esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 2 Design Principle
### Adjoint Method  
In optimization problems, direct computation of gradients can be computationally expensive, especially when the objective function depends on a large number of variables and their interactions. In contrast, the adjoint method provides a more efficient way to obtain gradient information. The key idea behind the adjoint method is to introduce an auxiliary problem called the adjoint problem, which is derived from the original problem through certain mathematical transformations. For the dual-mode demultiplexer, the gradient \(df/d\varepsilon_{r}\) can be expanded using the chain rule as follows [28]:  
\[\frac{df}{d\varepsilon_{r}}=\frac{df_{1}}{d\varepsilon_{r}}\cdot f_{2}+\frac{ df_{2}}{d\varepsilon_{r}}\cdot f_{1} \tag{3}\]  
Calculating \(df_{i}/d\varepsilon_{r}\) in Equation (2) directly from the finite difference parameters matrix of the electromagnetic field can be computationally expensive. However, the adjoint variable method provides an alternative approach for computing \(df_{i}/d\varepsilon_{r}\) by introducing the adjoint electric field and analyzing its electric field propagation in the adjoint problem [29]. The computation of \(df_{i}/d\varepsilon_{r}\) using the adjoint method is as follows:  
\[\frac{df_{i}}{d\varepsilon_{r}}=2k_{0}^{2}\text{Re}(\mathbf{E}_{i,adj}^{T}\cdot \mathbf{E}_{i,in}),\ i=1,\ 2 \tag{4}\]  
Here, \(k_{0}\) represents the wave number in free space, and the adjoint field, denoted as \(\mathbf{E}_{adj}\), can be regarded as the distribution of the electric field obtained by backwardly inputting the TE\({}_{0}\) mode of the target within the design region.  
By solving the adjoint problem alongside the original problem, the adjoint method enables the efficient computation of derivatives or gradients of the objective function with respect to the variables, which allows for the iterative optimization of all the design parameters in an efficient manner, guided by the directionality of the gradients.
## 2 Materials and Methods
### From DVS to SpiNNaker
Each packet is sent as 10 symbols followed by an 'end-of-packet' (EOP) symbol. Except for the EOP symbol, the 10 symbols are selected from 16 Hex values, and each Hex value is 4-bit. Both SpiNNaker input and output data buses are 7 bits. To send a symbol, the microcontroller only changes the state of two wires and keeps logical levels of the rest five wires unchanged, i.e., uses the 2-of-7 coding method [26]. In Table 4, '1' is represented by change of the state on the that line and '0' is represented with no change of the state. In other words, logical-1 is physically represented by a change of the voltage level and not by a specific value for the digital signal. Similarly, logical-0 is represented by no-change of the physical voltage on the wire, not by a specific value. After sending a symbol, the voltage levels of the 7 wires will not return to the initial state [27]. This mechanism increases the transmission speed and reduces power consumption. After the microcontroller changed the logical levels of two wires, SpiNNaker will send back an acknowledge (ACK) signal to indicate the symbol (data) has been received. For the microcontroller, it will send the next symbol, once it receives the ACK signal from SpiNNaker. Through this 2-phase handshaking protocol, packets are sent serially.
## 2 Materials and Methods
### Image Processing  
The central aim was to determine methane concentration images by means of BLL and the HITRAN database in a calibration-free manner. Consequently, a suitable mathematical procedure for DAS in terms of image processing needs to be applied and described.  
Figure 1: The artificial leak scenario consists of a shot-blasted aluminum target with a central hole as the “leak”. This non-cooperative target ensures sufficient diffuse reflection of the incident laser radiation (interband cascade laser, ICL, by Nanoplus) that can be detected by the mid-IR camera (Infrared) and recorded as an image sequence. Pure methane as the leakage gas is released through the hole at a flow rate of 0.25 mL/min.
## 5 Conclusions  
The findings of this study suggest that material directionality plays an important factor in the balance between overall stiffness and total volume of material used in the case of the continuum and structural models. On the other hand, distinguishing between cortical and trabecular bone leads to a reduction in the effect of material directionality on overall stiffness, potentially due to the directionality imposed through the use of shell elements to represent cortical bone. In cases where distinguishing between trabecular and cortical bone is important, both hybrid approaches and the structural approach seem appropriate with the caveat that a structural approach could potentially have a further advantage of directly providing architecture that can be additively manufactured. In addition, despite using two different approaches, the orthotropic, hybrid and structural models predicted similar outcomes while the isotropic predictive model compared least favourably with the CT derived model, indicating that using an isotropic approach to model bone adaptation might not be a suitable solution.  
In addition to providing information on the particularities of orthotropic and isotropic material modelling or the use of structural shell elements to model cortical bone, the adaptive models can be used to simulate the mechanical behaviour of bone seeded scaffolds designed to fill bone defects or to aid tissue regeneration. Furthermore, as the models are strain driven, they can be used with different materials to design scaffolds or implants that will be supporting the bone architecture surrounding them, as well as stimulating bone growth. The models can also be used in applications such as rehabilitation programs, informing the user on what type of physical activity is required to stimulate a specific region, or to predict the evolution of skeletal diseases, such as osteoporosis, by modifying the target strain, remodelling rate or set of activities within the adaptation process.  
**Supplementary Materials:** The following are available at [[http://www.mdpi.com/2076-3417/9/16/3320/s1](http://www.mdpi.com/2076-3417/9/16/3320/s1)]([http://www.mdpi.com/2076-3417/9/16/3320/s1](http://www.mdpi.com/2076-3417/9/16/3320/s1)).  
**Author Contributions:** Conceptualization, D.Z. and A.P.; methodology, D.Z. and A.P.; formal analysis, D.Z.; investigation, D.Z.; writing--original draft preparation, D.Z.; writing--review and editing, A.P.; supervision, D.Z.; project administration, A.P.; funding acquisition, A.P.  
**Funding:** This work was conducted under the auspices of the Royal British Legion Centre for Blast Injury Studies at Imperial College London. The authors would like to acknowledge the financial support of the Royal British Legion.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiments
### Singing Voice-Separation Experiment  
#### 4.1.1 Dataset  
We experimented with the DSD100 dataset made for the 2016 SiSEC [31]. The dataset consists of development and test sets. Each set has 50 songs and was recorded in a stereo environment with a sampling rate of 44.1 kHz. Each song has four music sources (_bass_, _drums_, _other_, _vocals_) and a mixture of sources. In the singing voice separation task, the mixture signal is separated into _vocals_ and _accompaniment_.  
Several studies in music source separation or vocal instrument separation mixed the instrument signals of the different songs to augment data [12; 15; 20]. In the DSD100 dataset, we also augmented the training data by mixing the instrument signal of different songs. To balance the other class, the training data was augmented by mixing other signal of different songs and bass, vocals, and drums instrument signal of the same song. Since the duration of the signal is different for each type of music, the signal is adjusted based on the duration of the shortest signal.
## 2 Methods
### CFD Method
#### 2.1.2 Governing Equation  
In numerical simulations of water jets, the fluid is typically considered a continuous and incompressible medium. The fluid flow process is governed by three fundamental equations: the continuity equation, the momentum conservation equation, and the energy conservation equation [23].  
\[\frac{\partial\rho_{\text{m}}}{dt}+\
abla\cdot\left(\rho_{\text{m}}\overset{ \rightarrow}{v}_{\text{m}}\right)=0 \tag{1}\]  
\[\begin{array}{c}\frac{\partial}{\partial t}\left(\rho_{\text{m}}\overset{ \rightarrow}{v}_{\text{m}}\right)+\
abla\cdot\left(\rho_{\text{m}}\overset{ \rightarrow}{v}_{\text{m}}\overset{\rightarrow}{v}_{\text{m}}\right)=-\
abla p +\
abla\cdot\left[\mu_{\text{m}}\left(\
abla\overset{\rightarrow}{v}_{\text{ m}}+\overset{\rightarrow}{v}_{\text{m}}\right)\right]\\ +\rho_{\text{m}}\overset{\rightarrow}{g}+\overset{\rightarrow}{F}+\
abla \cdot\left(\sum_{k=1}^{n}\alpha_{k}p_{k}\overset{\rightarrow}{v}_{\text{d}, rk}\overset{\rightarrow}{v}_{\text{d},k}\right)\end{array} \tag{2}\]  
where \(t\) is the time; \(\overset{\rightarrow}{v}_{\text{m}}\) is the velocity vector; \(p\) represents the pressure on the fluid micro-element; \(\mu_{\text{m}}\) is the viscosity of the fluid; \(F\) is the volumetric force; \(\alpha_{k}\) is the volume fraction of the \(k\)th phase; and \(\overset{\rightarrow}{v}_{\text{d},rk}\) is the slip velocity of subphase \(k\).  
In a deep-sea environment, the nozzle jet used to flush sediments on the crawler is classified as a submerged jet. This jetting process involves two mediums, water and air, necessitating the selection of an appropriate multiphase flow model for analysis. Ansys includes three types of multiphase flow models: the mixing model, the VOF (Volume of Fluid) model, and the Euler model. The Volume of Fluid (VOF) model is applicable in scenarios where the phases are immiscible, share a common momentum equation, and are characterized by phase fractions. These phase fractions must be determined using a pressure-based solver. The Eulerian model possesses the capability to simulate multiphase-separated flows, as well as the interactions among different phases, which encompass liquids, gases, and solids. However, it is associated with high memory consumption and presents challenges in achieving convergence. The Mixture model, known for its ability to handle multiphase fluid flow with varying velocities of each phase, is preferred due to its ease of convergence in calculations, high accuracy, and computational efficiency. A variety of turbulence modeling solutions are accessible within the Fluent software. There are three types of k-\(\epsilon\) models: the realizable k-\(\epsilon\) model, the RNG k-\(\epsilon\) model, and the standard k-\(\epsilon\) model. The realizable k-\(\epsilon\) model is used as the turbulence model due to the intricate nature of water jets, including high-speed flow, collision, entrainment, and other factors. This model can more correctly explain the diffusion velocity of the circular jet, and it meets the limits on the Reynolds stress, which can be kept consistent with the Reynolds stress of real turbulence.
# Evaluating the Performance of Lightweight Ciphers in Constrained Environments--The Case of Saturnin
###### Abstract
In this technical note, our preliminary analysis illustrates that Saturnin is much more efficient than AES in the environment we check, being able to reduce the Round Trip Time up to the half value (compared to AES). These results are compatible with the relative information given in the original submission of the Saturnin and, thus, they further reveal the performance benefits that can be achieved by the lightweight cryptography. The structure of the report is as follows. In Section 2, a short description of the lightweight cryptography and, especially, of the Saturnin is given. Section 3, being the main part of the paper, presents the experimental environment as well as the experimental results in terms of comparing the performance between Saturnin and AES. Finally, concluding remarks are given in Section 4.
## 2 Specim Design and Test Method
### Loading Protocol  
The shaking table tests were rigorously carried out to evaluate the seismic performance of the model by the increasing earthquake excitation magnitudes. Three ground input excitations, representative of actual earthquake records, which conform to the dynamic characteristics of the standardized response spectrum put forward by GB 50011-2010, were selected: El Centro wave (Imperial Valley earthquake, 1940), Kobe wave (Hyogoken-Nanbu earthquake, 1995), and Taft wave (California earthquake, 1952). The unidirectional alternative-loading method of the seismic wave was applied to investigate the dynamic characteristics of the model under different levels of earthquake intensity in this test.  
Firstly, the model was scanned by 3D white noise signals, for which the peak ground acceleration (PGA) level was limited to 0.05 g, to obtain the original dynamic characteristics, including the natural frequency and damping ratio, before applying the ground motion. Subsequently, a white noise excitation was applied to identify any variation in the dynamic characteristics of the test model after each level of adjusted PGA for the earthquake records.  
Three different levels of earthquake hazard with 63.2%, 10%, and 2% probabilities of exceedance in 50 years of the design reference period are referred to as \"frequent earthquake\", \"occaional earthquake\" and \"rare earthquake\", respectively. Table 6 lists the loading plan consisting of 47 seismic cases during the shaking table test program. The loading-scaled PGAs of input El Centro and Taft waves were increased from 0 to 1.0 g, with each case increased by 0.1 g, whereas the Kobe wave was scaled up to 1.6 g due to the different maximum oil pressures caused by the individual dynamic characteristics of seismic waves. The adjusted PGAs were involved in all levels of earthquake fortification intensity, from frequent 7 to rare 9, for the test model presented in Table 7. The representative seismic waves and corresponding frequency spectra, derived from converting the input time domain into the frequency domain by fast Fourier transform under PGA = 0.3 g, as an example for three ground records, are shown in Figures 14 and 15, from which the dominant frequencies of 1.82, 2.54, and 1.72 Hz were obtained for the El Centro, Kobe, and Taft earthquake waves, respectively.  
Figure 13: Layout of monitoring points.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Case** & **Earthquake** & \begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & **Case** & **Earthquake** & \begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & **Case** & **Earthquake** & \
\begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & **Case** & **Earthquake** &
\begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} \\ \hline
\begin{tabular}{c} White \\ noise \\ 10
# Review of Satellite Interferometry for Landslide Detection in Italy
## 7 Final Remarks
LaDIS is based on the comparison between landslide inventories derived from field survey evidence and satellite data. The coincidence between satellite data and ground truth is evaluated in four classes, ranging from 1--total coincidence--to 4--no coincidence [192].
## 5 Experimental Results and Analysis
### Evaluation Metrics  
To comprehensively evaluate the performance of our model, we employed several key metrics, including mean average precision, precision, recall, and floating point operations (FLOPs). These metrics provide a detailed insight into the effectiveness and efficiency of our model. First, precision measures the accuracy of the positive predictions made by the model. It is defined as in the following equation:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
TP (true positive) means the number of correctly detected instances of the target behavior, and FP (false positive) means the number of incorrectly detected instances, i.e., detections that were predicted as the target behavior but are actually not.  
Recall measures the model's ability to detect all relevant instances in the dataset. It is defined as the ratio of true positive detections to the total number of actual positive instances.  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
FN (false negative) means the number of instances that were not detected by the model, i.e., actual instances of the target behavior that the model missed. A high recall indicates that the model is effective at finding all relevant instances, which is important in applications where missing a positive instance can have significant consequences.  
We also used mAP to evaluate our model. Mean average precision (mAP) is a standard metric used in object detection tasks to evaluate the accuracy of a model. It considers both the precision and recall across different thresholds to give a single performance score. Mathematically, the mAP is defined as follows:  
\[AP=\int_{0}^{n}\text{Precision}\,d(\text{Recall}) \tag{15}\]  
\[\text{mAP}=\frac{1}{j}\sum_{i=0}^{j}\text{AP}_{i} \tag{16}\]  
Floating point operations (FLOPs) is a metric used to measure the computational complexity of a model. It indicates the number of floating-point operations required to process a single forward pass through the model. FLOPs is a critical measure of the efficiency of a model, particularly when deploying it in resource-constrained environments such as real-time systems or edge devices.  
\[FLOPs=\sum_{l=1}^{L}FLOPs_{l} \tag{17}\]  
For a deep learning model, FLOPs can be computed by summing the number of multiplications and additions performed in each layer. Lower FLOPs generally imply a faster and more efficient model, which is essential for real-time applications like classroom behavior recognition.
# Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
## 3 Methods
### Behavior Recognition  
#### 3.4.1 Quaternion to Euler Angles  
We converted the quaternion to Euler angles to check the amount of change in the rotating body, as shown in Equation (4).  
Figure 3: Example of MediaPipe hand model: (**a**) model output image with 21 landmarks and (**b**) distinction between right and left hands.  
\[\begin{cases}&\alpha=\text{atan2}(2(q_{w}q_{x}-q_{y}q_{z})/s,(1-2(q_{x}^{2}+q_{y}^{2} ))/s)\\ &\beta=\text{asin}(2(q_{x}q_{z}+q_{w}q_{y}))\\ &\gamma=\text{atan2}(2(q_{w}q_{z}-q_{x}q_{y})/s,(1-2(q_{y}^{2}+q_{z}^{2}))/s)\\ &s=2(q_{x}q_{z}+q_{w}q_{y})\end{cases} \tag{4}\]  
The rotation angles are \(\alpha\), \(\beta\), and \(\gamma\) with respect to each of the \(x\)-, \(y\)-, and \(z\)-axes. Next, we constructed continuous data by calculating the Euler angles on the rotating bodies of both hands in all frames of the input images and stored the values in the system.
## 3 Results and Discussions  
### Workability  
The workability of the fresh concrete mix is defined as the ease with which the concrete can be handled, placed, compacted, and finished. There are different methods available to measure the workability of concrete. However, the method that is commonly used at the site is slump cone test. The workability of FS-GPC mixes was observed by performing slump cone test following the ASTM C143/C143M-12 soon after mixing of concrete was completed. The results of slump cone test are presented in Figure 2 and Table 5.  
Generally, the workability of GPC is lower than the conventional concrete due to sticky characteristics of the alkaline activator solution. However, it can be compacted well by using mechanical compaction methods. Although the sphere-shaped particles of FA provide lubricating effect to the freshly mixed GPC, the SS and SH solutions used in GPC mix have more viscosity as compared to water, which gives it more cohesion and stickiness than the conventional concrete.
### Importance and Relevance of Machine Learning in the Maritime Industry  
Machine Learning's importance and relevance in the maritime industry cannot be overstated. The vast amount of data generated in the maritime environment, from vessel movements to environmental conditions, makes this field an ideal setting for leveraging the predictive and analytical capabilities of ML. With its potential for processing large volumes of data, making accurate predictions, automating decision-making processes, and ensuring regulatory compliance, Machine Learning presents an opportunity to overcome many of the maritime industry's challenges. In the following sections, we will delve deeper into these applications, shedding light on the depth and breadth of ML's impact on the maritime industry [7].  
Here is a closer look at why ML is relevant and important in the maritime industry:  
1. Data Analysis: Maritime systems generate huge volumes of data, from onboard sensors, AIS, radar systems, weather stations, and more. ML can help process this data, uncover hidden patterns, and provide valuable insights that can enhance operational efficiency and safety [8].
2. Predictive Capabilities: ML algorithms, particularly in the realm of predictive analytics, can provide forecasts about vessel movements, potential traffic congestion, and other significant factors. These predictions can assist in proactive decision making, minimizing risks and optimizing routes for fuel efficiency and timely arrivals [9].
3. Automation: ML, coupled with other AI technologies, can automate numerous processes in marine traffic management. From identifying potential hazards to routing decisions, automation can reduce human errors, improve response times, and increase overall operational efficiency [10].
4. Environmental Impact: ML can help monitor and predict environmental impacts associated with maritime activities. For instance, ML models can predict oil spill trajectories or emissions from vessels, helping in planning mitigating strategies [11].
5. Security: ML can enhance maritime security by identifying abnormal vessel behaviors or detecting potential threats, such as piracy or illegal fishing activities, thus aiding in timely intervention [12].
6. Regulatory Compliance: ML models can be trained to monitor and ensure compliance with various regulatory norms, such as emission levels, waste management, and safety standards, by continuously analyzing data from various sources [13].  
Given these advantages, ML can be seen as a valuable tool for managing complex maritime systems. Its ability to transform raw data into actionable intelligence, enable predictive decision making, and automate various processes is likely to be pivotal in addressing the current and future challenges of marine traffic management. The following sections will delve deeper into these applications, shedding light on the depth and breadth of ML's impact on the maritime industry.