esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed LI2A Scheme
### Forward Secrecy  
In authentication methods that use symmetric keys, session keys are often derived from shared keys. Recognized limits include the risk of insider compromise and the vulnerability to side-channel attacks. As a result, it is critical to protect previous communication sessions, emphasizing the importance of forward secrecy. This means that even if a long-term secret key is disclosed, the previous session key cannot be recovered. This feature enables the deployment of finite-lived session keys in our suggested solution for IIoT environments. In our proposed scheme, the FS stage starts every time \(\Delta T\) expires. The steps for forward secrecy are as follows:  
1. \(C_{i}\) triggers the FS session.
2. \(C_{i}\) generates a random number \(r_{i}\leftarrow\{0,1\}^{q}\).
3. \(C_{i}\) computes \(m_{f_{5_{1}}}=r_{i}\oplus k_{i-1}\).
4. \(C_{i}\) computes \(k_{i}=h(r\|T_{i}\|k_{i-1})\) and stores it.
5. \(C_{i}\) sends \(m_{f_{5_{2}}}=(m_{f_{5_{1}}},T_{i_{2}},h(T_{i_{1}}\|k_{i-1}))\) to \(IIoT_{j}\).
6. \(IIoT_{j}\) computes \(r_{i}=m_{f_{5_{1}}}\oplus k_{i-1}\) and confirms the authenticity of \(T_{i}\).
7. \(IIoT\) computes \(k_{i}=h(r_{i}\|T_{i}\|k_{i-1})\), stores it, and sends a key acknowledgment message \(m_{f_{5_{3}}}=(T_{ACK},h(T_{ACK}\|K_{s}))\) to \(C_{i}\).
8. Once \(C_{i}\) receives \(m_{f_{5_{3}}}\), it computes \(T_{i}=h(T_{ACK}\|K_{s})\) and compares the result to \(T_{ACK}\). If the result matches, this confirms that \(K_{s}\) is correct and synced.
9. \(C_{i}\) will then choose a new random time period for the timer \(\Delta T\). Figure 4 demonstrates the forward secrecy phase. It should be noted that for all exchanged messages, the LI2A protocol stores the last \(T_{i}\) as \(T_{i-1}\) and drops any messages that have \(T_{i}\leq T_{i-1}\).  
Figure 3: Key agreement phase.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 3 Performance Evaluation
### Performance Simulation of Synchronization Parameter Estimation
#### 3.2.2 Performance Simulation of Phase Offset Estimation  
Figure 4 shows the simulation of phase offset estimation accuracy. Simulation conditions: \(N=108\); the added phase offsets are \(-\frac{3}{4}\pi\), \(-\frac{1}{2}\pi\), \(-\frac{1}{4}\pi\), \(0\), \(\frac{1}{4}\pi\), \(\frac{3}{4}\pi\), and \(\frac{3}{4}\pi\). From the figure, we can see that the MSE of the algorithm closely tracks the MCRB, so its performance is excellent.  
Figure 5 shows the effect of different \(UW\) lengths on the phase offset estimation performance. Simulation conditions: the added phase offset is \(\frac{1}{4}\pi\), and the \(UW\) lengths used are \(N(N=108)\), \(3N/4\), \(N/2\), and \(N/4\). From the figure, we can see that the performance of the algorithm is proportional to the local \(UW\) length used.  
In summary, the ML-based phase offset estimation algorithm has better estimation performance, and its performance is directly proportional to the \(UW\) length. In actual projects, as long as the performance can meet the needs, the length of the local \(UW\) used can be appropriately reduced. For example, when the length of the local \(UW\) used in this  
Figure 4: Accuracy of the phase offset estimation algorithm.  
Figure 3: Estimation range of different frequency offset estimation algorithms.  
article takes \(N/2\), its accuracy still meets the requirements, and its complexity is reduced by half.
## 3 Results and Discussion
### Effect of Solution pH on Fluoride Removal  
Figure 3a illustrates the variations in the amount of fluoride adsorbed and the final pH corresponding to changes in the solution pH. The fluoride adsorption capacity was higher at an initial pH of 3 than at other pH values, indicating favorable adsorption under acidic conditions and showcasing its promising applicability in wastewater treatment. The low pH of industrial wastewater is primarily due to the use of hydrofluoric acid in various industrial processes. For instance, industrial wastewater generated in China contains fluoride concentrations as high as 30,000 mg/L, with a pH of 1.58 [48], while wastewater from semiconductor manufacturing typically has a pH of around 3.5 [49]. The results of the PZC analysis to investigate the surface charge of WA are presented in Figure S2, with a calculated PZC value of 12.97. This indicates that, under the initial pH conditions (pH 3-11) of the fluoride solution, the surface charge of WA was positive (pH < PZC) [50]. Under low-pH conditions, the surface of WA becomes more positively charged, promoting Coulombic attraction between WA and fluoride, consequently enhancing the fluoride adsorption capacity of WA [51]. As the pH of the fluoride solution was raised from 5 to 11, the amount of fluoride adsorbed by WA remained consistent at 116.01 \(\pm\) 0.8 mg/g. This observation contrasts with findings from previous studies, in which fluoride adsorption typically decreased as pH increased because the negatively charged and deprotonated surface of the adsorbents led to competition with OH\({}^{-}\) for adsorption sites [52]. This highlights the versatility of WA, demonstrating its effectiveness across a wide pH range.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Parameters** & \(R^{2}\) \\ \hline Langmuir & \(Q_{m}\) (mg/g) & \(K_{L}\) (L/mg) & 0.972 \\  & 156.78 & 0.071 & \\ \hline Freundlich & \(K_{F}\) & \(1/n\) & \\  & ((mg/g)\(\cdot\)(L/mg)\({}^{1/n}\)) & 0.276 & 0.949 \\  & 31.87 & & \\ \hline Temkin & \(b_{T}\) (J/mol) & \(A_{T}\) (L/g) & \\  & 96.27 & 1.20 & 0.985 \\ \hline \end{tabular}
\end{table}
Table 2: Equilibrium model parameters obtained by fitting the experimental data.  
Figure 3: Influence of (**a**) initial pH of the fluoride solution, (**b**) dose of WA, and (**c**) presence of coexisting anions on fluoride removal. The different letters (a, b, c, d, e, and f) indicate statistically significant differences among treatments, as determined by [PERSON]’s multiple range test at a 5% probability level.
## 3 Cooperative Schemes
### Cooperative Schemes for VLC
#### 3.2.5 Other Techniques  
NOMA was firstly proposed for VLC as a proper technology to boost the narrow modulation rate available for VLC systems [128]. Users are multiplexed in the power domain by using coding on the transmitter side and successive interference cancellation at the receiver. Later on, NOMA is proposed as an adequate technique for providing a fair coverage, regardless of user location, i.e., users located at the cell edge are benefited thanks to NOMA and the received inter-cell interference is reduced [48; 105; 129]. These NOMA techniques can be managed as single footprint scenarios.  
Spatial division multiple access (SDMA) can be considered as a clustering technique to reuse resources at a different group of users. SDMA may reduce the inter-user interference, but its greatest advantage is the reuse of multiple access resources such as time and frequency. Most of the state-of-the-art SDMA techniques for VLC involve the fabrication of a new transceiver capable of transmitting multiple beams, which increases the cost and complexity considerably [130; 131]. However, authors of [132] recently proposed a novel SDMA technique for MIMO-VLC systems that do not need a new transceiver design, and they demonstrate the superiority of SDMA against traditional OFDMA. On the contrary, SDMA techniques are classified into the multi footprint category.  
Figure 16: Relay-assisted technique using reflections [45].  
Both NOMA and SDMA can be combined for improving the performance of cell-edge users in a multi-cell VLC network [133]. Besides, SDMA may be employed as a grouping technique prior to JT-CoMP techniques [134].  
To conclude this section, Table 3 represents a summary of the previously described cooperative techniques in VLC. As can be seen, some of them focus on interference mitigation, whereas some others address the LoS-link blockage by providing additional ones. The choice of the cooperative technique must be determined depending on the scenario, permitted complexity and the target of the system.
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms
### Continous Sampling  
Continuous sampling helps overcome the limitations of discrete sampling by providing high frequency measurements over time [29; 30]. Such systems have been used for decades to measure parameters, such as salinity, temperature, depth (CTD), and chlorophyll fluorescence (ARGO floats; [31]). Recent improvements in technology have enabled the integration of optical sensors for measurements of irradiance, spectral absorption, fluorescence, and backscattering from buoys [32] from flow-through systems installed on-board ships (Figure 4) [19], and from profiling floats (BCG-Argo; [33]), or gliders [34]. These instruments provide high spatial resolution measurements, in both the lateral and vertical dimensions, with scales between 10 and 100 m depending on the speed of the ship and integration-time of the instruments (Figure 5) [35].  
Figure 4: Bot-wing equipped with a CTD and optical sensor for high spatial lateral and depth measurements (in-flow).  
Fixed platforms provide a different type of continuous sampling through a wide variety of techniques typically used with buoys or on hard-mounted platforms. The AERONET-OC network provides continuous and automatic measurements of the remote sensing reflectance in coastal waters [36]. GLEON (www.gleon.org, accessed on July 2021) has evolved to encompass nearly 100 of similarly purposed buoys across the globe. In addition to the data provided, continuous sampling also benefits the validation of remote sensing products. The dynamic changes that can occur in water quality variables throughout the day are shown in Figure 6. Another example of a single-lake system is Lac Leman Exploration (LeXPLORE; [[https://lexplore.info/](https://lexplore.info/)]([https://lexplore.info/](https://lexplore.info/)), accessed on July 2021), which is an experimental platform deployed in 2019 on Lake Geneva and equipped with instruments that measure coupled biogeochemical and physical processes at fine temporal and spatial resolution. The platform aims to provide a wide range of open access in situ data (from bio-optical to physico-chemical measurements and samples) to foster interdisciplinary research, inform stakeholders on the lake status, and communicate science to the public. The temporal resolution and the variety of data measured at such platforms make them effective for validating remote sensing measurements and model simulations.  
Figure 5: Examples of in-flow measurements of the back-scattering coefficients in French Guyana (**a**) and the Eastern English Channel (**b**).  
Figure 6: High resolution profiles of absorption line height peak at 676 nm measured between April 9 and May 2, 2020 with a WetLabs ACS mounted on a Thetis autonomous profiler near the platform LeXPLORE in Lake Geneva (Switzerland-France). Every 3 h, the Thetis profiles the top 50 m of the water column and produces various bio-optical and physical data at a centimeter resolution.
## 1 Introduction
One of the main advantages of space vector models is their ability to reduce the number of differential equations required, making them computationally efficient while still capturing important transient behaviors [46; 47]. Each model has its strengths and limitations, depending on the required details and available computational resources. Simpler models like the steady-state equivalent circuit may suffice for basic applications, while more advanced models like D-Q or space vector models are better suited for detailed startup simulations or real-time control [48]. Conventional mathematical models for induction motors typically assume a uniform current distribution in the rotor slots. This assumption overlooks the phenomenon of current displacement, where the current density varies along the height of the rotor slot due to changes in inductive reactance. As a result, conventional models tend to underestimate the starting current and overestimate the motor's acceleration during the direct-starting process. These inaccuracies can lead to significant discrepancies between predicted and actual motor behavior, particularly in high-power applications where precise control of starting dynamics is critical. This paper presents a mathematical model of a three-phase induction motor (IM) with a squirrel cage rotor, focusing on the analysis of its direct-starting mode using a model in the \(\alpha\beta\) coordinate system. Specifically, two models are analyzed: one that ignores current displacement in the rotor slots and one that accounts for it. Both models are used to study the direct starting of the motor and compare the dynamic characteristics in each case. The chosen model is particularly effective in simulating the start-up behavior of the motor, including phenomena such as rotor acceleration and the interaction between stator and rotor flux linkages. By incorporating the effect of current displacement, the model provides a more accurate representation of the starting current and torque profiles. This allows for a detailed examination of how current displacement influences motor performance during start-up. The results obtained from the mathematical models are compared with experimental data to validate their accuracy and reliability.
# Forecasting Irregular Seasonal Power Consumption.
## 3 Results
### Forecasts Comparison  
The greatest interest is in forecast reliability. To compare the results of the forecasts given by the different methods, the mean absolute percentage error (MAPE) as a percentage is used, as indicated in (33). This is a common indicator used to compare forecasts of demand [73].  
\[\mathrm{MAPE}(h)=\frac{1}{h}\sum_{t=1}^{h}\biggl{|}\frac{y_{t}-\hat{y}_{t}}{y_{t }}\biggr{|}. \tag{33}\]  
Here, \(h\) is the forecast horizon to evaluate. As the forecasts are made one hour ahead (ten units) throughout the validation subset, \(h\) can take values from one to ten time units. From the forecasts of one hour ahead, the MAPE is obtained by comparing these with the real values of the validation set, using the procedure described in [74]. The benchmark summary in Table 5 includes the average of the MAPE. The average is obtained as the mean of the MAPE(\(h\)) with \(h\) = 1,2,...,10. The best forecasts, on average, are produced by the NLR. The nHWT-DIMS models are revealed as a competitive method against the regular seasonal models, outperforming the other models.  
Figure 13 shows the MAPE of these forecasts as a function of the forecasting horizon. It is clear from the results obtained that traditional models with one seasonality are not capable of working with this type of series. The BATS and TBATS models of state spaces do not drop below 30% MAPE. The ARIMA model starts by making very short-term forecasts that have MAPE of below 15%, but beyond three time units it is not capable of making good predictions. The nHWT models improve the forecasts with respect to the previous ones, although the use of the DIMS allows the level of the predictions to be always kept below 20%. However, the method that produces the best results is NN-NLR. These models give forecasts that remain almost constant with an accuracy of about 14% of MAPE.
###### Abstract  
This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.  
transmission network expansion planning; clustering analysis; uncertainty; multi-objective optimization Article
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
## 4 Conclusions  
In this study, the effects of drying temperature, air velocity, and manure layer depth on the low-temperature drying characteristics of chicken manure in a humid environment were investigated, and five common drying models were evaluated to simulate the drying process. The following conclusions can be drawn:  
1. The drying effect was proportional to the temperature and air velocity, and was inversely proportional to the depth of the manure layer. The drying time of the low-temperature drying process in a humid environment was more than 24 h, which will increase the disposal cycle of chicken manure. Therefore, it was necessary to increase the drying temperature and air velocity or reduce the thickness of the manure layer to shorten the drying time to within 24 h.
2. The low-temperature drying process of chicken manure in a humid environment had only a falling rate period, with no accelerated or constant rate period, and there was a turning point in the falling rate period. Furthermore, with increasing temperature and air velocity and decreasing manure depth, the turning points of the drying rate curves appeared earlier.
3. The Exponential model was considered to be more suitable for describing the low-temperature drying process of chicken manure in a humid environment, which can be used to predict the drying rate and select reasonable drying parameters in actual production. The effective diffusivity of a low-temperature drying process of chicken manure in a humid environment changed between 6.37 \(\times\) 10\({}^{-7}\) and 2.17 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/h, and the values were proportional to the temperature and air velocity.  
Figure 7: (a) In (_MR_)-t curves and fitted curves of chicken manure drying at different drying temperatures; (b) In (_MR_)-t curves and fitted curves of chicken manure drying at different air velocities; (c) In (_MR_)-t curves and fitted curves of chicken manure drying at different manure depths.  
**Author Contributions:** Conceptualization, L.X.; data curation, Q.D.; formal analysis, Z.S.; funding acquisition, L.X.; investigation, L.X.; methodology, Z.S.; resources, X.K. and L.X.; software, X.L.; supervision, Z.S.; writing--original draft, X.L.; writing--review and editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Research Foundation for Doctor of Henan University of Animal Husbandry and Economy (2021 HNUAHEDF003), and Key Scientific Research of Higher Education of Henan Province (23B230004).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the author.  
The authors declare no conflict of interest.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
###### Abstract  
For the risk assessment of a satellite to determine whether the satellite tank explodes under the hypervelocity impact, the Walker-Wasley criterion is selected to predict the shock initiation of the satellite tank. Then, the minimum power density of liquid hydrazine is determined based on the tests, the expressions of shock wave pressure and pressure duration are constructed based on the one-dimensional wave theory, and the initiation criterion for the liquid hydrazine tank is established. Finally, numerical simulation and the initiation criterion are adopted to calculate the power density in the satellite tank under the debris impact at the velocity of 10 km/s. The calculated power density agrees well with the simulated power density, they are both larger than the minimum power density, demonstrating that the shock wave generated by the hypervelocity impact is sufficient to trigger an explosion in the satellite tank.  
shock initiation; satellite tank; debris; hypervelocity impact; numerical simulation +
Footnote †: journal: Applied Physics Letters
## 3 Results
The interpretation of the GPR results proposed above was validated using in situ drilling tests as an evaluation method. The drilling point, marked with a yellow dot in Figure 3, was in the middle of the construction site. As shown in Figure 7, water was encountered at approximately 5 m depth. The presence of water at the bottom of the borehole caused internal collapse of the soil from the sides of the borehole, and the release of pressure from the trapped water led to flooding within the borehole. This finding was crucial as it provided direct evidence of the water table depth, a critical factor for foundation engineering. Additionally, the photo in Figure 8 reveals the presence of yellow sandstone fragments at the drilling bottom, representing the natural rock layer at an estimated depth of about 10 m beneath the construction site's surface. This observation aligns with the subsurface composition revealed in the GPR results (e.g., Figure 6). The consistency between the field observations and GPR findings substantiates the accuracy and reliability of the applied aerial GPR technology, showcasing its potential for real-world applications in foundation engineering. Furthermore, additional hydro-geological analysis of GPR findings showed that they correlated with the natural water flow direction of the park's Wadi, descending from east to west (general water flow direction in Israel from hills at the east to Mediter  
Figure 7: The photographs of the drilling campaign. (**a**) The drilling machine used for the campaign aborted midway, (**b**) The closer view around the drilling borehole reveals a distributed soil (filling) composition, primarily consisting of approximately 90% CL/ML matrix [44] and 10% random concentrations of white chalk pebbles, (**c**) A zoomed-in image inside the drilling borehole, depicting the presence of water at a depth of 5 m and evident collapse in the pit’s sides. ranean sea at the west). This correlation reinforces the utility of GPR in not only mapping subsurface structures but also understanding hydro-geological properties. Figure 8 provides an updated photo capturing the process of drilling 14 m pile foundations at the construction site. The pervasive brown, wet, and distributed clayey soil is evident throughout the image, reflecting the challenges posed by the water table at around 5 m depth. To counter the risk of collapsing drilling pits, a bentonite mixture was employed during the drilling process to stabilize the surrounding soil. Yellow sandstone fragments at the drilling bottom, representing the natural rock layer, align with the GPR results, confirming the technology's accuracy. This comprehensive validation through drilling underscores the potential of aerial GPR for effective subsurface investigation in foundation engineering projects.