esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A technique for calculating the main solidification parameters for a two-dimensional columnar crystal growth during complete penetration laser beam welding of thin steel sheets was developed. Given that the weld pool interface is described by Lame curves (superellipses) within the horizontal plane of growth, general analytical solutions were derived for the geometry of the crystal axis and the corresponding growth rate and cross-sectional area of the crystal. A dimensionless analysis was performed to provide insights on the dependence of the solidification parameters on the shape and dimensions of the rear part of the weld pool boundary. The derived solutions were applied for the case of complete penetration laser beam keyhole welding of 2 mm thick 316L austenitic chromium-nickel steel sheets. It was shown that the reconstruction of the weld pool boundary with Lame curves provides higher accuracy and flexibility compared to results obtained with elliptical functions. The validity of the proposed technique and the derived analytical solutions was backed up by a comparison of the obtained solutions to known analytical solutions and experimentally determined shapes and sizes of the crystals on the top surface of the sheet. The dimensions of the calculated crystal axis correlated well with the experimentally obtained results.  
Keywords:general analytical solutions; two-dimensional solidification; columnar crystal growth; Lame curves; laser beam welding +
Footnote †: journal: Applied sciences
###### Abstract  
Panoramic images in indoor construction sites are gaining attention as valuable tools for process monitoring and quality assessment. However, despite the environmental complexity and the demand for high segmentation performance in indoor construction environments, the scarcity of specialized segmentation models and datasets has created a gap between technological advancements and practical application, thus hindering the effective utilization of panoramic images. To address these challenges, this study proposes a novel approach leveraging the Segment Anything Model (SAM), a perspective image segmentation foundation model, to enhance the performance of existing segmentation models. The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. The experimental study was conducted using Panoplane360, a model specifically designed for plane segmentation, as the target model. A quantitative evaluation was conducted to measure the exactness of label assignment, and two qualitative evaluations were performed to assess whether the assigned labels accurately represent the actual planar information. The evaluation results confirmed that the proposed method significantly improves segmentation performance compared to conventional approaches. The findings of this study highlight the potential of SAM-based methods to enhance segmentation accuracy in dynamic indoor construction environments. Furthermore, the proposed approach provides practical advantages, as it improves segmentation performance without requiring the construction of additional datasets. Future research will focus on resolving computational efficiency issues resulting from iterative SAM execution and will extend the applicability of the proposed approach to diverse segmentation tasks and models.  
Keywords:indoor construction sites; segment anything model; segmentation quality; panoramic image segmentation
## 4 Energy and Power Optimization Allocation
### Integrated Optimization Scheme  
#### 4.1.1 Optimization for Synchronous Networks  
For the single-slot synchronous ISAL network, the FIM of \(\boldsymbol{\theta}=[\mathbf{p}_{\mathrm{r}}^{\mathrm{T}},\mathbf{p}_{ \mathrm{t}}^{\mathrm{T}}]^{\mathrm{T}}\) is as in (18), and the power optimization allocation strategy is as in [25]:  
\[\mathfrak{B}_{1}: \min\limits_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\tau} }\mathrm{tr}(\mathbf{J}_{\mathrm{e}}^{\mathrm{T}-1})\] (26) s.t. \[0\leq P_{j}\leq P_{\mathrm{r,max}},\forall j\in\mathcal{N}_{ \tau} \tag{28}\] \[0\leq P_{j}\leq P_{\mathrm{a,max}},\forall j\in\mathcal{N}_{ \mathrm{a}}, \tag{29}\]  
where \(E_{\mathrm{total}}\) is the total energy of the ISAL network, and \(P_{\mathrm{a,max}}\) and \(P_{\mathrm{r,max}}\) are the upper limit of power allocated to each anchor and radar, respectively, within a single slot.  
According to [24], problem \(\mathfrak{B}_{1}\) can be reformulated into a standard semi-definite programming (SDP) problem as:  
\[\mathfrak{A}_{1}^{\mathrm{SDP}}: \min\quad\mathrm{tr}(\mathbf{H})_{\mathbf{p}_{\mathrm{t}}}\] (30) s.t. \[\mathbf{H}\succeq 0 \tag{31}\] \[\left[\begin{array}{cc}\mathbf{H}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\theta}^{\mathrm{syn}}\end{array}\right]\succeq 0\] (32) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}} P_{j}\leq E_{\mathrm{total}}/1\] (33) \[0\leq P_{j}\leq P_{\mathrm{r,max}},\forall j\in\mathcal{N}_{ \mathrm{r}}\] (34) \[0\leq P_{j}\leq P_{\mathrm{a,max}},\forall j\in\mathcal{N}_{ \mathrm{a}}, \tag{35}\]  
where \(\mathbf{H}\) is called the auxiliary matrix of the FIM, and \(\mathbf{I}\) represents the identity matrix whose dimension is the same as the FIM.  
For the dual-slot synchronous ISAL network, the FIM expression is as in (19). We search for the optimal energy allocation between two time slots through traversal, where the energy allocated to slot 1 and slot 2 is, respectively, \(E_{\mathrm{slot1}}\) and \(E_{\mathrm{slot2}}\), satisfying \(E_{\mathrm{total}}=E_{\mathrm{slot1}}+E_{\mathrm{slot2}}\). The power optimization allocation strategy within slot 1 is the same as that in the single-slot synchronous ISAL networks. The power optimization allocation within slot 2 applies the result in slot 1 as prior knowledge, which is shown as  
\[\mathfrak{A}_{2}^{\mathrm{SDP}}: \min\quad\mathrm{tr}(\mathbf{H})_{\mathbf{p}_{\mathrm{t}}^{(2)}}\] (36) s.t. \[\mathbf{H}\succeq 0 \tag{37}\] \[\left[\begin{array}{cc}\mathbf{H}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\theta}^{\mathrm{syn}(2)}\end{array}\right]\succeq 0\] (38) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}} P_{j}\leq E_{\mathrm{slot2}}/1\] (39) \[0\leq P_{j}\leq P_{\mathrm{r,max}},\forall j\in\mathcal{N}_{ \mathrm{r}}\] (40) \[0\leq P_{j}\leq P_{\mathrm{a,max}},\forall j\in\mathcal{N}_{ \mathrm{a}}. \tag{41}\]
# Spatial Variations in Microfiber Transport in a Transnational River Basin
## 4 Discussion
### Origin of the Microplastics  
Most of the transported particles were colored microfibers, and there were some plastic spheres too. These morpho-types clearly have a wastewater origin [46]; thus, probably the actual transport of particles is also influenced by the local input of wastewater. As microfibers dominate (84-97%) the suspended MP transport, such a homogeneity reflects the uniformity of the origin of the pollution according to [PERSON] et al. As some sources argue [80], we thus assumed that in case of the Tisza the microfibers definitely originate from wastewater drained into the Tisza.  
A similar human impact was indicated by several other rivers [43; 44; 46], where the main source of MP transport in the river was not surface run-off but wastewater input into the river. Considering that the year 2022 was a drought year with minimal/no surface runoff, it can be assumed that most of the transported MPs directly reached the Tisza and its tributaries through wastewater discharge.  
Comparing the MP pollution of the sections with the sewerage and wastewater management of the different areas, it can be concluded that the high pollution levels, especially in the Upper Tisza and its tributaries in Ukraine, and in the Lower Tisza in Serbia, are likely connected to the inadequate treatment of wastewater in the sub-catchments and the direct discharge of wastewater into the Tisza. The situation is better in the Middle Tisza in Hungary, where WWPSs and WWTPs are adequately developed, thus less MP particles can get into the water. However, it should be noted that the MP pollution could be trapped in the sediment deposited on the river bed [38], which could be mobilized by erosion downstream of dams or by floods, and thus MP particles could re-enter the water system.  
The dominance of microfibers in the water and in the sediments [35; 38] of the Tisza suggests that washing of clothes is the main source of MP microfibers in wastewater [22; 81; 82], and thus in the river system [37]. There is a clear connection between wastewater treatment facilities located near rivers and the persistence and replenishment of microfiber and MPpollution. Some of these wastewater treatment plants function as point sources of pollution in rivers, providing a continuous supply and a high relative frequency of microfiber pollution [44; 46], as only 64-99% of the MP particles can be removed by different wastewater treatment technologies during the treatment process [45].
# Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
## 4 Research Methodology
### Experimental Confirmation of DL Attack on Serpent  
This study experimentally verified and analyzed the DL attack conducted in [19] on a 12-round Serpent. The attack was based on the fundamental 11-round DL attack using a plaintext pair that provides the input differentials of 28 participating S-Boxes in round zero. Consequently, changing the Serpent algorithm was conceivable, and a 12-round attack against Serpent with 256-bit keys was obtained.  
[PERSON] et al. [19] tried all the possible input differences for round 1 that yielded the difference \(LT^{-1}(\Delta P)=200000000001.400E00400000000000_{x}\). The difference was not changed by S-Boxes 2, 3, 19, 23, and so on; those S-Boxes did not change the participating bits of \(LT^{-1}(\Delta P)\)[19]. Consequently, [PERSON] et al. [19] constructed plaintext structures, which took that fact into attention and obtained a 12-round attack on [PERSON]:  
1. [PERSON] et al. [19] selected \(N=2^{123.5}\) plaintexts that consisted of \(2^{11.5}\) structures, and each was selected by choosing the following: (a) an abitaray plaintext \(P_{0}\); (b) the plaintexts \(P_{1}\),..., \(P_{2^{112}-1}\), which differed from \(P_{0}\) by all the \(2^{112}-1\) possibilities of non-empty subsets of the bits which were used as inputs of all S-Boxes except 2, 3, 19, and 23 in round zero [19].
2. [PERSON] et al. [19] requested the cipher texts of the encrypted plaintext structures by using the private unknown key \(K\). 3. For every input 112-bit of \(K_{0}\) value using those 28 S-Boxes, partly encrypted all the plaintexts in the first round and utilize the original 11-round DL attack on Serpent [19].
3. Each experimental key revealed and provided [PERSON] et al. [19] 112 + 20 + 28 = 160-bit subkeys: 112-bit of round 0; 20-bit of round 1; and 28-bit of round 11, simultaneously with an accuracy test [19]. The accurate estimation of the 160-bit was anticipated to be the typical and frequently expected value with the appearance of more than 84% completion rate [19].
4. The remainder of the key bits were retrieved by supplemental techniques [19].  
The study experimentally verified that the data attack complexity was \(2^{123.5}\) chosen plaintexts. The time attack complexity \(2^{123.5}\times(\frac{28}{84})\times 2^{112}=2^{231.7}\) encryptions for the partial encryption in Step 3, and \(2^{137.4}\times 2^{112}=2^{249.4}\) for the repeated trials of the 11-round DL attack [19].  
The study further experimentally verified that on a 10-round DL attack of Serpent using 128-bit keys, the data complexity was \(2^{101.2}\) elected plaintexts, and time encryption complexity was \(2^{115.2}\).
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 3 Description and Formulation of BACASP
### The Dynamic Adaptive Mechanism
#### 4.3.2 Information-Enhanced Adaptive Abandonment Process  
The original MOCS algorithm performs an abandonment process (as shown in Equation (28)) after generating a new solution in order to maintain population diversity and enhance the ability to escape from local optima:  
\[s_{i}(t)^{\prime}\ =\ \begin{cases}s_{i}(t)+r_{i1}(s_{j}(t)-s_{k}(t))&r_{i2}<p_{a} \\ s_{i}(t)&r_{i2}\geq p_{a}\end{cases} \tag{28}\]  
where \(p_{a}\) denotes the probability of abandonment, \(r_{i1}\) and \(r_{i2}\) are uniform random numbers in \([0,1]\). \(s_{i}(t)\) and \(s_{j}(t)\) are the two solutions randomly chosen at the \(t\) iteration.  
However, if the MOCS algorithm does not receive enough information in the search for the optimum or obtains the wrong information, this can cause it to end the global exploration too quickly before undertaking local exploitation. In addition, a larger value of \(p_{a}\) increases the diversity of solutions and inhibits premature convergence, while a smaller value of \(p_{a}\) improves the search accuracy, but slows down the search process. Fixed values of \(p_{a}\) cause the algorithm to lack a proper balance between global exploration capability and local exploitation capability. Therefore, in order to reduce the probability of the MOCS algorithm falling into a local optimum, a dynamic adaptive \(p_{a}(t)\) strategy is proposed to enhance its ability to acquire information during the optimization search process. The improved abandonment process is shown in Equations (29) and (30):  
\[s_{i}(t)^{\prime}\ =\ \begin{cases}s_{i}(t)+r_{i1}((s_{j}(t)-s_{k}(t))+(s_{i}(t )-s_{m}(t)))&r_{i2}<p_{a}(t)\\ s_{i}(t)&r_{i2}\geq p_{a}(t)\end{cases} \tag{29}\]\[p_{a}(t)\ =\ 2\frac{p_{a}}{1+e^{6t/t_{\max}}} \tag{30}\]  
where \(p_{a}(t)\) denotes the adaptive probability of abandonment; \(t_{\max}\) is the maximum number of iterations; \(s_{j}(t)\), \(s_{k}(t)\), \(s_{l}(t)\), \(s_{m}(t)\) are the four solutions randomly selected in the \(t\) iteration; \(r_{i1}\) and \(r_{i2}\) are uniform random numbers in [0, 1].  
After obtaining more information about the local exploitation space, the improved vector provides more possibilities for the direction and distance of the abandonment process on the basis of the previous one, which increases the likelihood of the algorithm jumping out of the local optimum through various changes in distance and direction. In addition, the introduction of the dynamic adaptive \(p_{a}(t)\) enlarges the search space of the algorithm in the pre-iterative period, thus improving the global exploration capability. During the optimization search process, \(p_{a}(t)\) decreases, which enhances the local exploitation performance of the algorithm in the late iteration.
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
## 3 Proposed Algorithm
### Algorithm 1 for the Partial Blockage Scenario
Thus, the \(k\)-th element of \(\mathbf{s}^{i+1}\) can be obtained by the following equation, which is shown as  
\[\widetilde{\mathbf{Y}}_{k,:}=\mathbf{B}_{k,:}s_{k}+\mathbf{N}_{k,:}, \tag{13}\]  
where \(\widetilde{\mathbf{Y}}_{k,:}\), \(\mathbf{B}_{k,:}\) and \(\mathbf{N}_{k,:}\) denote the \(k\)-th row of matrix \(\widetilde{\mathbf{Y}}\), \(\mathbf{B}\) and \(\mathbf{N}\), respectively. Similarly, \(s_{k}\) denotes the \(k\)-th elements of \(\mathbf{s}^{i+1}\). Thus, we can utilize the least squares (LS) estimator to estimate \(s_{k}\), which can be expressed as  
\[LS:\ \ s_{k}=(\mathbf{B}_{k,:}(\mathbf{B}_{k,:})^{\mathrm{H}})^{-1}\widetilde{ \mathbf{Y}}_{k,:}(\mathbf{B}_{k,:})^{\mathrm{H}}. \tag{14}\]  
Repeat the above process until the number of iterations reaches \(N_{iter}\). We can then obtain the accurate estimation of \(\mathbf{s}\) and \(\overline{\mathbf{s}}\). Next, we analyze the complexity of Algorithm 1. The complexity mainly comes from Steps 4, 6, and 10. In Step 4, the complexity can be expressed as \(\mathcal{O}(MNT(N_{R})^{2}+{N_{R}}^{3})\) and \(\mathcal{O}(TN)\), where \(N_{R}\) denotes the number of the non-zero element in \(\overline{\mathbf{s}}\). After \(N_{iter}\) iterations, the total complexity can be expressed as \(\mathcal{O}(N_{iter}(N_{R}(MNT+NT(N_{R})^{2}+{N_{R}}^{3})+N^{2}T))\).
###### Abstract  
In this paper, we propose a novel multiple-symbol detector based on maximum likelihood metric for differential quadrature amplitude modulation in massive multiple-input multiple-output (MIMO) systems. While current research on differential modulation in massive MIMO has focused on two consecutive symbols, our proposed detector is based on multiple-symbol, which is larger than or equal to two. Moreover, we derive new distance based on the proposed detector. To encode and decode data, we apply existing look-up table algorithm using the proposed distance, which is known as optimum encoding algorithm for differential modulation. Simulation results show the improvement based on the bit-error-rate performance since the proposed detector and distance vary according to the channel statistic information.  
5G wireless networks; massive MIMO; non-coherent detection; QAM Article
# Comparison of Various Factors Affected TID Tolerance in FinFET and Nanowire FET
###### Abstract  
Analysis of the radiation effects in a device is of great importance. The gate all around (GAA) structure that contributes to device scaling not only solves the short channel effects (SCE) problem but also makes the device more resistant in radiation environments. In this article, the total ionizing dose (TID) simulation of nanowire FET (NW) and FinFET was performed. Both these devices were compared and analyzed in terms of the shift of threshold voltage (V\({}_{\text{T}}\)). The channel insulator was composed of two materials, SiO\({}_{2}\) and HfO\({}_{2}\). To improve the accuracy of the simulation, the interfacial trap parameter of SiO\({}_{2}\) and HfO\({}_{2}\) was applied. Based on the simulation result, the NW with a larger oxide area and larger gate controllability showed less V\({}_{\text{T}}\) shift than that of the FinFET. It was therefore proved that NW had better TID resistance characteristics in a radiation environment. The gate controllability was found to affect the TID effect more than the oxide area. In addition, we analyzed the manner in which the TID effect changed depending on the V\({}_{\text{DD}}\) and channel doping.  
Keywords:total ionizing dose (TID); radiation; threshold voltage (V\({}_{\text{T}}\)); nanowire FET; gate-all-around (GAA); FinFET; TID tolerance +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
### Organization of the Paper  
The remainder of the paper is organized as follows: the overview of the proposed concept is provided in Section 2. The detailed structure and specifications of the interface are presented in Section 3. The applicability of the proposed concept is validated in Section 4 with a case study. Lastly, the conclusions are given in Section 5.
# Cost-Effective Design Modification of a Sleeve Bearing with Large Bearing Clearance
## 3 Numerical Simulation  
### Rotordynamic Model  
The rotor was discretized into 53 Timoshenko beam elements, and the shear, rotary inertia, and gyroscopic effects were considered. The exciter, generator, and runner were added to the model as mass blocks with polar and diametral moments of inertia. As shown in Figure 2, the rotor was radially supported by three bearings, which were further attached to the ground via brackets. They were modeled as springs with isotropic stiffness and no damping. For operation at nominal rotor speed, the UMP of the exciter and generators was considered and modeled as a constant negative stiffness. Equation (1) is the governing equation of the hydropower unit:  
\[\mathbf{M}\ddot{\mathbf{q}}+(\mathbf{C}+\Omega\mathbf{G})\dot{\mathbf{q}}+ \mathbf{K}\mathbf{q}=\mathbf{f}_{\mathbf{u}}-\mathbf{f}_{\mathbf{b}} \tag{1}\]  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{C}\) is the damping matrix, \(\mathbf{G}\) is the gyroscopic matrix, \(\mathbf{K}\) is the stiffness matrix, \(\Omega\) is the rotor speed, \(\mathbf{f}_{\mathbf{u}}\) is the unbalance force vector, and \(\mathbf{f}_{\mathbf{b}}\) is the bearing restoring force vector of the UGB and LGB. Note that the bearing forces of the TB are represented by damping and stiffness coefficients and are included in the \(\mathbf{C}\) and \(\mathbf{K}\) matrices, respectively. The magnitude of \(\mathbf{f}_{\mathbf{u}}\) at the \(j\)-node is the product of an unbalance magnitude (\(m_{i}\cdot e_{j}\)) and the square of the rotor speed (Equation (2)). For unbalance response simulations, mass unbalances were introduced at two locations, i.e., at the generator (\(m_{GNF}\cdot e_{3}=400\) kg\(\cdot\)m) and turbine (\(m_{T}\cdot e_{7}=200\) kg\(\cdot\)m), which were more than five times higher than the ISO 1941-1 balance quality grade of G6.3 [19]. This high unbalance is not  
Figure 2: A schematic representation of a hydropower unit supported by the bearings (UGB, LGB, and TB), and three brackets at node 8 (UF: upper foundation), node 9 (LF: lower foundation), and node 10 (TF: turbine foundation). The springs in red represent the magnetic stiffnesses of the exciter (\(\mathbf{K}_{\text{UMP:EXR}}\)) and generator (\(\mathbf{K}_{\text{UMP:GNR}}\)). The nodes on the shaft correspond to the exciter (node 1), UGB (node 2), generator (node 3), LGB (node 4), TB (node 6) and runner (node 7).  
unusual in hydropower units, and it was found that this level of unbalance could represent the vibration measurement in the actual machine:  
\[f_{u}=m_{i}\cdot e_{j}\cdot\Omega^{2} \tag{2}\]  
where \(m_{i}\) is the mass of the generator or turbine, \(e_{j}\) is the amplitude of the trajectory of the journal center at the \(j\)-node, and \(\Omega\) is the rotor speed.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 3 Methods
### Loss Function  
The loss function utilized in the present study comprises two crucial components, wherein the first pertains to the utilization of the L1 smoothing loss function to ascertain the correlation between the predicted depth values and the actual depth values. The second component concerns the direction consistency loss function wherein the normal vector prediction values are compared against the normal vector ground truth. In doing so, we employed the cosine distance approach to measure the disparities between the normal vectors.