esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 3 Description and Formulation of BACASP
### The Initial Feasible Solution  
Numerous studies have shown that a high-quality and diverse initial population leads to higher performance when solving optimization problems. The greedy strategy chooses the current optimal choice at each decision step and is able to find a near-optimal solution quickly, thus finding a high-quality solution at the initial stage. Chaotic mapping is characterized by randomness, non-repeatability, and chaotic traversal, implying that it is more capable of leading to a uniformly distributed population than the random generation that relies on probability. Therefore, in this paper, the greedy strategy and tent chaotic mapping strategy are used to generate the initial population.  
On the one hand, half of the initial populations are generated using the greedy strategy. First, the berthing time \(y_{i}\) of each vessel is assigned as the expected arrival time, \(a_{i}\), and the berthing position, \(x_{i}\), is assigned as the desired position of the vessel. In addition, the number of QC assignments to each vessel is specified as \(q_{i}^{\max}\). On the other hand, the remaining initial populations are generated using an improved tent chaotic mapping strategy. Specifically, firstly, the berthing time, \(y_{i}\) of each vessel is randomly generated in the planning horizon, the berthing location is randomly generated from \([0,L-l_{i}]\), and the number of QC assignments to each vessel is randomly generated from \([q_{i}^{\min},q_{i}^{\max}]\). The initial population is then obtained by improved tent mapping function (as shown in Equation (23)).  
\[x_{n+1}\ =\ \begin{cases}\frac{v_{n}}{a}+rand(0,1)\times\frac{1}{N}&0\leq x_{ n}<\alpha\\ \frac{1-x_{n}}{1-x_{n}}+rand(0,1)\times\frac{1}{N}&\alpha<x_{n}\leq 1\end{cases},n\ =\ 1,2,\ldots,N \tag{23}\]  
Figure 4: Encoding and decoding processes for a small BACASP instance.  
where the chaos parameter \(\alpha~{}=~{}0.5\) and \(N\) is the number of individuals within the population. The introduction of the random variable, \(rand(0,1)/N\), not only maintains the randomness and traversal nature of the tent chaotic mapping strategy, but also effectively avoids falling into the unstable cycle point during the iteration process. After the initialization of the population is completed, some constraints in the model are satisfied. However, there are still many constraints that have not yet been satisfied, which results in many solutions in the population generated after initialization being infeasible, requiring heuristic algorithms (Algorithm 1) to obtain feasible solutions.
## 2 Materials and Methods
### Phase 2. Empirical Data Collection and Circularity Assessment
#### 2.2.1 Scenario Formulation and Empirical Data Collection  
Four scenarios were analysed to encompass the full spectrum of the CE transition:  
* Scenario 1 (S1) assessed the construction sector's current practices by analysing material linearity throughout their entire lifespan.
* Scenario 2 (S2) evaluated the circular performance of ongoing industry initiatives, as outlined in the EEEC reports [57,58], by implementing circularity strategies at the EOL stage.
* Scenario 3 (S3) examined circularity strategies applied to all materials, excluding concrete, across the complete lifecycle. This scenario highlighted the industry's growing interest, particularly within the steel sector, in incorporating a higher percentage of secondary raw materials while ensuring compliance with required physical and mechanical properties [64].  
Figure 4: Flowchart of the research methodology based on methods from [62,80,81] (Authors’ own).  
* Scenario 4 (S4) extended these circularity strategies to encompass concrete by adopting RAC as structural concrete, thereby promoting a more sustainable approach to construction materials. Empirical data were collected from the BIM model of the representative structure established in Phase 1. This virtual 3D building model was developed using Autodesk Revit 2023 (Autodesk Inc., Mill Valley, CA, USA) and integrates a database of its building elements [84]. The development of this process adhered to the verification flow methodology for BIM models of buildings, as outlined by [PERSON] et al. [81]. This methodology comprised three main sequential check phases [85; 86]: BIM validation, which evaluated the quality and internal consistency of the model to ensure the extraction of reliable results in subsequent analyses; clash detection, which identified physical conflicts between elements; code checking, which ensured the model's compliance with relevant standards.
# On the Capacity of Optical Backbone Networks
###### Abstract  
Optical backbone networks, characterized by using optical fibers as a transmission medium, constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is one of the key factors influencing optical network performance, it is important to comprehend its limitations and have the capability to estimate its value. In this context, we revisit the concept of capacity from various perspectives, including channel capacity, link capacity, and network capacity, thus providing an integrated view of the problem within the framework of the backbone tier. Hence, we review the fundamental concepts behind optical networks, along with the basic physical phenomena present in optical fiber transmission, and provide methodologies for estimating the different types of capacities, mainly using simple formulations. In particular, we propose a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects, in conjunction with capacitated routing techniques for traffic routing. We apply this method to three reference networks and obtain capacities ranging from tens to hundreds of terabits/s. Whenever possible, we also compare our results with published experimental data to understand how they relate.  
network capacity; channel capacity; optical networks; optical fiber communications +
Footnote †: journal: Accepted: 6 March 2024
## 3 Results
### DOX Causes DNA Damage but Does Not Disrupt Satellite Cell Activation and Myogenesis  
Since DOX-induced TOP2B upregulation is associated with cardiotoxicity [11], we examined for the first time if EXE would downregulate TOP2B and thus protect myotoxicity. TOP2B levels were higher in the SED-DOX group than in the SED-SAL and EXE-SAL groups; however, their levels were not statistically different among the SED-SAL, EXE-SAL, and EXE-DOX groups (Figure 3A,B). The CS levels have been used as a mitochondrial biogenesis indicator. Our data showed that CS levels were significantly increased in the EXE-DOX group compared to the SED-SAL and SED-DOX groups (Figure 3A,C); interestingly, EXE per se (EXE-SAL) did not promote CS upregulation.  
Figure 2: EXE-induced myocyte protection against DOX-induced myotoxicity in soleus muscle. (**A**) Hematoxylin and eosin (H&E) staining. The arrows show degenerating fibers. EXE-DOX shows protection against myocyte degeneration (\(n=3/\)group). (**B**) Modified Gomori Trichrome (MGT) staining (\(n=3/\)group). The arrows indicate pathological diagnostic of ragged red fibers. Total magnification is 200\(\times\), and the size of a scale bar is 20 \(\upmu\)m. (**C**) Quantification of ragged-red fibers/designated area. (**D**) Measurement of the cross-sectional area (CSA) of myofibers. (**E**) Numbers of myofibrer per designated area. Asterisks denote significant differences: \(p<0.01\) (**), and \(p<0.001\) (***). Values are presented as mean \(\pm\) SEM. SED: Sedentary; SAL: saline; DOX: doxorubicin; EXE: endurance exercise.  
Next, we measured the markers of satellite cells activation (PAX7) and myogenesis (MYOD1 and MYOG). Neither SED-DOX nor EXE (EXE-SAL and EXE-DOX) treatments modulated the expressions of those markers (Figure 3A,D-F).  
Figure 3: Effects of EXE on DNA damage, satellite cells activation, and myogenesis-related markers in soleus muscle against DOX treatment. (**A**) Representative western blot images. (**B**) Quantification of TOP2B protein expression. Both SED-DOX and EXE-DOX significantly upregulate TOP2B compared to the SED-SAL. (**C**) Quantification of CS protein expression. EXE-DOX significantly increases CS compared to the SED-SAL and the SED-DOX. (**D–F**) Quantification of PAX7, MYOD1, and MYOG protein expression, respectively. All target proteins were normalized by Ponceau-stained total proteins (_n_ = 6–7/group). Asterisks denote significant differences: \(p\) < 0.05 (*) and \(p\) < 0.01 (**). Values are presented as mean \(\pm\) SEM. SED: Sedentary; SAL: Saline; DOX: Doxorubicin; EXE: Endurance exercise; TOP2B: Topoisomerase II-beta; CS: Citrate synthase; PAX7: Paired box 7; MYOD1: Myogenic differentiation 1; MYOG: Myogenin.
# Numerical Evaluation of Sample Gathering Solutions for Mobile Robots
## Problem Formulation
The utility relates to the necessary time required for collecting all samples (being a maximum value over utilities of elements of obtained partition), while the partition is acceptable if each robot has enough energy to collects its samples. The maximization over individual utilities show that our problem is more complicated than standard SPPs. Since an SPP is NP-hard [41], we conclude that Problem 1 is also NP-hard. Therefore, we expect computationally intensive solutions for optimally solving Problem 1, while sub-optimal relaxations may be used when an optimal solution does not seem tractable._  
Since the sample deployment and robot energy limits are known, the searched solution is basically an off-line computed plan (sequence of nodes) for each robot such that the mission requirements are fulfilled. The first requirement from Problem 1 can be regarded as a global target for the whole team (properly assign robots to collect samples such that the overall time for accomplishing the task is minimized), while the last two requirements are related to robot capabilities. As in many robot planning approaches where global tasks are accomplished, we do not account for inter-robot collisions when developing movement plans. In real applications, such collisions can be avoided by using local rules during the actual movement, and the time (or energy) offset induced by such rules is negligible with respect to the total movement time (or required energy). Clearly, in some cases Problem 1 may not have a solution due to insufficient available energy for robots, these situations will be discussed during solution description. **Example 1**. : _For supporting the problem formulation and solution development, we introduce an example that will be discussed throughout the next sections. Thus, we assume an environment abstracted to the graph from Figure 1, composed by \(10\) nodes \((v_{1},v_{2},\ldots,v_{10})\), with the deposit \(v_{10}\). The costs for moving between adjacent nodes are marked on the arcs from Figure 1, e.g., \(c(v_{10},v_{8})=2\). The team consists of 3 robots labeled with elements of set \(R=\{r_{1},r_{2},r_{3}\}\). For simplicity of exposition, we consider \(\gamma=1\) (the constant that links the moving energy with necessary time) and equal amounts of energy for robots, \(\mathcal{E}(r)=100\), \(\forall r\in R\). There are \(14\) samples scattered in this graph (labeled with numbers from \(1\) to \(14\)), with locations given by map \(\pi\), e.g., \(\pi(9)=\pi(10)=v_{3}\)._  
_The problem requires a sequence of movements for each robot such that all the \(14\) samples are gathered into storage \(v_{10}\), each robot being able to carry one sample at any time._
## 5 Conclusions  
In this paper, a method for noise-robust voice conversion using high-quefrency boosting via sub-band cepstrum conversion and fusion using BLSTM and statistical filtering was proposed. A divide-and-conversion scheme of high-dimensional cepstral coefficients and signal filtering methods were applied to improve the noise robustness of the system by comparing it with several recently proposed methods of voice conversion on the CMU ARCTIC and THCHS-30 databases. It was found that the converted voice obtained by the proposed method showed a relatively high naturalness and similarity. Both objective and subjective experiments confirmed the effectiveness of the proposed method. Meanwhile, the proposed method was able to improve the robustness of the voice conversion system, especially with the noisy inputs of source speakers.  
Due to the increase of the network size and the expansion of the feature dimension, the time required for conversion of the proposed method was slightly longer than those of the baselines.  
Figure 6: Spectrogram of the methods of voice conversion: (**a**) source voice with babble noise at 15 dB, (**b**) clean target voice, (**c**) BLSTM conversion, (**d**) the proposed method, (**e**) DIFFGMM conversion, and (**f**) GMM conversion.  
As future work, we would like to study how to improve the network structure to further improve the robustness of the system and to accelerate the conversion to make it real-time.  
Conceptualization, X.M. and M.S.; methodology, X.M.; validation, X.M. and M.S.; formal analysis, Y.W.; investigation, M.S.; data curation, X.M.; writing--original draft preparation, X.M. and M.S.; writing--review and editing, M.S. and X.Z.; supervision, M.S. and X.Z.; project administration, M.S. and X.Z.; funding acquisition, M.S. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Jiangsu Province (grant number BK20180080) and the National Natural Science Foundation of China (grant number 61471394).  
The authors declare no conflict of interest.
## 3 Critical Metrics for Industrial Monitoring and Control
### Scalability  
As industrial processes increase in complexity, the number of points that need to be monitored and controlled increases rapidly. This makes it essential to design network architectures which are capable of scaling up. In other words, the objective is to ensure optimal network performance even when the network size or rate of data generation increases.  
Current wireless technologies designed specifically for industrial applications such as WirelessHART, WIA-PA (in centralized management scheme) and ISA100.11a mostly use a centralized approach for managing resources. While centralized approaches are technically easier to develop and manage, they are unable to cope with sudden changes that might occur frequently in a harsh industrial environment. This problem is further exacerbated as the network is scaled up. For example, a motor capable of running at different speeds may cause radio interference at different frequencies as it changes its operational speed. Wireless nodes operating in the vicinity of the motor should ideally reorganize their communication protocols using distributed techniques as and when interference is detected to quickly adapt to the changing environment. Traditional centralized approaches are unable to cope with such sudden unexpected changes, as they would then require detailed network statistics to be sent back to the central system manager which would then clog up the limited network resources. Thus, the larger the scale of the deployment, the more important it is to utilize distributed approaches to ensure that the system continues to perform optimally.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 3 Locata Development History and Mathematical Models
### Locata Mathematical Model
#### 3.2.4 Measurement Errors and Biases  
(1) Clock biases  
There is no transmitter clock bias present in the measurement due to the tight time synchronisation of the LocataLites. However, the receiver clock is not synchronised to the LocataNet and therefore is subject to drift. The receiver clock offset has either to be introduced as an additional unknown into the LS estimation algorithm or eliminated by forming SD observables [33; 35; 37].  
(2) Cycle slips  
Cycle slips may occur in the Locata carrier phase measurement due to signal loss, signal obstructions, or internal receiver tracking problems. It has been reported that Locata cycle slips may be multiples of half cycles [35].  
(3) Tropospheric delay  
Traditional GNSS tropospheric models cannot be used since the Locata signal travels very close to the Earth's surface [48]. Microwave-based Electronic Distance Measurement (EDM) tropospheric correction models are recommended, such as the RTCA (2000) model, the Werner model [49; 50], the Bouska and Raquet model [34], the local differential corrections model [51], as well as the length-based correction models for EDM [52; 53].  
(4) Multipath  
Multipath is a serious error source due to the low elevation angles in a typical Locata scenario [21]. [PERSON] et al. investigated the influence of issues, such as different power levels and shielding options (metal plate and a choke ring) [54]. Nowadays, using the VRay antenna is perhaps the best option for mitigating against multipath disturbance.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 3 Analysis on 3D IoU Losses
The representation of the loss function is as follows,  
\[L_{EIoU_{3D}}=1-\text{IoU}+\frac{d^{2}(P,G)}{c^{2}}+\frac{d^{2}(w^{P},w^{G})}{ c_{w}^{2}}+\frac{d^{2}(l^{P},l^{G})}{c_{l}^{2}}+\frac{d^{2}(h^{P},h^{G})}{c_{h}^{2}} \tag{9}\]  
In Equation (9), in addition to the estimation of the distance between the centers of the boxes, the aspect ratio of the individual sides is taken into consideration, addressing the shortcomings of \(L_{DIoU}\) and \(L_{CIoU}\). Since this technique does not involve the sorting operation utilized in skewIoU, the loss function's convergence is also faster. However, the orientation of an object \(\theta\) needs to be more used in loss estimation. Two boxes with the same centers and aspect ratio but different orientations can have the same loss value. This requires additional operations in the analysis of the orientation of the objects [68]. The drawbacks of existing IoU loss functions can be summarized as follows:  
* Various IoU losses converge to simple IoU in cases of complete overlap of boxes, boxes with the same centers, and the same aspect ratio. * Axis-aligned IoU losses suffer poor regression for rotated bounding boxes. * Performance gap due to orientation of the objects. Figure 1: (**a–d**) Examples of axis aligned and rotated bounding boxes. Ground truth boxes are green, and prediction boxes are red.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 2 Functional Effects of EGCG
### Prevention/Alleviation of Bone Loss and Inhibition of Bone Resorption  
The preservation of proper bone structure can occur through several mechanisms beyond the promotion of osteogenic differentiation or mineralisation processes. [PERSON] et al. [34] showed that EGCG can prevent bone loss due to the suppression of the SAPK/JNK pathway in cloned osteoblast-like MC3T3-E1 cells derived from newborn mouse calvaria. In contrast, [PERSON] et al. [35] proved that EGCG treatment ameliorates bone loss and improves bone microarchitecture by enhancing osteogenesis after BMP-2 expression in the in vivo study conducted on ovariectomy-induced osteogenic rats (their proximal tibia and third lumbar spine were compared).  
The main cell population responsible for bone resorption is the osteoclast. Under physiological conditions, they maintain the proper mechanical strength of bones. Sometimes, under the influence of various factors, a pathological increase in bone resorption may occur. This can be prevented, for example, by inducing osteoclast death, thereby preventing/reducing bone resorption. [PERSON] et al. [36] found that EGCG treatment leads to the inhibition of bone resorption by inducing the apoptotic death of crude murine osteoclast-like multinucleated cells (OCLs), which involves the Fenton reaction affecting caspase-3 activity. Also, [PERSON] et al. [46] noted an apoptotic osteoclast cell death in the murine monocyte/macrophage cell line, RAW 264.7-cell-derived osteoclasts treated with EGCG. This process was caused by DNA fragmentation and caspase-3 activation. On the other hand, [PERSON] et al. [37] showed that the expression of matrix metalloproteinases-9 (_Mmp-9_) genes derived from primary osteoblastic mouse cells stimulated by _Porphyomonas gingivalis_ could be inhibited by EGCG. This study suggested that bone resorption may be inhibited not only through the induction of osteoclast apoptosis but also by modulating osteoblast-osteoclast signalling pathways.
## 3 Design Optimization
### Metamodeling  
To optimize the motor shape using the DOE results, metamodels must be established. Metamodeling is the process of approximating the DOE results in the form of a function. Although different metamodeling techniques could be used for different design problems, in previous studies, metamodels of objective functions and constraints were established by applying a single metamodeling technique [10; 11; 12; 13; 14; 15; 16]. In this study, we established metamodels by applying 13 techniques for each multi-objective function and constraint, and the most accurate technique was selected through accuracy evaluation. The accuracy of the metamodels was evaluated in terms of the root mean squared error (RMSE) [21]:  
\[\text{RMSE}=\sqrt{\frac{1}{\text{nEXP\_ts}}\sum_{i=1}^{\text{nEXP\_ts}}[y(X_ {i})-\hat{y}(X_{i})]^{2}} \tag{4}\]  
where nEXP_ts is the number of test points, \(y(X_{i})\) is the real function value, and \(\hat{y}(X_{i})\) is the metamodel value. Table 4 shows the results of the accuracy evaluation of the metamodels. It can be seen that when the custom multi-layer perceptron (MLP) technique, which is a deep learning algorithm, is applied, the accuracy of the metamodel is the best for the demagnetization rate and torque. When the simple cubic polynomial regression (PRG) technique and the Krigging technique are applied to the efficiency and torque ripple, the metamodel has the highest accuracy.  
Neural networks are complex structures made up of artificial neurons that can take multiple inputs and produce a single output. Neurons transform data mathematically, and neural networks can use these neurons to learn complex patterns. Neurons are mathematical functions, also known as perceptrons. One or more inputs are multiplied by weights and then passed the result to a nonlinear function called the activation function. A layer made up of multiple neurons is called a hidden layer. The MLP has been used for the shape optimization of electric devices [22]. MLP is a type of feedforward artificial neural network and has the advantage of being able to represent a nonlinear relationship between input and output variables. The metamodels of the demagnetization rate and torque are generated using the MLP technique. Various hyperparameters required to apply the MLP technique are optimized through the hyperparameter optimization function of PIAnO [21]. The number of hidden layers of the demagnetization rate is 2, and the number of neurons in each layer is 178 and 2. The number of hidden layers of the torque is 4, and the number of neurons in each layer ranges from 8 to 178. A rectified linear unit is used as the activation function of demagnetization rate and torque.  
Figure 7: Sensitivity analysis using screening.