esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
###### Abstract  
Contemporary murals and street art play a critical role in urban culture, serving as platforms for social activism and reflecting the vibrancy of city life. This study within the SuperStaAr project framework examines the challenge of graffiti removal while safeguarding the original synthetic paint layers. Through a detailed investigation using Q-Switch and Long Q-Switch lasers (Nd:YAG), we evaluate the effectiveness and safety of laser cleaning techniques on both unaged and artificially aged mural mock-ups. The initial findings highlight the Q-Switch and Long Q-Switch lasers as promising for removing graffiti without compromising the paint integrity. Our assessment criteria--encompassing residue presence, surface roughness, color changes, cleaning effectiveness, and pigment pickup--were validated through empirical evaluation and supported by colorimetric, micro-ATR-FTIR, and Py-GC/MS analyses. Notably, the incorporation of a passive sampling system for Py-GC/MS analysis facilitates a deeper understanding of the ablated materials without direct sampling from the artwork. This research contributes a foundational framework for the evaluation of laser cleaning in mural conservation, emphasizing the importance of tailored strategies to enhance the sustainability of urban art conservation efforts.  
laser cleaning; street art; sustainable conservation; cultural heritage; acrylic paint; alkyl o paint +
Footnote †: journal: Applied  
0000-0001-4300-2277-2233]C.R.R.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 2 Related Research
### Overview of Satellite Task Planning Algorithms
[26] designed a heuristic algorithm to solve the multi-satellite multi-orbit task planning problem, with the goal of satisfying user requests and various observation constraints. [PERSON] et al. [27] designed a heuristic neighborhood search algorithm to solve the problem of satellite and ground receiving station scheduling problems. When [PERSON] [28] studied the task planning of the Earth observation satellite, he used a two-stage planning algorithm based on relaxation map planning, which combines a heuristic search with plan evaluation technology, and obtained a time-stamped sequential planning solution. [PERSON] [29] designed an improved heuristic search algorithm, combining the conflict-oriented algorithm and the arc-compatible algorithm to improve the efficiency of the solution. [PERSON] et al. [30] proposed a rolling plan heuristic algorithm, which uses successive multiple local plans, instead of one global plan. [PERSON] et al. [31] designed a multi-satellite joint task planning and data transmission scheduling algorithm framework for multi-satellite ground observation system joint task planning and designed a task scheduling algorithm, based on task priority. While the intelligent optimization algorithm has better solutions, the computational efficiency is lower. If the satellite task changes greatly and cannot be processed in time, the intelligent optimization algorithm cannot adapt to the requirements of in-orbit dynamic tasks. Heuristic algorithms are used to solve specific problems. While it is not guaranteed to find the best planning results, it can find a solution that meets the planning requirements in a short time. It has the advantages of a simple and easy implementation, fast and efficient solution, and intuitive and easy participation in the formulation of rules. It can be used as the basis for the algorithm design of intelligent satellite task planning.
## 6 Magnetic Properties
### Power Loss
#### 6.2.2 Eddy Current Loss  
The generation of eddy currents in the core results in resistive loss. The magnitude of this loss is inversely proportional to the resistance of the core material. One effective way of mitigating eddy current loss is by laminating the core material. Lamination involves using thin sheets of the core material, which helps reduce the flow of eddy currents.  
According to [PERSON]'s law, an adjustment in the magnetic field induces a counteracting electromotive force (emf). For of this induced emf, currents are produced within the core that do not contribute to the useful output power; instead, they dissipate as heat. This phenomenon is known as eddy current loss is a significant portion of total energy loss in electrical devices. To minimize eddy currents, various approaches can be employed, such as using thinner laminated sheets, increasing the silicon content, and reducing the grain size. Thinner laminations confine the flow of electrons within the layers, preventing the formation of wider current loops, as depicted in Figure 10.  
While reducing the thickness of the core material seems advantageous with respect to minimizing eddy current loss, there are some drawbacks to consider. Thinner sheets can lead to instability in secondary-recrystallization behaviour, and the decrease in sheet thickness can also contribute to higher hysteresis loss due to increased surface roughness, resulting in more overall loss [58]. Therefore, finding an optimal thickness is essential to strike the right balance.  
To enhance the resistivity of steel and reduce eddy currents, it can be beneficial to increase its silicon content. For instance, [PERSON] [59] raised the silicon content from 0.21-2% in an electrical steel, resulting in an increase in resistivity from 15-38 \(\upmu\Omega\)-cm.  
Eddy currents, in the case of uniform magnetic fields, uniform materials, and no skin effect, can be calculated using Equation (1) [58].
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 2 Materials and Methods
### Problem Formulation
#### 2.1.3 Neural Networks  
NNs are usually applied to classification or regression tasks [43; 44; 45; 46]. The neural network algorithm consists of inputs, weights (parameters), aggregation functions, activation functions and outputs. When we apply the multilayer networks, the inputs of the first layer are then the predictors from the training set, but the inputs of the next layers are defined as outputs of the activation function of the current layer. For the \(j\)th layer the aggregation function is a linear combination of the inputs from the previous layer \(Z_{j-1}(x)\) and weights \(w_{j}\) including the bias \(\alpha_{j}\), thus the aggregation function is defined as \(w_{j}Z_{j-1}(x)+\alpha_{j}\) for \(j=1,2,...,K\). The result of the aggregation function is an argument of the activation function \(g_{j}\), as follows:  
\[Z_{j}(x)=g_{j}\big{(}w_{j}Z_{j-1}(x)+\alpha_{j}\big{)}, \tag{8}\]  
The result obtained from neural the network is an outcome of the activation function form the last layer (\(K\)th layer). Thus, from (8) the last layer is defined as follows:  
\[\begin{array}{l}Z_{K}(x)=g_{K}(w_{K}g_{K}(w_{K-1}Z_{K-2}(x)+\alpha_{K-1})+ \alpha_{K})=\\ \qquad\qquad\qquad\qquad\qquad\qquad=g_{K}(w_{k}g_{K-1}(w_{K-1}g_{K-2}(\dots g _{2}(w_{2}g_{1}(w_{1}x+\alpha_{1})+\alpha_{2})\dots)\\ \qquad\qquad\qquad\qquad\qquad\qquad+\alpha_{K-1})+\alpha_{K})\end{array} \tag{9}\]  
Neural network identification for regression problems consists of solving the following problem:  
\[\min R\big{(}\{w_{j}\}_{1\leq j\leq K},\big{\{}\alpha_{j}\}_{1\leq j\leq K} \big{)}. \tag{10}\]  
where the objective function denotes the mean squared error between the output variable and the last layer \(Z_{k}(\cdot)\) of the neural network and is defined as follows:  
\[R\big{(}\{w_{j}\}_{1\leq j\leq K},\big{\{}\alpha_{j}\}_{1\leq j\leq K}\big{)}= \frac{1}{n}{\sum_{i=1}^{n}(y_{i}-Z_{K}(x_{i}))^{2}}. \tag{11}\]  
The backward propagation algorithm is usually applied to solve task (10).
# Pullout Behavior of Nail Reinforcement in Nailed Soil Slope
## 2 Materials and Methods for Soil Nail Reinforcement Pullout Test
### Nailed Soil Slope Modeling  
The laboratory models with a scale of 1:10 and a soil slope of 400 were prepared to determine the pullout resistance of the nails at different footing pressures. The slope height (H) is taken as 700 mm. The construction of the slope-nailed system and loading stages are shown in Figure 8. The footing pressure is applied on the slope nailed system through a footing plate of a width (B) of 150 mm. The footing plate is placed at 1.5 times the width of the plate (i.e., X = 225 mm) from the crest of the slope. The length of horizontal nails (Ln) is taken to be equal to slope height (700 mm) in the models. The vertical spacing (Sv) and horizontal spacing (Hv) of nails are provided as 0.4 times the slope height. Readings for force and displacement in the nail are measured during the construction of the slope and after each increment of footing pressure (q) from 5.0, 10.0, 20.0, to 30.0 kPa.
## 5 Conclusions  
This study proposed a semantic map, a so-called value structure that is fit to a product or service usage so that it may be used as an evaluation framework. Values may vary over contexts and target products or services. Hence, a value structure for a certain product or service may vary. To confirm the possibilities of developing a value structure using network analysis, this study conducted a case study of smart TV gesture interactions. Network analysis was then used to extract representative value dimensions from participant response during and after gesture interactions.  
In terms of gesture interaction on smart TVs, convenience, fun, and fatigue turned out to be core value dimensions among associated value dimensions such as a desire to use, independent, novel, learnability, easy, quality of life, familiar, and compatibility. Some nodes such as gesture shape, type of control, and exactness were designated as design variables, which were supposed to be properties of smart TV gesture interactions or to depend on system performance. In addition, value structure models, based on node strengths, were formulated and confirmed with a post hoc analysis.  
One of the major contributions of network analysis is to transfer qualitative descriptions to quantitative data. Users' perceptions or opinions generally consist of narrative descriptions. Once evaluation dimensions such as the luxuriousness of a certain product are given, users may rate the product on a certain numerical scale. However, manipulated numbers risk distorting UX in reality. If a user's descriptions toward a specific product or service can be quantitatively analyzed, the loss of information can be decreased.  
Although this study suggested a methodology for building a value structure for a specific product or service, it could have limitations, and future work remains to be done. First, probing for latent or unconscious values, some of which may turn out to be important, should be explored to enhance compatibility between value structure and network analysis. Second, applying network analysis to other products or services should be done to validate the reliability of the framework. Consequently, the value structuring method suggested in this study is expected to be used by designers to examine alternative analyses of various product or service families.  
**Author Contributions:** Conceptualization and methodology, J.H.L.; formal analysis, Y.L.R.; writing and editing, J.H.P.  
**Funding:** This work was supported by the Incheon National University Research Grant in 2018 (Grant No.: 2018-0242).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Results
### Main Result
Compared with the Swin-Unet model, the dice coefficients are increased by 0.86%, 0.86%, and 0.75% respectively. Swin-Unet is a pure medical image-segmentation model similar to UNet. First, the tagged images are input into the encoder-decoder architecture based on the Transformer. Moreover, skip connections are used to learn local and global semantic features. Swin-Unet has a higher segmentation accuracy and a weaker ability to extract local semantic feature information compared with our model. Moreover, the model slices 3D feature maps into 2D feature maps and inputs the model without a good integration of 3D spatial semantic feature information. In addition, we compared the proposed model with the classic model nnU-Net in the field of medical image segmentation. The dice coefficients for the ET, TC, and WT brain-tumor regions increased by 1.72%, 0.31%, and 0.08%, respectively. nnU-Net is an ensemble of multiple models that has performed well in various medical image-segmentation challenges, including brain-tumor segmentation. The comparison results with the nnU-Net model also demonstrate the wide applicability and efficiency of our proposed model in the field of medical image segmentation.
## 2 Materials and Methods
### Delivery Vehicles and Trip Details  
The average vibration levels of different last-mile delivery vehicles within Seoul, Korea, were measured; more than half (58%) of Korea's parcel delivery volume is concentrated in the Seoul capital area [38]. Seoul was divided into four quadrants: northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Then, two to three last-mile delivery vehicles from each quadrant were selected for use in measuring the vibration levels. In total, the last-mile vibration levels of 10 small box trucks were measured (Figure 1). Measurements took place from the beginning to the end of their last-mile delivery service. All last-mile delivery vehicles traveled to the local delivery area from the sub-terminals where they belonged. The sub-terminals covering the Seoul area were mostly located in areas slightly beyond the direct delivery zones, where the population density is relatively lower. Therefore, during the initial stages of delivery, they accessed these locations using highways. Upon entering the delivery zones, delivery vehicles navigated through urban roads, allowing them to experience various road conditions. How long each truck was driving, but not idling (duration in motion), was determined to avoid artificially lowering the measured vibration intensity (described in detail in Section 2.3) and to provide recommended lab testing durations. The average length of time in motion of the investigated vehicles was 81 min (Table 1).  
Two different models of small box trucks that were similar to each other and common for last-mile delivery in Korea were selected for data collection: Bongo 3 (Kia, Seoul, South Korea) and Porter 2 (Hyundai Motor Company, Seoul, South Korea). Load capacity differed, even between the same models, due to modified (specialized) cargo boxes on some of the trucks and model year differences. The specifications and delivery areas of each box truck are listed in Table 1.
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
###### Abstract  
With the development of precise positioning with multi-GNSS, the inter-system bias (ISB) has become an issue that cannot be ignored. ISB is introduced from the differences among satellite reference clocks and different receiver hardware delay biases. To analyze the characteristics of multi-GNSS ISB, the precise point positioning (PPP) with full-rank uncombined model was derived for GLONASS, BDS, GALILEO, while the GPS receiver clock was selected as the reference. In addition, a recommended ISB parameter processing model was adopted. Data of 28-days from the Multi-GNSS Experiment (MGEX) station was used to estimate and analyze the ISB parameters. Based on a statistical analysis of the acquired data, results demonstrate that: (a) The rms of multi-GNSS PPP positional bias can reach 4.6 mm, 3.4 mm and 8.5 mm for E, N and U directions, respectively, which guarantees the reliability and accuracy of the ISB parameter solution. (b) The intra-day ISB time series of the three groups is relatively stable with standard deviations less than 0.6 ns. The ISB parameters between the GALILEO and GPS system are the most stable and the standard deviation was the smallest, at about 0.37 ns, which may be related to the good signal quality of the GALILEO system. (c) The mean of the single-day solution of the ISB parameter is not stable and the amplitude of the jump can be up to 60 ns. However, each station shows a similar variation for the same ISB parameter on the same day. The situation is independent of the type of receiver and antenna; however, it may be affected by the satellite reference clock of different systems. (d) There is a clear relationship between the ISB parameters and receiver types.  
uncombined model; inter-system bias; multi-GNSS; receiver type; precise point positioning +
Footnote †: journal: Sensors
###### Abstract  
In our previous study, _Lentzea_ sp. JNUCC 0626 was isolated from Hwasun Gotjawal on Jeju Island, and its melanogenic effects were confirmed in B16F10 melanoma cells through the identification of 1-acetyl-\(\beta\)-carboline. In this study, we conducted a comprehensive taxonomic characterization of _Lentzea_ sp. JNUCC 0626, including enzymatic activities, carbohydrate metabolism, growth conditions, and cellular composition. Major fatty acids identified were iso-C16:0, iso-C15:0, and C15:0 anteiso, with polar lipids such as diphosphatidylglycerol, phosphatidylethanolamine, and several unidentified lipids. Ubiquinone Q-9 was determined as the predominant respiratory quinone. Enzymatic activity analysis (API ZYM) showed alkaline phosphatase, esterase (C4), esterase lipase (C8), and leucine arylamidase activities, while carbohydrate metabolism analysis (API 50 CHB) indicated acid production from esculin alone. Complete genome sequencing revealed a 10,602,950 bp linear chromosome and a 177,940 bp plasmid. This plasmid encodes essential plasmid-related genes, including a Type IV secretion system and ParA proteins critical for plasmid transfer and stability. These findings suggest that the plasmid in _Lentzea_ sp. JNUCC 0626 could be utilized for developing host-vector systems to facilitate the combinatorial biosynthesis of novel bioactive compounds. Comparative genomic analysis identified _Lentzea_ pudingi CGMCC 4.7319 as the closest relative, but significant genetic divergence (dDDH 46.7%, ANI 88.02%) strongly supports the classification of _Lentzea_ sp. JNUCC 0626 as a novel species. AntiSMASH analysis revealed 34 biosynthetic gene clusters (BGCs), highlighting the strain's capacity to produce diverse bioactive compounds. Finally, the JNUCC 0626 extract exhibited concentration-dependent NO inhibition in LPS-stimulated RAW 264.7 cells, demonstrating significant anti-inflammatory activity. This suggests that the secondary metabolites inferred from genomic analysis may contribute to these observed bioactivities.  
Keywords:genome sequence analysis; Gojawal; _Lentzea_; mining; RAW 264.7 cells; secondary metabolites; taxonomy +
Footnote †: journal: Acta Microbiol. Hell.
## 3 Optimization of Vehicle Edge System Model
### Optimization Model  
The vehicle task is configured so that it cannot be fully locally computed or fully unloaded while still meeting the user's requirements for service quality. To put it another way, the scale factor is dynamically adjusted to accommodate the task's dynamic unloading, and the numerical relation is \(0<a_{vi}^{\tau}<1\). At the same time, in order to reduce the energy consumption of the system, before the load task processing, the vehicle in the system is matched to the appropriate service node, the matching vector is introduced \(V_{i}^{j}=[V_{1}^{j},V_{2}^{j},\dots,V_{N}^{j}](\tau)\), and the element \(V_{N}^{j}\) is the vehicle \(i\) selection to unload part of the task to the node \(j\).  
The total system energy consumption includes the local computing energy consumption, transmission energy consumption, and computing energy consumption, which can be expressed as \(e=E_{vi}^{local}+E_{vi,j}^{un}+E_{mcc}^{cal}\). In short, an optimization model is developed to minimize the system's overall energy usage, as illustrated below.  
\[\text{P1} \underset{\{V_{i}^{j}\mu_{vi}^{\tau},P_{vi}B_{vi}\}}{min} e\] \[s.t\] \[max\Big{\{}T_{vi}^{local},T_{vi,j}^{un}+T_{vi,j}^{wait}(\tau)+T_{vi,j}^{cal}\Big{\}}\leq T_{vi}^{max} \tag{7a}\] \[Q_{j}(\tau)\leq Q_{max}\,,\forall j\in J\] (7b) \[B_{vi}>0,\forall vi\in N\] (7c) \[\sum\limits_{vi=1}^{N}B_{vi}\leq B_{max}\] (7d) \[0<a_{vi}^{\tau}<1\] (7e) \[P_{vi}>0,\forall vi\in N\] (7f) \[\sum\limits_{vi=1}^{N}P_{vi}\leq P_{max}\] (7g) \[\eta =\{0,1\} \tag{7h}\]Constraint (a) ensures that the task processing time stays within the maximum allowable delay; constraint (b) ensures load balancing, and the backlog of each node cannot exceed the maximum threshold \(Q_{max}\). Constraints (c), (d), (f), and (g) stipulate that the bandwidth and computing power allocated by the server to the user cannot surpass its own maximum threshold \(B_{max},P_{max}\). Constraint (e) guarantees the requirements of the unloading ratio on the premise of satisfying the quality of service; constraint (h) represents two unloading instructions.
## 5 Experiments
### Experiment of Interference Monitoring  
To demonstrate the results discussed in the previous sections, three experiments were designed. The hardware setup for the experiments is shown in Figure 7. As can be seen the software receiver is used to capture the IF data to be analyzed and a SMU200A Vector Signal Generator (Rohde & Schwarz GmbH & Co. KG, Munich, Germany) is used to generate the broadband and narrowband interferences. The interferences are transmitted by a G5 Ant-52 PT1 transmitting antenna (Antcom corporation, Torrance, CA, USA) which are combined with the real GPS signal received by a HX-BS781A antenna (Harxon corporation, Shenzhen, China).  
Figure 5: Influences of different RFIs on _COP_\(\sigma\).  
Figure 6: The variation curves of _COP_\(\sigma\) to _COP_ mean ratio under different RFIs.
## 2 Materials and Methods
### Lower Limb Power Assessment  
To evaluate lower limb power, vertical jumps were conducted under the following conditions: The jumping exercises performed were the Squat Jump (SJ) and the Counter Movement Jump (CMJ). An optical measurement system, the Opto Jump Next(tm) (Microgate, Bolzano, Italy), was used to collect the data. Each transmission bar contained 96 LED lights (1.0416 cm resolution) in continuous communication with the receiving cell, measuring the contact times during the jumps with an accuracy of 1/1000 per second.  
Prior to data collection, the athletes performed a short warm-up (15 min) consisting of 6 min of light jogging, 5 min of dynamic stretching, and 4 min of exercise at the participart's discretion, followed by familiarization with the vertical jump protocol. Each subject performed three familiarization attempts, followed by three additional attempts for each jump condition (squat jump and countermovement jump), with a 30 s interval between them. To analyze the data, the height average (m) of the three valid jumps was calculated, and the relative power (W/kg) was determined for both CMJ and SJ.  
In the SJ, the athlete assumed a static position with the knee angle at approximately 90\({}^{\circ}\), attempting to maintain a vertical alignment of the trunk and with the hands positioned on the waist. At the assessor's command, the athlete performed a maximum vertical jump from a single concentric action. In the CMJ, the athlete commenced from a static standing position with their hands on their waist and proceeded to perform a maximum vertical jump utilizing the stretch-shortening cycle. The assessor directed the subjects to flex their knees to an angle of approximately 90\({}^{\circ}\) in the eccentric phase and jump as high as possible, utilizing the elastic energy generated. In both tests, the subjects were prohibited from flexing their knees during the flight phase of the jumps [19; 20; 21].