esa-sceva/satcom-chunk-collection huggingface dataset:
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 5 SRv6-Based Lossless Migration  
In this section, we first present service migration in the considered 5G-MEC scenario. Then, considering the two operation modes described in Section 4.2, two corresponding migration procedures are explained, providing the control plane and data plane details. The service migration procedure described must guarantee service continuity, i.e., service relocation must remain hidden from the client. Our solution enables making the client not aware of the location of the service, and we ensure that the latter always maintains the same IP address, regardless of the MEC Host where it is running. Moreover, we have to guarantee connection persistence, a lossless workload migration, and preservation of performance experienced by the application. The first one is the Enhanced Mode, i.e., a full 5 GC network, whereas the second one is the Enhanced Mode with Unchanged gNB, i.e., the integration of a non-SRv6-enabled gNB with an SRv6 core network. Finally, we describe the End.Buffer and End.M.GTP6.D.Buffer behaviors, proposed for the Enhanced Mode and Enhanced Mode with Unchanged gNB, respectively.
# A New Architectural Approach to Monitoring and Controlling AM Processes
###### Abstract  
The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method.  
Keywords:additive manufacturing; monitoring and control; functional architecture; multi-loop control architecture; clustering +
Footnote †: journal: Applied Sciences
## 1 Introduction
For acquiring the in-depth information, two confocal outputs with these detectors in different confocal setups are utilized. For the signal processing, the algorithm referred to as the tracking local minimum (TLM) method has been proposed [27]. In the TLM method, the normalized confocal output is defined as the ratio of the measurement detector output to the reference detector output. This algorithm can theoretically eliminate the influences of the nonuniform spectrum of a mode-locked femtosecond laser source. Meanwhile, the intensity around the first local minimum of the reference detector output spectrum is relatively small, and hence the normalized output can easily be influenced by out-of-focus noise [25; 32; 33; 34]. To address the issue, in this paper, a major modification is made to the conventional differential chromatic confocal setup in the mode-locked femtosecond laser differential chromatic confocal probe. Figure 2 shows a schematic of the confocal setup of the reference detector unit employed in the newly proposed method. As can be seen in the figure, the fiber detector is placed at the focal plane of  
Figure 1: Schematics of the relative position between the fiber core and the collecting lens in measurement and reference detectors in the femtosecond confocal probe. (**a**) A setup for the measurement detector. (**b**) A setup for the reference detector with a defocus \(d\). the collecting lens while an off-axis detector shift \(d_{\rm m}\) is given to the fiber detector, instead of giving a defocus \(d\); this modification improves the signal-to-noise ratio of the normalized chromatic confocal output to be obtained in the differential confocal setup. This optical configuration is also expected to make the optical alignment of the differential confocal setup easier than the conventional setup shown in Figure 1. Theoretical analysis and experimental examination are implemented to verify the feasibility of the proposed off-axis differential method for the differential chromatic confocal probe with a mode-locked femtosecond laser source. It should be noted that the work described in this paper is mainly intended to focus on the proposal of the preliminary idea of an off-axis differential method for the improvement of the signal-to-noise ratio (SNR), the measurement range and the measurement resolution of the femtosecond laser chromatic confocal probe. The subsequent application of this new method is needed in the imaging of three-dimensional (3D) microstructures in our future work. Regarding the spectral range (from 1480 to 1640 nm) and the chromatic objective lens employed in the experiments, a measurement range of 46 \(\upmu\)m is expected to be achieved through the above improvements by the proposed off-axis differential method.
## 1 Introduction  
The modeling of 3D objects and environments from real-world measurements enables new possibilities across diverse applications, including automatic inspection, manipulation, and service robotics. In the context of Industry 4.0, where production environments are increasingly dynamic and flexible, the ability to rapidly generate or update 3D models of a robotic workcell is particularly crucial. An accurate, up-to-date 3D model is foundational for tasks like collision avoidance, offline programming, and robotic task adaptation in these rapidly evolving settings [1, 2]. This on-demand model creation offers a significant advantage over traditional approaches that rely on pre-existing CAD models, which may not reflect recent changes or the true state of a flexible workcell.  
The process of autonomously determining a sequence of sensor poses to build such a model is known as Next-Best-View (NBV) planning [3], as illustrated in Figure 1. However, developing an efficient NBV planner for complete workspace exploration with a robot arm presents several technical challenges.  
Many existing methods are designed for high-resolution object scanning and rely on techniques like volumetric ray-casting to evaluate information gain. Other approaches, such as frontier-based exploration developed for mobile robots, do not easily translate to the kinematic constraints and limited reach of a fixed-base manipulator. Finally, while powerful, recent learning-based methods often require extensive, object-specific training data and can struggle to generalize to novel workcell layouts, a significant drawback in flexible manufacturing. These general limitations of existing methods--high computational cost, platform incompatibility, and a lack of on-the-fly adaptability--hinder their practical deployment in dynamic industrial settings.  
To address these challenges, this paper proposes a novel NBV method specifically designed for the complete and efficient exploration of a robotic arm's entire, initially unknown workspace. The main contribution of this work is a holistic framework that integrates several key features. We introduce a computationally efficient information gain metric that relies on a fast, collision-based assessment, avoiding the high cost of traditional ray-casting. This is paired with a lightweight potential field method to generate and orient candidate views from the frontiers of the known space, effectively directing the robot while respecting its kinematics. These components are unified within a single, tunable fitness function that balances the critical trade-off between maximizing information gain and minimizing motion cost. The robustness and practical viability of this integrated approach are demonstrated through rigorous testing in diverse simulated environments and, crucially, through validation on a physical industrial robot.  
In all setups, our method successfully explores the majority of the robot's workspace, providing a practical solution for enabling advanced automation in modern manufacturing.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
###### Abstract  
There exist several challenges in the implementation of proportional multiple quasi-resonant (PMQR) control strategies in single-phase grid-connected H6 inverters, such as high computational costs and design complexity. To overcome these challenges, this paper proposes a proportional multiple quasi-resonant (PMQR)-type repetitive control (PMQR-type RC) scheme for single-phase grid-connected H6 inverters. In the control scheme, a repetitive controller and a proportional controller run in parallel. The repetitive controller is to improve the steady-state harmonics compensation ability, while the proportional controller can enhance the transient performance of the system. Both theoretical stability analysis and detailed design steps regarding the proposed control scheme are introduced. Finally, comparison results on a typical single-phase grid-connected H6 inverter with LC filter under a variety of control methods verify the capability of suppressing harmonics and the robust performance of the proposed control strategy against grid disturbances.  
Keywords:grid-connected H6 inverters; proportional multiple quasi-resonant control; repetitive control; harmonic suppression +
Footnote †: journal: Applied
## 3 Results and Discussion
### Influence of Ambient Conditions--Surrounding Temperature and Ventilation (Parameter Sweep Study)  
The performance of cooling systems is heavily influenced by the climatic conditions in which power tools are used. To evaluate two of the main factors, ambient temperature and ventilation (i.e., the convection coefficient in the model), a parameter sweep (variation in one single input parameter while keeping all other parameters constant) was conducted varying these two parameters (Figure 5). The study was performed for the whole cycle using heat-conductive polymer I as the cooling material.
## 4 Experimental Results
### Feature Matching  
During the loop detection algorithm, the vocabulary CF tree is constantly updated, providing information for feature matching and similarity calculation between images. Figure 7 illustrates two sets of matching image results in the KITTI07 dataset. Both sets of matching images share common words, but the top result is the correct match while the bottom one is incorrect. By calculating the similarity through the vocabulary CF tree, the similarity score of the correct match is 0.391704, while the similarity score of the incorrect match is 0.0849734, showing a significant difference.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Elliptic Curve Criptography (ECC)  
The ECC is a type of cryptography which is encompassed in the public-key cryptography class. Its functioning leverages on the algebraic structure of the elliptic curves defined over finite fields. It could be used for several tasks related to the field of cyber security, but the two more important are secure key exchange (with the Elliptic Curve Diffie-Hellman Algorithm, also known as ECDH), digital signature (with the digital signature algorithm (DSA) extended to the use of the elliptic curve, which is called ECDSA). Many works propose to use ECC in the Internet of Things (IoTs) field, for example making the MQTT  
Figure 4: RSA sent packet.  
Figure 5: Confidentiality, Integrity and Authentication in RSA.  
protocol more secure by mitigating data tampering, eavesdropping and replay attacks such as [33; 34]. The ECC algorithm was developed after RSA due to advancements in terms of computational power and more advanced factorization techniques. These advancements, as a consequence, determine the use of higher key sizes and, thus, make the ciphering/deciphering operation as well as the generation mechanism of the keys more resource-hungry. The security of the ECC is based on the infeasible discrete logarithmic problem (DLP) [35], which by now is an unsolved problem, not solvable in a polynomial time. And this problem is also more difficult to solve when extended to the ECC field, the so called ECDLP [36]. Therefore, as a consequence, considering the same security degree, this kind of cryptography needs public keys of lesser size, and thus it is more suitable to this constrained context than the RSA. The NIST stated that a key of 256 bits used in ECC, for example, can guarantee the same security of 3072 bits used by RSA.
## 2 Channel Model and Signal-to-Noise Ratio
### Signal to Noise Ratio  
In general, the received signal-to-noise ratio can be expressed by:  
\[SNR=\left(R\;P_{S}\right)^{2}/\sigma^{2} \tag{21}\]  
where \(P_{S}\) is the received optical signal average power, \(R\) is the photodetector responsivity and \(\sigma^{2}\) represents the total noise variance [4; 25]. The total noise variance can be calculated by the sum of the contributions from the background light-induced shot noise and thermal noise due to the amplifier:  
\[\sigma^{2}=\sigma_{shot}^{2}+\sigma_{thermal}^{2} \tag{22}\]  
The shot noise variance can be approximated by:  
\[\sigma_{shot}^{2}\approx 2 qR_{2}R_{b}P_{bg} \tag{23}\]  
where \(q=1.6\times 10^{-19}\) C is the electron charge, \(I_{2}=0.562\) is a noise bandwidth factor, \(R_{b}\) is the bit rate, and \(P_{bg}\) is the incident optical power from natural and artificial ambient light.  
Assuming the use of a FET-based transimpedance preamplifier [4], the thermal noise variance can be expressed as:  
\[\sigma_{thermal}^{2}=\frac{4 kT}{R_{f}}I_{2}R_{b}+\frac{16\pi^{2}kT}{g_{m}} \left(\Gamma+\frac{1}{g_{m}R_{D}}\right)C_{T}^{2}I_{3}R_{b}^{3}+\frac{4\pi^{2} KI_{D}^{a}C_{T}^{2}}{g_{m}^{2}}I_{f}R_{b}^{2} \tag{24}\]  
where \(k\) is [PERSON]'s constant, \(T\) is absolute temperature, \(R_{f}\) is the feedback resistor, \(g_{m}\) is the transconductance, \(C_{T}\) is the total input capacitance of receiver, \(R_{D}\) is the polarisation resistance, \(\Gamma\) is the FET channel noise factor, \(K\) and \(a\) are the FET \(1\)/\(f\) noise coefficients, \(I_{D}\) is the FET drain current, and \(I_{2}\), \(I_{3}\) and \(I_{f}\) are noise-bandwidth factors. Neglecting stray capacitance [10; 22], the total input capacitance \(C_{T}\) is given by \(C_{T}=C_{d}+C_{g}\), where \(C_{d}\) and \(C_{g}\) are the detector and FET gate capacitances, respectively. The detector capacitance \(C_{d}\) is proportional to the detector area \(A_{R}\), i.e., \(C_{d}=\eta\;A_{R}\), where \(\eta\) represents the fixed photodetector capacitance per unit area. Therefore, replacing \(C_{T}\) by \(\eta\;A_{R}+C_{g}\) in Equation (24) yields:  
\[\sigma_{thermal}^{2}=\frac{4 kT}{R_{f}}I_{2}R_{b}+\frac{16\pi^{2}kT}{g_{m}} \left(\Gamma+\frac{1}{g_{m}R_{D}}\right)\left(\eta\;A_{R}+C_{g}\right)^{2}I_{ 3}R_{b}^{3}+\frac{4\pi^{2}KI_{D}^{a}\left(\eta\;A_{R}+C_{g}\right)^{2}}{g_{m}^ {2}}I_{f}R_{b}^{2} \tag{25}\]
## 3 Acquisition of Raw Data
### A Piezolectric-Sensor Embedded Necklace
The application acts as an interface between the intake monitoring system and the user. The application facilitates the experiment by providing different daily meal options, calorie calculation methods, food types, and caloric intake for each meal, and makes suggestions to the user based on their hydration level and caloric intake (see screenshots of the application in Figure 2). In contrast to [11; 13], the user can extend the battery power of the intake monitoring system by switching the active mode of the Simblee microcontroller to ultra-low power (sleep) [31]. The weight of each food is estimated separately, based on the swallow count. The calories for each meal of the day are calculated by Equation (1), which aggregates the calories of all the ingested food types. The calories for each food type are calculated as the 1-g caloric weight, \(c_{i}\) (coefficient), multiplied by the estimated food weight, \(food_{i}\) (swallow counts). Thereafter, the calculated calories for all ingested foods are aggregated to yield the calories for a particular meal of the day. There are two methods of caloric coefficient insertion: user-defined and application-default. User-defined caloric coefficients are entered by the user, while application-default caloric coefficients are pre-defined or pre-programmed based on average values in order to generalize varieties of the same food. Figure 4: Food ingestion pattern waveform: (**a**) The eating pattern of all the ingested food categories; (**b**) Extracted frames from the ingestion pattern of foods are enclosed by rectangles. The calorie estimation performance of these two methods are compared in Section 6.2. The caloric intake is simply estimated with Equation (1). \[\text{caloric intake}=\sum_{i}c_{i}food_{i} \tag{1}\]  
In this work, we considered five food types: chips, cookies, nuts, pizza, and water. As water was consumed in our experiment and does not contain any calories, the caloric coefficient for water is 0. For other foods, coefficients are entered by the user or left as the application default. Default values are calculated as the average number of calories from different varieties of the same food. Though the default values estimate calories less accurately for some foods, as discussed in Section 6.2, they also generalize the calorie equation well over different varieties of foods. In our experiment, foods were consumed in sequences of bites weighing about 1 g, corresponding to 3.01 calories for pizza, 5.67 for nuts, 5.2 for chips, and 5.125 for cookies for the estimated 1-g bolus ingested during each swallow.
# Precise Channel Estimation Approach for a mmWave MIMO System
###### Abstract  
Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm's convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.  
m _Applied_  
_sciences_
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 2 Materials and Methods
### Building New Inter-Defects Three-Dimensional Correlation Matrices
* For each detected defect (defect \(k\)), the corresponding row in the defects-diagnosis methods correlation matrix is read;
* For each second defect (defect \(j\)), the corresponding row in the defects-diagnosis methods correlation matrix is read;
* For each column, the product of the correlation indexes of both rows (for defects \(k\) and \(j\)) in the defects-diagnosis methods correlation matrix is calculated;
* To obtain the correlation index for each defect \(Cl_{kj}\), the products are summed up: \[Cl_{kj}=\sum_{i=1}^{N}C_{ki}C_{ji}\] (1) where \(N\) is the total number of diagnosis methods;
* To obtain the maximum theoretical percentage correlation index \(I_{Mk}\), for each detected defect \(k\), the corresponding row in the defects-diagnosis methods correlation matrix is read; the correlation indexes of defect \(k\) with the diagnosis methods are multiplied by 2 (which is the maximum index of correlation in the correlation matrices used as a basis to apply the described algorithm,. three indexes are available: 0 (no correlation), 1 (indirect correlation), and 2 (direct correlation)), and then, those products are summed up: \[I_{Mk}=\sum_{i=1}^{N}(C_{ki}\times 2)\] (2) where \(N\) is the total number of diagnosis methods;
* To determine the theoretical percentage correlation index between defect \(k\) and defect \(j\), \(Cl_{j\times kj}\), the quotient between the correlation index \(Cl_{kj}\) and the maximum theoretical percentage correlation index \(I_{Mk}\) is calculated: \[CI_{\gamma kj}=\frac{CI_{kj}}{I_{Mk}}\] (3) Following the described procedure, one can obtain correlation indexes between defects according to the possibility of prescription of the same type of diagnosis methods for different defects (based on the defects-diagnosis methods correlation matrix; Table 6) and according to the possibility of recommendation of the same type of repair techniques for different defects (based on the defects-repair techniques correlation matrix; Table 7). As such, two new three-dimensional inter-defect correlation matrices were built. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Defects**} \\ \hline
**Defects** & **A-A1** & **A-A2** & **A-A3** & **A-A4** & **A-A5** & **A-A6** \\ \hline
**A-A1** & - & 13\% & 8\% & 0\% & 0\% & 0\% \\
**A-A2** & 50\% & - & 33\% & 0\% & 0\% & 0\% \\
**A-A3** & 50\% & 50\% & - & 0\% & 0\% & 0\% \\
**A-A4** & 0\% & 0\% & 0\% & - & 0\% & 0\% \\
**A-A5** & 0\% & 0\% & 0\% & 0\% & - & 0\% \\
**A-A6** & 0\% & 0\% & 0\% & 0\% & 0\% & - \\ \hline \hline \end{tabular} Note: refer to Table 3 for the designation of each defect.  
\end{table} 
Table 6: Excerpt of the door and window frames’ layer of the new inter-defects correlation matrix based on the defects–diagnosis methods correlation matrix from the global inspection system developed by [PERSON] (2012).
## 2 Results and Discussion
### Method Validation
#### 2.3.6 Recovery  
The mean recoveries of TFME extraction were 59.01%, 56.13% and 73.54% for piperacillin prepared at concentrations of 0.06, 0.25 and 0.5 mg/L, respectively, while the mean recoveries for impenem prepared at concentrations of 0.15, 0.5 and 1.0 mg/L were 26.10%, 19.05% and 22.83%, respectively. The results confirm that the proposed TFME-LC-MS/MS method meets the FDA Guidelines for Bioanalytical Method Validation.