esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Secure, and Private Data Filtering, and Aggregation  
In this section, we present the data acquisition layer, consisting of the General Purpose Data and Query Privacy Preserving Protocol described in [13]. The communication protocol presented in [13] is characterized by messages containing a layered object made of several encryption layers similar to the one employed in the onion routing [46]. Each layer of the layered object contains the IP address of the next receiver of the message, and since layers are produced using public key cryptography [47], the message must travel through the exact sequence of nodes defined at message construction. The technique of encoding the message path in the message is commonly known as source routing [48]. Path information is carried in encryption layers to restrict the knowledge obtained by nodes processing the message, which only learn about the sender and the next receiver of the message. Therefore, the whole message path is not revealed to any node receiving the message.  
In addition to the layered object, messages specified by the communication protocol in [13] include a payload. The payload consists of computer code specified in a general-purpose programming language and a binary string that stores an aggregate. Therefore, it includes instructions specifying the data to retrieve and the aggregated data of nodes in the message path. In the following, we will refer to the onion message (OM) as a structure consisting of the layered object and the aforementioned payload. The OM payload is secured by symmetric key encryption to prevent malicious actors from tracking the OM and obtain values added by sensor nodes by comparing the aggregate pre and post OM processing. Moreover, encryption keys required to decipher the OM payload are delivered only to specific nodes in the OM path by enclosing symmetric encryption keys in the layered object. Nodes in the OM path are either: (a) processing the OM or (b) emulating OM processing.  
1. Nodes processing the OM obtain two symmetric encryption keys and the next-hop IP address from layer decryption of the layered object. The first symmetric encryption key is used to access the content of the OM payload. Next, the node executes the computer code and embeds results in the binary string. The OM payload is then encrypted using the second symmetric encryption key, and after a time-span affected by randomness, the OM is forwarded to the next-hop node.
2. Nodes emulating OM processing only obtain the next-hop IP address from layer decryption of the layered object. These nodes retain the OM without accessing the payload for a time-span similar to nodes processing the OM, and then the message is forwarded to the next-hop node.  
Therefore, external actors observing network communications are not able to identify nodes contributing to the aggregated result; consequently, they cannot associate activities occurring in the monitored environment with messages transiting network nodes.  
Figure 6: Wireless-enabled force sensors.
## 3 Theoretical Analysis of Ore Pass Wall Damage
### Theoretical Analysis of the Normal Impact
#### 3.2.1 Elastic Compression Stage  
Based on the [PERSON] contact theory [32], the contact model of the ore rock impacting the ore pass wall was constructed, as shown in Figure 2(a), and the velocity decomposition of both rock and ore impacting the shaft wall is shown in Figure 2(b). According to the [PERSON] contact theory, and based on the actual situation of the ore-rock block moving through the ore pass, the analytical solution to the elastic contact problem of the ore-rock block under normal contact pressure \(F(r)\) is as follows [16,17]:  
\[F(r)=\frac{3F_{\rm n}}{2\pi a^{2}}\bigg{[}1-\left(\frac{r}{a}\right)^{2}\bigg{]} ^{\frac{1}{2}} \tag{7}\]  
where \(F_{\rm n}\) is the normal contact force in the elastic stage, N, \(a\) is the contact radius of the ore-rock block and ore pass wall, m, and \(r\) is also the contact radius, which is smaller or equal to \(a\). The normal contact force \(F_{\rm n}\) and the contact radius \(a\) can be calculated as follows:  
\[F_{\rm n}=\frac{4E^{*}}{3}R\frac{1}{2}\cdot\delta\frac{3}{2} \tag{8}\]  
\[a=\left(\frac{3F_{\rm n}\cdot R}{4E^{*}}\right)^{\frac{1}{3}} \tag{9}\]  
where \(\delta\) is the depth of the ore-rock block intruding into the shaft wall, m, and \(R\) is the radius of the spherical ore-rock particles, m. The relationship among \(\delta\), \(R\), and \(a\) can be described as in Equation (10). The term \(E^{*}\) from Equation (9) is the equivalent elastic modulus satisfied by Equation (11). \[a^{2}=R\delta \tag{10}\]  
\[\frac{1}{E^{*}}=\frac{1-\
u_{1}^{2}}{E_{1}}+\frac{1-\
u_{2}^{2}}{E_{2}} \tag{11}\]  
where \(E_{1}\), \(\
u_{1}\) and \(E_{2}\), \(\
u_{2}\) are the elastic modulus and Poisson's ratio of the ore pass wall and of the spherical ore-rock particles, respectively. [PERSON] and [PERSON] [18] used the Von Mises yield criterion to derive the functional relationship between the initial yield intrusion depth \(\delta_{y}\) and the initial yield contact force \(F_{y}\) at the elastic stage as follows [35]:  
\[\left\{ \begin{array}{l}\delta_{y}=\left(\frac{\pi\cdot C\cdot\sigma_{y}}{2E ^{*}}\right)^{2}\cdot R\\ F_{y}=\frac{4}{3}\left(\frac{R}{E^{*}}\right)^{2}\cdot\left(\frac{\pi}{2}C \cdot\sigma_{y}\right)^{3}\end{array} \right. \tag{12}\]  
where \(\sigma_{y}\) is the yield limit stress of the shaft wall and \(C=1.295\)e\({}^{0.736\
u_{1}}\). Figure 3: Contact model between the moving ore-rock block and the ore pass wall. (**a**) Calculation model of the moving ore-rock block impacting the ore pass wall and the yellow part is the deformation area of the ore pass wall; (**b**) decomposition of the velocity of the moving ore-rock block when it collides with the ore pass wall.
## 2 Experimental Overview
### Experimental Apparatus and Equipment
#### 3.1.3 Elastic Modulus  
Due to the influence of temperature on the elastic modulus of concrete after exposure to high temperatures, there is no obvious elastic phase. Therefore, the secant modulus of elasticity E\({}_{\text{c}\text{T}}\) is calculated as 40% of the slope of the line connecting the origin of the stress-strain curve to the peak stress point. The relationship between the elastic modulus and temperature is shown in Figure 10. At room temperature, the elastic modulus of PPF concrete was higher than that of ordinary concrete PL0C0, and the increase in elastic modulus was particularly significant, with the largest increase observed in PL3C1.5 and PL15C1.5, which had increased ratios of 21.65% and 17.42%, respectively. The addition of PPF could effectively suppress the cracking of concrete, and there was a positive correlation between the increase in PPF content and length and the elastic modulus. With an increase in temperature, the elastic modulus of PPF concrete with different contents and lengths decreased. However, there was no significant correlation between the content and length of fibers and the change in the elastic modulus. The presence of fibers at temperatures between 200 \({}^{\circ}\)C and 300 \({}^{\circ}\)C reduce the elastic modulus of concrete, as high temperatures, above 170 \({}^{\circ}\)C, cause the PPF to melt, increasing the internal porosity and weakening the cross-section of the concrete. The average loss of elastic modulus at 300 \({}^{\circ}\)C was 44.97%. The critical temperature at which the elastic modulus decreased most significantly was 400 \({}^{\circ}\)C, where it decreased by 70% compared to that at room temperature [54]. The elastic modulus of fiber-reinforced concrete at 400 \({}^{\circ}\)C was higher than that of ordinary concrete,  
Figure 9: The relationship between peak strain and temperature.  
with the highest increase in strength reaching 64.84%. When the temperature rose to 600-800 \({}^{\circ}\)C, the influence of fiber content and length on the elastic modulus could be ignored, as, at high temperatures, the development of concrete cracks is dominated by macroscopic cracks, and the role of the internal micro-structure disappears. At higher temperatures, the deterioration of the concrete material itself makes the elastic modulus maintain a relatively stable value, of only 1% to 3% of that at room temperature.
# A Study on ML-Based Sleep Score Model Using Lifelog Data
## 2 Materials and Methods
### Second Step: Intermediate Sleep Score
#### 2.3.1 Data Preparation: Training and Test Data Set  
The dataset, classified into good sleep and bad sleep data, is used as the training data, and the primary sleep habit score described in Section 2.2 is set as the training data's target. The second sleep habit score is derived by setting the data set classified as the intermediate sleep state as predictive (test) data, and the sleep habit score for the intermediate sleep state is predicted using machine learning and deep learning stacking models. Specifically, the stacking machine learning model trains with training data of 5494 data (good sleep: 326, bad sleep: 5168), and estimates sleep habit scores for 10,559 test data.
## 4 Results
### Multiple Regression Using PLS  
An elbow method was performed in the PLS analysis to determine the number of components using the MSE metric. This method aims to create a regression with PLS and consider its MSE error. The Figure 6 shows the results of the number of components for each season. In all the seasons, the error stabilizes between three and four components. Therefore, it was decided to use three components.  
Figure 7 and Table 4 show the results obtained for the internal temperature prediction using PLS multiple regression.  
Both MLR and PLS regression produced similar results for summer (Figure 7d) but faced challenges forecasting temperatures below 10 \({}^{\circ}\)C and above 50 \({}^{\circ}\)C in fall (Figure 7a). The PLS regression also had difficulty covering the entire temperature range in winter (Figure 7b) due to temperature peaks. Interestingly, PLS and MLR showed a notable difference in their performance for spring (Figure 7c), with PLS having a considerably higher RMSE than its counterpart.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **R2** & **RMSE** & **MAE** & **MAPE** \\ \hline Fall & 0.9462 & 2.9244 & 2.0621 & 0.1163 \\ Winter & 0.9244 & 4.6499 & 3.0969 & 0.1066 \\ Spring & 0.9077 & 3.6264 & 2.9595 & 0.1096 \\ Summer & 0.9680 & 1.9574 & 1.5139 & 0.0520 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results for MLR model.  
Figure 6: Number of components for multiple regression models using PLS. Componentes for fall (**a**), winter (**b**), spring (**c**) and summer (**d**).
###### Abstract  
The paper's main aim is to present the impact on the city's road traffic generated by the Port of Gdynia's operations and propose the optimal solution for transport network development around the port. Firstly, the authors demonstrate a case study determining the impact of heavy goods vehicles (HGVs) generated by port facilities on local traffic. To this end, the average travel time of cars in the network on selected measurement sections is conditioned on the varying number of HGVs generated by the port. Next, based on the data obtained from the traffic monitoring system, PTV Vissim software is used as a modelling tool to analyse and assess the impact on local traffic. Finally, considering the analysis' results, the vulnerability of the transport network is discussed. The optimal solution for the transport network around the port's area is proposed. The paper is an extended version of the materials presented at the XIX Maritime Traffic Engineering Conference.  
transport modelling; microsimulation analysis; intelligent transport; vulnerability; transport networks +
Footnote †: journal:  
can be a severe obstacle to the development of the port, and thus the flow of cargo. In the literature, there are studies on port cities in Germany [9], which shows the dynamic relationship between urban space and port space in the process of urban development in Europe, or, for example, in China [10], which shows the emergence of urbanised seapports, with the theoretical help of a formal economic geography model.  
Gdynia, a port city, struggles with sharing infrastructure for transporting cargo and residents. Due to the need for more relevant research on the impact of road traffic generated by port areas on urban road traffic, we propose a microsimulation model and a case study of the Port of Gdynia, located in the northern part of Poland. The port is challenging because it is located in the central part of the city [11; 12]. It even divides the city into a northern half and southern half. It is worthwhile undertaking research that addresses how to improve the current transport infrastructure, so that the traffic coming from the port does not negatively affect the entire network of the city of Gdynia.  
This paper presents the relationship between the port and the city road network. To this end, a micro-simulation model is developed by applying the PTV Vissim tool [13; 14; 15; 16; 17; 18] on a microscopic scale [16; 19; 20]. These results are then used to determine the vulnerability of the transport network. This, in turn, made it possible to identify the best solution for the problem of burdening Gdynia's transport network.  
This way, we try to answer the research question, \"how to better optimise and coordinate port and urban transport through network planning or management?\".  
Our results show how small increases in the number of vehicles generated by the port can significantly affect the performance of the road transport network. Thus, it affects the city's development and the level of service perceived by the inhabitants.
## 4 Proposed Model
### Model Validation
#### 4.4.2 Events Simulation Software Platform (ESSP)  
The Events Simulation Software Platform (ESSP) is a sophisticated simulation framework that models a hybrid edge and fog computing environment in massively multiplayer online (MMO) games. It supports the execution of user interactions such as connections, disconnections, migrations between clusters, and stationary server usage. A dynamic load balancer ensures optimal request distribution across servers, aiming to minimize latency and enhance quality of experience (QoE). The simulator is particularly suited for MMO games that use cluster-based architectures. Dividing the game world into clusters managed by servers enables efficient handling of user interactions, such as migrations between clusters or connections to servers. This architecture ensures that system resources are used optimally to maintain consistent performance. Rationale for Developing the ESSP  
The development of the ESSP was driven by the need for a tailored simulation platform capable of addressing the unique requirements of hybrid edge and fog computing environments in MMO games. While there are several mature simulators available, these often lack the flexibility or specific features needed to model the dynamic behaviors of such architectures. In particular,  
* **Customization for specific architectures:** Existing simulators are often designed for generic cloud environments and do not account for the unique interactions and load distribution mechanisms in hybrid fog and edge architectures. * **Dynamic behavior simulation:** The ESSP supports complex scenarios, including real-time migrations between clusters, dynamic load balancing, and the interplay of stationary and mobile server usage. This granularity is essential for evaluating the performance of the proposed model. * **Integration of key QoE metrics:** The ESSP allows for precise latency and load distribution measurements. Factors specific to gaming environments are often overlooked in more generic simulators. * **Optimized resource utilization:** The ESSP provides a more realistic evaluation of system performance under varying load conditions by simulating hybrid environments with edge and fog nodes. * **Validation and benchmarking:** The ESSP was designed to align closely with the architectural and operational requirements of the proposed model, ensuring the accuracy and relevance of the results. Simulation Workflow  
The simulation process begins with initializing the system's foundational components, including game servers, fog nodes, and clients. Each client is assigned a game plan with probabilistic actions, such as connecting to servers, disconnecting, or migrating between clusters. The simulation operates over a defined time period, during which events are processed iteratively.
## 2 Materials and Methods  
### Methodology  
#### 2.1.1 Calculation of the Linke Turbidity Factor, \(T_{\mathrm{L}}\)  
\(T_{\mathrm{L}}\) was introduced by [PERSON] [6; 7] as a parameter to indicate how many (hypothetical) clean and dry atmospheres are necessary to derive the observed attenuation of the extraterrestrial radiation caused by the real atmosphere under clear-sky conditions. \(T_{\mathrm{L}}\) is typically bounded between 1 and 10. According to [PERSON]:  
\[B_{e}=S\cdot G_{e,\rho}\cdot sin\gamma\cdot exp(-m^{\prime}\cdot\overline{k_{ r}}\cdot T_{L}) \tag{1}\]  
where \(B_{\mathrm{e}}\) is the direct horizontal solar radiation equal to \(G_{\mathrm{e}}\) - \(D_{\mathrm{e}}\), \(S\) is the correction factor for the Sun-Earth distance [29] given by Equation (3), \(G_{\mathrm{e,o}}\) is the solar constant equal to 1361.10 \(\mathrm{Wm}^{-2}\)[30], \(\gamma\) is the solar elevation, \(m^{\prime}\) is the pressure-corrected optical air mass (Equation (5)), which includes the altitude of the site at which \(T_{\mathrm{L}}\) is to be estimated, and \(\overline{k_{r}}\) is the mean attenuation of the direct solar radiation due to Rayleigh scattering alone [31; 32]. Solving Equation (1) for \(T_{\mathrm{L}}\), we obtain  
\[T_{L}=\frac{lnG_{e,\rho}-lnB_{e}+lnS+lnsin\gamma}{\overline{k_{r}}\,m^{\prime}} \tag{2}\]in which  
\[S=1+0.033\,cos\Big{(}\frac{2\,\pi\,D}{365}\Big{)} \tag{3}\]  
\[\overline{k_{r}}=6.5567+1.7513\,m^{\prime}-0.1202\,m^{\prime}\,\,2+0.0065\,m^{ \prime}\,\,3-0.00013\,m^{\prime}\,\,4 \tag{4}\]  
\[m^{\prime}=\frac{P_{z}}{P_{o}}\,m \tag{5}\]  
\[m=\,1\,/\,\Big{[}\,sin\gamma+0.50572(\gamma+6.07995)^{-1.6364}\Big{]} \tag{6}\]  
The optical air mass, \(m\), is given by [PERSON] and [PERSON] [33]. In Equation (5), \(P_{z}\) is the atmospheric pressure (in hPa) at an altitude \(z\) (in m), and \(P_{0}\) the atmospheric pressure at sea level. Its value is usually taken as equal to 1000 hPa or 1013.25 hPa; the latter value has been adopted in this work. If \(P_{z}\) is not known, it can be calculated via [34]  
\[P_{z}=P_{o}\cdot exp\Big{(}-\frac{z}{8435.2}\Big{)}. \tag{7}\]  
Unfortunately, \(T_{\rm L}\) has the disadvantage of depending on air mass. To overcome this difficulty, various authors have tried to derive air-mass-independent \(T_{\rm L}\) expressions. The most popular method to date is the normalization of the estimated \(T_{\rm L}\) values at \(m\) = 2 (\(T_{\rm L2}\)) [11; 35]. [PERSON] recognized this problem of the turbidity factor and tried--without success--to introduce a new extinction coefficient based on an atmosphere with a water-vapor content of 1 cm. Nevertheless, the present study does not adopt \(T_{\rm L2}\) as this factor refers to a specific range of solar elevation angles equivalent to \(m\) = 2 throughout a year. For a climatology of \(T_{\rm L}\) over a region, the statistics of \(T_{\rm L}\) should be based on all estimated \(T_{\rm L}\) values that represent the real atmospheric conditions over the area for a year or a number of years. This last statement has been considered in the present work.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
###### Abstract  
It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows: (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt failure increase zone. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching four to five times the initial permeability. (3) When the static loading stage and dynamic load are constant, the rate of change in coal permeability decreases with increasing adsorption amounts. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in the static loading stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static loading stages and decreased sensitivity with greater adsorption amounts.  
cyclic dynamic loading: gas-bearing coal; permeability; complete stress-strain process; sensitivity analysis +
Footnote †: journal: Applied