esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### AgriLogger Structure  
The system belongs to a family of battery-powered sensors able to collect, hold and transfer, through standard IoT policy, the measurements of two essential environmental parameters: temperature degree and relative humidity. The agrosensor has been derived from ESC(tm) (Easy Sense & Connect) architecture (Figure 1), which is able to reduce the energy consumption through the suppression of the IoT network interface as LoRa (Long Range) or NB-IoT (Narrow Band IoT). It is formed of a transducer, a data logger based on low power embedded microcomputer family designed by Nordic NRF52832 and supported by Cypress FM24V05, containing 512 Kbit of non-volatile memory and employing an advanced ferroelectric process, which guarantees data retention for several years. Moreover, a wireless data transfer represented by BLE (Bluetooth Low Energy) transceiver with PCB printed antenna which transfers the collected data to the requesting host. a real-time clock based upon MCP79412 Microchip, and supported by a 32 kHz crystal in order to take trace of elapsed time in terms of fiscal year, month, day, hour, minute and second. The clock is set through a smartphone APP, supplied with the sensor, at the time of battery installation. Subsequently, a wake-up device is modulated according to the forecasted application. Each device is limited to waking up at a certain interval of time, taking the data and storing it in an internal memory. The awakening of the device is driven by the internal real time clock, and the process can be launched whether by using a need relay throughout approaching a magnet to one of the ends of the external box, which reactivates the internal microcontroller, allowing Bluetooth LE connectivity. Alternatively, a directive radio wave transmitter enabled on the host, in our case a drone, can wakes up the sensor. The maximum distance between the radio wave transmitter and the awaking circuit is about 20 m. The waking-up circuit embedded in the sensor harvests energy from the radio wave. AgriLogger can connect to gateways enabled for the connection. If there is a wakeup call but there is no gateway nearby the device returns to a deep sleep state. Furthermore, the device structure contains a power manager which regulates the embedded battery pack (2 xAA alkaline batteries with limited self-discharge) and provides sleep/awake status for the device.  
A series of capacitors are charged to reach a certain level of electrical voltage used as an input for the microcontroller that wakes up and enables Bluetooth LE connectivity.  
Figure 1: ESC(tm) (Easy Sense & Connect) architecture used for AgriLogger manufacturing.
## 2 Research Background
### AR and BIM Integration Approaches  
Early attempts at AR adoption in the construction industry [30; 31] used models created from 3D CAD drafting tools or 3D rendering software (e.g., Blender, 3 ds Max, Maya, etc.). These models were subsequently converted into standard vector file formats, such as VRML or STL, for projection using legacy AR devices. However, these approaches only allowed the projection of the 3D graphical components of the models and were consequently limited in their intractability and data accessibility. Thus, AR use cases were restricted to simple projections for equipment arrangement [30; 31], interference checks [32], and engineering education [33].  
The increased adoption of BIM in the industry has made it natural to use BIM models as a direct source of AR content [22]. Meanwhile, game development engines, once a tool exclusively used for creating animations and 3D gaming scenes, have been extended and applied to engineering disciplines. Consequently, engineering CAD models and BIM models can be readily imported into these engines and transformed into formats acceptable for AR environments. More importantly, these transformations retain the semantic definitions and attributes of the individual elements in BIM models, thereby enabling project data to be displayed over real-world scenes via AR devices. Such advances have enabled BIM models to be used with AR devices as an interactive tool in the field for progress monitoring [24; 25], safety inspections [26; 28], and quality management [16; 27].  
The two representative game engines include Unity 3D and Unreal. Both engines provide add-ins within Autodesk Revit for the conversion of BIM models to AR-acceptable model formats: 'Reflect' for Unity 3D and 'Datasmith' for Unreal Engine.  
Table 2 provides a summary of the characteristics of both engines. Unity3D has a larger user base and is suited for rapid 3D model development, whereas Unreal is suitable for models requiring high-end graphical performance [34]. Moreover, Unreal's Datasmith can only be run on Windows, whereas Unity's Reflect runs on both Windows and Mac  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Projection Method** & **Object Target** & **3D Model Target** & **Image Target** & **Spatial Target** \\ \hline \multirow{4}{*}{Methodology} & \multirow{4}{*}{Extracting feature points after a 360\({}^{\circ}\) shooting using an object scanner} & \multirow{4}{*}{Set 3D CAD file as a target using Model Generator} & \multirow{4}{*}{Determine feature points of 2D photo data and recognize them as targets} & \multirow{4}{*}{Recognize planes that can project models} \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various Augmented Reality (AR) Model Projection Methods.  
environments. This study used Unity3D's Reflect for BIM-to-AR integration, using an iPad Pro as the primary device for AR implementation.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 3 The Smart Collaborative Routing Protocol for IoT
### IN Election  
In this part, the CH node stipulate that only its MNs can start the IN campaign. Because the IN node must be within the communication range of CH to monitor its behaviors. The selection of the CH and IN is crucial to improve the network life-cycle and energy efficiency [48; 49]. In the GIN protocol, the IN election is based on a non-cooperative and repeated game theory. The game is expressed as:  
\[G=\{N,S,U\} \tag{1}\]  
The players are represented by \(N\); each player has the same strategy space named \(S\); the utility of them is given by \(U\). The selection of the strategies is to become an IN or a MN, and the set of strategies can be represented as:  
\[S=\{IN,MN\} \tag{2}\]  
The process of the game can be described as a cost and payment model. If a node chooses one of the strategies, it will get a specific payoff. All the nodes look forward to selfishly select the strategy for maximizing their payoff and consuming the least energy. If a node decides to work as an IN, it can obtain more responsibilities. On the contrary, refusing to be an IN can save its power. Therefore, the set of utility functions is described as:  
\[U(s_{i})=\left\{\begin{array}{cc}0&\text{when }s_{i}=MN,\ \forall_{i}\in N \\ \frac{1}{C_{in}}&\text{when }s_{i}=IN\\ \frac{1}{C_{nm}}&\text{when }s_{i}=MN\end{array}\right. \tag{3}\]  
where \(C_{in}\) and \(C_{mn}\) represent the cost of being the IN and MN, respectively. Then, we can obtain \(C_{in}<C_{mn}\). For example, there are two players joining in the game. The payoffs of them are described in Table 1. According to the payoff matrix, if the first player selects to be the strategy IN, the other player will only choose the strategy MN, because the strategy combination (IN,MN) receives more payoffs for the second player, i.e.,  
\[\frac{1}{C_{mn}}>\frac{1}{C_{in}} \tag{4}\]  
If not, the other player will select the strategy IN, because the strategy pairs (MN,IN) receives more payoffs for the second player than (0,0), i.e.,  
\[\frac{1}{C_{in}}>0 \tag{5}\]  
In another case, if the selection order is changed, both players will still choose the strategy pairs (IN,MN) and (MN,IN). The payoffs of the selection will be  
\[\left(\frac{1}{C_{in}},\frac{1}{C_{mn}}\right) \tag{6}\]  
and  
\[\left(\frac{1}{C_{mn}},\frac{1}{C_{in}}\right) \tag{7}\]  
Therefore, the best result is that one player decides to be an IN and another selects to be a MN, which is a pure Nash Equilibrium.  
The game schematic is shown in Figure 6.
## 5 Conclusions and Outlook  
This paper details the optimization of traditional thermochemical iodine-sulfur cycle water-splitting hydrogen production technology. It introduces the concept of a small cycle, which enhances the thermal efficiency of hydrogen production to 53% under conditions where the electrical energy conversion efficiency is 40%. Based on this optimization, the hydrogen production pilot platform at Qingshan Lake Energy Research Base of Zhejiang university was modified. To address the corrosion problem during sulfuric acid evaporation, an innovative three-stage H\({}_{2}\)SO\({}_{4}\) reactor was developed and validated through CFD simulations, preventing the corrosive issues that occur during high-temperature H\({}_{2}\)SO\({}_{4}\) decomposition. A heat recovery-utilizing HI decomposition reactor was adopted to improve heat recovery efficiency and increase hydrogen production efficiency. Subsequently, in January 2025, a successful 24 h thermochemical iodine-sulfur cycle hydrogen production test was conducted, with a peak hydrogen production rate of 1536 L/h, stabilizing at approximately 300 L/h in the long term.  
In future industrialization processes, the use of electric heating elements in small-scale and pilot tests is not advisable. The standard for industrialization is to use actual high-temperature heat sources for heat exchange. Therefore, the development of reactors for high-temperature actual heat source heat exchange and the design of fully industrial automation control are future research directions.  
Conceptualization, Z.W. and Y.H.; methodology, W.S. and W.W.; software, W.S.; validation, W.W.; formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and W.S.; resources, W.W.; data curation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, Z.W.; visualization, Z.W.; supervision, Y.H. and Z.W.; project administration, Y.H. and Z.W.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52125605) and the Fundamental Research Funds for the Central Universities (2022 ZFJH04).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Figure 15: Comparison of I-S hydrogen production in various research institutions (hydrogen production volume, running time).  
**Acknowledgments:** Thank you to [PERSON] and [PERSON] from Dongfang Electric Co., Ltd., for their support of this project work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 2 Materials and Methods
### Simulation Description
#### 2.5.2 Network Details  
Regarding the packets sent through the network, we consider that the sensed data have a length of 37 bits for each measurement. The headers of the packets sent through LoRa comprise a preamble of 8 bytes and a header of 13 bytes. The data packets that are forwarded over LoRa will have a minimum size of 141 bits, which corresponds to a message with one measurement. Furthermore, the maximum packet size is 4176 bits, which is the maximum size allowed by LoRa. Lastly, the LoRa acknowledgements (ACK) have a size of 21 bytes which corresponds to the preamble and the header.  
The simulation for the energy consumption of the LoRaWAN nodes is performed according to the energy consumption model presented in [17]. The total energy consumption of the node is presented in Equation (1).  
\[\mathrm{E_{Total}}=\mathrm{E_{Microcontroller}}+\mathrm{E_{Sensors}}+ \mathrm{E_{tx}}+\mathrm{E_{rx}} \tag{1}\]  
where \(\mathrm{E_{Microcontroller}}\) is the energy consumed by the microcontroller, \(\mathrm{E_{Sensors}}\) is the energy consumed to activate the sensors and gather the data, \(\mathrm{E_{tx}}\) is the energy consumed to transmit the data, and \(\mathrm{E_{rx}}\) is the energy consumed to receive the ACK from the gateway, which according to [17] is 0.27 mJ.  
Equation (2) is utilized to determine the energy consumption per bit at the transmission.  
\[\mathrm{E_{bit}}=\mathrm{(Power\:Consumption\:x\:ToA)/(8\:x\:(Preamble\:+Header\:+ Payload))} \tag{2}\]  
As for the LoRaWAN parameters, considering that our system requires a minimum range of 2 km, all possible settings would meet the requirement in the scenario of free space for a frequency of 868 MHz (See Figure 8). However, as there are vegetation and trees in the area, we consider a scenario with few obstacles, as presented in Figure 9. Therefore, for a transmission power of 7 dBm, the settings of BW250-SF7 and BW500-SF7 would not meetthe requirement. For the case of 433 MHz, the required maximum theoretical distance is reached with all settings.  
Considering the results of the theoretical distances for each LoRa setting, the settings selected for our system are the EU 863-870 frequency band, BW 125 kHz, and SF 8. Therefore, for the selected settings and a selected transmission power of 13 dBm with its power consumption of 92.4 mW/h, the energy consumed per bit for each sensor is 45.55 \(\upmu\)J.
# University Student Dropout Prediction Using Pretrained Language Models
## 4 Experiments
### Experimental Results  
Table 4 presents the results of our experiments, comparing the performance of the six baseline models with that of our proposed STUD model. Overall, our experiments demonstrated that STUD, which prepends continuous hypotheses to the layers, significantly outperformed the baseline methods. In particular, the F1-score of STUD\({}_{SimCSE-BERT}\) was 9.00% higher than that of MSNF, an existing state-of-the-art model.  
This result demonstrated that our model effectively captured the relationships between structured and unstructured data when predicting dropout. In addition, STUD\({}_{SimCSE-BERT}\) exhibited a 10.84% higher performance in terms of the F1-score than the MLP models using only conventional numerical forms. This result showed that our attempts to formulate numeric data in textual form and train them were effective. Moreover, we observed that STUD\({}_{SimCSE-BERT}\) outperformed STUD\({}_{BERT}\) by 3.60% in terms of the F1-score. This showed that the model additionally trained on the NLI dataset was effective for the dropout prediction task, recast as an NLI task.  
Another insight that we observed from these results was that the precision was higher than the recall for the proposed method. This indicated that the model was generally passive in predicting students who drop out. In real-world applications, it may be more beneficial for administrators to use models with a high precision. By implementing a counseling program for students who are considering dropping out, universities can proactively prevent dropouts and improve student retention rates. However, if such a program is applied to students who have no intention of dropping out, it can have a negative impact on their academic experience. Balancing the precision and recall of the model can be considered in further work.
## 4 Results and Discussions
### Direct Tensile Strength Test  
The tensile strength of the interface between the RCA and asphalt is shown in Figure 10. The results indicate that the interface tensile strength significantly increases after SCA modification under both dry and wet conditions. Under dry conditions, the interface tensile strength between RCA and asphalt without SCA modification is 97 kPa. After SCA modification, the interface tensile strength increases by 72.2%. Under wet conditions, the interface tensile strength without SCA modification decreases by 56%. After SCA modification, the interface tensile strength increases by 119.7%.  
Figure 10: The tensile strength of the interface and adhesion rate of asphalt on RCA surface.  
Figure 9: Boiling water test: (**a**) unmodified RCA before the test; (**b**) unmodified RCA after the test; (**c**) SCA-modified RCA before the test; (**d**) SCA-modified RCA after the test.  
The influence of SCA modification on the interface failure types was analyzed by the interface failure forms shown in Figure 11 and the corresponding adhesion rates in Figure 10. The results showed that the adhesion rate of asphalt on the surface of RCA increased after SCA modification, changing the form of interface failure. In the unmodified RCA, the main type of interface failure was adhesion failure between the asphalt and RCA, with an adhesion rate of approximately 27.2%. However, after SCA modification, the type of interface failure was mostly cohesive failure, and the adhesion rate increased significantly to 98.4%. This is because when the adhesive force of the RCA/asphal interface is greater than the cohesive force of asphalt itself, the asphalt undergoes cohesive failure. This indicates that SCA modification reinforces the adhesive strength of the interface, making the adhesive force more significant than the cohesive force of asphalt itself. At the same time, SCA modification also showed good performance under wet conditions, increasing the adhesion rate from 9.5% in the unmodified state to 81.2%.
## 5 Stability Analysis of TSWs  
### Stress Distribution and Yield Zone Analysis of TSWs  
Under large localized lateral loads, as well as transverse and longitudinal loads, significant bending stresses are induced in the thin sidewalls (TSWs), resulting in elevated maximum principal stresses (MPS) on both sides of the structure [34], which may compromise stability. As derived in Section III, the MPS and corresponding yield zones in the TSWs are primarily influenced by the lateral load center position (LP) and the spatial coordinates of the TSW.  
Furthermore, because the host rock contains pre-existing joints and cracks and is affected by processes such as blasting, weathering, and hydraulic penetration [35; 36; 37; 38], the rock's cohesion should be reduced by a safety factor (SF). The yield behavior of the rock mass is assessed using the allowable cohesion, which incorporates this safety factor [9]. When the actual SF is lower than the allowable value, the rock enters a yield state; conversely, when the SF exceeds the allowable value, the rock remains relatively stable. In mining practice, a typical SF exceeds 1.25 to ensure safety; in this study, a value of 1.5 is adopted.  
To analyze the MPS distribution and yield zones, a case study was conducted based on the actual conditions at the Jubankeng Tungsten Mine. The average TSW dimensions were set to 50 m (length) \(\times\) 50 m (height) \(\times\) 3 m (thickness), with a lateral load width (LW) of 4 m. The MPS and yield zone distributions were evaluated for LP values of 5 m, 10 m, 15 m, 20 m, and 25 m. The corresponding cloud diagrams of stress and yield zones are shown in Figures 5 and 6.  
As shown in Figure 5, when the LP is small, the zone of elevated MPS is concentrated near the left boundary of the TSW. As the LP increases, stress concentrations develop at the upper and lower boundaries and the lateral load center zone, while stresses at the left boundary gradually decrease. Eventually, the MPS at the upper/lower boundaries and center zone surpass those at the left boundary. This indicates that peak stress points tend to occur at the midpoint of the left boundary (0, h/2), at the lower boundary's lateral load center (LP, 0), and at (LP, h/2). Additionally, the stress distribution becomes more uniform with increasing LP.  
As shown in Figure 6, for smaller LP values, the yield zone appears primarily at the left boundary. With increasing LP, yield zones begin to appear at the upper and lower side boundaries, while the yield zone at the left boundary diminishes. Eventually, yield zones also emerge near the right boundary. Overall, yield zones become more evenly distributed across the TSW, indicating a gradual decrease in structural stability.  
Figure 5: MPS cloud under different LP conditions. (**a**) LP = 5 m; (**b**) LP = 10 m; (**c**) LP = 15 m; (**d**) LP = 20 m; (**e**) LP = 25 m.
# Semi-Active Vibration Control Based on a Smart Exciter with an Optimized Electrical Shunt Circuit
## 3 Numerical Analysis of Vibration Reduction Potential
### Simulation Framework
#### 3.1.2 Modeling of Smart Exciter with Electric Shunt  
After successful export of the state space matrices from COMSOL, the SE coupled with the electric shunt (series connected resistor and inductor) was modeled in MATLAB/Simulink. According to the Equations (1)-(3), the acceleration of the SE, \(\ddot{x_{e}}\), and the electric current, \(I\), can be described as follows:  
\[\ddot{x_{e}}=\frac{K_{e}}{M_{e}}\cdot u_{s}+\frac{D_{e}}{M_{e}}\cdot\dot{u_{s} }+\frac{Bl}{M_{e}}\cdot I-\frac{K_{e}}{M_{e}}\;x_{e}-\frac{D_{e}}{M_{e}}\cdot \ddot{x_{e}} \tag{4}\]  
\[I=-\frac{Bl\cdot\left(\dot{x_{e}}-\dot{u_{s}}\right)}{R_{e}+R_{2}}-\frac{L_{e }+L_{2}}{R_{e}+R_{2}}\cdot\dot{I} \tag{5}\]  
The resulting Simulink model of the SE and the RL-shunt is shown in Figure 4. As the blue sections show, the input signal to the SE is the displacement of the panel. The SE's output signal is the exciter force. The black sections in Figure 4 represent the relationship in Equation (4). The green (electrical components) and orange segments (mechanical components) represent Equation (5). For this model, the solver ode4 (Runge-Kutta) with a step size of 0.0001 s has been used.  
Figure 3: Geometry of a plate model with three coupling positions.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 5 Performance Measurements  
Mobile networks for space, air, ground, and sea applications involve the use of channels of different characteristics that are affected by particular environmental conditions. To cover a wide range of cases while still allowing reasonable comparisons among the different cases, the evaluation study focused on a common network topology (depicted in Figure 1). Different cases were then evaluated by applying different conditions to the channel features and operating conditions.