esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Results of Software Evaluation by Experts  
#### 2.3.1 Max and Music Technology Expert Ratings  
For the evaluation of the first functional version of the software, the team requested the collaboration of five experts with recognised experience both in programming with Max and in the field of music technology. The criteria for their selection were to have more than 10 years of experience in Max programming and to teach music technology in educational centres.  
In order to collect their quantitative assessment, the survey was sent to each of them. For the Max and technology experts, the survey consisted of seven evaluation blocks, one for each software module. Each block included 5 or 6 questions depending on the block, with an ordinal 5-point scale where 1 was the lowest score and 5 the highest.  
For the analysis of the experts' quantitative assessment, the maximum possible score for each block was calculated to obtain the sum of the total of the experts' assessment and the corresponding percentage of the maximum possible score for each block.  
The data in Table 1 show that all modules were rated above 60% by the experts, which can be considered a high rating, but there is also a wide difference between the module with the lowest rating, the BPFB (_bandpass filter bank_) module, and the rest of the modules. The best rated module is the _looper_ module, with 78%.  
Figure 21: Max programming of a looper.  
The assessment of the modules is fairly homogeneous, with a difference of only 0.177% between the lowest and highest rated module.  
The high ratings of the individual modules translate into a high overall rating of the software by the experts.  
The overall rating of the software is 82% of the highest possible score, which can be considered as a very satisfactory rating as a starting point for the programming part of the first functional version of the software but with a wide margin for improvement, as indicated by the experts in their qualitative assessments and proposals for improvement.  
In the qualitative assessments and proposals for improvement, most of the experts agree that a series of modifications and improvements to the BPFB module could be necessary. We found a large coincidence in the responses, suggesting the addition of controls that would allow for greater intervention in the module. These considerations refer mostly to the ability to choose the number of filters to be activated without having only two pre-set selection options, as well as adding the functionality to manually set, from the module interface, the different parameters necessary for the operation of the filters, including cut-off frequency, filter gain, bandwidth and Q factor, or the frequency attenuation curve.  
In their replies, the experts considered the inability to manually intervene in the filters to be important, which explains the low rating of this module.  
On the other hand, another of the most recurrent suggestions from experts is to add _presets_ to the _reverb_ module that allow for the selection of reverberations that imitate specific architectural spaces, such as a church, auditorium, or room.
## 4 Evaluation
### Experimental Design  
As shown in Figure 3, topology TA2 from Survivable fixed telecommunication Network Design library (SNDlib) [32] was used for the performance evaluation. TA2 has 66 nodes and 108 links. Considering the convenience of control, the controllers were assumed to be connected with SFDs. As discussed in Section 3.1, in hybrid SDNs, there are some available paths, which can be used for routing control messages. The more paths that can be used in routing, the more chances there are to maintain stability and recover from link outage. Thus, in this experiment, the location of controllers was chosen greedily with the maximum number of paths between controllers and SFDs. About 30% of forwarding devices were selected randomly to be deployed with the SDN capability. The links between devices had the same transmission speed \(k_{e}=5\) us/km. We assumed that LFDs had the same configuration with the service rate being \(\mu=5\) M packets/ms and the estimated queue length being \(m=40\) M packets. The solution of the dynamic assignment was computed on Intel Core i7 (2.9 GHz) with 16 GB memory. The Gurobi Optimizer, which is a programming solver, was chosen to solve this optimization problem in this work.  
The assignment scheme Legacy Ignored Assignment (LIA) [15] was used for comparison. It ignores the influence of the LFDs to the latency of control message. In this section, we focus on two metrics: the control message latency and the loss rate caused by the LFDs.
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.2 Correlation of Adjacent Pixels  
The amount of redundant information in digital photographs is extremely large, resulting in a strong correlation between adjacent pixels within the image [44]. This correlation of adjacent pixels is expected to be reduced by encryption. We used the formula in Equation (7) to compute the correlation coefficients of both plain and encrypted standard photos.  
\[r=\frac{cov(p,q)}{\sqrt{D(p)}\sqrt{D(q)}} \tag{7}\]  
where  
\[D(p)=\frac{1}{S}\sum_{i=1}^{S}(p_{i}-\overline{p})^{2}\]  
\[cov(p,q)=\frac{1}{S}\sum_{i=1}^{S}(p_{i}-\overline{p})(q_{i}-\overline{q})^{2}\]  
* \(p_{i}\) and \(q_{i}\): two neighboring pixels. (horizontal or vertical).
* \(S\) is the number of (\(p_{i}\), \(q_{i}\)) couples.
* \(\overline{p}\) and \(\overline{q}\) are the mean values of \(p_{i}\) and \(q_{i}\).  
The correlation coefficients of both plain and encrypted standard images are shown in Table 3. The results suggest that the pixels of encrypted images show low correlation in every direction (horizontal, vertical, and diagonal), which is graphically illustrated in Figure 13.
## 2 Materials and Methods
### Deep Neural Network Models
#### 2.4.1 Feed-Forward Multilayer Perceptron  
The Feed-Forward Multilayer Perceptron (MLP) is a widely used Deep Neural Network (DNN). In the context of Inverse Kinematics (IK), it serves as a model that represents a non-linear relationship between the input vector (specifically, the desired Cartesian target for the end-effector, \(X_{d}\)) and the output vector (the corresponding joint space positions that obtain this target, \(\theta_{cmd}\)). Each MLP consists of an input layer, multiple hidden layers, and an output layer [29].  
Three different MLPs are built to solve the IK problem. The first of them consists of an input layer, one hidden layer, and an output layer. The input and output sizes are set depending on the shape of the training dataset. The number of neurons in the hidden layer has been optimized using a hyperparameter tuner, and finally set to 160. An instance of this architecture can be seen in Figure 4. The hidden layer makes use of the ReLU activation function while the output one, after multiple tests, uses the tanh activation function. This first MLP will be referred to as SimpleMlp.  
The second proposed MLP architecture expands on the first one, adding more hidden layers. Specifically, the model consists of an input layer, 80 hidden layers, and an output layer. The first and last 20 hidden layers contain 128 neurons each, while the middle 40 hidden layers contain 256 neurons each. The input and output sizes are again set depending on the shape of the training dataset. An instance of this architecture can be seen in Figure 5. As before, the hidden layers use ReLU as their activation function, and the output layer uses the tanh activation function. This second MLP will be referred to as DeepMlp.  
Figure 4: SimpleMlp: Fully connected feed-forward Deep Neural Network for learning Inverse Kinematics. The input size is set for a 3D Cartesian position and quaternion orientation, the hidden layer contains 160 neurons, and the output size corresponds to the 7 DOFs of TIAGo’s arm.  
Finally, the third model follows the idea of [30], where each joint has an associated subnet with one hidden layer. The original architecture is expanded to include TIAGo's 7 DOFs and therefore contains one input layer, 7 subnets, and one output layer, as can be seen in Figure 6. The units for each subnet are set to 16, 272, 496, 496, 496, 496, and 272, respectively. All layers use a linear activation function. It will be referred to as SubnetMlp.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### MAWILab  
MAWI was built in 2001 and consists of a set of labels locating traffic anomalies in the MAWI archive [15]. This dataset contains tcpdump packet traces captured from an operational testbed network in a link between Japan and the United States. The archive contains 15 min of daily traces. This dataset is huge with a very long period. The labeled MAWI archive is known as MAWILab, obtained from a graph-based methodology that combines different and independent anomaly detectors [16]. MAWI archives labeling is based on inferences that results in no ground-truth traffic that can be used for evaluation. The label has three classes: anomalous for a true anomaly, suspicious indicates that the traffic is likely to be anomalous, and notice is assigned as an anomaly but it does not reach a consensus from all anomaly detectors. Several researchers used MAWILab for anomaly detection [17] and generating labeled flow [18]. The limitation of this dataset is that the packet traces are captured for 15 min each day. The header information is available in the packet traces but the payload is removed.
## 2 Materials and Methods
### Analysis Equipment and Methods  
The current and voltage were measured using current (6585, Pearson Electronics, Palo Alto, CA, USA) and high-voltage probes (P6015A, Tektronix, Beaverton, OR, USA) with a digital storage oscilloscope (DPO 4054 B, Tektronix, Beaverton, OR, USA). The optical emission spectrum of the plasma was obtained using an optical emission UV-NIR spectrometer (HR4000 CG-UV-NIR; Ocean Optics, Inc., Orlando, FL, USA). The temperature and concentration of ozone were measured at the gas outlet point of the DBD reactor using  
Figure 1: (**a**) Diagram of the compact air-cooled coaxial DBD reactor with bubbler configuration to disinfect water-based cutting fluids. (**b**) Image of compact air-cooled coaxial DBD plasma.  
thermometer (905-T1, Testo, Titisee-Neustadt, Germany) and ozone monitor (OM-1500B, OZONETECH, Daejeon, Republic of Korea), with a measurement error of \(\pm\) 0.2%. Water-based cutting fluids (CIMPERIAL 1311D; Cimcool Industrial Products Inc., Cincinnati, OH, USA) were diluted with water to a concentration of 10% to form fresh cutting fluids. Water-based cutting fluids (Aryung Machinery Ind. Co., Ltd., Damyang, Republic of Korea) aged > six months were used in this study. We measured the changes in water-based cutting fluids' concentration, pH, and composition by ozone treatment using a cutting fluid refractometer (PAL-102S, Atago, Tokyo, Japan) with a measurement error of \(\pm\)0.2%, a pH meter (HI 98194, HANNA Instruments, Carrollton, TX, USA) with errors of pH (\(\pm\)0.02%), and a gas chromatograph/mass spectrometer (GC/MS), respectively. The complex odor was measured using an Odor Mater (OMX-SRM, Shinyei Technology, Kobe, Japan), and the concentration of the complex odor was acquired from values averaged over triplicate measurements.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 3 Deep Learning-Based Target Detection
### Transfer Learning  
For all the neural network models used in this paper, we use the transfer learning approach. The neural network models used are pre-trained on the ImageNet dataset [31], and the last fully connected layer of the network is changed from 1000 to 4 layers. The network is initialized using pre-trained parameters and trained by fine-tuning the entire network parameters with new data generated by the method in Section 2 to improve the performance of the network on the desired task. All other networks are frozen, and all network parameters are fine-tuned according to the pre-trained model parameters. The transfer learning approach based on fine-tuning each layer of the network is shown in Figure 8. A transfer learning approach based on fine-tuning entire layers of the network to train a new dataset with weights that have been trained on ImageNet.
## 3 Materials and Methods
### Electromagnetic and Magnetic Field Response Modeling
#### 3.1.3 Unified Modeling and Consistency Verification  
Both the electromagnetic and magnetic field responses are governed by the magnetic polarizability tensor \(M\), ensuring a consistent physical representation of the target's magnetization across both methods. While TEM modeling captures the time-decaying secondary EM field, magnetic modeling provides a static magnetic field representation. By employing a shared MPT and the effective magnetic moment formulation, both forward models converge within a single, unified framework, enabling integrated analysis and enhancing the robustness of inversion results.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 2 Insulator Identification and Location Based on Mask R-CNN
### Model Output  
The loss function is an important function to judge whether the algorithm converges. By reducing the value of the loss function, the accuracy of the algorithm's classification prediction can be improved. As shown in Equation (1), the loss function of the Mask R-CNN algorithm mainly consists of five items: one part is the foreground/background classification loss \(L_{\text{rpn\_cls}}\) and the target frame regression loss \(L_{\text{rpn\_bbox}}\) generated by the region proposal network (RPN), the other part is the classification loss \(L_{\text{cls}}\), regression loss \(L_{\text{bbox}}\) and instance segmentation loss \(L_{\text{mask}}\) caused by ROI classification branch network.  
\[L=L_{\text{rpn\_cls}}+L_{\text{rpn\_bbox}}+L_{\text{cls}}+L_{\text{bbox}}+L_{ \text{mask}} \tag{1}\]  
After the insulator identification model based on the Mask R-CNN algorithm converges, the minimum circumscribed rectangle is designed and cut according to the mask predicted by the algorithm, and the inclined insulator is automatically rotated to the horizontal position for later judgment of insulator damage.
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 2 The Resource Scheduling Model in Containerized Microservice
### Microservice Container Dependencies  
The microservice architecture's characteristic is to differentiate an extensive service project into individual modules for a single service. There exists a calling relationship between these modules, which creates a dependency relationship between the deployed containers. Therefore, the dependency relationship between containers is a factor that must be considered in the resource scheduling process for containers. This means that containers with call dependencies are allocated to the same physical host as much as possible, which can reduce the time for containers to call across physical hosts, minimize the waste of network resources, and improve the QoS.  
In this article, the calling distance, \(v\big{(}k_{i},k_{j}\big{)}\), is used to quantify the dependence between the container \(i\) and \(j\), as shown in Equation (4).  
\[v\big{(}k_{i},k_{j}\big{)}=\left\{\begin{array}{cc}0,&\text{no direct calling relationship between container $i$ and $j$}\\ &1,\text{else}\end{array}\right. \tag{4}\]  
Through \(v\big{(}k_{i},k_{j}\big{)}\), we can calculate the sum of the dependency relationship between the container \(i\) in the host \(t\) and other used containers, as shown in Equation (5). When the \(i\) container in the physical host \(t\) has a stronger dependence on different containers, this may indicate that the \(i\) container is more suitable for deployment on the physical host \(t\).  
\[v_{ti}=\sum\limits_{j=1}^{w}v\big{(}k_{i},k_{j}\big{)} \tag{5}\]  
Finally, we can calculate the dependency correlation between all containers in the physical machine \(t\), as shown in Equation (6). When the value of \(v_{t}\) increases, the total dependency of all containers in the same physical host t will be higher. At the same time, this also means that the number of network calls across physical hosts generated in the system is less, and the response time of client requests is shorter  
\[V_{t}=\sum\limits_{i=1}^{w}v_{ti} \tag{6}\]
# Fire Safety Risk Analysis of Conventional Submarines
## 4 Fire Safety Risk Considerations--Technology Review
### Fire Risk-MESMA Steam Turbine
#### 4.6.3 Technology Maturity  
Although similar technologies exist, MESMA was developed exclusively for use in a submarine and has no development or service history in other industries or applications. Development of MESMA began in the mid-1980s when France-based Bertin Company designed a system initially fuelled by methanol [67]. Since then the technology has been jointly developed with Naval Group Australia and successfully integrated into a number of conventional submarines.  
The first submarine fitted with MESMA was an Agosta 90B submarine built for the Pakistan Navy and commissioned in 2008. A further two Agosta 90B submarines have since been retrofitted with MESMA and were delivered in 2011 [68]. A completed MESMA module, ready for integration into one of the Agosta 90B submarines, is shown in Figure 12(a). The inside of a MESMA module built for the Agosta 90B submarine is shown in Figure 12(b).  
MESMA is also offered on the latest AM-2000 variant of the Scorpene class submarines of which the Indian Navy currently have three on order and the Brazilian Navy four, although none of these submarines have been completed or commissioned [71].  
\begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Component** & **Failure** & **Effect** & **Consequence** \\ \hline \multirow{4}{*}{LOX distribution} & & Reduced or loss of oxygen supply to combustion chamber, release of liquid and gaseous oxygen, localised rapid cooling from cryogenic liquid & Loss of MESMA power, embrittlement and possible failure of components exposed to cryogenic liquid, oxygen enriched atmosphere leading to ignition of surrounding materials and explosion/fire \\ \cline{2-4}  & & Reduced or loss of fuel supply to combustion chamber, release of fuel in the form of a drip or spray & Loss of MESMA power, spray of flammable liquid onto hot surface or accumulation of flammable liquid leading to ignition and fire \\ \hline \multirow{2}{*}{Combustion chamber} & & Reduced or loss of steam pressure to turbine, high or low pressure release of fuel, exhaust gases and or oxygen & Loss of MESMA power, spray or leakage of heated, flammable liquid-gas mixture leading to ignition and fire \\ \hline \hline \end{tabular}
\end{table}
Table 5: MESMA System Failures Leading to Fire.  
MESMA has been under development for over 30 years and has nearly 10 years' service history in conventional submarines, though those submarines are considered to be medium sized at around 2000 tonnes displacement. MESMA currently has no service history in large conventional submarines of 3000 tonnes or greater.
The literature indicates that both professional football players and elite youth football players [3; 22] perform decades of accelerations and decelerations in a match. The 1-4-3-3 formation is a widely utilized tactical setup in soccer, offering a balanced approach between offense and defense. The four lines of the formation consist of the goalkeeper (1), the four defenders (4), the three midfielders (3), and the three forwards (3). This formation is highly effective for teams that emphasize possession, pressing, and attacking football. Its structure provides width in attack and allows for effective pressing strategies. Historically, teams like the Netherlands in the 1974 and 1978 World Cups employed the 1-4-3-3 to great effect, emphasizing fluid movement and positional interchange. In modern football, clubs such as FC Barcelona under [PERSON] and Liverpool under [PERSON] have adopted this formation to dominate possession and apply high defensive pressure. The 1-4-3-3's flexibility enables teams to adapt their midfield configurations and attacking patterns to exploit opponents' weaknesses, making it a preferred choice for many top-tier teams. However, all the above studies about running distances concern professional players, while for amateur and developmental football, studies are extremely limited [23; 24]. Previous studies in developmental football have reported the wide range of athletic profiles in different age categories [24]. The information that coaching staff will obtain from forming these athletic profiles can be used to individualize load according to playing position, making the coaching process more effective. Additionally, in elite youth football, this information will be used to prepare players according to their position for the next step in their football career (the next team). Specifically, by knowing the running profile of the winger in the U19 team in the 1-4-3-3 formation, the U17 player who plays in the same position can aim to approach these values to be better prepared (in terms of physical condition) for the transition from the U17 team to the U19 team. According to the above, the aim of this study was to create the athletic running profile of high-level football players under 17 years of age (U17) in the 1-4-3-3 formation and to investigate the differences between the playing positions in the formation. According to the existing literature, which primarily concerns professional football players, we hypothesize that (a) the greatest total distance and high-intensity distance will be covered by side players and midfielders.
# Passive Earth Pressure and Soil Arch Shape: A Two-Dimensional Analysis
## 3 Analysis of Soil Arching Under Passive Conditions
### Comparison with Circular and Parabolic Arches
In contrast, the derived soil arch curve is affected by the magnitude of the surcharge. As the surcharge increases, the derived arch curve becomes slightly flatter. This finding indicates that the proposed arch shape reflects the internal stress redistribution within the entire soil wedge, rather than being governed only by boundary conditions.
## 2 Related Work
### Color Distortion Correction for the OST-HMD  
A lookup table (LUT) that defines the relationship between the input colors and the ones actually observed appears to be one possible way of dealing with the color distortion of an OST-HMD. But, creating a 16-million-color LUT is time-consuming (it may take two to three days), and the OST-HMD would require a considerable amount of memory to store the LUT. To reduce the size, both [PERSON] et al. [19] and [PERSON] et al. [20] utilized a binned-profile method [21] to divide the CIELAB color space into bins with a size of \(5\times 5\times 5\). To get rid of the LUT, [PERSON] et al. [14] created a  
Figure 1: Dlatonization process.  
channel-specific color-correcting model that consists of two portions, a linear transformation matrix and a nonlinear function, according to the physical optics represented in the rendering and perception of an OST-HMD and a user's eyes. [PERSON] et al. [22] used synthesized images that were captured by a user-perspective camera to estimate real-space colors and performed color correction accordingly. [PERSON] et al. [23] tackled nonlinear color changes through localized regression; they modeled the changes and used hue-constrained gamut mapping for color correction.