esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this section, we compare the INSGA-II with other algorithms in terms of performance and solutions and evaluate the effectiveness of the dynamic adjustment by comparing the route solutions of the initial route and the dynamic adjustment stage. Considering the variations in individual preferences, constraints, and requirements in the practical applications, 20 tourist cases are designed in Table 4 according to the behavioral characteristics from mobile tracking data, in terms of category preference weights, objective importance weights, time budget, departure location, and final arrival location. The non-dominated sorting genetic algorithm II (NSGA-II), the multi-objective particle swarm optimization (MOPSO), the multi-objective ant colony optimization (MOACO), which are classical and widely used in solving the multi-objective TTDP [35], are used as baselines to compare with our INSGA-II. In addition to the above multi-objective optimization algorithms, the weighted sum method (WSM), one of the classical exact methods for multi-objective problems [63], is also involved in the comparison. To reduce random errors, each tourist's experiment is conducted independently 20 times using each method.  
Figure 10: Routes before and after the dynamic adjustment.
## 2 Methods
### Kalman Filter  
Kalman filter has a key role in derivation of stochastic subspace algorithm. The estimated state sequence \((\hat{x}_{k})\) of the system using non-steady Kalman filtr can be obtained through noise covariance. By solving the Lyapunov equation, the estimation value of the covariance matrices (\(Q\), \(R\) and \(S\)) are inferred. Knowing the system matrices (\(A\) and \(C\)) and the noise (\(W\) and \(V\)) together with covariance matrices are the parameters required for Kalman filtering and \(\Lambda\), \(G\), \(C\), and \(\Lambda_{0}\) could be simply derived.  
\[\left[\begin{array}{cc}\hat{Q}&\hat{S}\\ \hat{S}^{T}&\hat{R}\end{array}\right]=\frac{1}{j}\left[\begin{array}{cc}W \\ V\end{array}\right]\left[\begin{array}{cc}W&V\end{array}\right], \tag{18}\]  
\[\Sigma=E\left[x_{k}^{\prime}\left(x_{k}^{\prime}\right)\right]=A\Sigma A^{T}+Q, \tag{19}\]  
\[\Lambda_{0}=C\Sigma C^{T}+R, \tag{20}\]  
\[G=A\Sigma C^{T}+S, \tag{21}\]A forward innovation model is introduced in the formula of the Riccati equation as shown in Equations (22) and (23). The Riccati equation is solved using Schur decomposition.  
\[P=APA^{T}+\left(G-APC^{T}\right)\left(\Lambda_{0}-CPC^{T}\right)^{-1}\left(G-APC ^{T}\right)^{T}, \tag{22}\]  
\[K=\left(G-APC^{T}\right)\left(\Lambda_{0}-CPC^{T}\right)^{-1}, \tag{23}\]  
The Kalman filter gain \((K)\) is obtained from Equation (23). The state-space model of the output-only dynamic system can be presented by:  
\[\left\{\begin{array}{c}x_{k+1}=Ax_{k}+Ke_{k}\\ y_{k}=Cx_{k}+e_{k}\end{array}\right., \tag{24}\]  
The Kalman gain is estimated through the Kalman process to update the future data corresponding to a certain pattern, independent of the noise covariance data.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Feature Extraction
#### 3.2.2 Local Feature Extraction Module  
A complete footstep typically includes the processes of heel strike, forefoot contact, and toe-off, which manifest as distinct signal waveforms in the time domain. These waveforms contain local features, such as shape, duration, and amplitude variations, which are crucial for distinguishing different walking patterns and gait information and for precise motion localization. Therefore, following global feature extraction, this section utilizes a 1D-ResNet to thoroughly extract the local time-frequency domain characteristics of the signal and addresses the performance degradation issue associated with increasing network depth.  
A 1D-ResNet consists of an initialization module and four structurally similar residual modules. The feature matrix \(\overline{\mathbf{U}}^{r}\) first passes through the initialization module with a convolutional kernel size of \(1\times 7\) and a stride of 2, which preliminarily identifies key shapes in the signal, such as rising and falling edges, sharp changes, flat sections, etc., to obtain an initial feature representation \(\mathbf{D}_{0}^{r}\). Subsequently, the signal enters the four stacked residual modules, each outputting channel quantities of 64, 128, 256, and 512. Each residual module contains two residual units, each consisting of two convolutional layers and a residual connection. To fully capture local time-frequency characteristics, small \(1\times 3\) convolutional kernels are used in each convolutional layer [25]. This study draws on the idea of residual structure, different from traditional networks that directly learn input-output mappings. In this study, the \(l\)-th residual unit is retained to learn shallow features \(\mathbf{D}_{l}^{r}\) capturing local motion trends, which are used as input to the \((l+1)\)-th residual unit to further learn subtle changes in pedestrians' movements, resulting in local fine-grained residual features \(\Delta\mathbf{D}_{l}\). When the sizes of \(\mathbf{D}_{l}^{r}\) and \(\Delta\mathbf{D}_{l}^{r}\) are mismatched, \(\mathbf{D}_{l}^{r}\) is dimensionally aligned through a \(1\times 1\) convolution operation in the branch, followed by addition with \(\Delta\mathbf{D}_{l}^{r}\); otherwise, they are directly added. Finally, to enhance the model's nonlinear expressive power, the sum is passed through the ReLU function to obtain the output \(\mathbf{D}_{l+1}^{r}\) of the \((l+1)\)-th residual unit:  
\[\mathbf{D}_{l+1}^{r}=\text{ReLU}(\mathbf{D}_{l}^{r}+\text{F}(\mathbf{D}_{l}^{ r},W_{1},W_{2})) \tag{6}\]  
Here, F denotes the residual function, and \(W_{1}\) and \(W_{2}\) are the weight coefficient matrices of two convolutional layers. After computations through four residual modules, the output is the spatiotemporal feature \(\mathbf{D}_{8}^{r}\).
## 3 Results
### Microscopy Analysis  
Microscopic characterization was performed to examine filler dispersion, morphology, and interfacial features in the epoxy composites across different seashell powder (SSP) loadings. Digital microscopy images (Figures 7-11) illustrate the progressive changes in surface morphology with increasing SSP content. The pure epoxy polymer (Figure 7) exhibits a smooth and homogeneous surface without any particulate reinforcement, serving as a reference baseline. At 0.71 g (1.4 wt.%) SSP (Figure 8), slight particle dispersion begins to appear, suggesting initial integration into the matrix. At 1.75 g (3.5 wt.%) (Figure 9), the filler distribution becomes more pronounced, indicating improved but still moderate dispersion. At 2.79 g (5.5 wt.%) (Figure 10), filler dispersion is more evident, but microvoids and localized clustering begin to appear. Finally, at 3.5 g (7 wt.%) SSP (Figure 11), significant filler agglomeration and interfacial discontinuities are observed, indicating poor dispersion at higher loading levels.  
Figure 8: Microscope image of 0.71 g (1.4 wt.%) seashell-reinforced epoxy composite.  
Figure 10: Microscope image of 2.79 g (5.5 wt.%) seashell-reinforced epoxy composite.  
Figure 9: Microscope image of 1.75 g (3.5 wt.%) seashell-reinforced epoxy composite.  
Microscopic analysis confirmed that while moderate SSP loadings promote relatively uniform dispersion and acceptable interfacial bonding, higher loadings lead to clustering, voids, and degraded interfacial integrity. These morphological changes directly support the mechanical testing results and highlight the importance of optimizing SSP content to balance reinforcement effectiveness and matrix compatibility.
## 2 Materials and Methods
### HPLC-ESI-TOF-MS Analysis  
The analysis of the artichoke by-products PLE extracts was carried out by high-performance liquid chromatography (HPLC) with a diode array detector (DAD) coupled to time-of-flight mass spectrometry with an electrospray interface (ESI-TOF-MS). The instrument was an Agilent 1200-RRLC system (Agilent Technologies, Palo Alto, CA, USA) equipped with a vacuum degasser, a binary pump, an auto-sampler, a thermostatically controlled column compartment, and a DAD detector. Mass detection was performed using a micrOTOF II analyzer from Bruker Daltonik (Bremen, Germany). The extracts were dissolved in aqueous ethanol (50:50, v/v) at 5 mg/mL and analyzed in triplicate. Before the HPLC injection, the samples were filtered through a 0.45 \(\upmu\)m syringe filters of regenerated cellulose to avoid solid particles.  
The separation occurs in an Agilent Zorbax Eclipse Plus C18 column (150 mm \(\times\) 4.6 mm id, 1.8 \(\upmu\)m). The mobile phases consist of water with 0.1% formic acid (A) and methanol (B) at a flow rate of 0.8 mL/min. The analytical method used an elution gradient according to the following linear multi-step profile: 0 min, 5% B; 3 min, 50% B; 25 min, 75% B; 35 min, 100% B, 40 min, and 5% B plus 5 min of stabilization at initial conditions before the next analysis. The analyses were carried out at 25 \({}^{\circ}\)C injecting 10 \(\upmu\)L of sample in each run.  
Detection was performed within a mass range of 50-1000 \(m\)/z operating in negative ionization mode. In order to achieve a stable ionization, the flow rate from the HPLC was split before the connection to the ESI interface (Agilent Technologies) until 125 \(\upmu\)L/min. Ultrapure nitrogen was used as drying and nebulizing gas with flows of 9.0 L min\({}^{-1}\) and 2.0 bar, respectively. The operating parameters applied for the ionization and ion transfer were: capillary voltage of +4.5 kV; drying gas temperature, 190 \({}^{\circ}\)C; capillary exit, \(-150\) V; skimmer 1, \(-50\) V; hexapole 1, \(-23\) V; RF hexapole, 100 Vpp; and skimmer 2, \(-22.5\) V.  
In order to recalibrate the mass spectra acquired during the analysis to achieve accurate mass measurements with a precision better than 5 ppm, 10 mM sodium formate solution was used as calibrant. This mixture is automatically injected at the beginning of each run by means of a 74900-00-05 Cole Palmer syringe pump (Vernon Hills, IL, USA) directly connected to the ESI interface, equipped with a Hamilton syringe (Reno, NV, USA). All data acquisition and processing operations were controlled with HyStar 3.2 and Data Analysis 4.0 software, respectively (Bruker Daltonics GmbH, Bremen, Germany). The software provides a list of possible elemental formulas using the Generate-Molecular Formula Editor. This information provided by the analytical platform was used for identification purposes together with the one reported in databases and bibliography.
## 3 Results and Discussion
### Characterization of Films Processed at Pilot Scale
#### 3.2.5 Overall Migrations  
The results of the overall migration tests carried out are shown in Table 4 for the three food simulants used and for all the developed materials. A row with the values of the ML material without the layer containing \(\beta\)-carotene has also been included for the sake of comparison. Obtained results indicated that in all tested conditions, the determined overall migration values are below the established limit of 10 mg dm\({}^{-2}\). It is important to note that migration values obtained using the alternative simulants were corrected according to the legislation (REGLAMENTO (UE) No 10/2011).  
A Tukey test for multiple pairwise comparisons (confidence level 95%) was applied to evaluate differences in overall migration data. Results indicated that, related to the ML films, the obtained overall migration values by using isooctane as simulant were higher than those registered with the ethanol-based simulants. When the material containing the layer with \(\beta\)-carotene was tested, results for the overall migration by using 10 % (\(v/v\)) ethanol was lower than those attained with both 95% (\(v/v\)) ethanol and isooctane. As it was expected, higher overall migration values were obtained for the films containing \(\beta\)-carotene in comparison with the control ones. It should be stressed that during the migration assays, the layer that was in contact with the simulant was that containing \(\beta\)-carotene, hence the increase of the overall migration values could be attributed to material migrating from this layer.  
The obtained results are in line with previous works which describe the addition of an Azteca marigold extract, rich in carotenoids, in mono and bilayer polyethylene films, and its diffusion into 95% (\(v/v\)) ethanol simulant [36]. The described films had a positive effect on soybean oil oxidative stability as a result of the migration of the carotenoids, among them astaxanthin, present in the films [21].  
In this line, a positive effect of the developed films on the oxidative stability of fatty foods was expected because of the higher amount of carotenoids migration in ethanol 95 % (\(v/v\)) and isooctane in comparison with ethanol 10% (\(v/v\)), as it can be observed in Table 4. The obtained results highlight the importance of food simulant selection in the analysis of food-packaging interaction depending on the final application of the developed materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EHOH 10\% (\(v/v\))** & **EHOH 95\% (\(v/v\)) *** & **Isooctane *** \\ \hline ML & 0.2 \(\pm\) 0.1 & 0.09 \(\pm\) 0.04 & 0.44 \(\pm\) 0.02 \\ ML/LDPE\_CAR & 1.6 \(\pm\) 0.5 & 2.62 \(\pm\) 0.16 & 3.32 \(\pm\) 0.14 \\ \hline \hline \end{tabular}
* Corrected.  
\end{table}
Table 4: Overall migrations values (in mg dm\({}^{-2}\)) in ML and ML/LDPE_CAR films.
###### Abstract  
MISTiC Winds is an instrument and constellation mission approach to simultaneously observe the global thermodynamic state and the vertically resolved horizontal wind field in the troposphere from LEO SSO. The instrument is a wide-field imaging spectrometer operated in the 4.05-5.75 \(\upmu\)m range, with the spectral resolution, sampling, radiometric sensitivity, and stability needed to provide temperature and water vapor soundings of the atmosphere, with 1 km vertical resolution in the troposphere-comparable to those of NASA's atmospheric infrared sounder (AIRS). These instruments have much higher spatial resolution (<3 km at nadir) and finer spatial sampling than current hyperspectral sounders, allowing a sequence of such observations from several micro-satellites in an orbital plane with short time separation, from which atmospheric motion vector (AMV) winds are derived. AMVs for both cloud-motion and water vapor-motion, derived from hyperspectral imagery, will have improved velocity resolution relative to AMVs obtained from multi-spectral instruments operating in GEO. MISTiC's extraordinarily small size, low mass (<15 kg), and minimal cooling requirements can be accommodated aboard an ESPA-class microsatellite. Low fabrication and launch costs enable this constellation to provide more frequent atmospheric observations than current-generation sounders provide, at much lower mission cost. Key technology and observation method risks have been reduced through recent laboratory and airborne (NASA ER2) testing funded under NASA's Instrument Incubator Program and BAE Systems IR&D, and through an observing system simulation experiment performed by NASA GMAO. This approach would provide a valuable new capability for the study of the processes driving high-impact weather events, and critical high-resolution observations needed for future numerical weather prediction.  
atmospheric motion-vector winds; vertical wind profile; infrared temperature sounding; water vapor sounding; moisture sounding (Not export controlled per ES-C4 ISR-073019-0179, Approved for Public Release; Unlimited Distribution)
# Optimizing Service Placement for Microservice Architecture in Clouds
## 1 Introduction
In order to achieve a desired performance of a service-based application, cluster schedulers have to carefully place each service of the application with respect to the resource demands and traffic demands. Recent cluster scheduling methods mainly focus on the cluster resource efficiency or job completion time of batch workloads [1; 2; 3]. For instance, Tetris [4], a multi-resource cluster scheduler, adapts heuristics for the multi-dimensional bin packing problem to efficiently pack tasks on multi-resource cluster. Firmament [5], a centralized cluster scheduler, can make high-quality placement decisions on large-scale clusters via a min-cost max-flow optimization. Unfortunately, these solutions would face difficulties for handling service-based applications, as they ignore the traffic demands when making placement decisions. Some other research works [6; 7] concentrate on composite Software as a service (SaaS) placement problem, which try to minimize the total execution time for composite SaaS. However, they only focus on a set of predefined service components for the application placement. For traffic-aware scheduling, relevant research solutions [8; 9] are proposed to handle virtual machine (VM) placement problem, which aims to optimize network resource usage over the cluster. However, these solutions rely on a certain network topology, while most of existing cluster schedulers are agnostic to network topology. In particular, it is hard to get the network topology information when deploying a service-based application on a virtual infrastructure. In this paper, we propose a new approach to optimizing the placement of service-based applications in clouds. The objective is to minimize the inter-machine traffic while satisfying multi-resource demands for service-based applications. Our approach involves two key steps: (1) The requested application is partitioned into several parts while keeping overall traffic between different parts to a minimum. (2) The parts in the partition are packed into machines with multi-resource constraints. Typically, the partition can be abstracted as a minimum k-cut problem; the packing can be abstracted as a multi-dimensional bin packing problem. However, both are NP-hard problems [10; 11]. To address these problems, we first propose two partition algorithms: _Binary Partition_ and _K Partition_, which are based on a well designed randomized contraction algorithm [12], for finding a high quality application partition. Then, we propose a packing algorithm, which adopts an effective packing heuristic with traffic awareness, for efficiently packing each part of an application partition into machines. Finally, we combine the partition and packing algorithm with a resource demand threshold to find an appropriate placement solution. We implement a prototype scheduler based on our proposed algorithms and evaluate it on testbed clusters. The results show that our scheduler outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.
## 6 Conclusions  
This study provides the coverage range analysis of the LoRa and NB-IoT technologies for LPWAN-based agricultural vehicle tracking applications. The main contribution is comparing the upper bounds of LoRa and NB-IoT technologies in urban, suburban,  
Figure 12: The coverage analysis of LoRa connectivity with XIRIO online tool.  
Figure 13: The coverage analysis of NB-IoT connectivity with XIRIO online tool.  
and rural propagation environments by deriving explicit mathematical expressions. On this basis, the comparative analysis was made using the Hata path loss model, which investigates the power decay based on the frequency and antenna heights. Also, to show the effect of terrain irregularities on the coverage range, the obtained simulation results were supported by the coverage maps from the XIRIO Online radio planning tool. From the presented theoretical coverage analysis, it can be concluded that the NB-IoT provides extended coverage compared to the LoRa. Since agricultural activities are usually carried out in rural areas worldwide, both the LoRa and NB-IoT technologies are viable solutions for vehicle-tracking applications in large agricultural fields. Thanks to its cellular network independent cloud access opportunity, LoRa connectivity offers a more cost-effective operation without paying monthly access fees to mobile operators for connected vehicles. Besides, NB-IoT connectivity can be attractive due to the service continuity guarantee advantage via existing mobile communication infrastructure when LTE BS is deployed in the agricultural vehicles' coverage area.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 7 Experiments Results and Analysis
### Experimental Dataset and Setting  
In order to evaluate the performance of our personalized recommendation, experiments were conducted on a dataset sampled from YouTube-8M dataset. YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs (Identity Documents) and associated social tags from a diverse vocabulary of 4700+ visual entities. Since each video in our dataset is between 120 and 500 seconds long, this dataset is available for our mobile video recommendation experiments.  
The deep feature extraction and key frame detection are implemented under the Ubuntu system using TensorFlow platform (Google, Mountain view, California, USA) and Python programming language. The remaining experiments are completed in a PC with 3.60 GHz CPU, 4.00 G memory and windows 7 operating system, Intellij IDEA programming, and SQL Lite database. It's worth mentioning that Intellij IDEA is an integrated environment for Java programming language development. We used Java as the main technical framework in Windows.  
To verify the superiority of the proposed method, we compared with other user preference modeling methods.  
* Video topic method [22]: this method represents user preference modeling by a topic model that has a closer distribution with the user's history.
* Tag ranking method [24]: this method builds user preference modeling by ranking all the social tags associated with all videos that the user has watched.