifications to the NeRF model architecture or involve an increase in the number of network parameters, which extends rendering times. This can lead to increased decision-making times, making them potentially unsuitable for robotic frameworks.  
Consequently, we adopt volumetric uncertainty by calculating the entropy of ray weight distribution, as in [17, 44]. Using entropy-based uncertainty allows for a focused analysis of an object's 3D geometry and enables rapid uncertainty calculation without the need for additional training or NeRF model modification.
## 4 Acoustic Measurements  
Acoustic measurements were carried out inside the National Theater of Zagreb to understand the acoustic response of the hall based on the standard requirements as stated in ISO 3382-1 [7], considered the international reference standard for objective acoustic parameters. During the surveys, thermos-hygrometric conditions were taken into consideration [8]. The equipment employed for the survey is the following:  
* Equalized omnidirectional loudspeaker (Look Line);
* Binaural dummy head (Neumann KU-100);
* B-Format microphone (Sennheiser Ambeo);
* Omnidirectional microphone (Bruel&Kjaer 4165);
* 32-channel spherical array (Mh Acoustic em32 Eigenmike(r)).  
The sound source was placed at 1.6 m from the finished floor, simulating the actor singing on the stage, while the receivers were installed at 1.2 m across the stalls and inside a few selected boxes to cover the audience areas. Given the axial symmetry of the volume, the receiver points selected for measurements were considered enough to understand the acoustic response of the hall. The excitation signal feeding the sound source was an Exponential Sine Sweep (ESS) [9] with a duration of 15 s in a uniform sound pressure level from 40 Hz and 20 kHz. The employment of an ESS signal feeding the loudspeaker instead of a white/pink noise is due to a good level of signal to noise ratio (S/N), other than to minimize the variability of directivity and repeatability that other types of sound sources (e.g., balloon pops, clapper boards) are more subject to [10]. This follows the reference standard to employ an excitation signal that should have sufficient energy at each relevant octave band. The measurements were undertaken in unoccupied conditions and with the stage completely empty [11]. Figure 5 shows the measurement positions of the instrumentations across the sitting areas.  
During the post-processing analysis, the impulse responses (IRs) can be obtained by the convolution of the recorded sine sweep signals (measured in the hall) with the inverse filter generation. Once we obtained the IRs, the acoustic parameters can be calculated using Aurora [12], a plugin suitable for Audition 3.0.
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2.6 Update Security and Privacy Requirements  
The goal of this stage is to update the security and privacy requirements with the list of security and privacy risks which require controls to mitigate. As risk analysis on the requirement analysis stage uses the initial product requirements, the updated security and privacy requirements will feed into the final product requirements. The following security and privacy requirements can be used as a starting point:  
* [noitemsep,topsep=0 pt]
* Assure data confidentiality by protecting sensor nodes, and database server from unauthorized access. Assure data integrity by protecting data from external modification during transmission or while in storage.
* Assure that data will always be available to an authorized entity of the application.
* Assure privacy of the data during collection, processing and transmission. Allow access of the data only to authorized entities.
* Use a lightweight, memory and energy-efficient cryptographic algorithm for encryption.
* Facilitate a key management service for key generation, key refreshing, key agreement, key distribution and key revocation.
* Include a firewall and intrusion detection system to identify and block suspicious activity on a network.
* Include logging for auditing and accountability.
* Include a data backup strategy to assure high availability of the application.  
After identifying the security and privacy requirements the following two tasks need to be conducted:  
* [noitemsep,topsep=0 pt]
* Update the initial product requirements with security and privacy requirements.
* Document the security and privacy requirements in the security assessment report.
## 2 Methods
### Polar Code
#### 2.2.2 Decoding with Polar Codes  
Decoding Polar Codes efficiently is performed using the Successive Cancellation (SC) algorithm [18], which exploits the recursive structure of \(G\). The SC decoder operates by estimating the transmitted bits sequentially, taking advantage of the previously decoded bits to refine the estimates of the following bits. For each bit, the decoder calculates the likelihood of it being a '0' or '1', based on the received values and the decisions made for the preceding bits.  
The performance of the SC decoder can be significantly improved with techniques such as Successive Cancellation List (SCL) decoding, which maintains multiple decoding paths simultaneously, or utilizing soft decision decoding to enhance the reliability of each estimated bit.  
These decoding techniques ensure that Polar Codes not only achieve high efficiency but also maintain robustness in environments with high noise levels, making them highly suitable for applications requiring reliable data transmission such as in 6G communications.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 3 Results
### Novelty Detection  
The One Class SVM algorithm was trained using standard parameters defined in the scikit-learn package [52]. As kernel type, the Radial Basis Function (RBF) was used. The kernel coefficient was set to scale, so it was dependent on the number of features and their variance. The coefficient related to the upper bound on the fraction of training errors and the lower bound of the fraction of support vectors (called nu) was set to 1 because the novelty detection algorithm was trained entirely on only non-anomalous data. Similarly to anomaly detection results presented in Section 3.1, so far, anomalies detected during the project are consulted on an ongoing basis with domain experts from PONAR Wadowice S.A. The solution undergoes evaluation in production.
## 3 Background
### Convolutional Neural Network (CNN)  
A convolutional Neural Network (CNN) is a type of fully connected feedforward neural networks that utilizes convolutional operations to automatically extract features of data [36]. CNN is widely used in various scenarios such as image pattern recognition and classification due to its powerful feature extraction capabilities on data with a grid structure [37]. A standard CNN architecture usually contains three types of layers involving linear and nonlinear operations, namely the convolution layer, subsampling layer, and fully connected layer. In the convolution layer, the output of the previous layer is convolved with the learnable kernels and obtains a sets of arrays called feature maps. Each element of the feature maps will pass through a non-linear activation function. The parameter-sharing mechanism used in the CNN network reduces the weights and number of connections. The subsampling layer is used to reduce the computation by producing low-resolution maps. In the fully connected layer, the feature maps are transformed into a one-dimensional array, and the layer outputs the classes of the respective objects. In this study, we utilized trained CNN models to detect the vehicle anomaly behaviors by classifying the constructed continuous wavelet transform scalograms of sensor signals.
## 1 Introduction
Nevertheless, the design process relied on the assumption of ideal conditions and did not consider the engineering constraints arising from the limited design space for the correction mechanism parameters. In recent years, various approximate modeling methods, such as radial basis function neural networks, back propagation neural networks, and support vector machines, have been widely applied to reduce computational costs in the field of aerospace [17; 18] and intelligent munitions design [19; 20; 21], becoming important technical means for multidisciplinary analysis and optimization. Among them, the Kriging surrogate model has received more attention because of its outstanding non-linear approximation modeling ability in medium- to low-dimensional problems when compared to other methods [22]. For uncertain design problems, a stochastic Kriging (SK) surrogate model based on the prototype of deterministic Kriging (DK) can be established. By effectively predicting the mean and variance of the performance, the SK model can significantly reduce computational costs while ensuring a certain level of analysis accuracy. The robustness advantage is also more apparent [23]. However, there have been no reported studies on the integrated optimal design of the aerodynamic shape and exterior ballistics of the pulse-controlled correction projectile using robustness approximation-based modeling methods in the field of pulse-controlled correction projectile research. In summary, this paper focuses on trajectory correction projectiles with lateral pulses as the research object. It considers the inherent uncertain factors in engineering calculations and carries out a robust optimization design for the aerodynamic shape and exterior ballistics. To solve the mathematical problem, a stochastic Kriging surrogate model is constructed to obtain a rapid response of the mean and variance of the projectile's performance with respect to design variables. Furthermore, based on the non-dominated sorting multi-objective sparrow search algorithm (MOSSA) [24], simulation and experimental results are compared and analyzed for deterministic Pareto optimal solutions and robust Pareto optimal solutions. This analysis aims to verify the practical engineering significance of robust design for pulse-controlled correction projectiles.
###### Abstract  
Marjoram (_Origanum majorana_ L.) infusion has been used as folk medicine against depression and anxiety. However, no studies have been carried out yet to prove those activities scientifically. In this study, the anxiolytic, antidepressant-like effects, and memory impact of the hydro-ethanolic extracts of majoram were evaluated in mice. The hydro-ethanolic extracts (250 and 500 mg/kg) were evaluated for their central nervous effect using six different behavioral tests such as light-dark box (LDB) and open field (OF) for anxiety, forced swim test (FST), and tail suspension test (TST) for depression, and object recognition test (ORT), Morris water maze (MWM) for the impact on memory. The experiments were realized on days 1, 7, 14, and 21 of treatments and compared with bromazepam for anxiety (1 mg/kg) and paroxetine for depression (11.5 mg/kg). The phytochemical screening was performed by HPLC, and the acute and sub-acute toxicities were performed following OCED guidelines (N\({}^{\circ}\)423 and 407) with biochemical parameters evaluation and histopathological analysis. Oral administration of majoram hydro-ethanolic extract induced significant anxiolytic and antidepressant-like effects without memory impairment, increasing the exploration and time spent in the light area in the LDB test in a similar way to that of bromazepam. In the FST and TST, the extract was as effective as paroxetine (11.5 mg/kg, p.o.) in reducing immobility. The phytochemical screening showed the presence of ferulic acid, naringin, hydroxytyrosol, geraniol, and quercetin. This study approves the traditional use of this plant and encourages further investigation on its bioactive compounds.  
**Keywords:** anxiolytic; antidepressant; memory impact; _Origanum majorana_ L.; OECD 423, 407; light-dark box test; Morris water maze test; object recognition test; open field test; forced swim test; tail suspension test
## 3 Application to Vibration Control  
### Seat Suspension Model  
As mentioned in the Introduction, to validate the effectiveness of the proposed control, the vehicle seat suspension model with MR damper shown in Figure 1 is adopted. The governing equations are derived as follows [9].  
\[m_{sd}\ddot{x}_{s}=-k_{s}(x_{s}-x_{0})-c_{sd}\Big{(}\dot{x}_{s}-\dot{x}_{0} \Big{)}+k_{1d}(x_{1}-x_{s})+c_{1d}\Big{(}\dot{x}_{1}-\dot{x}_{s}\Big{)}+F_{dMR} \tag{39}\]  
\[m_{1d}\ddot{x}_{1}=-k_{1d}(x_{1}-x_{s})+c_{1d}\Big{(}\dot{x}_{1}-\dot{x}_{s} \Big{)} \tag{40}\]  
The above equations can be rewritten using the state-space model as follows.  
\[\left\{\begin{array}{c}\dot{x}_{11d}=\dot{x}_{s}=x_{22d}\\ \dot{x}_{22d}=f_{11d}(x_{11d},x_{22d},x_{33d},x_{44d})+g_{11d}(x_{11d},x_{22d},x_{33d},x_{44d})u\\ \dot{x}_{33d}=\dot{x}_{1}=x_{44d}\\ \dot{x}_{44d}=f_{22d}(x_{11d},x_{22d},x_{33d},x_{44d})\end{array}\right. \tag{41}\]  
where  
\[f_{11d}(x_{11d},x_{22d},x_{33d},x_{44d})=-\frac{k_{d}}{m_{sd}}(x_ {11d}-x_{0})-\frac{c_{sd}}{m_{sd}}\Big{(}x_{22d}-\dot{x}_{0}\Big{)}+\frac{k_{ 1d}}{m_{sd}}(x_{33d}-x_{11})+\frac{c_{1d}}{m_{sd}}(x_{44d}-x_{22d}),\] \[g_{11d}(x_{11d},x_{22d},x_{33d},x_{44d})=\frac{1}{m_{sd}},u=F_{MR},\] \[f_{22d}(x_{11d},x_{22d},x_{33d},x_{44d})=-\frac{k_{1d}}{m_{1d}}(x _{33d}-x_{11d})-\frac{c_{1d}}{m_{1d}}(x_{44d}-x_{22d}).\]  
The damping force of the MR damper is determined as [9]:  
\[F_{MRd}=(c_{a}+c_{b}\
u)(x_{44d}-x_{22d})+k_{0d}(x_{33d}-x_{11d})+(\alpha_{a}+ \alpha_{b}\
u)\phi \tag{42}\]  
where \(\dot{\phi}=-\kappa[x_{44d}-x_{22d}]+\lambda(x_{44d}-x_{22d})\big{|}\phi\big{|} +\varphi(x_{44d}-x_{22d})\). Using Equation (42), the voltage to be applied to MR damper \(\
u\) is obtained as.  
\[\
u=\frac{F_{MRd}-[c_{a}(x_{44d}-x_{22d})+k_{0d}(x_{33d}-x_{11d})+\alpha_{a} \phi]}{c_{b}(x_{44d}-x_{22d})+\alpha_{b}\phi} \tag{43}\]  
Figure 1: Mechanical model of the seat suspension with MR damper.  
The voltage \(\
u\) is changed to the applied current, and then this current is used as the input with the relationship of \(u=F_{MRd}\). The parameters for the seat suspension and MR damper are given in [9].
## 3 Distributed Mission Scheduling Model
### APSOA for Single Satellite Scheduling  
#### 3.3.1 Solution Coding Scheme  
Coding scheme is directly related to the design of model and efficiency of algorithm. In the data preprocessing stage, observation windows defined in Section 2.2.2 are calculated and numbered, and each window can be uniquely indexed by a window ID. As observation can only happen during the window time period, an optimal scheduling solution is actually a combination of observation windows that not only satisfy the model constrains but maximize the observation payoff. In this paper, the solution coding is organized in a chromosome manner with gene fragments on it (the number of gene fragments is equal to the total number of targets). In each gene, there is a non-negative decimal number which corresponds to the observation window ID (0 indicates that the current target has not been scheduled for observation). For satellites with the same payload type, they share the same sub-solution coding (the length of sub-solution is equal to the number of targets with the required observation type), the final overall solution will be the accumulation of sub-solutions with same payload type, and connections of sub-solutions with different payload types, as shown in Figure 2.
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 5 Exploratory Imbalance Analysis
### Results and Discussion
#### 5.2.3 Statistical Analysis of Confusion Matrices

Table 13: ARI, AMU and difference in F1-scores for uniform distribution vs. equal majority–minority distribution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Uniform Distribution vs. Many Majority Distribution**} \\ \hline
**\% Labeled Data** & **ARI** & **AMU** & **Difference in F1-Scores** \\ \hline
80 & 0.30672 & 0.44755 & 0.05 \\
60 & 0.20373 & 0.33497 & 0.06 \\
40 & 0.26017 & 0.37980 & 0.12 \\
20 & 0.36146 & 0.46094 & 0.14 \\
10 & 0.24457 & 0.31041 & 0.13 \\
5 & 0.36115 & 0.41877 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: ARI, AMU and difference in F1-scores for uniform distribution vs. many majority distribution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Uniform Distribution vs. Few Majority Distribution**} \\ \hline
**\% Labeled Data** & **ARI** & **AMU** & **Difference in F1-Scores** \\ \hline
80 & 0.43037 & 0.49422 & 0.02 \\
60 & 0.18946 & 0.37004 & 0.08 \\
40 & 0.41235 & 0.50588 & 0.17 \\
20 & 0.33234 & 0.39484 & 0.07 \\
10 & 0.34888 & 0.43448 & 0.04 \\
5 & 0.31430 & 0.35841 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: ARI, AMU and difference in F1-scores for uniform distribution vs. few majority distribution. published, each aiming to improve the efficacy of the FixMatch algorithm in situations where the training dataset is imbalanced. While these algorithms have reported major improvements on benchmarking datasets such as CIFAR and SVHN, running each of them on this chest X-ray dataset might allow a deeper insight into how effective SSL can truly be in situations where new diseases have lower amounts of available data.
## 6 Fully-Distributed Clock Synchronization Algorithms  
The centralized signal processing techniques can only help address the problem of pairwise synchronization. As discussed in Section 1, the network-wide synchronization requires a specified network structure to extend the pairwise synchronization approach to all of the sensor nodes. This increases the overhead of the communication and makes the network vulnerable to node failures. More recent works on clock synchronization mainly concentrated on applying decentralized signal processing methods (e.g., distributed estimation techniques) to develop clock synchronization algorithms in a fully-distributed manner.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 2 Zero-Copy DMA Technology Overview  
When the amount of data to be processed is large, CPU copies and hardware interrupts can take up many CPU cycles and lead to interrupt livelock [8]. Data copying and context switching can consume many memory and clock cycles. To cope with the performance bottlenecks caused by hard interrupts and CPU copies, the Industry has proposed the concept of zero-copy to improve the efficiency of sending and receiving data [9]. The most representative technologies and frameworks are pf_ring zc, Netmap, AF_XDP, and DPDK [10; 11; 12; 13], among which the best performance is DPDK [14], the leading research target. Table 1 shows the comparison of several zero-copy techniques.