esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods
### Methods  
The purpose of this study was to examine the possible effects of HSR on the interregional flow of population and industrial restructuring. The specific mechanism can be found in Figure 2.  
We think that both city scale and the construction of HSR are essential to the dependency of a specific city upon another one. As the cross-section data only consider city characteristic variables and the time series data only reflect the development of HSR, the panel data integrate time-series dynamics with the cross-sectional shapes, which allow us to examine the effect of HSR on dependency from both perspectives at the same time. In view of the general form of the panel data model, we constructed the following model to deal with the empirical analysis in this study:  
\[RD_{ijt}=\alpha_{0}+\beta_{0}ktx_{it}+\sum\beta_{1}X_{it}+\delta_{i}+\rho_{t}+ \varphi_{it} \tag{2}\]  
where \(RD_{ijt}\) indicates the dependency of city \(i\) upon city \(j\) in year \(t\). \(ktx_{it}\) is a dummy variable that represents the connection of city \(i\) to the HSR network in year \(t\) (1, connected; 0, not connected). \(X_{it}\) is a set of city characteristic control variables, and \(\alpha_{0}\) is the intercept, which is independent of \(i\) and \(t\). \(\delta_{i}\) represents the time constant unobserved heterogeneity, \(\rho_{t}\) is the time dummy to control affecting factors that vary with time. \(\varphi_{it}\) denotes the error term, varies over \(i\) and \(t\).  
There are two forms of panel data model: the fixed effect model and random effect model. The former assumes that the individual effect is constant within a group, the intercept varies over individuals, the latter assumes that the intercept is constant over individuals and inter-individual variability is random. The suitable form of the model depends on the results of the Hausman test.  
Figure 2: Mechanism of how the KTX (Korea Train Express) construction affected interregional dependency.  
With respect to this study, the results of the Hausman test show that the fixed effect model is more suitable here (the specific results are not shown due to the limited space, but are available upon request).
## 4 Experiment Results and Discussion
### Experiments on the MNIST and Fashion-MNIST Dataset
#### 4.1.2 Conversion to SNN and Performance Evaluation
Figure 7: Average firing rates for the first convolutional layer of the SNN for the MNIST dataset [25; 26]. \[R_{k}^{l}=\frac{\sum_{i=1}^{n_{k}^{l}}\frac{S_{i}}{T_{inf/\sigma}}}{n_{k}^{l}} \tag{3}\]  
where \(n_{k}^{l}\) is the number of neuron at the \(k\)-th channel, \(i\) is the neuron index at the \(k\)-th channel, and \(S_{i}\) is the number of spikes of the \(i\)-th neuron for a latency of \(T_{infer}\). Figure 8 shows the performance (accuracy versus latency) for the compared methods. The proposed method (green line) shows better performance than the other methods [25; 26], even with low latency. The SNN models converted by the proposed method achieved a competitive accuracy of 92.11% in 512 time steps with a conversion loss of less than 0.1%. While reducing the latency up to 16 time steps, they maintained their accuracy without drastic loss. Table 3 shows the classification accuracy for different latencies of the compared methods on the Fashion-MNIST dataset. The proposed method shows better performance than other methods even for low latencies.
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach
### Security Policies of Information Flow in Architecture
#### 3.3.2 Security Policies of Information Flow under the Goals of Integrity  
An integrity policy of information flow is intended to prevent information from flowing to subjects or data with a higher integrity level and ensures that information only flows inside a security class or to a class with lower security level. Integrity refers to the feature that information cannot be destroyed, lost, or modified without authorization when it is transmitted, exchanged, stored, or processed. There are two kinds of modification, direct and indirect. Direct modification means service A performs the writing operation on service B, and indirect modification means that service A performs the reading operations on service B. Aiming at reading, writing, as well as reading and writing operations that occur during service invocation, we propose security polices as follows:  
**Policy 4.**_Policy for writing operation in service invocation under the goal of integrity._  
If there is an invoking service A and an invoked service B, A can perform the writing operation on B if and only if \(\text{I}_{\text{a}}\geq\text{I}_{\text{b}}\). The information flow is \(\text{A}\rightarrow\text{B}\). \(\text{I}_{x}\) stands for the integrity level of service \(x\).  
**Policy 5.**_Policy for reading operation in service invocation under the goal of integrity._  
If there is an invoking service A and an invoked service B, A can perform the reading operation on B if and only if \(\text{I}_{\text{a}}\leq\text{I}_{\text{b}}\). The information flow is \(\text{B}\rightarrow\text{A}\).  
**Policy 6.**_Policy for reading and writing operation in service invocation under the goal of integrity._If there is an invoking service A and an invoked service B, A can perform both reading and writing operations on B if and only if \(\text{I}_{\text{a}}=\text{I}_{\text{b}}\). The information flow is A \(\leftrightarrows\) B.
# Experimental and Numerical Investigation of Spray Scrubber Dust Collection Efficiency
###### Abstract  
Spray scrubbers are widely used in gas purification applications and allow fulfillment of the demands of air quality norms introduced all over the world. They effectively remove harmful gases like sulfur dioxide (SO\({}_{2}\)), nitrogen oxides (NO\({}_{3}\)), and particulate matter from industrial emissions, reducing their impact on air quality. Moreover, by capturing greenhouse gases such as carbon dioxide (CO\({}_{2}\)) in certain applications, spray scrubbers contribute to efforts to reduce climate change. Optimization of scrubber internal elements leads to reduced energy usage and lowered water mass flow while maintaining high pollutant removal efficiency. This makes them cost-effective in the long term. The demister is an additional device which is often used in scrubbing systems, and its main task is to prevent the water droplets from escaping through the upper part of the scrubbing chamber. In this article, the pollution (MgO particles) is introduced to the system upstream the scrubber inlet, the working fluid is air under atmospheric pressure, and water droplets are generated by a single nozzle placed inside the scrubber. Before the experimental part, a preliminary numerical analysis of gas velocity inside scrubber is performed and expectations of particle behavior are indicated. Then, the authors present the spray scrubber laboratory stand designed by them and carry out experimental research on it. Each element of the test stand is described in the article including the self-designed fluidizer, which effectively mixes MgO powder with air. The authors investigate the effect of their innovative construction of demister on separation efficiency and compare the results to the case without demister. The impact of water mass flow rate generated by the nozzle and gas inlet velocity on separation efficiency is presented for several investigated cases. The results show that demisters significantly improve the separation efficiency at lower water mass flow rates and successfully prevent water droplets from reaching the scrubber outlet. The measured separation efficiency was in the range of 80% for lower water mass flow rates up to 97% for the highest water flows.  
spray scrubber; experimental research; solid particles; gas cleaning; demister Article
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Propagation Models  
We examined how various propagation models affect the performance of PURs, focusing particularly on energy consumption and end-to-end latency, as illustrated in Figures 14 and 15. We considered four models: Winner Plus [30], Two-ray Ground [31], Long-Distance Pathloss [32], and ITU-R [33]. Each model has distinct characteristics influenced by environmental factors and propagation effects.  
Figure 14: Energy consumption of PURs under different propagation models.  
Figure 15: End-to-end latency of PURs under various propagation models.  
The Winner Plus model, commonly used in urban macro-cell environments, shows a moderate increase in energy consumption and latency with distance due to realistic urban propagation effects like shadow fading and multipath reflections. On the other hand, the Two-ray Ground model, which considers both direct and ground-reflected paths, results in significantly higher energy consumption and latency over long distances due to destructive interference and signal loss.  
The Long-Distance Pathloss model demonstrates a logarithmic increase in energy consumption and latency, reflecting compounded signal losses and increased power requirements over extended distances. Meanwhile, the ITU-R model, with its power-law path loss relationship, provides a balanced approach with moderate energy consumption and latency increases, making it suitable for various environments.  
While these findings highlight the influence of the chosen propagation model on energy consumption and latency, it is important to note that in practical scenarios, the energy consumption of the UE is also significantly affected by its operational behavior in response to the network conditions. For instance, the number of repetitions required to achieve reliable communication, adjustments in CE levels, and other adaptive behaviors of the UE play a crucial role in determining the actual energy consumption. Therefore, the selection of an appropriate propagation model should be complemented by a consideration of these UE behaviors to optimize the trade-off between energy efficiency and latency in NB-IoT networks.
## 4 Case Study
### Results in Terms of Damped Rocking  
The seismic response of the case study is discussed by comparative analyses in terms of the damped rocking expressed in terms of the force ratio (RF) and the displacement ratio (RD), where  
* The RF is the ratio between the shear force evaluated in the node connecting the roof to the facade in the equivalent FEM, and the total shear force evaluated at the base of the structure;
* The RD is defined as the ratio between the transversal displacement of the roof and the target displacement (chosen as the 0.5% of the total height of the perimeter walls).  
As an example, the first vibration mode shape, either for the 3D model or the equivalent FEM, is depicted in Figure 10, from lateral and top views, to show the transverse deformation that involves a great part of the structure's mass. Concerning the equivalent FEM adopted for the analyses, the first twenty natural periods and the corresponding percentage of the masses involved in the vibrational mode shapes are shown in Table 3. Moreover, the status of the yielding of the inelastic hinges is shown in Figure 11 for one of the accelerograms at the end of the seismic event so to detect whether the hinges are beyond the elastic limit. When the optimal value of the hysteretic variable \(\beta_{\text{-}HYS\text{-}opt}\) is reached (Section 2.2), it is possible to observe that the RD trend is the opposite of the RF one (Figure 12). When the value of \(\beta_{\text{-}HYS}\) exceeds 1.5, the RD does not significantly decrease, as occurs for lower \(\beta_{\text{-}HYS}\) values, while the RF continues to increase. This means that an over-stiffness configuration is not recommendable in the roof structure optimization to avoid the onset of extreme shear actions on the facade. The RD and RF values plotted in Figure 12 are calculated as the average ones obtained for the seven spectrum-compatible accelerograms.  
Figure 10: Deformed shape related to the first transversal vibrational mode for the 3D model (Iso and top views, **left**) and the equivalent FEM (Iso and top views, **right**).  
Figure 9: **(a)** The identification of the inelastic hinges in the FEM; **(b)** the cyclic responses of the roof’s inelastic hinges (on the **left**) and an example of an extracted single loop for hinge 1 with the SD model under accelerogram 1 (on the **right**); **(c)** the elastic–plastic responses of the walls’ inelastic hinges (on the **left**) and an example of an extracted single cycle for hinge 1 under accelerogram 1 (on the **right**).
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.6 Summary and Future Direction  
OSW is among the key components of ocean/atmosphere circulation. Accordingly, accurate knowledge of OSW is of substantial importance to understand and quantify different ocean-related characteristics. Despite the tremendous progress in the utilization of RS technology for quantitative and qualitative assessment of OSW, there are still different issues and challenges that need to be addressed in future research. These include but are not limited to improving the measurement accuracy and consistency of OSW products by integrating different data sources (e.g., buoys, satellite, and NWP), proposing novel automatic and/or semiautomatic methods for concurrently retrieving OSW and other related parameters (e.g., ocean currents and precipitation), developing new RS sensors to enhance the temporal resolution and spatial extent of global OSW products, integrating active and passive sensors to provide OSW products in all-weather conditions with the highest accuracy, and employing downscaling methodologies to enhance the temporal and spatial resolutions of OSW products.
## 1 Introduction  
To date, vaccines have been able to overcome the evolution of antibiotic-resistant strains, which makes vaccination one of the most cost-effective measures for fighting infectious diseases [1]. _Mycobacterium tuberculosis (Mtb)_ is included as 'critical' in the WHO priority list of research and development for new antibiotics effective against current resistant strains of tuberculosis (TB) [2]. Concurrently, new efficacious vaccines will be a very important tool to fight antimicrobial resistant TB (AMR TB) and are called to play an important role against this serious health issue [1].  
The potential of using TB vaccines to combat AMR TB has generally been undervalued. This could be partly due to the lack of integral efficacy of the present vaccine Bacillus Calmette-Guerin (BCG) failing to reduce the numbers of TB cases, which makes new efficacious vaccines against respiratory forms of TB a critical necessity to help combat AMR TB. Currently, one of the biggest threats in TB is the emergence of multidrug-resistant (MDR) _Mtb_ strains, resistant to isoniazide and rifampicin, and extensively drug-resistant (XDR) strains, resistant to at least four of the core anti-TB drugs. In 2018, half a million people were diagnosed with MDR-TB and is estimated that fifty million people were infected with MDR _Mtb_ strains, creating a reservoir for future cases of active TB making treatment extremely difficult [2]. AMR poses a threat in TB; both the World Health Organization (WHO) and International Centres for Disease Control and Prevention (CDC) have expressed concern about antibiotic treatments for TB. New TB vaccines are necessary to complement existing and in-development pipeline TB treatment and diagnostic strategies. Considering there has been no evidence to suggest that molecular mechanisms of drug resistance in _Mtb_ could affect immune control susceptibility, it is likely that vaccine efficacy against MDR-TB and drug-sensitive TB will be equivalent [3].  
In the present work we review the current use of BCG and the non-specific effect against other pathogens and we summarized the diversity of new TB vaccine candidates in clinical trials and their indications. We discuss the need to keep the pipeline of new TB vaccine candidates and the current clinical trial designs employed in the field for efficacy determination of new TB vaccines which include designs for prevention of infection (POI), prevention of disease (POD), and prevention of recurrence of TB (POR), or trials for evaluating the therapeutic effect of TB vaccine candidates when applied in combination with current TB drug regimens with the aim to shorten duration of treatment. The pipeline includes new prime TB vaccines for administration at birth, which are expected to protect better than BCG, and for use in adolescents and adults, as revacination strategies in individuals previously vaccinated with BCG at birth, as per WHO Preferred Product Characteristics (PPC) for new TB vaccines [3,4]. Novel TB vaccine strategies, which are safe and effective, are imperative in the global efforts to halt dissemination of drug-sensitive and AMR TB [3].
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 3 Vessel Data Preprocessing
### Trajectory Compression  
The essence of trajectory compression is to represent the raw trajectory data in a simpler way while maintaining its key spatial and temporal relationships as well as trend features. In AIS data applications, the difficulty of processing and storing arises from the large scale of AIS data and the presence of dense and redundant information recorded during vessel operations. Therefore, to reduce data volume and improve data usability, it is necessary to compress trajectories.  
In this study, the TR algorithm is selected for trajectory compression, which is based on the synchronized Euclidean distance (SED) error for trajectory compression. The synchronized Euclidean distance error can measure the distance between two positions at the same time, which is the Euclidean distance between the point \(I\) on the trajectory segment and its time-synchronized point \(I^{\prime}\) as shown in Formulas (1)-(3),  
\[r=\frac{I_{time}-S_{time}}{E_{time}-S_{time}}, \tag{1}\]  
\[I^{\prime}_{lat}=S_{lat}+r(E_{lat}-S_{lat}), \tag{2}\]  
\[I^{\prime}_{lon}=S_{lon}+r(E_{lon}-S_{lon}), \tag{3}\]  
Figure 1: Pipeline for Trajectory Extraction.  
where \(r\) represents the time ratio of \(\overline{TS}\) and \(\overline{ES}\), and \(S,E\) represents the start and end points of the trajectory segment.
## 3 Methods
### Linear Mixed Effects Model
where \(SM_{ij}\) is the soil moisture at a spatial site \(i\) on a day \(j\), \(\sigma_{ij}^{0}\) is the backscattering coefficient of the grid cell corresponding to site \(i\) on the same day; \(\alpha\) and \(u_{j}\) are the fixed and random day-specific intercepts, respectively; \(\beta\) and \(v_{j}\) are the fixed and random day-specific slopes, respectively; \(s_{i}\ \sim\ N\big{(}0,\ \sigma_{S}^{2}\big{)}\) is the random intercept of site \(i\); \(e_{ij}\) is the error term at site \(i\) on a day \(j\), and, \(\Sigma_{\beta}\) is the variance-covariance matrix for the day-specific random effects. Making use of the restricted maximum likelihood method, the fixed and random parameters in the LME model were estimated using the \"Imer\" function in the lme4 package for R [54]. The effect of polarization on the SM-\(\sigma^{0}\) relationship was inspected by comparing the LME models developed using different polarized \(\sigma^{0}\) images, which were VV polarization, VH polarization and VV + VH polarizations, respectively. Moreover, the LME models with and without the site-specific term \(s_{i}\) were compared to provide a measure of the sensitivity of the model-derived time varying SM-\(\sigma_{0}\) relationship to site locations. Another approach that allows for the SM-\(\sigma^{0}\) relationship to vary temporally would be fitting a linear fixed effects model separately for each day when both radar image and soil moisture measurements are available. The linear fixed effects model follows the form of  
\[SM_{j}=\alpha_{j}+\beta_{j}\times\sigma_{j}^{0}+\varepsilon_{j}, \tag{3}\]  
where \(\alpha_{j}\) and \(\beta_{j}\) are the fixed intercept and slope on a day \(j\), respectively; \(\varepsilon_{j}\) is the error term on a day \(j\). In this model, the SM-\(\sigma^{0}\) relationship is constant for all of the sites on a certain day and the spatial differences are not considered. Model performances of the linear mixed and fixed effects models were compared to reveal whether the LME model was able to improve the prediction of soil moisture.
## 1 Introduction
Hence, the family of time series of ROIs is transformed into a family of time series of SPD matrices. Each obtained time series of SPD matrices is a curve on the SPD manifold. The family of curves is the feature curves of the short video, which is the basis of short video recognition. 4. Using the dynamic time warping (DTW) [19] with Riemannian metrics [1; 2; 20] and divergences [21; 22] on the SPD manifold, the similarity measure between curve families on the SPD manifold is established, so as to realize the recognition of short video. Taking face recognition as an example, the overview of our framework is shown in Figure 1. Thus far, no similar algorithm has been reported. The experiments on three databases show that our framework is superior to seven other related algorithms published in the top issue in recent years. The main contributions of this paper are as follows:  
1. Proposing a short video recognition framework feasible for different applications, as well as providing optional strategies for stringing time series of ROIs, constructing RCDs, and providing recognition between families of feature curves. 2. Different from viewing a video as an image set-based SPD matrix, which ignores the temporal correlation of features across image frames, our framework models each video as a family of feature curves on the SPD manifold, considering both temporal and spatial features of video. 3. ROI and motion estimation in video encoding are applied in our framework to reduce computational burden due to redundancy across image frames. Compared with using global frame images, ROIs convey more accurate spatial features. Moreover, tracing ROI with motion estimation can effectively reduce the computation of feature detection. 4. Encoding major spatial features by ROI-based covariance descriptors helps to build SPD geometry and provides a discriminant Riemannian metric for recognition. The programming of this paper is as follows. In Section 2, we present notations and preliminaries. In Section 3, several related works are introduced. Section 4 provides the details of our proposed framework. Section 5 introduces the application of our proposed framework in face recognition from video. Section 6 introduces seven SPD-based video/image set recognition algorithms in recent years as comparison algorithms. Section 7 shows the experimental results on three datasets with seven comparison algorithms. Conclusions are given in Section 8.