esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description
### Optimization Constraints
#### 2.3.1 Task-Dependency Constraint  
The sequence of operations is especially important in the cooking process. This means that some tasks can only be executed after the previous tasks have finished. Therefore, some constraints that enforces dependencies on the task, such as execution of the task in order, must be reflected in the optimization process. To effectively represent multiple characteristics of each task, a hierarchical set called _taskset_ is defined. **Definition 1**. : _A tasksset \(\mathbb{T}\) is an array of \(\mathcal{D}\) of quadruples consisting of \(\mathcal{T}\), \(\tau\), \(\mathcal{L}_{s}\), \(\mathcal{L}_{e}\), \(\mathcal{A}\), and \(c\), where \(\mathcal{D}\) is the set of dish, \(\mathcal{T}\) is the set of task, \(\tau\) is the set of time duration required to complete task, \(\mathcal{L}_{s}\) and \(\mathcal{L}_{e}\) are the set of location indices where the \(\mathcal{T}\) starts and ends, \(\mathcal{A}\) is the set of number of robotic arms required to perform a task, and \(c\) is the set of parameters that represents the continuity of a task. If the value of \(c\) is 1, then the following task should be executed immediately after the current task finishes. \(t\) is the set of execution time of tasks. The purpose of optimization is to obtain \(t\) from each task._  
Each set has components: for example, if the number of dish is \(n\), \(\mathcal{D}\) can be expressed as \(\mathcal{D}=\{\mathcal{D}_{1},\cdots,\mathcal{D}_{n}\}\). The tasks in \(i\)th dish can be expressed as \(\mathcal{T}^{i}=\mathcal{T}^{i}_{1},\cdots,\mathcal{T}^{i}_{m}\), when there are \(\mathfrak{m}\) tasks. The other components can also be expressed as \(t^{i}_{j}\) (execution time of the \(j\)th task in \(i\)th dish), \(\tau^{i}_{j}\) (time duration of the \(j\)th task in the \(i\)th dish), etc. The superscript can be omitted when it is clear which task is being referred to. To illustrate what is the _taskset_, an example \(\mathbb{T}\) is shown in Table 1. There are three dishes in \(\mathbb{T}\), which are pancakes [6], hand drip coffee [44], and chicken salad [45], and those can be written as \(\mathcal{D}_{1}\), \(\mathcal{D}_{2}\), and \(\mathcal{D}_{3}\), respectively. Each recipe was simplified for implementation into our environment, assuming that the ingredients were prepared in bowls before the start of cooking. Taking the second task \(\mathcal{T}^{1}_{2}\) as an example, the time duration of this task is 10 seconds, and it moves from the 6 th location to the 11 th location in the environment. Moreover, it only requires one arm for execution, and the following task \(\mathcal{T}^{1}_{3}\) does not need to be executed immediately. Especially in the task related to fire (e.g., boiling, frying, etc. ), \(c\) needs to be set as 1; otherwise, the food can be burned or overcooked.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 4 Software Defined Network (SDN)
### Software Defined Optical Access Networks
For instance, CNTs and OLTs are now programmable units, which can, in principle, allow on-demand adjustment of overarching rules and policies to match incoming traffic flows to logical connections. This can be applied as required to suit diverse and new services. One specific example of such flexibility and programmability is its application to the Dynamic Bandwidth Allocation algorithms in the PON. From early approaches based on creating isolated groups of PON users [65], to complete frameworks for the virtualisation of the DBA (vDBA), previously introduced in Section 3.2. This extends the OLT virtualisation down to the data plane [66], by also virtualising the OLT scheduler [67]. The result is the ability for different VNOs to operate different DBA scheduling algorithms, so that they can fine-tune the scheduling to suit specific business models, or even applications, running in their PON slice. The concepts were also showcased at two public testbed demonstrations [68; 69]. Similar ideas were also proposed for QoS handling in the downstream direction [70], where a novel scheduler was designed to handle QoS differentiation simultaneously at the service, customer (i.e., ONT), OLT and VNO level [71].
## 2 State of the Art
### Conclusions  
The analysis of 12 research works on security culture was used as a basis to obtain a group of meaningful characteristics for the implementation of these processes. A comparative analysis between these pieces of research was also carried out. Each of the characteristics is described in the following section. * Group 1. Characteristics oriented towards the application of framework regulations. * Standardization: The implementation of the process will be based on the regulations related to the management of information security systems. * Certification: This process will permit users to obtain any type of temporary certification. * Measurement: This system will make it possible to measure the company's level of security culture. * Cultures: This system not only takes into account the internal cultural aspects, but also local, sectorial and international legislation. * Group 2. Characteristics focused on users' human aspects. * Progressive adaptation: The process will permit the users' security culture to be adapted in a progressive manner. * Theoretical Approach: The process will be based on an appropriate theoretical and regulative approach. * Practical Approach: The process will be oriented towards a practical application as regards users. * Critical Aspects: The human aspect will be considered critical in the preparation process. * Psychological Factors: Disciplinary actions, clauses, rewards for points, etc. and their effects on the users' psychology when using the process will be taken into consideration. * Group 3. Other desirable aspects for the process. * Oriented towards SMEs: It must have an orientation if it is to be valid for both big companies and SMEs. * Low cost resources: It should be oriented towards low costs as regards the implementation and maintenance of the system
* Core of the ISMS: The security culture process is the main process in information security management. (ISMS)
* Dynamic Knowledge Base: This process is able to learn from the security incidents and change those weaknesses into strengths by incorporating the incidents into the knowledge base in order to reinforce the security culture of those aspects. As will be observed in Table 1, the core of each of the proposals is one or several of these characteristics. The security culture process of MARISMA has been created in order to provide a solution to these characteristics, but without it being at the core of ISMS.
# An Extended Functional Design Approach Based on Working Principle and Property and Its Application
## 6 Discussions  
Compared with the existing functional design approaches mentioned in Sections 1 and 2, the main contribution of this work is that, based on the analysis and discussion of the working principle and properties, a new approach is proposed to generate structure solutions to the desired functions. The proposed approach does not rely on existing established design case resources, structure analogy resources, etc. The working principle determines the realization possibility of desired functions, which provides a basic guarantee for the feasibility of structure solutions. Meanwhile, scientific and technical principles are integrated to help designers to explore new structure solutions at the level of principles, which is different from the existing research into principles. In the existing research, properties are an internalization of the structure concept, and most of the existing research advocates finding the structure through its function. By contrast, this paper advocates finding the working principle through its function, determining the property through the working principle, and combining the properties as a conceptual structure. A structure is only an externalized expression of the final design solution. The property is the core of the lower layer, and the fundamental task of conceptual design is to find the appropriate properties.  
However, the main research objective of this work is to realize the transformation from function to structure through the working principle and property. Some other issues closely related to conceptual design are not considered; for instance, the transformation from customer needs to function, function decomposition, structure synthesis, design solution evaluation, etc. These issues will be addressed in further research on conceptual design.  
Figure 9: The mapping relation of the working principle/property/structure of the lifting mechanism.
## 4 Design of DDNB
### SN-BN Communication Process  
One final component of our layered architecture is the node agents between SN and blockchain network. Node agents are mediators between two networks and all accesses from SNs to the blockchain network pass through the node agents. There can be multiple node agents. If a node agent receives a request from an unauthorized SN, the node agent registers the SN into a blacklist and blocks additional requests for a certain period of time. In addition, node agents serve as a load balancer to prevent load concentration in one blockchain node and as a dispatcher of events from BNs.  
Finally, using this 3-layer architecture, DDNB implements various functions to support application business logic. For example, TN can connect to a randomly selected SN (with function '_FindHost_'),  
Figure 4: Service Node Verification Protocol.  
Figure 3: The process of ‘adding new node’.  
TN can get _nonce_ from BN through SN (with function '_GetNonce_'), TN can get an account balance of specific address (with function '_GetBalance_'), and TN can send a coin amount (virtual currency) from an account to another one (with function '_SendCoin_').  
* _FindHost_ is the function that TN uses to connect to randomly selected SN through seed node. As the function does not reach BN, execution time is expected to be fast and stable. Changes in the number of SNs is what the function is most sensitive to.
* _GetNonce_ requests a _nonce_, a hashed string sent from BN, to identify the transactions which are disassembled by several steps. Since any step in any transaction is processed on a randomly selected SN, BN publishes a tiny hashed string for discrimination.
* _GetBalance_ is a function that returns the balance of an account. It internally executes _FindHost_ twice, _GetNonce_ once, and then finally a _GetBalance_ query. This represents a single step transaction that queries the network several times.
* _SendCoin_ is the most representative transaction which invokes changes in the blockchain. It consists of _InfoTrans_ query which validates syntactic errors first, and then _CreateTrans_ query which validates semantic errors and executes the function as a final step. Every steps of the process is shown in Figure 5 in detail.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 3 Problem Formulation  
This section formulates the problem of single target tracking in UWSNs. The issues to be covered include network model, target state model, measurement model and multi-sensor Particle Filter (PF) for target tracking.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 4 Manipulator Robot Simulation  
An adequate simulation that shows the capacity of a controller to follow the joint trajectory of a robot requires a precise design of the manipulator robot's mathematical model, as this model provides its kinematic and dynamic characteristics. These characteristics are represented by equations shown in Section 2.  
From Equation (5), obtained through the Lagrange-Euler formulation, it may be deduced that:  
\[\ddot{\textbf{q}} = \textbf{M}^{-1}(q)\left[\boldsymbol{\tau}-\textbf{C}\big{(}q,\dot {q}\big{)}\ -\ \textbf{G}(q)-\textbf{F}\big{(}\dot{q}\big{)}\right] \tag{18}\]  
Equation (18) is applied in the MatLab/Simulink software to simulate the dynamics of the 2-DoF manipulator robot, whose block diagram is shown in Figure 2. The internal structure of each module is as follows: inertia matrix (inverse, i.e., M \({}^{\sim}\) - 1); robot's friction force estimation vector (F(q\({}^{\prime}\))); Centrifugal and Coriolis force vectors (C(q,q\({}^{\prime}\))); and gravitational force vector (G(q)). This structure is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Link 1** & **Link 2** & **Units** \\ \hline \(m_{1}=0.392924\) & \(m_{2}=0.094403\) & \([\text{kg}]\) \\ \(l_{1}=0.2032\) & \(l_{2}=0.1524\) & \([\text{m}]\) \\ \(l_{c1}=0.104648\) & \(l_{c2}=0.081788\) & \([\text{m}]\) \\ \(l_{1}=0.0011411\) & \(l_{2}=0.0020247\) & \([\text{kg}\cdot\text{m}^{2}]\) \\ \(F_{v1}=0.141231\) & \(F_{v2}=0.3530776\) & \([\text{\frac{N\text{m}\text{m}}{\text{rad}}}]\) \\ \(Fex1=0.05\) & \(Fex2=0.05\) & \([\text{N}\cdot\text{m}]\) \\ \(Fexb1=0.05\) & \(Fexb2=0.05\) & \([\text{N}\cdot\text{m}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Manipulator robot parameters.  
Figure 1: Scheme of the 2-DoF planar manipulator robot.  
based on an Embedded MATLAB Function, in which are introduced the corresponding equation with the parameter values shown in Table (matrix or vector equations are shown in Section 2).  
As observed in Figure 2, the block diagram delivers the following variables: position q, speed q', and acceleration q\". In addition, it needs the variable \"tau\" as an input, which comes from the actuators block. In this work, the actuators used are direct current servomotors whose dynamic model is developed in [39].  
Finally, the complete structure of the 2-DoF manipulator robot is displayed in Figure 3.
## 1 Introduction
In addition, we apply dynamic loss functions and point cloud initialization pruning to suppress reconstruction artifacts such as smearing or ghosting effects caused by dynamic objects. These enhancements ensure that the system is not only accurate but also efficient and capable of operating in real-time even in complex and dynamic environments. Experimental results demonstrate that our approach effectively removes dynamic interference, accurately recovers camera poses, and supports multi-sensor input for robust scene tracking and reconstruction. The proposed fusion framework minimizes both computational and memory overhead, enabling real-time rendering and efficient processing. Our system outperforms existing state-of-the-art 3 DGS-SLAM methods in terms of dynamic object handling and overall reconstruction quality, making it a promising solution for real-world robotic applications. The key contributions of our work are summarized as follows:  
* A 3 DGS-SLAM system designed specifically for dynamic environments, incorporating tracking, 3D segmentation, and mapping threads to enable real-time dynamic scene tracking and reconstruction with both vision and LiDAR inputs. This system addresses key challenges in dynamic scene reconstruction and improves the robustness of SLAM in real-world environments. * A hybrid uncertainty-based 3D segmentation method accurately segments dynamic LiDAR point clouds and effectively removes uncertain dynamic points at the boundaries. This method ensures that moving objects and other uncertainties do not interfere with the static map reconstruction. * A dynamic rendering loss function and a sensor-fusion-based keyframe selection strategy ensure accurate removal of dynamic artifacts while minimizing computational overhead. This approach allows the system to operate in real time, even in large-scale and highly dynamic environments. * Evaluation of our approach on multiple public and in-house real-world datasets demonstrates superior performance across multiple metrics, achieving high-fidelity reconstruction and effective dynamic interference removal. Our system consistently outperforms existing methods in both dynamic object handling and overall map quality.
# Development of Automatic Electric Drive Drilling System for Core Drilling
## 5 Rig Performance
### Optimized ROP Based on Power Optimization Experiment  
ROP is a multivariable vector function determined by various parameters, including WOB, RPM, flow rate (Q), torque on bit, formation type, and drill bit properties [36; 37; 38]. By varying control variables, torque and WOB act as system response quantities. A drilling system can optimize ROP by monitoring and controlling these variables [39; 40; 41]. The optimization objective of ROP based on power optimization is to reduce the total energy consumption of cutting unit rock volume while increasing the drilling rate.  
Figure 13: Test results for constant-power drilling mode: the curves of WOB at different setpoints.  
Drill bits were divided into six groups. One group was the no-load control group, which was operated under the no-load condition without drilling the samples. The other five groups were drilled at the ROP of 2.6 m/h, 2.3 m/h, 2.0 m/h, 1.7 m/h, and 1.4 m/h, respectively, and the rotary speed ranged from 300 to 1000 rpm. The instantaneous powers of the two motors were recorded during drilling.  
Power calibration is required to calculate the average value of the instantaneous power results obtained under no-load operation. As shown in Figure 14, the no-load fluctuation of the rotation motor is significant, the instantaneous average power is 400 W, and the hosting motor has an average power of 855 W.  
In addition to energy consumption in the hole, mechanical consumption also occurs during drilling. The results are different when the ROP changes and RPM remains unchanged, as shown in Figure 15.  
From the power perspective, the best rotary speed is between 700-1000 rpm, and the best ROP is between 2.0-2.6 m/h for the rock samples and drill bits used in the experiment.  
In this chapter, four drilling modes are evaluated by experiments. The control of WOB under constant-pressure drilling mode is affected by drilling rules, and the control error of WOB under appropriate drilling rules is small. The maximum error of ROP control under constant-speed drilling mode is no more than 1.4%. Constant-torque drilling mode cannot achieve torque control, and it is prone to sticking, so it is not suitable for drilling in this system, but it can meet some specific innovative experiments. In the constant-power drilling mode, the performance was achieved by drilling at a given power within the appropriate range for a given drilling schedule. Therefore, the constant-speed drilling mode was selected as the main drilling mode, and was the best among the four drilling modes. Subsequently, a power-based optimization experiment was conducted for this drilling mode, and encouraging results were obtained. For the cement sample used in the experiment, the optimal rotary speed was in the range of 700-1000 rpm, and the optimal ROP was in the range of 2.0-2.6 m/h.  
Figure 14: Motor no-load operation diagram: the rotation motor is the blue line, and the hosting motor is the orange line.
## 1 Introduction
As a flexible power supply, the energy storage system (ESS) provides a new idea to mitigate the power fluctuations of intermittent RES. The application of ESS in ADN may provide auxiliary services and enhance system reliability, as well as improve system economy [23; 24]. As one typical DG, the distributed ESS was applied to improve voltage profile in [25; 26]. A coordinated dispatch problem of a wind farm with ESS was proposed in [27]. The results indicate that ESS can greatly improve the dispatchability of wind farm. The integration of ESSs with different capacities into the power grid has been studied in [28] to analyze the improvements in the power quality. Reference [29] has formulated a stochastic programming framework to choose optimal energy and reserve bids for ESS owners based on the electricity market. [PERSON] et al. [30] considered the unit commitment problem for microgrids; the optimal capacity and operation strategy of ESS for both the grid-connected and islanded modes of microgrids were analyzed. Reference [31] investigated the coordinated optimal dispatch of ESS with renewable energies in microgrids to minimize the electricity costs. [PERSON] et al. [32] analyzed the stochastic optimal operation of microgrid with ESS; the influences of ESS on the optimization results were fully investigated. Based on the above discussions, a coordinated optimization model for ADN with ESSs is proposed in this paper, where the active and reactive power are handled simultaneously. In particular, the reactive power capabilities of DGs and ESS are fully utilized to minimize power losses and improve voltage profiles. The remainder of this paper is organized as follows: Section 2 describes the mathematical formulation of the coordinated optimization problem for ADN with ESS. The SOCP relaxation method is used to transform the original MINLP problem into a tractable mixed integer SOCP (MISOCP) problem in Section 3. Section 4 presents the case studies. Finally, Section 5 concludes the paper.
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 3 OPS-SAT SDR Data Processing for Spectrum Monitoring  
This section presents signal processing operations applied to the I/Q samples collected from OPS-SAT. Specifically, Figure 3 reports the PSD computed on the I/Q samples retrieved from the recorded telemetries.  
Figure 3: Power Spectral Density computed on four acquisition windows of I/Q samples, collected on 26 September 2023.  
Figure 2: Jamming signal generation setup for the OPS-SAT experiment during the Norwegian Jammertest, 2023 [5].  
It is worth noting that four PSDs are reported on the same figure, i.e., one for each acquisition window. There are different spurious/harmonics, which are always at the same frequencies for all the acquisition windows. In other words, these spurious/harmonics are independent of the satellite movement and of the Doppler shift. Hence, these lines are necessarily introduced by the satellite circuitry and can be filtered away with ad hoc digital filters. Examples of these spurious elements include the spectral lines around \(-400\) kHz, between 0 and 100 kHz, and around 600 kHz.  
The jammer signal is clearly visible between 200 and 400 kHz. It is centered at different frequencies for each acquisition window, due to the Doppler shift caused by the LEO satellite motion. Let us remember that the jammer has been transmitted centered at L1 plus 250 kHz. Considering that the OPS-SAT front-end sampled the signal centered at 1.5754 GHz, the jammer would be centered exactly 270 kHz above the 0 frequency in the digital spectrum in absence of Doppler shift and carrier frequency error (due to the receiver crystal). This is in line with what is shown through the PSD analysis, but is better highlighted in Figure 4, which reports an example of spectrograms computed on the I/Q samples collected from OP-SAT.  
This spectrogram is specific for the Acquisition 2, collected (26 September 2023 over Norway at 18:26:29) as reported in Section 2. Similarly, to the PSD analysis, the spectrogram shows the presence of the jammer transmitted from ground and visible in the frequency region between 200 and 400 kHz. It is worth observing that the frequency variation following the chirp profile is visible in the spectrogram, thanks to proper selection of the FFT size. In fact, the inclined red segments represent the saw tooth statement of the instantaneous chirp frequency, matching features of the jammer signal, transmitted from the Jammertest location.