esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 2 System Model and Related Works  
### Internet of Things Architecture  
Since IoT is to connect a considerable number of heterogeneous things through the Internet, this technology is significantly dependent on the existence of a flexible layer architecture. In other words, IoT is to bridge the real world and virtual world such that dynamic communication is ensured [9]. Therefore, IoT requires a novel architecture in order to obviate the inherent challenges as well as providing acceptable scalability and Quality of Service (QoS) in separate applications [10]. According to the presented architectures in [11; 12; 13; 14], layers of IoT are illustrated in Figure 1.  
1. Perception layer: This layer is generally known as the physical or hardware layer, and is particularly allocated to sensors and edge recognizers of a network. In other words, physical and environmental parameters are converted into sensed data in this layer. The output of this layer will be considered as the input of the network layer.
2. Network layer: The main goal of this layer is to provide both direct and indirect communications between all things and IoT equipment, such that all of them are capable of sending and receiving data in the network. Note that the communicative infrastructures managed by network layer include all wireless communication technologies.
3. Middleware layer: The main task of this layer is to combine names and addresses in order to serve other components of the network. Programmers of IoT are endeavoring to connect heterogeneous things under a communicative platform, such that a united concept of network is created and exchanged data in databases can be saved and restored.
4. Application layer: This layer usually merges and evaluates the services provided by other layers. This layer can present high-quality services for responding to the final user's applications.
5. Business layer: Also known as the management layer of all components of the IoT network, is to analyze and schematize data.
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
### EV Characterization  
The most common EV isolation methods include ultracentrifugation, density gradient centrifugation, the size exclusion chromatography approach, ultrafiltration, and immunoprecipitation assays [27]. Once isolated, EV subpopulations are characterized using quantitative and qualitative criteria. EV morphology can be detected via transmission electron microscopy (TEM) and scanning electron microscope (SEM). To determine the vesicle size and concentration, single-particle interferometric reflectance (SP-IRIS) and nanoparticle tracking analyses (NTAs) are used, which are methods that are able to detect the particles' intrinsic Brownian movement [28]. The number of particles can also be established via standard flow cytometry to evaluate large EVs and high-sensitivity flow cytometry (hsFCM) for small EVs [29], while resistive pulse sensing (RPS) is based on the pore size and adapted for a wide range of sizes [30]. Cryogenic electron microscopy (cryo-EM) and other technologies [31] are also used. EV characterization occurs via the identification of specific antigen-antibody recognition such as the use of an ELISA, Western blotting, specific antibodies for membrane proteins (tetraspanins) and cytosolic proteins (ALX), peculiar markers of EV subgroups and the exclusion of other co-isolated materials,  
Figure 3: Schematic representation of alternative exosome-mediated cell–cell communication via different modes: (1) endocytosis-mediated communication; (2) direct surface-bound receptor-mediated communication; and (3) exosome fusion with the recipient cell membrane. EVs feature as diagnostic and prognostic biomarkers, and their implications for therapeutic approaches and clinical applications are also reported.  
and the multiple-reaction-monitoring (MRM) approach [32]. The super-resolution microscopy approach, direct stochastic optical reconstruction microscopy (dSTORM) [33; 34], is able to ascertain how and where EVs are clustered and localized; while with advanced fluorescence microscopy, it is possible to detect single EVs in real time, observe EVs during their interactions and uptake within living cells, and visualize their post-internalization movements in a time lapse. To better investigate this specific subject, the authors refer readers to the MISEV 2023 guidelines [35].
## 5 Discussion
### The Performance Improvement brought by the Relation Graph and the Universality of Relgraph  
The results in Table 2 demonstrate that Relgraph outperforms ordinary GAT, regardless of the representation learning method used. Specifically, Relgraph-Rotate offers a 5.6% improvement over GAT-Rotate, while Relgraph-TransE offers a 5.9% improvement over GAT-TransE on the FB15K-237 dataset. This highlights the value of introducing relation graphs to capture predicate interactions.  
The results in Table 2 also indicate that the Relgraph framework is versatile and can be seamlessly integrated with other transitive representation learning methods on KGs, enhancing their performance. Notably, the performance improvement of Relgraph-RotatE over the original RotatE is 56.5%, and the improvement of Relgraph-TransE over the original TransE is 72.3%. As Figure 3 shows, this improvement is consistent across datasets, highlighting the generalizability of the Relgraph framework for enhancing knowledge graph reasoning.
###### Abstract  
The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.  
**Keywords:** grassed channels; check dam system; stormwater; infiltration capacity; water resources modelling; modified Kostiakov model +
Footnote †: journal: Applied Sciences
# Sensitivity Estimation for Differentially Private Query Processing
## 4 Sensitivity Estimation for Join Queries
### Sampling-Based Sensitivity Estimation
#### 4.2.1. Estimation for One Residual Query
: _Sample complexity. With a probability of at least \(1-\eta\), Algorithm 2 outputs the upper bound for the largest group and draws \(O\left(\hat{I}_{2\eta}^{2}\sum_{i=1}^{g}\frac{\log(\frac{g}{g})+\log\log( \frac{1}{\mu_{i}})}{\alpha_{i}^{2}}\right)\) samples. Here, \(\alpha_{i}=\max\{\frac{|\mu_{max}-\mu_{i}|}{4},\tau_{0}\}\)_  
Proof. : We first prove that the sampling stops for each group \(i\) once the \(\tau\leq\max\{\frac{|\mu_{max}-\mu_{i}|}{4},\tau_{0}\}\). If group \(i\) is removed from \(G\) before \(\tau\) reaches \(\tau_{0}\), i.e., \(\tau>\tau_{0}\), then \(C_{i}+\tau<C_{max}-\tau\). Since the confidence interval always contains the true result, \(\mu_{max}\in[C_{max}-\tau,C_{max}+\tau]\), and \(\mu_{i}\in[C_{i}-\tau,C_{i}+\tau]\). To make sure \(C_{i}+\tau<C_{max}-\tau\) holds for the worst case, we get \(\tau\leq\frac{|\mu_{max}-\mu_{i}|}{4}\). If group \(i\) is not removed from \(G\) until \(\tau\) reaches \(\tau_{0}\), then \(\tau=\tau_{0}\). Thus, \(\alpha_{i}=\max\{\frac{|\mu_{max}-\mu_{i}|}{4},\tau_{0}\}\) is the half-width of the confidence interval for group \(i\) when we stop adding samples. Let \(\epsilon=\alpha_{i}\); then, we get  
Figure 3:1  
The sample complexity for all the groups is  
Figure 3:2  
From the theorem above, we can infer that the sample size complexity is closely linked to the distance between the size of the largest group and that of the other groups. The greater the distance, the fewer samples required.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 3 Methodology
### Timing Optimization Model
#### 3.1.3 Constraints  
Firstly, when a vehicle is not affected by other vehicles, it can travel towards the intersection at free-flow speed and arrive at the conflict zone smoothly. Therefore, there exists a lower bound constraint on the decision variable, which can be expressed as  
\[t_{ij}^{+}\geq t_{ij}^{-}+\frac{l}{v_{i}},i\in\mathcal{I},j\in\mathcal{N}_{i}. \tag{4}\]  
To ensure the driving safety of vehicles in the same direction, there is a minimum headway constraint between successive vehicles. Assuming this minimum headway is \(\tau\), the constraint can be expressed as  
\[t_{ij}^{+}\geq t_{ij(-1)}^{+}+\tau,i\in\mathcal{I},j\in\mathcal{N}_{i}\smallsetminus 1, \tag{5}\]  
where \(\tau\) represents the minimum headway between successive vehicles in the same direction.  
In addition, to ensure the traffic safety of vehicles from conflicting directions, vehicles from these directions cannot simultaneously enter the intersection conflict area. If the minimum time difference for vehicles from conflicting directions to enter the intersection conflict area is \(\omega\), this constraint can be expressed as follows:  
\[\left|t_{ij}^{+}-t_{rf}^{+}\right|\geq\omega,\forall\left(i,i^{\prime}\right) \in\mathcal{C},j\in\mathcal{N}_{i},j^{\prime}\in\mathcal{N}_{i^{\prime}}. \tag{6}\]  
where \(\omega\) represents the minimum time difference for vehicles from conflicting directions to enter the intersection conflict zone. \(\mathcal{C}\) is the set of conflicting directions at the intersection, which can be represented as \(\mathcal{C}=\left\{(1,2),(1,4),(2,3),(3,4)\right\}\).  
Referring to [2], introduce a 0-1 variable and use the Big-M method to transform Equation (6) into Equations (8) and (9):  
\[B_{ii^{\prime}jj^{\prime}}\times M+t_{ij^{\prime}}^{+}-t_{ij}^{+}\geq\omega, \forall\left(i,i^{\prime}\right)\in\mathcal{C},j\in\mathcal{N}_{i},j^{\prime} \in\mathcal{N}_{i^{\prime}} \tag{7}\]  
\[\left(1-B_{ii^{\prime}jj^{\prime}}\right)\times M+t_{ij}^{+}-t_{rf}^{+}\geq \omega,\forall\left(i,i^{\prime}\right)\in\mathcal{C},j\in\mathcal{N}_{i^{ \prime}},j^{\prime}\in\mathcal{N}_{i^{\prime}} \tag{8}\]  
where \(B_{ii^{\prime}jj^{\prime}}\) is a 0-1 variable. If \(j\)-th vehicle enters the intersection conflict zone first, its value is 0; otherwise, its value is 1.  
In summary, the objective function of this optimization model is given by Equation (3), and the constraints are Equations (4), (5), (7), and (8), forming a typical linear programming model. Commercial software, such as Gurobi and MATLAB, can be used to solve it. In this paper, the interior point method is adopted to solve this model. However, when the optimization time window is long, meaning that the number of vehicles to be optimized is large, the problem scale becomes significant, and directly solving it using commercial software can be inefficient. Therefore, a better solution algorithm must be designed based on the problem's characteristics.
# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 3 Validation of Mobility Models
### Speed Decay Problem
As a conclusion, we can state that _in order to avoid the speed decay problem, a stable mobility model must remain steady within an acceptable speed range_, as for the MGMM and the NCMM. Consequently, it must respect motion laws in every mobility decision by taking into consideration the relation between time, speed, and distance while moving. Figure 2: The Speed Decay Problem. (**a**) The RPPMM; (**b**) the MGMM; (**c**) the RPGMM; (**d**) the NCMM; (**e**) SLAW; (**f**) SMOOTH.
## 5 Discussion  
### Effect of the Position Change in Monitoring Points on the Models  
In this paper, deep learning methods are employed to predict the displacements of the Tangjiao 1# landslide. The prediction methods utilize the FCG to connect all the monitoring points for predictions. This approach avoids the repeated adjustment of parameters for each monitoring point and enhances the efficiency of landslide prediction. Simultaneously, the landslide monitoring system is an integrated whole, and there exists a certain spatial relationship among the monitoring points. The spatiotemporal prediction methods can better reveal the displacement changes of the entire monitoring system.  
In this paper, the adjacency matrix Aw is utilized to represent the relative distance between monitoring points. That is, the geographical location information of the monitoring points determines the spatial relationship of the models. During the process of landslide deformation, the positions of monitoring points may change, which might reduce the model accuracy [57]. However, in our case, the positions of GPS3, GPS9, and GPS10 have changed to some extent, yet the accuracies of the T-GCN and A3T-GCN models are still higher than those of GRU. It is speculated that the monitoring point GPS6 with excessive deformation has been removed when the FCG is constructed in this paper. Meanwhile, the position changes of the remaining monitoring points are relatively small, having little influence on the adjacency matrix and thus limited impact on the accuracies of the prediction models. In the next step, more cases will be collected to analyze the influence of the position change in monitoring points on the models.
## 1 Introduction
In this study, we present and test 18 different CNN models that use the above-mentioned approaches for the pooling process, but also different numbers of convolution layers to classify whether the P300 is present or absent in single-trial EEG readings from an auditory BCI experimental setup. For the experiment, nine subjects were presented with auditory stimuli (100 ms of white noise) for six virtual directions following the oddball paradigm and were asked to attend to the stimuli coming from a specific direction at a time and count in silence every time this happened to potentially increase the correct production of the P300. The BCI approach followed in this work is a reproduction of the one presented in [25] as it has relevant characteristics for auditory BCI (especially portability) such as the use of earphones to present the auditory stimuli and the capacity to simulate sound direction through them. Unlike the work of [25], which considers only one trial interval of 1100 ms between stimuli presentation, we considered variant time intervals (200, 300, 400 and 500 ms) between presentations of the auditory stimuli for all 18 CNN models to evaluate the extent to which this variation could affect the performance of the classifier. This paper is organized as follows: Section 2 contains the information regarding the conformation of the dataset used, such as the experimental setup and data processing. The structure of proposed CNN models, specific parameters considered for this study and the details of the selected models are described in Section 3. A summary of the obtained results is presented in Section 4, with a strong focus on the similarities between the observed patterns in the performance of the structures. Finally, in Sections 5 and 6, we discuss our results and ideas for future work.
# Surface Patterns for Drag Modification in Volleyballs
## 3 Results and Discussion
At the 2008 Beijing Olympics, a surface-modified volleyball (Mikasa MVA200) with 8-sheet dimples was introduced as the official ball and in turn adopted in international competitions (e.g., FIVB World League and Olympics). Another volleyball (Molten V5M5000) with hexagonal protrusions on the ball surface was developed and used as the official ball in many league games, including the American League (USA Volleyball) and the National Collegiate Athletic Association (NCAA) League. Similar changes appear in footballs: the aerodynamic force applied to the ball changes significantly depending on the surface panel shapes and consequently affects the aerodynamic performance [16; 17; 24; 27; 30]. Despite recent developments in design of volleyballs, aerodynamic validation has yet to be reported. For traditional volleyballs, a few reports were found on the numerical simulations [31; 32; 33] and the experimental measurements of the flight trajectory or the vortex structure [26; 32; 34]. For surface-modified volleyballs, a comprehensive understanding of the aerodynamic singularity is still lacking [35]. The surface conditions of volleyballs are different for the tested volleyballs as demonstrated in Figure 2. The aerodynamics and flight characteristics are analyzed from the wind tunnel experiments. Figure 4: The lift and side forces acting on the ball taken in the wind tunnel experiment (10 s, wind speed 15 m/s) are quite isotropic from the balls with isotropic (Hexagon and Dimple) surface patterns. Blue and red represents transverse and diagonal directions, respectively. Big circles and squares are the average values with error bars taken from the standard deviations.
## 2 Materials and Methods
### Correlations  
Table 5 provides the Pearson linear correlation results for the different phytoconstituents in the solid residue, highlighting their possible relationships. As shown in the table, TPC showed a moderate positive correlation (r = 0.494-0.537, \(p<\) 0.01) with antioxidant capacity (FRAP and CUPRAC). Our result matches the report of [PERSON] et al. [47]; they prepared aqueous-ethanolic extracts of cold-pressed solid residues of different seeds and nuts (seeds of sesame, sunflower, pumpkin, and flaxseed, and the nuts of pecan, macadamia, almond, and hazelnut) to study TPC and antioxidant capacity (ABTS, DPPH, reducing power, and ferrous ion-chelating ability) and observed a positive correlation between TPC and ABTS, reducing power, DPPH, and chelating activity, although to varying levels [47].  
Our result does not match the report by [PERSON] et al. [48] and [PERSON] et al. [49]. [PERSON] et al. [48] prepared aqueous methanolic or ethanolic extracts from the cold-pressed solid residues of different oil seeds (camelina, linseed, rapeseed, and white mustard) to study TPC and antioxidant capacity, such as reducing power, DPPH, and iron-chelating capacity, and observed a lack of positive correlations among the different antioxidant activity assays and TPC. Similarly, [PERSON] et al. [49] conducted [PERSON]'s correlation analysis to understand the relationship between TPC and antioxidant capacity (scavenging of DPPH radicals, scavenging of ABTS radicals, FRAP, and total antioxidant activity, determined using phosphomolybdate assays) in the solid residue of _Opuntia ficus-indica_ L. They reported a negative correlation between TPC and antioxidant capacity [49].  
Such a different relationship between total phenolic content and antioxidant capacity might be due to many reasons, including the type of plant material, the extraction conditions, and the presence of different phenolic types and other phytoconstituents in the plant material [50; 51; 52]. For example, [PERSON] et al. [52] collected various tea plants, prepared tea infusions, and investigated TPC, antioxidant capacity, and minerals (iron and copper). They reported that the relationship between TPC and antioxidant capacity in tea infusions is influenced by factors such as iron and copper content [52]. In fact, the total phenolic content does not incorporate all the antioxidants, and, therefore, even different species with the same total phenolic content might have different ranges of antioxidant capacity and vice versa [53]. Having said that, it is also worth noting that antioxidant capacity may be related to the presence of some individual phenolic compounds [53].  
Furthermore, it is also notable that there was a weak, positive correlation between FRAP and CUPRAC (r = 0.237, \(p\) < 0.05). Additionally, TQ showed a weak, positive correlation with TPC (r = 0.314, \(p\) < 0.01) but did not show any correlation with antioxidant capacity (FRAP and CUPRAC) despite being a known antioxidant. Moreover, this study also recorded no strong correlation between fatty acids, TPC, and antioxidant capacity.
## 3 Methodology
### Derivation of Promising Evaluation Indicators and Development of an Integrated Evaluation Index
Since the sum of the information gains of all promising indicators is 1, the derived value can be utilized as a weight. Next, normalization should be performed when computing the integrated index due to the different units of each promising indicator and the range of indicator values. Min-max normalization was used, a normalization method that represents different indicators as values between 0 and 1, as shown in Equation (7) [29]. For each scenario, a total of four integrated indices were computed, with a higher value indicating a more hazardous section. The integrated index can be computed by using Equation (8) for each scenario. \[V_{x}=\frac{x-Min.(x)}{Max.(x)-Min.(x)} \tag{7}\]  
where \(V_{x}\) is the normalized value of the evaluation indicator and \(x\) is the evaluation indicator value. \[Y_{MPR_{j}}=w_{1}*(\overline{V_{1}})+w_{2}*(\overline{V_{2}})+\ldots+w_{n}*( \overline{V_{n}}) \tag{8}\]  
where \(Y_{MPR_{j}}\) is the integrated evaluation index for the MPR \(j\), \(w_{i}\) is the information gain weight of evaluation indicator \(i\), and \(\overline{V_{i}}\) is the average normalized value of evaluation index \(i\) for each section. The variables that normalize the evaluation indicators used to develop the integrated evaluation index are presented as follows. First, the TTC-based conflict count is \(V_{TTC}^{CC}\), the SDI-based conflict count is \(V_{SDI}^{CC}\), and the DRAC-based conflict count is \(V_{DRAC}^{CC}\). Second, the average spacing and average headway indicate greater safety as their values increase. Therefore, the integrated evaluation index uses the inverse of these two indicators, so that larger values represent more hazardous sections. This is shown as \(V_{spac.}^{1/Avg. }\) and \(V_{htary.}^{1/Avg.}\). Finally, the normalized average DRAC is expressed as \(V_{DRAC}^{Avg. }\), and the time-varying volatility and standard deviation are represented as \(V_{indicator}^{VF}\) and \(V_{indicator}^{Std. }\).