esa-sceva/satcom-chunk-collection huggingface dataset:
# Study About the Performance of Ascon in Arduino Devices
## 5 Performance Analysis of the Ascon Implementation
### Analysis of the Results
Additionally, a high-performance implementation of Ascon was evaluated on an ESP32 Dev Kit under optimized conditions. According to tests by RWeather [60], Ascon-128a achieved an average of 0.86 cycles per byte for 128-byte message blocks, corresponding to approximately 27.9 Mbps, or more than 3,400,000 operations per second. This performance significantly exceeds the observed throughput on the Arduino Nano ESP32 and high lights the potential of Ascon when deployed with careful memory management, buffering strategies, and full CPU allocation. These comparative results illustrate a clear relationship between platform complexity and cryptographic performance. While Ascon scales well with increasing hardware capabilities, its low memory footprint and computational efficiency make it a practical solution even for ultra-constrained platforms such as the Mega2560 and Nano Every. Consequently, Ascon emerges as a robust and scalable cipher for lightweight cryptography across a wide spectrum of IoT and embedded applications.
## 3 Results and Discussion
### Self-Rotating Bit Design Considerations  
Pump pressure is one of the most important parameters determining how well the jet bit will break rock. The higher the pump pressure is, the better the jet bit will break rock. However, high pump  
Figure 10: Illustration showing the influence of different impact angles on rock-breaking range by jet rotational impact.  
Figure 9: Effect of water jet impact angles on depth, diameter, and volume of crushed pits: (**a**) water jet impact angles on depth for a nonrotating jet; (**b**) water jet impact angles on depth for a rotary jet; (**c**) water jet impact angles on diameter for a nonrotating jet; (**d**) water jet impact angles on diameter for a rotary jet; (**e**) water jet impact angles on volume of crushed pits for a nonrotating jet; and (**f**) water jet impact angles on volume of crushed pits for a rotary jet.  
pressures mean that this better rock breaking will use more water and that the energy used by the pump will increase. When the jet pressure is too high, the energy of the jet cannot be fully utilized. Hence, when a self-rotating jet bit is used to break rock, the pump pressure should be adjusted according to the performance of the drilling system. The self-rotating bit has an optimal rock-breaking rotational speed, and this speed correlates positively with the pump pressure. A speed-limiting mechanism must therefore be added when designing the bit to prevent the bit from rotating too fast. In addition, the speed at which the bit rotates should be adjustable. When the pump pressure is high, the bit rotation speed can be increased appropriately. The optimal rock-breaking angle for the jets on a self-rotating bit is about 10\({}^{\circ}\). When a large volume of crushed pit is desired, the impact angle can be increased.
## 3 5G Signal and Detection Performance Analysis
### Features of 5G Signal Physical Layer
#### 3.2.3 Time-Frequency Resources  
In the 5G protocol, many time-frequency resource units are defined, such as the resource element (RE), resource block (RB), physical resource block (PRB), and time-frequency resource grid. The RE is the smallest resource unit in 5G, occupying one OFDM symbol in the time domain and representing one subcarrier in the frequency domain. An RB contains 12 subcarriers in the frequency domain and is not defined in the time domain. PRB is a physical RB within the bandwidth part (BWP). BWP is a partial continuous bandwidth configured for UE in the transmission channel. Within each BWP, PRB is independently numbered, and, in order to manage different BWPs, a common resource block (CRB) is defined in 5G. The schematic diagram of BWP is shown in Figure 1[10], where \(N_{grid}^{start}\) is the offset of the cell containing this BWP relative to CRB0, \(N_{BWP}^{size}\) is the number of RB contained in BWP, and \(N_{BWP}^{start}\) is the offset of BWP relative to CRB0. The resource grid contains two dimensions, the time domain and frequency domain, and it is a method of signal representation. Each grid represents a subcarrier in the frequency domain and an OFDM symbol in the time domain.
## 3 The Proposed Constraint Multi-Objective Optimization Algorithm
### Algorithm Framework  
Algorithm 1 presents the pseudocode of the proposed algorithm.  
```
1:\(P_{t}\leftarrow\) Initialize Population (\(N\)) \(\triangleright\) Randomly generate \(N\) chromosomes
2:\(t\gets 1\)\(\triangleright\) Initialize generation count
3:while\(t\leq MaxGen\)do
4:\(MatingPool\leftarrow\) Binary Tournament Selection (\(P_{t}\))
5:\(Q_{t}\leftarrow\) Hybrid Crossover And Mutation (\(MatingPool\))
6:\(R_{t}\gets P_{t}\cup Q_{t}\)
7:\(Rank\leftarrow\) Non Dominated Sorting-MC (\(R_{t}\))
8:\(Dis\) = Compute Crowding Distance (\(R_{t}\))
9:\(P_{t}\leftarrow\) Selection (\(R_{t}\), \(Rank\), \(Dis\))
10:\(t\gets t+1\)
11:endwhile
```  
**Algorithm 1** NSGA-II-MC Algorithm Framework  
The detailed demonstration of the proposed NSGA-II-MC algorithm is as follows:  
1. Design the structure of the chromosome with a grouping matrix coding strategy;
2. Initialize the population \(P_{t}\) with \(N\) randomly generated chromosomes, each representing a energy management solution of the microgrid (Line 1);
3. Initial the generation count as \(t=1\) (Line 2);
4. Prepare the mating pool by performing binary tournament selection on \(P_{t}\) (Line 4);
5. Perform hybrid crossover and mutation operators on the mating pool to generate the offspring \(Q_{t}\) (Line 5);
6. Combine parent and offspring population \(R_{t}=P_{t}\cup Q_{t}\) (Line 6);
7. Perform the non-dominated sorting on \(R_{t}\) to obtain the rank of individuals in \(R_{t}\). A multi-stage constraint-handling strategy is used to rank the individuals to handle the complex constraints (Line 7);Compute the crowding distance (Line 8);
* Perform the selection operator on \(R_{t}\) according to the rank to obtain \(N\) individuals as the new \(P_{t}\). Individuals with larger crowding distance are preferred if they have the same rank (Line 9);
* Increment \(t\).
* Repeat steps 4 to 10 until \(t>MaxGen\).  
In the subsequent sections, we introduce each component of the designed algorithm in detail.
## 2 Proposed Bearing Fault Diagnosis Method
### Signal Preprocessing  
The vibration signals acquired from electrical machines are nonstationary as they contain bearing fault information modulated for variable rotational speeds and surrounding environmental noise. The frequency of the obtained signal is continuous, and its time-domain statistical features change with time. Nonstationary signals typically lack useful information in the time or frequency domains but may have useful information through merge representation within the time-frequency \((t,\ f)\) space.  
In varying working conditions of bearings (various types of single and compound faults and various degradation levels at variable rotational speeds), traditional signal processing methods such as envelope spectrum analysis and wavelet package transform are challenging to obtain meaningful information. Under these conditions, the signal's spectrum varies greatly in amplitude and frequency. Therefore, it is troublesome to recognize the deficiencies of bearings under adverse working conditions using conventional signal processing methods. For appropriate analysis and synthesis of nonstationary signals, Short-Time Fourier Transform (STFT) is a typical Fourier transform (FT) applied to create the spectrograms of signals. The spectrogram is a visual representation of the signals in both the time and frequency domains, using a color scale of the image to indicate the frequency's amplitude.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Underwater  
There are several specific underwater observatory systems deployed on the seafloor using power lines for energy supply and fiber optical for communication. Such system have large communication and power capacities, and can be used to provide power recharge and data sinks (see Section 4.4). There are also various types of underwater sensors/devices with communication capabilities, such as subsurface buoys and automatic underwater vehicles (AUVs) as well as underwater acoustic networks. They all suffer from the same problem, i.e., limited power supply of battery and drifting randomly with sea wave and current.
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 2 Establishment of an Inhomogeneous Particle Scattering Model
### Model Building Based on Polarization Monte Carlo Method
Then, we use the equivalent analysis method and relative humidity formula in Sections 2.1 and 2.2 to calculate the equivalent refractive index of inhomogeneous particles. The results are used as the simulation parameters for the next step of photon scattering. After the inhomogeneous particles scatter a large number of photons, the photons leave the transmission medium with polarization information. Finally, the transmission simulation of inhomogeneous particles is completed. The inhomogeneous particle scattering model based on Monte Carlo is constructed. Figure 3 shows the schematic diagram of the inhomogeneous particle scattering simulation. Figure 3: Schematic diagram of inhomogeneous particle scattering simulation. The workflow of the inhomogeneous particle scattering model is as follows: First, the wavelength or relative humidity is set. The refractive index calculation input module calculates the corresponding equivalent refractive index through the equivalent analysis method or relative humidity formula. Parameters such as particle size, optical thickness, and photon number are also set. Then, photons are emitted. The sampling of the free path by photon scattering is performed. The scattering angle and azimuth angle are sampled by the rejection method, and the Stokes vector is updated after scattering. Finally, the photons pass through the boundary and leave the transmission medium. Figure 4 shows the flowchart of an inhomogeneous particle scattering model.
## 2 Literature Review
### Markov Processes in Logistics and Transportation  
This section examines the use of Markov processes in logistics and transportation. These processes help model the probabilistic transitions and behaviors within these systems, offering insights for optimizing and predicting performance.  
The study [25] explores the application of Markov models in analyzing transportation processes within logistics systems. By using discrete and continuous time Markov models, the research examines how changes in supplier selection impact vehicle readiness and overall system efficiency. Initially, the system faced random and chaotic supplier choices. However, after a detailed analysis and selection of optimal suppliers, the study demonstrated improved system readiness through Markov processes. The proposed models offer a robust framework for diagnosing and forecasting operational indicators in various logistics contexts, including military, emergency services, and civil transport. These models are particularly effective for systems where readiness is crucial and data on task completion are accurately recorded. The study highlights the practical value of Markov models in optimizing logistic operations, ensuring reliable supply, and facilitating system evaluation and improvement.  
Another paper [26] addresses the modeling of light utility vehicle operations within military transport systems. Effective system performance hinges on maintaining vehicles at optimal technical readiness, which involves efficient fuel refilling, maintenance, and repair processes. Failures significantly impact transport capacity and hinder task execution. To manage and optimize vehicle operations, the paper proposes using advanced mathematical methods, particularly Markov processes. It introduces three key performance indicators: functional readiness, technical efficiency, and airworthiness. A stochastic exploitation model, incorporating a nine-state phase space and a semi-Markov model, is used to assess these indicators based on empirical data. Sensitivity analysis indicates that reducing vehicle repair wait times by 50% could improve performance metrics from 0.91 to 0.95, demonstrating the potential for significant operational enhancements.  
The research [27] focuses on the operational analysis of Honker 2000 light utility vehicles within the Polish Armed Forces. The study identifies four key operational states: task execution, awaiting a task, periodic maintenance, and repair. Functional readiness and technical suitability are used as performance metrics. A Monte Carlo simulation model, developed in MATLAB, was used to analyze these states. The model accounted for periodic maintenance as a deterministic process, while other states were modeled as stochastic. The simulation, validated against 16 models with different cumulative distribution functions (CDFs), showed that the results aligned within 6% of those from a semi-Markov model. Sensitivity analysis revealed that the Monte Carlo model is effective for forecasting vehicle performance and can be a valuable tool for analyzing and predicting operational outcomes.
## 4 Experimental Results and Analysis
### Experimental Environment and Hyperparameter Configuration  
All studies in this paper were conducted on a laptop with a GPU configuration of GTX 1650. Because the training and testing results of the neural network model are strongly influenced by the parameters, so the learning rate is set to 0.0001 and the loss function of the model is compared every 20 epochs during the training process. The learning rate is reduced by a factor of 0.1 if the validation loss is not reduced. The batch size is set to 128 and the epochs are set to 1200. The training is completed in about 6 hours. The mean square error (MSE) loss function is used. It should be noted that the network will not converge if the initial learning rate for model training is set too large, causing the loss function value to oscillate.
## 2 Background and Related Work
### Problem Definition  
In this subsection, we formally define the partitioning problem as a partitioning objective-function optimization problem subject to constraints. First, we define a function that returns 1 if an element \(n\) is assigned to partition \(p\) and 0 otherwise:  
Figure 1: Example of: (**a**) how a fully connected neural network may be represented as a dataflow graph; and (**b**) how it can be partitioned for execution on two devices.  
\[partition(p,n)=\left\{\begin{array}{ll}1,&\text{if $n$ is assigned to $p$;}\\ 0,&\text{otherwise.}\end{array}\right. \tag{4}\]  
The partitioning problem can be defined as a partitioning objective-function optimization problem subject to memory constraints:  
\[\begin{array}{ll}\text{optimize}&cost\\ \text{subject to}&\sum_{n=1}^{N}m_{n}\times partition(p,n)+\sum_{j=1}^{L}m_{ sb\eta_{j}}\times partition(p,j)\leq m_{p},\forall p\in[1...P],\end{array} \tag{5}\]  
in which _cost_ is the objective function (detailed below), \(N\) is the number of neurons in the DNN, \(m_{n}\) is the memory required by element \(n\), \(L\) is the number of layers of the DNN, and \(sbp\) is the shared parameters and biases.  
If we want to reduce communication, we can define a function that returns \(1\) if two elements are assigned to different partitions and \(0\) otherwise:  
\[\text{diff}(i,j)=\left\{\begin{array}{ll}1,&\text{if $i$ and $j$ are assigned to different partitions;}\\ 0,&\text{otherwise.}\end{array}\right. \tag{6}\]  
Then, we can define the communication cost as  
\[\text{communication cost}=\sum_{i=1}^{N}\sum_{j=1}^{adj(i)}\text{edge weight}_{ij}\times\text{diff}(i,j), \tag{7}\]  
in which \(adj(i)\) are the adjacent neurons of neuron \(i\) and edge weight\({}_{ij}\) is the edge weight between neurons \(i\) and \(j\).  
If we want to maximize the inference rate, then Equation (1) represents the cost function and, to formally define the optimization problem, we can rewrite the computational load of device \(d\) of Equation (2) as  
\[\text{computational load}_{d}=\sum_{i=1}^{N}\text{computational load}_{i}\times partition (d,i), \tag{8}\]  
and the communication load between devices \(d\) and \(q\) of Equation (3) as  
\[\text{communication load}_{dq}=\sum_{i=1}^{N}\sum_{j=1}^{adj(i)}\text{edge weight}_{ij}\times\text{diff}(i,j)\times partition(d,i)\times partition(q,j). \tag{9}\]
## 1 Introduction
Even if the test methods and certification criteria are crafted quickly for NCPs, the verification process should be accompanied, taking into account the real-world environment of the product use. Since most NCPs did not exist before, it is not easy to develop the test methods and related criteria that sufficiently reflect the actual use environments. In general, an accredited test for certification checks whether a product works normally in much worse conditions than the actual use environment [7]. Therefore, without an accurate understanding of the actual use situations, the appropriateness of the test methods and threshold values that are newly developed cannot be completely confirmed. As a result, the verification process for NCPs may take longer time than other existing products. Given these challenges, this study focuses on finding ways to improve the verification process for NCPs in effective ways and reduce the time required efficiently. Specifically, this study presents an interesting and feasible idea that livings labs, which are real-life spaces or settings for innovative and participative research, can be effectively used in verifying the certification test methods and criteria for NCPs. Despite the growing interest in living labs, little is known about how to utilize living labs for the verification purposes. This study fills this gap by proposing a conceptual framework that helps analyze the real-use environment of NCPs and derive significant design implications for living labs. Then, the effectiveness of the framework will be assessed by applying it to real NCP cases. To the best of my knowledge, this is among the first elaborating the idea of using living labs as the methodology for verifying the test methods and certification criteria for NCPs. The analytic tools developed in this study effectively link one of the core properties of living labs, i.e., realism, with the necessity in the area of the certification, i.e., understanding of real-use environment, for the new products such as NCPs. The work in this study is not only a useful tip for practitioners, but also has theoretical significance.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 5 Performance Analysis of CNN Based Robot Face Recognition Models
### Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multicolumn{2}{c}{**Without**} & \multirow{2}{*}{**AES**} & \multirow{2}{*}{**RCP**} & \multirow{2}{*}{**Hill**} & \multirow{2}{*}{**RSA**} & \multirow{2}{*}{**DNA**} & \multirow{2}{*}{**Bit Slice**} & \multirow{2}{*}{**Chacos**} & \multirow{2}{*}{**GA**} \\  & \multicolumn{1}{c}{**Enc.**} & & & & & & & & & \\ \hline PCA & 80.4 [37] & 2.50 & 2.51 & 2.91 & 2.91 & 3.33 & 70.0 & 72.50 & 80.4 \\ LeNet & 82.5 [38] & 6.25 & 7.50 & 8.75 & 9.38 & 6.25 & 80.62 & 84.38 & 82.50 \\ Alexnet & 84.3 [39] & 6.88 & 7.50 & 6.25 & 8.75 & 6.88 & 82.50 & 81.88 & 85.00 \\ VGG16 Net & 85.6 [13] & 7.50 & 8.12 & 9.38 & 8.12 & 6.88 & 80.62 & 86.87 & 85.62 \\ GoogLeNet & 85.7 [14] & 8.12 & 9.38 & 6.88 & 8.75 & 8.24 & 81.25 & 82.50 & 88.75 \\ ResNet & 85.3 [39] & 9.38 & 10.00 & 8.12 & 8.75 & 7.50 & 80.62 & 82.50 & 87.50 \\ DenseNet & 87.9 [40] & 7.50 & 11.25 & 8.12 & 6.88 & 6.25 & 80.62 & 83.13 & 85.00 \\ MobileFaceNet & 88.6 [41] & 11.7 & 12.34 & 11.34 & 9.25 & 10.54 & 81.35 & 86.25 & 87.92 \\ ShuffleNet & 90.3 [41] & 13.2 & 15.34 & 12.31 & 11.2 & 11.21 & 83.34 & 87.32 & 87.91 \\ EffNet & 93.5 [19] & 14.5 & 17.98 & 14.32 & 13.57 & 12.65 & **84.34** & **88.54** & **88.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the studied CNN models. Figure 17: Loss and Accuracy of **DenseNet** from left to right and top to bottom: DNA, AES, Bit Slice, Chaco, GA, Hill, RCP, and RSA.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 3 Model of an ASK-Type AQC System  
In this section, we describe the model of the AQC system that is constructed using the entangled states and the description of Bob's received quantum states in the AQC system.  
A diagram of the model of the AQC system using the entangled state is shown in Figure 1. Binary information is sent from Alice to Bob. The dashed and solid arrows in the figure represent mode S and mode A of the entangled state, respectively. The AQC system protocol is given as follows:  
**Protocol of an AQC system (Figure 1).**  
1. Bob (receiver) inputs the light corresponding to mode A directly into their detector.
2. Bob radiates the light corresponding to mode S toward one of the two beam splitters operated by Alice (sender); Alice then switches the two beam splitters with reflectivities of \(R_{0}\) and \(R_{1}\) (\(R_{0}<R_{1}\)) to encode the binary information.
3. The subsequently reflected light with reflectivity of either \(R_{0}\) or \(R_{1}\) is then collected by Bob's detector.
4. Bob decodes the binary information received at their detector by performing an optimum quantum measurement, i.e., a joint measurement of both light beams.  
In this paper, we use the same model for a different purpose than that of reference [9], and therefore must first clarify the differences in this case when compared with the basic characteristics presented in [9]. We focus on investigation of Bob's error performance as the basic characteristic of the ASK-type AQC system. In addition, the AQC system is characterized as an \"ASK-type\" system because Alice encodes information using different reflectivities, i.e., the differences in the amplitude of the reflected light corresponding to mode S.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 1 Introduction
### Contributions  
In this paper, we describe a distributed MIMO channel sounder design. A whole new multi-ink measurement system has been developed to measure the dynamic properties of distributed antenna channels. As in all measurement setups, there is a need for calibration; here, we describe a practical implementation of how this can be done over-the-air (OTA), paving the way for even more advanced OTA calibration algorithms to be developed for more accurate system designs. With this uniquely designed sounder, we conducted a measurement campaign in a realistic and dynamic industrial-like setting. We analyzed the channel characteristics essential for reliability and nonstationarity aspects stemming from the large array and the dynamic environment. Finally, we exploited delay and/or Doppler information in order to explore the possibilities of sub-6 GHz channels for integrated communication and sensing in a mesh setup.