esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Scheme
### Concepts and Definitions
is the nearest integer function, \(S_{x,y}\) is the total number of successful interactions of node \(x\) with \(y\) during time \(\delta t\), and \(U_{x,y}\) is the total number of unsuccessful interactions of node \(x\) with \(y\) during time \(\delta t\). After calculating trust value, a node will quantize trust into three states as follows:  
\[Mp(T_{x,y})=\left\{ \begin{array}{cc}\mbox{trustworthy}&100-f\leq T_{x,y}\leq 1 00\\ \mbox{uncertain}&50-g\leq T_{x,y}<100-f\\ \mbox{untrustworthy}&0\leq T_{x,y}<50-g\end{array} \right\}. \tag{3}\]  
where, \(f\) represents half of the average values of all trustworthy nodes and \(g\) represents one-third of the average values of all untrustworthy nodes. Both \(f\) and \(g\) are calculated as follows:  
\[f_{j+1}=\left\{ \begin{array}{cc}\left[\frac{1}{2}\left(\frac{\sum_{i\in R_{ x}}T_{x,i}}{|R_{x}|}\right)\right]&0<|R_{x}|\leq n-1\\ f_{j}&|R_{x}|=0\end{array} \right. \tag{4}\]\[g_{j+1}=\left\{ \begin{array}{cc}\left[\frac{1}{3}\left(\frac{\sum_{i\in M_{x}}T_{ x,i}}{|M_{x}|}\right)\right]&0<|M_{x}|\leq n-1\\ g_{j}&|M_{x}|=0\end{array} \right. \tag{5}\]  
where [.] is the nearest integer function, \(R_{x}\) represents the set of trustworthy nodes for node \(x\), \(M_{x}\) the set of untrustworthy nodes for node \(x\), and \(n\) is the total number of nodes that contains trustworthy, untrustworthy and uncertain nodes. The initial trust values of all nodes are 50, which represents the uncertain state. Initially \(f\) and \(g\) are equal to 25 and 17 respectively, although other values could also be used by keeping the following constraint intact: \(f_{i}-g_{i}\geq 1\), which is necessary for keeping the uncertain zone between a trusted and untrustworthy zone. The values of \(f\) and \(g\) are adaptive. During the steady-state operation, these values can change with every passing unit of time which creates dynamic trust boundaries. At any stage, when \(|R_{x}|\) or \(|M_{x}|\) becomes zero, the value of \(f_{j+1}\) or \(g_{j+1}\) remains the same as the previous values (\(f_{j}\) and \(g_{j}\)). The nodes whose values are above \(100-f\) will be declared as trustworthy nodes (Equation 3), and nodes whose values are lower than \(50-g\) will be consider as untrustworthy nodes (Equation 3). After each passage of time, \(\Delta t\), nodes will recalculate the values of \(f\) and \(g\). This trust calculation procedure will continue in this fashion. The time window length (\(\Delta t\)) could be made shorter or longer based on the network analysis scenarios. If \(\Delta t\) is too short, then the calculated trust value may not reflect the reliable behavior. On the other hand, if it is too long, then it will consume too much memory to store the interaction record at the sensor node. Therefore, various parameters can be used to adjust the length of \(\Delta t\).
# Energy-Saving Network Ventilation Technology of Extra-Long Tunnel in Climate Separation Zone
## 2 Materials and Methods
### Calculation Methods of the Natural Wind Velocity in the Tunnel
#### 2.1.1 Calculation Method of the Natural Wind Velocity in the Tunnel without Inclined Shafts  
For tunnels without inclined shafts (Figure 1), the natural wind velocity inside can be calculated as follows [28; 29]. The calculation method of natural wind resistance for the tunnels without shafts is shown in Equation (1).  
\[\Delta P=\left(\xi_{en}+\xi_{ex}+\lambda_{r}\cdot\frac{L}{D_{r}}\right)\cdot \frac{\rho_{i}}{2}\cdot{v_{i}}^{2} \tag{1}\]With the factors influencing the natural wind velocity in the tunnels, \(\Delta P\) is composed of ultra-static pressure difference (\(\Delta P_{u}\)), ventilation-wall pressure difference (\(\Delta P_{w}\)) and thermal-potential pressure difference (\(\Delta P_{t}\)), as shown in Equation (2).  
\[\Delta P=\Delta P_{u}+\Delta P_{w}+\Delta P_{t} \tag{2}\]  
During the design phase, the natural wind velocity is often set as 2 m/s-3 m/s. Based on Equations (1) and (2), [PERSON] et al. proposed a new calculation method for the natural wind velocity inside the tunnel, which is briefly introduced below [30].  
If there is no airflow in a tunnel, the static pressure difference between the tunnel portals is equal to the air pressure inside the tunnel produced by gravity. Otherwise, the equation will be false. The differential pressure is called ultra-static pressure difference. The ultra-static pressure difference is calculated as:  
\[\Delta P_{u}=P_{en}-P_{ex}-\rho_{i}\cdot g\cdot H \tag{3}\]  
The ventilation-wall pressure difference refers to the difference between dynamic pressure and static pressure. After blowing to the tunnel entrance, the natural wind outside the tunnel is decelerated by mountains and then part of the dynamic pressure is converted into static pressure, which is called \"ventilation-wall pressure difference\". It is calculated as follows [30]:  
\[\Delta P_{w}=0.35\times\rho_{en}\cdot(v_{aen}\cdot\cos\alpha_{en})^{2}-0.35 \times\rho_{ex}\cdot(v_{aex}\cdot\cos\alpha_{ex})^{2} \tag{4}\]  
The difference in the air temperature between inside and outside the tunnel results in different air density. The temperature difference and the elevation difference between two portals of the tunnel will cause pressure difference with airflow, which is called thermo-potential difference [31]. It can be calculated as Equation (5):  
\[\Delta P_{t}=(\rho_{avg}-\rho_{i})\cdot g\cdot H \tag{5}\]  
Combining of Equations (1)-(5), the natural wind velocity inside the tunnel can be obtained as:  
\[v_{i}=\sqrt{2\times\frac{P_{en}-P_{ex}-2\times\rho_{i}\cdot g\cdot H+0.35 \times\left[\rho_{en}\cdot\left(v_{aen}\cdot\cos\alpha_{en}\right)^{2}-\rho_{ ex}\cdot\left(v_{aex}\cdot\cos\alpha_{ex}\right)^{2}\right]+\rho_{avg}\cdot g \cdot H}{\rho_{i}\cdot\left(\lambda_{r}\cdot\frac{L}{D_{r}}+\xi_{e}+1\right)}} \tag{6}\]
## 1 Introduction
These interactions can affect learning efficiency, that is, with the course (cognitive presence), among peers (social presence) and with teachers/instructors (teaching presence). In this context, the main goal of this paper is to evaluate whether the use of these three different types of presences significantly influences in the performance of predictive models to early detect at-risk students. To achieve this goal, we evaluate whether a given type of presence in the LMS is more suitable to early predict at-risk students or the data collected through a survey may help to improve the performance of the models. Since the number of samples is small and unbalanced, we needed to find a solution to overcome this problem. Previous studies have shown that the use of the SMOTE (Synthetic Minority Over-sampling Technique)technique to balance datasets helps on improving the performance of the models [24]. As we are dealing with highly unbalanced datasets, we also applied SMOTE balancing technique to check if its use interferes in the models' performance. Before initiating the courses, social and demographic data were collected from the students. Since we get only the interaction count from Moodle logs, we included this information in the study to evaluate to which extent this information could help to improve the performances of the models. We base our studies on previous work that use solely the count of interactions to predict studentsat-risk, with the justification of using information that is not restricted to a given type of learning management system [25]. The idea here is to test the sole use of the interaction count to see if it achieves a satisfactory result. In this work, we do not distinguish our analysis between students' failure and dropout. Therefore, if it is not explicitly said the difference, the term \"at-risk students\" means students that got a final grade lower than 6.0 (on a scale from 1.0 to 10.0). In this context, students who dropped out got zero in their final grade and, consequently, are included in this definition. For those reasons, the present work come up with the following research questions:  
1. Which are the most appropriate datasets to early predict at-risk students? 2. Is the sole use of the count of students interactions sufficient to early predict students' failure in the course? 3. Does the use of oversampling techniques (SMOTE) help the models to achieve better performances? 4. Does the use of data from questionnaires applied at the beginning of the course help to improve model performance? The remainder of this paper is divided into 4 sections--Section 2 presents related works. Section 3 presents the methodology, the process of data collection, its description, the dataset generation and the model evaluation. Section 4 reports the results obtained in this study and Section 5 shows the works' conclusion and discussion.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 3 Optimization Model
### The Prediction Deviation  
In a power system that includes wind farms, the prediction deviation is mainly from the load forecast error and the prediction error of the wind farm output. Firstly, the load forecast deviation is as follows:  
\[\Delta P_{\mathrm{L},t}=P_{\mathrm{L},t}-\overline{P}_{\mathrm{L},t}, \tag{25}\]  
where \(\overline{P}_{\mathrm{L},t}\) is the forecast load and \(\Delta P_{\mathrm{L},t}\) is the load forecast deviation, which follows the normal distribution \(N(0,\sigma_{\mathrm{L}}^{2})\).  
The wind farm output prediction deviation is  
\[\Delta P_{\mathrm{W},t}=P_{\mathrm{W},t}-\overline{P}_{\mathrm{W},t}, \tag{26}\]  
where \(P_{\mathrm{W},t}\) and \(\overline{P}_{\mathrm{W},t}\) are the actual and forecast values of wind farm output, respectively, and \(\Delta P_{\mathrm{W},t}\) is the wind power output prediction deviation, which follows the normal distribution \(N(0,\sigma_{\mathrm{w}}^{2})\). The standard prediction deviation at the time \(t\) can be calculated as  
\[\sigma_{\mathrm{w},t}=Kt\cdot\overline{P}_{\mathrm{W},t}+\frac{1}{50}W_{ \mathrm{I}}, \tag{27}\]where \(K\) is the factor of wind power prediction error and \(W_{\rm I}\) is the total installed capacity of the wind farms.  
The actual electric demand of the power system can be calculated as  
\[\tilde{P}_{\rm I,t}=P_{\rm L,t}-P_{\rm W,t}=\overline{P}_{\rm L,t}-\overline{P} _{\rm W,t}+\Delta P_{\rm L,t}-\Delta P_{\rm W,t}=P_{\rm Q,t}+\Delta P_{\rm Q,t}, \tag{28}\]  
where \(P_{\rm Q,t}\) is the electric demand forecast of the whole power system, and \(\Delta P_{\rm Q,t}\) is the electric demand deviation of the whole power system, which is assumed to follow the normal distribution \(N(0,\sigma_{\rm Q}^{2})\). The standard deviation \(\sigma_{\rm Q,t}\) at the time \(t\) is  
\[\sigma_{\rm Q,t}=\sqrt{\left(\sigma_{\rm L,t}\right)^{2}+\left(\sigma_{\rm W, t}\right)^{2}} \tag{29}\]  
According to the [PERSON], et al. [17], the continuous probability density function of the electric demand deviation is defined as  
\[f(\Delta P_{\rm Q,t})=\frac{1}{\sqrt{2\pi}\sigma_{\rm Q}}\exp(-\frac{\Delta P _{\rm Q,t}{}^{2}}{2\sigma_{\rm Q}^{2}}) \tag{30}\]
## 2 Engineering Background and in Situ Monitoring Methods
### In Situ Monitoring Method of Surrounding Rock Pressure
#### 2.2.4 In Situ Monitoring Results  
The data acquisition interface is illustrated in Figure 7. The collected data include key parameters such as the pressure sensor model, identification number, measured pressure values, ambient temperature, and steel string frequency. The data results are archived to establish a comprehensive dataset. This dataset is then processed through calculations and statistical analysis to determine the real pressure values for each monitoring point on a daily basis. Subsequently, the spatiotemporal variation trends of the surrounding rock pressure are analyzed.  
Figure 5: Installation effect of pressure box.  
Figure 6: Pressure box installation position and number calibration.
## 5 Evaluations
### Performance Analysis of Text Extraction and Database Matching Function
#### 5.3.1 Experimental Scenarios  
We measured the performance of the _text extraction function_ and the _database matching function_ by implementing _INSUS_ in the #2 and #3 Engineering Buildings in Okayama University. The experiment was conducted in two scenarios. The first scenario involved text extraction using only the _PaddleOCR_ model. The second scenario combines the _YOLOv8_ model and the _PaddleOCR_ model. For both scenarios, we utilized the same environment, and for each room on each floor, we measured at a distance of one meter from the door. This distance was chosen to ensure consistency, image clarity, and relevance to real-world usage. The specifications of the evaluation environment are shown in Table 4.  
The brightness value on each floor in the building is represented using Illuminance (LUX). The illuminance value is measured using the light sensor on smartphones [58]. Based on [59], the average illuminance value of the implementation building shows that the lighting conditions are visible. During the experimentation, we utilized two devices with different operating systems. The specifications of the experimental devices are detailed in Table 5.  
Figure 7: (a) _Precision_ and _recall_ during the training process; (b) [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 results during the training process.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion
### Differences in SOC Storage among Different Cultivating Systems  
[PERSON] et al. (2009) [37] investigated the effects of crop production systems on different SOC components in India, where crops were grown in different climates and soil types. They studied eight production systems: cultivation systems based on lowland rice, sorghum, maize, pearl millet, toesinte, soybean, peanut, and cotton. They found that SOC storage was highest under soybean-based production systems (62.3 Mg/ha), while the lowest SOC storage was found under pearl millet and teosinte-based production systems. The inorganic carbon storage (SIC) was highest under cotton production systems (275.3 Mg/ha), followed by sorghum production systems (243.7 Mg/ha), while the lowest SIC storage was found under lowland rice production systems (18.15 Mg/ha). Differencesin carbon component changes under different crop production systems can be attributed to climate, aboveground and belowground biomass, root-stem ratio, quality of root and stem biomass (e.g., cellulose, protein, polyphenols, and lignin), decomposition rate, and soil characteristics.
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
###### Abstract
The concept of the maximum power curve for analyzing the power stability of the single-infeed LCC-HVDC system is extended to the hybrid dual-infeed HVDC situation. The calculation method of the power stability margin of the hybrid dual-infeed HVDC system is proposed. 2. Based on the power stability index, the influence of VSC-HVDC on the power stability of the hybrid dual-infeed HVDC system is investigated. Furthermore, it is verified that there is a relatively clear quantitative relationship between the power stability and the ESCR in the hybrid dual-infeed HVDC system. This provides a theoretical basis for using the ESCR index to guide the planning and operation of the hybrid dual-infeed HVDC system. 3. The influence of system parameters on the CESCR is discussed, and the value range of CESCR is determined. Then, the evaluation criteria of power stability applicable to the two VSC-HVDC control modes are, respectively, proposed. The effectiveness of the calculation results is verified based on the electromechanical transient simulation software. This paper is organized as follows: the mathematical model of the hybrid dual-infeed HVDC system is described in Section 2. The power stability analysis of the hybrid dual-infeed HVDC system is presented in Section 3. The power stability evaluation criteria of the hybrid dual-infeed HVDC system are proposed in Section 4. The effectiveness of the calculation results is verified in Section 5. Finally, conclusions are drawn in Section 6.
## 1 Introduction
### IIoT  
The industrial Internet of Things (IIoT), Internet of Vehicles (IoV), and Internet of Drones (IoD) are three important technologies of next generation networks (NGNs). IIoT is referred to as the industry of the future. IIoT refers to interconnected instruments, devices, and sensors networked with industrial applications on computers, such as for production and energy management. This connectivity enables data gathering, sharing, and analysis, possibly promoting productivity and efficiency increases and other economic benefits [9]. The IIoT is a development of a distributed control system (DCS) [10] that enables more automation by refining and optimizing process controls utilizing cloud computing.
## 3 Results
### No Factor Evolution  
The first case we analyze is the one without the evolution of users opinions (\(\varepsilon=0\)).  
The time evolution of the structure is extremely slow. As reported in Figure 2a, there are often early big fluctuations in the community indicator, which slowly converges to a final value. The convergence is slower for large values of \(\tau\), since, in this case, the message is sent only to a limited number of people.  
In the case of a small number \(N\) of users and large number \(L\) of factors, spurious communities can appear because the small number of items will be sparse in a space of \(L\) dimensions. We noted that we obtained very similar results if the number of users is scaled with the number of factors.  
On the contrary, when there is a large number of users with respect to factors, no community structures appear in the users overlap (Figures 3a and 4a).  
Figure 2: (a) Time evolution of the community indicator for a simulation with \(\varepsilon=0\)\(L=40\), \(N=100\), \(M_{0}=200\), \(\tau=0.5\), \(f=0.4\), \(\varepsilon=0\), \(M=40{,}000\). The unit of time corresponds to the sending/receiving of one message, and the time scale (number of messages sent) is in unit of \(10^{2}\). (b) Dependence of the maximum jump indicator on the threshold \(\tau\) in a system without evolution (\(\varepsilon=0\)). The other parameters are \(L=20\), \(N=100\), \(M_{0}=200\), \(\tau=0.5\), \(f=0.4\), \(M=10{,}000\).  
By waiting a sufficiently long time, the cluster structure emerging from the opinion database is the same as the real user overlap, as shown in Figure 3. This is consistent with the fact that users overlap can be recovered by examining the opinions expressed on the messages [17].  
By changing the selection factor \(\tau\) for a fixed evolution time \(M\) we see a decreasing of the clustering indicator (maximum jump), as reported in Figure 2b. This is due to the fact that for large values of \(\tau\) the messages are sent only to neighboring users (in the factor space). So the initial database structure, which is given by the small number of initial messages \(M_{0}\) remains almost frozen, until, slowly, users express opinions on messages sent by intermediate users updating the database and making the opinion correlation slowly converge to the real overlap \(O\).
## Abstract  
Energy expenditure through metabolic equivalent (MET) prediction during resistance exercises in humans can be modeled by using cardiorespiratory parameters. In this study, we aimed to predict MET during six moderate-intensity resistance training sessions consisting of three different exercises. Eleven participants were recruited into two groups; an untrained (n = 5; with no resistance training experience) and a trained group (n = 6; with 2 months resistance training experience). Each participant completed six training sessions separated with a rest interval of 1-2 days. While wearing a mask for indirect calorimetric measurements using Cortex Metalyzer 3B, each participant performed training sessions consisting of three types of dumbbell exercises: shoulder press, deadlift, and squat. The metabolic equivalents (METs), respiratory exchange ratio (RER), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), blood lactate (BL), and Borg rate of perceived exertion (RPE) were measured. The MET was predicted using generalized estimating equations (GEE) for repeated measure data collected during exercise and rest periods. It was observed that during exercise period, RER, HR, SBP, and BL for the training group (QIC = 187, 95% CI = \(-\)0.012-0.915, \(p=0.000\)*\(-\)0.033*) while RER, HR, SBP, DBP, and RPE (QIC = 48, 95% CI = \(-\)0.024-0.422, \(p=0.000\)*\(-\)0.002*) during resting period for untrained group significantly predicted MET for moderate-intensity interval resistance training. It is concluded that the cardiorespiratory variables are significantly related to MET. During exercise, RER and HR significantly predicted MET for both groups along with additional parameters of SBP and BL for the training group. While during the resting period, RER, HR, SBP, DBP, and RPE related significantly for untrained and BL for training group respectively.  
strength training; METs; GEE modeling; cardiorespiratory variables
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 4 Experiments
### Datasets and Metrics  
For evaluation purposes, experiments are carried out on three datasets, namely VeRi-776 [43] and VehicleID [44], which are pivotal in vehicle re-identification research.  
The VeRi-776 dataset [43] comprises 51,035 images representing 776 vehicles captured by 20 cameras. Specifically, the training set consists of 37,778 images of 576 vehicles, while the testing set includes 13,257 images of 200 vehicles. The testing set is further divided, where 1678 images form the query set and the remaining serve as the gallery. Each probe image in the query set corresponds to multiple images with the same identity in the gallery set.  
The VehicleID dataset [44] contains 221,763 images of 26,267 vehicles, with 110,178 images of 13,134 vehicles designated for training and the remainder for testing. The test set is segmented into three subsets of varying sizes: small, medium, and large, containing 800, 1600, and 2400 vehicles, respectively. Each query image is matched with only one image in the gallery. Unlike VeRi-776, the VehicleID dataset predominantly features two vehicle orientations: front and back.  
To quantitatively assess the efficacy of our proposed method, we employ two standard metrics: mean Average Precision (mAP) and Cumulative Match Curve (CMC). While both indicators effectively reflect the model performance, each has its distinct emphasis in a re-identification task. The mAP metric is particularly well-suited for an n vs. n scenario, where each probe image corresponds to multiple matching images in the gallery, as exemplified by the VeRi-776 dataset. Conversely, the CMC metric is more applicable to an n vs. one scenario, characterized by the presence of only one matching image in the gallery, typical of datasets like VehicleID.