esa-sceva/satcom-chunk-collection huggingface dataset:
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 2 Clock Recovery in Coherent Optical Receivers  
In communication systems, the synchronization between the sample rate, generated at the receiver clock, and the symbol rate, generated at the transmitter clock, is necessary for a correct extraction of the transmitted information [2]. The main objective of the symbol synchronization is then to determine, with the best precision as possible, the optimal decision instant where the signal has the maximum energy and signal-to-noise ratio.  
Some synchronization solutions in literature use data-aided algorithms [23], which implies the transmission of a clock signal or a pre-determined symbol training sequence. However, such solutions require either higher power, to transmit an off-band clock tone, or higher transmission rates, to introduce an overhead, thus penalizing the transmission. On the other hand, non-data-aided algorithms [24; 25] are capable of extracting the intrinsic timing information from the received signal, therefore, the success of such algorithms rely directly on the quantity and quality of the clock information available at the received signal. Factors such as bandwidth limitation, intersymbol interference or low signal-to-noise ratio tend to impair the performance of these methods.
## 5 Conclusions  
In this study, a signal processing algorithm was proposed to reconstruct a PC display image from the common-mode noise emitting on the PC power cable, which was easily measurable by using the current probe. The complicated beats, which were caused by the up-step and the down-step responses of the signal, concealed the meaningful information included in the common-mode noise. The proposed signal processing algorithm was used to exclude the beats from the measured common-mode noise and to reconstruct the original monitor signal. An alphabetic character string composed of, at most, \(9\times 9\) dots was reproduced clearly from common-mode noise for both cases, where the monitor resolutions of \(800\times 600\) and \(1280\times 1024\) pixels were used. Although this work was carried out under the condition of removing shielding and filters from cables and only for monochromatic images, the results indicate that nearly fine images and a character string displayed on the monitor could be reconstructed relatively easily with appropriate signal processing as long as the common-mode noise could be detected. Although the experiments were carried out for the simple character string, the proposed method may be extended for reconstructing more complex images or motion scenes in real-time processing. While the Internet of Things (IoT) technology is being used in a wide range of industry and usual life, and the number of IoT devices is increasing day by day, such security issues as those mentioned in this paper should be considered more seriously.  
**Author Contributions:** Conceptualization, M.S.; methodology, M.S. and T.N.-V.; measurement, T.N.-V.; analysis and validation, M.S. and T.N.-V., programming, T.N.-V.; writing--original draft preparation, T.N.-V.; writing--review and editing, M.S. and T.N.-V.; supervision, M.S.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction
### Contributions of this Study  
The SS-HSA provides a cost-effective and efficient solution that resolves issues related to privacy as well as LoS. The major contributions of the proposed system are as follows.  
Figure 1: Percentage of crimes reported in homes and buildings.  
1.2.1. Decreases Losses in the Event of Larceny/Theft and Burglary  
Expanding the number crimes such as theft, larceny, burglary, and other crimes detectable in a home requires an optimal security framework. The most affected areas in this regard are houses and buildings, where larceny/theft and burglary account for 60% and 81% of crimes, respectively. By using GMS with GSM, a proactive security framework can efficiently prevent the loss of property due to larceny/theft and burglary.
# Efficient Data Exchange between WebAssembly Modules
###### Abstract  
In the past two decades, there has been a noticeable decoupling of machines and operating systems. In this context, WebAssembly has emerged as a promising alternative to traditional virtual machines. Originally designed for execution in web browsers, it has expanded to executing code in restricted and secure environments, and it stands out for its rapid startup, small footprint, and portability. However, WebAssembly presents significant challenges in data transfer and the management of interactions with the module. Its specification requires each module to have its own memory, resulting in a \"share-nothing\" architecture. This restriction, combined with the limitations of importing and exporting functions that only handle numerical values, and the absence of an application binary interface (ABI) for sharing more complex data structures, leads to efficiency problems; these are exacerbated by the variety of programming languages that can be compiled and executed in the same environment. To address this inefficiency, the Karmem was designed and developed. It includes a new interface description language (IDL) aimed at facilitating the definition of data, functions, and relationships between modules. Additionally, a proprietary protocol--an optimized ABI for efficient data reading and minimal decoding cost--was created. A code generator capable of producing code for various programming languages was also conceived, ensuring harmonious interaction with the ABI and the foreign function interface. Finally, the compact runtime of Karmem, built atop a WebAssembly runtime, enables communication between modules and shared memory. Results of the experiments conducted show that Karmem represents an innovation in data communication for WASM in multiple environments and demonstrates the ability to overcome challenges of efficiency and interoperability.  
Data-Oriented Design; inter-process communication; performance optimization; serialization; WebAssembly +
Footnote †: journal:  
Article
# Quantum Key Distribution in Kubernetes Clusters
## 2 Background
### Kubernetes and Operators  
Developing microservices instead of monolithic applications has become a widely adopted approach in distributed systems. Microservices leverage lightweight virtualisation technologies that run applications in sandboxes called containers. Kubernetes is the defacto standard for container orchestration and allows the management of the whole life cycle of these objects.  
Kubernetes sees everything related to an infrastructure as a resource: physical or virtual nodes, applications, exposed services, and even configurations. A set of physical or virtual nodes can be aggregated in clusters. Inside a cluster, nodes are labelled as masters or workers depending on their role: masters manage the cluster and retain sensitive information and configuration about the infrastructure, workers are deputed to execute the containerised applications and expose services. Here are definitions of some Kubernetes resources needed in the rest of this paper:  
* _Pod:_ is the smallest deployable object in Kubernetes, and it could be seen as a set of containers that share the same IP address. In order to run those containers, a Container Runtime is available on the node (e.g., Docker, containerd, cri-o);
* _Service:_ is an abstract resource that represents a way to expose an application that could be composed of one or more pods, i.e., replicas of the same applications;
* _Deployment_: is an object that provides declarative updates for Pods. In practice, it allows managing changes to their status through the Deployment controller. It is possible to describe the desired number of replicas of a specific pod and the policies for the rollout or rollback of the application to previous versions;
* _Secret_: is an object that contains sensitive data such as passwords, tokens, or keys. Generally is used to avoid including sensitive information in an application or code;
* _Custom Resource (CR)_: this is a way to extend the Kubernetes API by creating a new resource that allows storing objects of a certain type;
* _Custom Controller_: combined with the CR, it continuously monitors the cluster and based on the state of a CR, it applies changes to the cluster. In this sense, Kubernetes implements a declarative approach.  
To summarise, Kubernetes allows the deployment of containerised applications over a distributed infrastructure, managing those resources and automatically scaling them when needed.  
The relatively recent introduction in Kubernetes of the _Operator pattern_ is also worth mentioning. As we already discussed, one of the principles of Kubernetes is the control loop where resources are declared, and controllers continuously monitor them taking actions if needed. The Operator pattern follows this logic by using CRs and controllers to monitor distributed applications and manage their life-cycle by instantiating, updating, and configuring them. The ultimate goal of an Operator in Kubernetes is to mimic the behaviour of a human operator who is in charge of managing resources in a Kubernetes cluster.
## 3 Results and Discussion
### Physical Characterization  
#### 3.2.1 Thermal Stability Analysis  
The proximal analysis carried out on rice husk in its natural state is detailed in Figure 7. According to the TGA, the fiber began to lose weight at 120 \({}^{\circ}\)C. This was observed, corresponding to 7.88% being mainly related to moisture, and subsequently, a more significant mass loss of 55.13% was observed between 120 \({}^{\circ}\)C and 759 \({}^{\circ}\)C, associated with the volatile compounds hemicellulose and cellulose. Once the environmental change to oxygen took place, carbon losses of 17.07% were observed between 758 and 798\({}^{\circ}\), corresponding to lignin. Finally, the percentage of ashes was 19.92%, and this was displayed at 800 \({}^{\circ}\)C. The rice husk fiber presented stability until 120 \({}^{\circ}\)C, indicating that the temperature used for applying or processing should have been lower than this value to avoid thermal degradation. Similar results are shown for other natural fibers [30,32].  
Figure 6: (**A**) Rice husk blown into the specimen, (**B**) Rice husk applied by the blowing technique.  
The differential curve TGA(DTGA) shows in its shoulders the three stages mentioned. The first one corresponds to moisture with a peak at 85.32 \({}^{\circ}\)C, the second one with a peak at 371 \({}^{\circ}\)C corresponds to cellulose, and the last one with a peak at 553 \({}^{\circ}\)C corresponds to lignin. As can be seen in Figure 7, the rice husk stays thermally stable up to 120 \({}^{\circ}\)C; above this temperature, thermal degradation starts, but the rice husk shows a temperature peak for maximum degradation at 371, which is similar to that reported in other studies of rice husk TGA [12; 13]. In addition, based on this analysis, the rice husk fiber was found to remain stable up to 120 \({}^{\circ}\)C, indicating that its processing temperature should remain below this threshold to avoid thermal degradation. In this regard, it was noted that the blowing application process did not cause a rise in the material's temperature, ensuring that no degradation occurred during its application.
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 2 The Beam-Like Model
### The Inter-Storey Shear and Torsional Stiffness  
The equivalent beam shear stiffness in the two principal directions, denoted as \(\hat{S}_{x,k}\), \(\hat{S}_{y,k}\), and the torsional stiffness, denoted as \(\hat{C}_{k}\), are obtained for the \(k\)-th inter-storey, by considering the contributes of all the \(n_{E}\) columns of the building whose base points have planar coordinates \((x_{i,k},y_{i,k})\). The adopted inter-storey stiffness values are:  
\[\hat{S}_{x,k}=\sum_{i=1}^{n_{E}}S_{xi,k}\hskip 28.452756 pt\hat{S}_{y,k}=\sum_{i=1 }^{n_{E}}S_{yi,k}\hskip 28.452756 pt\hat{C}_{k}=\left(\sum_{i=1}^{n_{E}}C_{i,k}+ \sum_{i=1}^{n_{E}}S_{xi,k}y_{i,k}^{2}+\sum_{i=1}^{n_{E}}S_{yi,k}x_{i,k}^{2}\right) \tag{2}\]  
In case of concrete columns, the flexural \(S_{xi,k},S_{yi,k}\) and torsional \(C_{i,k}\) stiffness are:  
\[S_{xi,k}=\frac{12 Ej_{yi,k}}{h_{k}^{3}}\hskip 28.452756 ptS_{yi,k}=\frac{12 Ej_{ xi,k}}{h_{k}^{3}}\hskip 28.452756 ptC_{i,k}=\frac{Gj_{zi,k}}{h_{k}}, \tag{3}\]  
where \(E\) is [PERSON]'s modulus; \(J_{xi,k},J_{yi,k}\) the moment of inertia with respect to the \(x\), \(y\) axis and \(J_{zi,k}\) the rotational inertia moment of the \(i\)-th column of the \(k\)-th inter-storey.  
In order to take into account the unknown or not properly identified structural properties (floor out-of-plane deformability, beam-column stiffness ratios and uncertainty on the position of the center of mass at each floor), three correction coefficients are introduced. These coefficients, denoted as \(k_{x},k_{y},k_{c}\), correct the shear stiffness along the \(x\) and \(y\) directions and the torsional one that are listed in Equation (2). The actual stiffness values are therefore reported as follows:  
\[S_{x,k}=k_{x}\hat{S}_{x,k}\hskip 28.452756 ptS_{y,k}=k_{y}\hat{S}_{y,k}\hskip 28.452756 ptC _{k}=k_{c}\hat{C}_{k}, \tag{4}\]  
As it will be shown in the following, the three correction coefficients can be evaluated by means of an iterative optimization procedure.
# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
## 3 Results
### Vertebral Corner Point Extraction  
We evaluated vertebral corner point extraction results using the Euclidean distance between the ground truth and the detected points. Table 9 shows the evaluation results for models. Resnet152V2 achieved the lowest average error distance of 4.63 mm on the  
Figure 16: Results of lumbar vertebrae detection (LA view): (**a**) normal patient with 5 lumbar vertebrae; (**b**) lumbarization patient with 6 lumbar; and (**c**) sacralization patient with 4 lumbar.  
Figure 15: Results of lumbar vertebrae detection (AP view): (**a**) normal patient with 5 lumbar vertebrae; (**b**) lumbarization patient with 6 lumbar; and (**c**) sacralization patient with 4 lumbar.  
AP view and DenseNet201 achieved 4.91 mm on the LA view. According to statistics in our dataset, the average width of lumbar vertebrae in the AP view was 52.45 mm, which means the average error in our detection was 8.83%. The average width of lumbar vertebrae in the LA view was 40.54 mm, which means the average error in our detection was 12.11%. When comparing the average error in the percentage of verteb width with the Meyerding classification system from Table 7, where each grade range is 25%, we observed relatively high average errors of 8.83% and 12.11%, respectively. This suggests that further improvements are needed in our feature point extraction models to achieve higher accuracy. Addressing this challenge remains an essential area of focus for researchers aiming to enhance the system's performance.  
The vertebral corner points provide valuable information for algorithms to predict spondylolisthesis, as shown in Figures 17 and 18. We can measure the extent of vertebral slip by analyzing the relative positions of the upper with the adjacent lower vertebrae. Moreover, the direction of the slip can be crucial information in classifying the types of spondylolistheses.
# Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario
## 3 Development of the LSM-CBPS Model
### Problem Description
With the construction mode, the following information for subactivity \(i_{k}\) can be obtained: the required resources, \(rs_{i,k}\); rate, \(pr_{i,k}\), or duration, \(dur_{i,k}\), of construction and the direct cost per mile, \(cs_{i,k}\). 3. For linear activity \(i\), each crew has a choice between forward/reverse construction directions. Forward construction progresses from small mileage to large mileage, while reverse construction proceeds from large mileage to small mileage. Each crew can select only one construction direction, and it will be maintained until the crew completes all its tasks. The construction direction also determines the construction sequence. For example, if \(seq_{1,1}\) = [1,2,3] and crew 1 of activity 1 selects a forward direction, then the construction sequence is [1,2,3]. If crew 1 selects the reverse direction, the construction sequence becomes [3,2,1], as shown in Figure 1.  
Linear project-scheduling optimization considering various practical scenarios such as bidirectional construction is depicted in Figure 2. The aim of this problem was to determine the construction crew, start time, construction mode, and construction direction for each subactivity. Consequently, the project schedule can fulfill various constraints and achieve an optimal objective function.
## 2 Preliminaries
### Support Vector Machines  
Support vector machines (SVMs) [1] are generally used for binary classification. Given \(n\) pairs of instances with input vectors \(\{\mathbf{x}_{1},\ \mathbf{x}_{2},\ \dots,\ \mathbf{x}_{n}\}\) and response variables \(\{y_{1},y_{2},\dots,y_{n}\}\), where \(\mathbf{x}_{i}\in\mathbb{R}^{p}\) and \(y_{i}\in\{-1,\ 1\}\), SVMs present a decision function in a hyperplane that optimally separates two classes:  
\[y=sign(\mathbf{w}^{t}\mathbf{x}+b) \tag{1}\]  
where \(\mathbf{w}\) is a weight vector and \(b\) is a bias term. The margin is the distance between the hyperplane and the training data nearest the hyperplane. The distance from an observation to the hyperplane is given by \(\big{|}d(\mathbf{x})\big{|}/\mathbf{w}\). To find the hyperplane that maximizes the margin, we solve the problem by transforming it to its dual problem, introducing the Lagrange multipliers. Namely, in soft-margin SVMs with penalty parameter \(C\), we find \(\mathbf{w}\) by the following optimization problem:  
\[\begin{split} max\sum\limits_{i}^{n}\alpha_{i}-\sum\limits_{i}^{ n}\sum\limits_{j}^{n}\alpha_{i}\alpha_{j}y_{i}y_{j}K\big{(}\mathbf{x}_{i},\mathbf{x}_{j} \big{)},\\ \text{subject to}\quad\sum\limits_{i}^{n}\alpha_{i}y_{i}=0,\\ 0\leq\alpha_{i}\leq C/n,i=1,\dots,n.\end{split} \tag{2}\]  
where \(C>0\), \(\alpha_{i}\), \(i=1,\dots,n\), are the dual variables corresponding \(\mathbf{x}_{i}\), and all the \(\mathbf{x}_{i}\) corresponding to nonzero \(\alpha_{i}\) are called support vectors. By numerically solving the problem (2) for \(\alpha_{i}\), we obtain \(\alpha_{i}^{*}\) and compute \(\mathbf{w}^{*}=\sum_{i}\alpha_{i}^{*}y_{i}\mathbf{x}_{i}\) and \(b^{*}=y_{i}-\mathbf{w}^{*}\mathbf{x}_{i}\) for \(0<\alpha_{i}^{*}<C/n\). The kernel function \(K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}\) is the inner product of the mapping function: \(K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\phi(\mathbf{x}_{i})^{T}\phi\big{(}\mathbf{x}_{j} \big{)}\). The mapping function \(\phi(\mathbf{x})\) maps the input vectors to high-dimensional feature spaces. Well-known kernel functions are polynomial kernels, tangent kernels, and radial basis kernels. In this research, we chose the radial basis kernel function (RBF) with a free parameter \(\gamma\) denoted as  
\[K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\text{exp}\big{(}-\gamma\|\mathbf{x}_{i}- \mathbf{x}_{j}\|^{2}\big{)} \tag{3}\]  
Notice that the radial basis kernel, possessing the mapping function \(\phi(\mathbf{x})\) with an infinite number of dimensions [27], is flexible and the most widely chosen.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 2 System Model
### Single Park Operation Model  
The set of park is denoted as \(\mathcal{P}=\{1,2,\ldots,n\}\). Each single park consists mainly of a PV device, a power storage device and users. The park manager can get the operation schedule policy from the cloud and then deals with the external grid or other parks for electricity.  
We use net metering to calculate the total electricity cost of each park, which means that we record the electricity delivered to and obtained from the external grid or other parks by the distributed PV park through a bidirectional meter, and settle according to certain pricing rules. Therefore, the operating cost of electricity of the park \(i\) in the time slot \(t\) is  
\[C_{i,t}=\sum_{t=1}^{T}\left(C_{i,t}^{grid}+C_{i,t}^{in}\right) \tag{1}\]  
where \(C_{i,t}^{grid}\) and \(C_{i,t}^{in}\) are total transaction costs with the external grid and other parks, respectively, which can be given as  
\[C_{i,t}^{grid}=p_{t,b}^{grid}E_{i,t,b}^{grid}-p_{t,s}^{grid}E_{i,t,s}^{grid} \tag{2}\]  
\[C_{i,t}^{in}=p_{t,b}^{in}E_{i,t,b}^{in}-p_{t,s}^{in}E_{i,t,s}^{in} \tag{3}\]  
where \(p_{t,b}^{grid}\), \(p_{t,s}^{grid}\) denote the price purchased or sold of the external grid, \(p_{t,b}^{in}\), \(p_{t,s}^{in}\) denote the price in the internal trading market, analogously. \(E_{i,t,b}^{grid}\), \(E_{i,t,s}^{grid}\) denote bought or sold electricity from the external grid, and \(E_{i,t,b}^{in}\), \(E_{i,t,s}^{in}\) denote the bought or sold electricity from the internal trading market, respectively.
## 3 Simulation Results Considering Typical Working Conditions with Mount
### Starting Condition  
The proportional integral differential control is adopted. The engine starts at a constant speed. Starting at 0.5 s, the engine speed gradually increases from an idle speed to 1800 r/min, which is maintained. The speeds of the clutch-driving plate and clutch 1 driven plate are synchronized at approximately 2 s, and the staring process is completed. The simulation results of the vehicle with and without the mount during the starting process are presented in Figure 6.  
The overall trend of the speeds of the driving and driven ends of the clutch is the same with and without the mount, as shown in Figure 6a. However, at the beginning of the starting process, fluctuations in speeds with the mount are observed. For the starting process, the fluctuation in the speed of the driving and driven ends of the clutch gradually  
Figure 6: Results of the mount influence on the starting condition: (**a**) clutch speed; (**b**) vehicle jerk; (**c**) the enlarged view of curve a in figure a; (**d**) the enlarged view of curve b in figure a; (**e**) longitudinal force of each mount; (**f**) the longitudinal force of the powertrain on the vehicle.  
decreases (Figure 6c,d). The jerk of the vehicle, depicted in Figure 6b, reveals that when the mount is considered, the engine speed fluctuation causes the vehicle's jerk to fluctuate with a large amplitude during the starting process and then gradually decreases and becomes stable. The maximum fluctuation amplitude of the jerk increases from 4 m/s\({}^{-3}\) without the mount to 54 m/s\({}^{-3}\) with the mount. When the starting process is being completed, the jerk of the vehicle also has a fluctuation due to the inconsistent acceleration of the driving and driven plates of the clutch. At the beginning of the starting process, the torque produced by the engine crankshaft torsion directly acts on the powertrain, and the longitudinal forces on the left, right, and rear mounts of the powertrain fluctuate, causing deformation of the mounts. For the starting process, the longitudinal force on the left and right mounts tends to be stable. As the vehicle accelerates, the longitudinal force on the rear mount increases. When the starting process is completed, the vehicle acceleration decreases and fluctuates. Therefore, the longitudinal force acting on the rear mount decreases and fluctuates, as displayed in Figure 6e. Figure 6f shows that the longitudinal force exerted by a powertrain on a vehicle through the mount fluctuates at the beginning of the starting process. With an increase in the force acting on the vehicle during starting, the force acting on the vehicle decreases significantly after starting. In summary, at the beginning of the starting process, the mount causes large fluctuations in the vehicle jerk.