esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Future Research Directions
### Optimized Secure Offloading  
Mobile terminals are expected to triple over the 6G network. Consequently, optimal computation offloading is required to ensure the effective usability of mobile terminals. ML integration with MEC has proved to be a solution to challenges such as overhead delays, cost, and reduced QoS during data transmission [15; 223]. The DRL approaches are mostly explored for task scheduling or offloading applications. Optimal performance is often measured by the average delay/QoS satisfaction against the amounts of spectrum resources, computing capacity, and caching resources in the networks [216]. Other KPIs for ML-assisted MEC and UAV applications in 6G networks include average energy consumption and average offloading delay [215; 224].
## 2 Materials and Methods
### Phosphogypsum: Preparation and Analysis  
Raw phosphogypsum (PG) was obtained from [PERSON] (El Jadida, Morocco). The material was crushed, ground, washed with distilled water, dried at 105 \({}^{\circ}\)C, and sieved to obtain a homogeneous powder with a particle size of 0.5 mm. Initially, physico-chemical analyses were conducted on the material using commonly used techniques such as SEM(scanning electron microscopy, JSM-6700F, JEOL Ltd., Tokyo, Japan), XRF (X-ray fluorescence, Axios, Overijssel, PANalytical, Almelo, The Netherlands), XRD (X-ray diffraction, using a D8 Advance diffractometer with Cu K\(\alpha\) radiation at 1.54 A, Bruker Corporation, Billerica, MA, USA), TGA-DT (thermogravitetric analysis and differential thermal analysis, using a DTG-60H system, Shimadzu, Kyoto, Japan), and FTIR (Fourier-transform infrared spectroscopy, using a Vertex 70, Bruker Corporation, Billerica, MA, USA). Subsequently, a set of physico-chemical properties such as density (pycnometer), pH (H12210 pH-meter, HANNA Instruments, Woonsocket, RI, USA), conductivity (HI 99300 Conductimeter, HANNA Instruments, Woonsocket, RI, USA), organic matter content, mineral matter content (calcination method in an oven), specific surface area (V-Sorb 2800S Analyzer, Japan) and heavy metal contents (Thermo 7400 ICP-OES Analyzer, Thermo Fisher Scientific, Waltham, MA, USA) were determined.
## 4 Results
### Model Efficiency and Real-Time Inference Evaluation  
To further evaluate the deployability of BiCA-LI in real-world and edge computing environments, we conducted a quantitative analysis of model size, inference latency, and real-time feasibility. Table 6 summarizes the key computational characteristics of BiCA-LI and the two baseline models.  
Despite having a larger parameter count (1.47 M), the BiCA-LI model maintains a moderate model size of 5.6 MB and achieves competitive inference efficiency. On GPU, BiCA-LI requires only 3.95 ms for a single sample and 8.09 ms for a batch of 64 samples--well within the latency bounds of typical industrial applications.  
To verify its applicability for real-time monitoring in Internet Data Centers (IDC), we compare each model's throughput against common IDC sampling rates (1-10 Hz). Table 7 shows the derived maximum sampling rate and margin.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Params (M)**} & **FP32 Size** & **.pth Size** & **Latency** & **Latency** & **Training** \\  & & **(MB)** & **(MB)** & **Device** & **(ms/Sample)** & **(ms/64)** & **Time (s)** \\ \hline Informer & 0.43 & 1.7 & 4.1 & CPU & 8.87 & 44.39 & 196.0 \\ Informer & 0.43 & 1.7 & 4.1 & GPU & 2.18 & 3.28 & 78.0 \\ LSTM & 0.28 & 1.1 & 1.1 & CPU & 53.61 & 93.41 & 156.6 \\ LSTM & 0.28 & 1.1 & 1.1 & GPU & 2.35 & 1.78 & 58.2 \\ BiCA-LI & 1.47 & 5.6 & 5.6 & CPU & 41.73 & 138.35 & 349.8 \\ BiCA-LI & 1.47 & 5.6 & 5.6 & GPU & 3.95 & 8.09 & 103.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model size and inference latency across platforms.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Device**} &
\begin{tabular}{c} **Latency** \\ **(ms/Sample)** \\ \end{tabular} & **Max Rate (Hz)** & **IDC Margin** \\ \hline Informer & CPU & 8.87 & 113 & 11–113\(\times\) \\ Informer & GPU & 2.18 & 459 & 46–459\(\times\) \\ LSTM & CPU & 53.61 & 19 & 2–19\(\times\) \\ LSTM & GPU & 2.35 & 426 & 43–426\(\times\) \\ BiCA-LI & CPU & 41.73 & 24 & 2–24\(\times\) \\ BiCA-LI & GPU & 3.95 & 253 & 25–253\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Real-time performance relative to IDC sampling rates.  
The results confirm that all three models meet the minimum real-time requirement of 10 Hz. Notably, BiCA-LI delivers a balance of performance and efficiency, with a 25-253\(\times\) margin over the required rate on GPU. This indicates strong potential for deployment in real-time and resource-constrained environments.  
Future work will explore further model compression techniques, such as pruning, quantization, or knowledge distillation, to optimize BiCA-LI for ultra-low-power edge scenarios.
## 3 Results
### Patients Treated with DOACs  
#### 3.4.1 Tranexamic Acid  
[PERSON] et al. [38] performed a placebo-controlled trial on the use of 10% tranexamic acid mouthwash 3-times-a-day for 3 days in patients treated with DOACs (Rivaroxaban, Apixaban, Edoxaban, Dabigatran). No differences were found between the groups in terms of post-extraction bleeding, number of bleeding events, and procedural bleeding score. Delayed bleeding and bleeding after multiple extractions showed lower occurrence in the tranexamic acid group.
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 4 Case Analysis of Static Wind Instability and Parameter Influence of Xiangshan Harbor Bridge
### Calculation of Critical Wind Speed for Static Wind Instability of the Bridge
According to the given wind speed solution strategy for static wind instability in Section 2.1.2 and the above program modules, the variation curve of the maximum displacement of the main beam with the wind speed can be plotted, as shown in Figure 9, where it can be seen that the maximum displacement of the main beam increases nonlinearly with the increasing wind speed. When it is close to the instability wind speed, the displacement increases sharply, and the instability wind speed can be inferred according to the changing trend of the displacement. When the initial wind angle of attack \(\alpha=0^{\circ}\), the instability wind speed is \(298\) m/s, which is unlikely to reach in a practical environment. However, for some bridges, the wind speed of aerostatic instability is much smaller, and the static wind stability even becomes the limiting factor of bridge wind-resistant design. Some examples are given in Ref. [5]. Therefore, this paper provides a method for static wind instability analysis, which can be used for all bridges. Of course, due to the limitations of software and models, some factors such as strength failure are temporarily ignored, where such high wind speed may have caused static failure to the bridge structure. Figure 8: ANSYS solution process diagram. (**a**) User Toolbar; (**b**) Parameter input dialog box.
## 4 Test Results
### Quantitative Analysis of Particle Structure of Silt under Freeze-Thaw Cycles by LST
#### 4.2.2 The Particle Characteristics of Silt under Freeze-thaw Cycles  
Under the condition of vertical confining pressure 150 kPa, effective confining pressure 75 kPa, freezing temperature \(-20\)\({}^{\circ}\)C, and relative bulk density 0.827, the values of \(B_{\rm f}\) of silt with different freeze-thaw cycles are shown in Figure 12. It shows that total particle size index increases with freeze-thaw cycles. The initial freeze-thaw cycles have a greater degree of influence on total particle size index. With the more freeze-thaw cycles the silt has experienced, the influence degree of single freeze-thaw cycle on the particle size becomes smaller.  
To quantitatively describe the effects of freeze-thaw action on the total particle size index, a hyperbolic type total particle size variation model is established under freeze-thaw cycle number:  
\[B_{\rm f}=\frac{N^{a}}{b+cN^{a}} \tag{5}\]  
where \(a\), \(b\), \(c\) are model parameters, which respectively relate to accumulative total particle size index, total particle size index in the first freeze-thaw cycle, and the final total particle size index.  
A hyperbolic type total particle size variation model was used to fit the relationship between total particle size index of silt and freeze-thaw cycles (show as Equation (6)), and the data fits well with the correlation coefficient \(R^{2}\) value of 0.98.  
\[B_{\rm f}=\frac{N^{0.973}}{1.113+0.056N^{0.973}}R^{2}=0.98 \tag{6}\]  
Under the condition of vertical confining pressure 150 kPa, effective confining pressure 75 kPa, freezing temperature \(-20\)\({}^{\circ}\)C, and relative bulk density 0.827, the total particle shape index values of silt with different freeze-thaw cycles are shown in Figure 13. It shows that the total particle shape index increases with freeze-thaw cycles. This means that freeze-thaw action causes the total particle shape of silt to become more spherical. The initial freeze-thaw cycles have a greater degree of influence on total particle shape index. With more freeze-thaw cycles, the influence degree of a single freeze-thaw cycle on the particle shape of silt becomes smaller and smaller.  
Similarly, a hyperbolic type model is established to describe the effects of freeze-thaw cycles on total particle shape index of silt:  
\[B_{\rm s}=\frac{N^{d}}{e+fN^{d}} \tag{7}\]  
where \(d\), \(e\), \(f\) are model parameters, which are respectively related to accumulative growth rate of total particle shape variation, total particle shape index in the first freeze-thaw cycle, and the final total particle shape index.  
Figure 12: The effect of freeze–thaw cycles on the total particle size of silt.  
A hyperbolic type total particle shape variation model was used to fit the relationship between total particle shape index of silt and freeze-thaw cycles (shown as Equation (8)), and the data fit relatively well with the correlation coefficient \(R^{2}\) value of 0.97.  
\[B_{\rm s}=\frac{N^{1.249}}{10.45+0.091N^{1.249}}R^{2}=0.97 \tag{8}\]
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 5 Conclusions  
The phenomobile was able to acquire and store the desired field data as planned. Its peculiarity is to be easy to handle, being handheld, but, meanwhile, capable of acquiring complex and informative data. If compared to industrial systems, the proposed one gives the opportunity to expand phenotyping applications in open-field and/or greenhouses conditions at a low price tag. Being its movements are separated from the acquisition set, and thus not triggered together, sensors, after proper installation and calibration, can be swapped, further reducing the total price, or if the application type requires acquisition within a different wavelengths range. This system represents an opportunity to integrate modern non or minimally invasive technologies into commonly used protocols for characterizing plant responses to growing environmental challenges and for field and controlled experimental trials. In addition, the separation between the movements and the acquisition  
Figure 16: Illustrations of NDVI (**a**, tomato; **d**, durum wheat), GDVI (**b**, tomato), and RGB images (**c**, tomato; **e**, durum wheat) obtained from the sensors of MAIA spectral camera.  
triggering gives the possibility to swap and add self-triggered sensors. In conclusion, the study positively tested the prototype built for in-field phenotyping purposes, showing great potential as an aid to geneticists to speed up the selection process regarding new cultivars, with the aims to create plants with an increased productivity and or resistance to pathogens or environmental parameters shifted due to climatic changes. Future studies will expand the present preliminary test to an extended experimental design in comparison to professional systems operating within similar spectral bands, with the aim to assess in detail differences in terms of the output, acquisition time, and costs.  
Conceptualization, C.C., S.F. and F.P.; methodology, C.C., S.F., L.O., E.S. and F.P.; software, S.F. and L.O.; validation, F.A., C.C., S.F. and F.P; formal analysis, C.C., S.F., E.S. and L.O.; investigation, F.A, C.C., S.F. and F.P; resources, C.C.; data curation, C.C., F.P., R.M. and F.A.; writing--original draft preparation, R.M. and F.A.; writing--review and editing, R.M., L.G. and F.A.; visualization, C.C., F.A. and F.P.; supervision, C.C. and F.P.; project administration, C.C. funding acquisition, C.C and F.P. All authors have read and agreed to the published version of the manuscript.  
This technical note was funded by the Italian Ministry of Agriculture, Ministry of Agriculture, Food Sovereignty and Forestry (MASAF), national program sub-project \"Tecnologie digitali integrate per il raforzamento sostenibile di produzioni e trasformazioni agrofilimentari (AgroFiliere)\" (AgriDigit program) (DM 36503.7305.2018 of 20/12/2018).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Simulation Results
### Estimation Phase Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Error (Hip, Knee, Ankle)**} \\ \cline{2-6}  & **MAE** & **MAPE (\%)** & **RMSE** & **RMSPE (\%)** & **R\({}^{2}\)** \\ \hline [PERSON] et al. [12] ID & (40, 9, 21, 6, 59) & (696, 361, \(\sim\)) & (153, 732, 17.2) & (616, 1246, \(\sim\)) & (0.004, 0.004, \(\sim\)) \\ IID & (213, 9, 90, 203) & (362, 168, \(\sim\)) & (117, 552, 11.8) & (1998, 939, \(\sim\)) & (0.002, 0.007, \(\sim\)) \\ [PERSON] et al. [12] NDO & (21, 212, 24, 60) & (360, 207, \(\sim\)) & (26,1, 122, 4.65) & (444, 208, \(\sim\)) & (0.226, 0.931, \(\sim\)) \\ INDO & **(0.718, 0.609, 0.521)** & **(212, 104, \(\sim\))** & **(0.939, 803, 0.650)** & **(16.9, 136, \(\sim\))** & **(0.959, 0.973, \(\sim\))** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Human–robot interaction torque estimation methods performance indices during a leg press exercise with SNR \(=\) 40 dB and 20% BSIPs uncertainties. Bold numbers highlight the best metric value. Figure 10: Comparison of (**a**) IID and [PERSON] et al. [12] ID method, and (**b**) INDO and [PERSON] et al. [12] NDO method, during a leg press exercise with SNR \(=\) 40 dB and 20% BSIPs uncertainties. the [PERSON] et al. NDO method. Hence, an adequate estimation of the interaction torque is obtained with the INDO. However, it does not entirely reduce the noise effect to zero. Moreover, it may add a delay to the estimates. Defining the gain \(\alpha\) of the NDO changes this sensitivity to noise and delay. The design for our INDO was made with the LMI, but an analytical and optimal design can be evaluated in further studies. The proposed methods have no negative or positive effect on the sensitivity to sensors noise. Moreover, we can observe that the ID-based methods have more significant errors than the NDO-based methods. To summarize, we can state that, in this case, ID-based methods have more sensitivity to sensor noise than the NDO-based methods. To sum up, we can see that the errors for the IID are significantly lower than the errors obtained with the [PERSON] et al. ID method under BSIP's uncertainties. Moreover, we can denote that the errors for the INDO are also significantly lower than the errors obtained with the [PERSON] et al. NDO method under BSIP's uncertainties. This significant error reduction with both proposed methods under BSIP's uncertainties, compared to the methods reported by [PERSON] et al. [12], is the most significant contribution of this paper. Therefore, we have proposed two human-robot interaction torque estimation observers for a simulated LLRR that works despite the BSIP's uncertainties. This proposal extends the work reported by [PERSON] et al. [12].
## 5 Numerical Simulation and Verification  
In this section, the validity and robustness of the proposed method were investigated using numerical and simulated signals.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 3 Pretreatment of Feedback to Enhance Biogas Production
### Physical or Mechanical Pretreatment  
For lignocellulosic materials, physical or mechanical pretreatment methods are usually employed to promote microbial accessibility to hydrolysable polymers and to reduce the cellulose crystallinity and its degree of polymerization. This includes a change in the structure or appearance of the materials using different techniques [56; 57]. The process includes crushing or breakdown of the feedstock particles, resulting in increased surface area that provides more contact between the substrate and the microorganisms, and presumably increases the process efficiency [57]. Comminution, such as ball milling, is a physical pretreatment method that increases the feedstock surface area and decrystallizes cellulose. Indeed, ball milling was found to be an effective pretreatment, with similar glucose yield and superior carbohydrate yield to steam explosion pretreatment. In this context, the effect of grinding on wheat straw was investigated, and the results showed that ball milling samples yield 46% total carbohydrates and 72% glucose as a result of the reduction in the cellulose crystallinity from 22% to 13% [58]. Ball milling was also applied to non-degraded digestate in order to feed it back into the digestion process [59]. Enhanced methane production of 9% was reported in the case of two-stage maize silage digestate, and an increase of 17% was detected when using two-stage hay/straw digestate. Irradiation is also an effective pretreatment method that disorganizes the structure of the biomass cell wall and decreases the crystallinity of the cellulose [60]. Solubilization of microalgae as a substrate for biogas production was evaluated using microwave pretreatment. The biogas production rate was increased by 27-75% at specific energy rates of 21.8, 43.6, and 65.4 MJ/kg TS.  
The advantages of this method are that there is no risk of forming inhibitory compounds, and there is improvement in the methane yield in some cases caused by size reduction. However, the main disadvantage is the high energy requirements [55]. In some cases, this method is not viable as a stand-alone pretreatment for industrial applications, but it can be combined with other pretreatments that are more cost-effective.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 1 Introduction  
As a key enabler for intelligent transportation systems (ITSs), vehicular networks have attracted considerable attention in recent years due to the potential to improve road safety and traffic efficiency and support infotainment requirements. There are two prominent communication technologies for vehicular networks: dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X) [1, 2]. DSRC is supported by the IEEE 802.11p standard and faces many challenges, such as limited mobility support and unacceptable latency under a high vehicle density. As an alternative to DSRC, C-V2X possesses various advantages, including strong scalability, a high data rate, and a quality of service (QoS) guarantee [3, 4]. In release 14 [5], the 3 GPP developed two operational modes for direct vehicular communications in C-V2X: mode 3 and mode 4. In mode 4, vehicles do not require the cellular coverage and autonomously select and reserve the subchannel resources for information transmissions using a distributed scheduling scheme, while in mode 3, the orthogonal subchannel resources are scheduled by the cellular networks. Compared to mode 4, the scheduling scheme of subchannel resources is more efficient in mode 3 due to the comprehensive knowledge collected by the cellular network from all vehicles in its coverage.  
The high mobility in vehicular networks causes channel conditions to change rapidly over time, impairing communication efficiency [6, 7]. Thus, the resource allocation method plays a vital role in vehicular networks and needs to cater for the varying channel conditions to improve and maintain communication efficiency.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 3 Proposed Quasi-Uniform Quantizer for SCMA System
### Max-Log MPA Detector for the SCMA System  
Observe (7), it is obvious that the computational complexity of Log-MPA mainly lies in the exponential operation within \(\max^{*}(\cdot)\) in the VN message updating. Note that  
\[\max^{*}\{a_{1},a_{2}\}=\ln(e^{a_{1}}+e^{a_{2}})=\max\{a_{1},a_{2}\}+\ln(1+e^ {-|a_{1}-a_{2}|}), \tag{11}\]  
where \(\max\{\cdot\}\) is the maximum operation, \(\ln(1+e^{|a_{1}-a_{2}|})\) is the correction element, and this element can be eliminated. For the case with more than two numbers, by recursively applying this equation, the Jacobian approximation [49,50] can be obtained as  
\[\max^{*}\{a_{1},a_{2},\cdots,a_{n}\}\approx\max\{a_{1},a_{2},\cdots,a_{n}\}. \tag{12}\]  
By using the Jacobian approximation above, the resource node updating in (7) can be simplified as [31]  
\[V_{k\to j}^{t}(\mathbf{x}_{j})\approx\max_{\mathbf{x}_{k}:u\in\mathcal{V}_{k} \setminus j}\left\{P_{k,j}(\mathbf{x})+\sum_{u\in\mathcal{V}_{k}\setminus j} R_{u\to k}^{t-1}(\mathbf{x}_{u})\right\}. \tag{13}\]  
In this sense, we achieve the low complexity implementation of Max-log MPA for SCMA detection.
## 4 Main Result  
### Control Law Design  
Suppose that each satellite has the onboard navigation equipment, e.g., the GLONASS/GPS receivers [85], for determining its position and is able to share this information via the digital inter-satellite communication channel. Therefore it is assumed that the onboard control system of each satellite is supplied by the values of all the relative coordinates \(x_{12}=x_{2}-x_{1}\), \(z_{12}=z_{2}-z_{1}\) and their time derivatives. Control signals for each satellite are generated so as to provide the difference \(u=u_{2}-u_{1}\), generated in accordance with the chosen control law \(u=U(x_{12},z_{12},\dot{x}_{12},\dot{z}_{12})\). In the present study, for control law design the pole-placement technique is employed. The design procedure is made for a LTI system model under the assumption that the restriction on the control signal is not \"active\", i.e., \(u\) does not go beyond the boundaries of the linear region, that is \(|u|\leq u_{\mathrm{max}}\). State-space equations of the closed-loop system (disregarding disturbances) have the form:  
\[\dot{\chi}=(A-BK)\chi, \tag{14}\]  
where matrices \(A\), \(B\), \(K\) are of form (13).  
The design problem consists of choosing the coefficients of the controller \(k_{i}\) so as to provide the required spectrum \(\{\lambda_{ABC}\}\) of the matrix \((A-BK)\) of the closed-loop system (14). The fourth order Butterworth polynomial:  
\[D(s)=s^{4}+2.6131\Omega\,s^{3}+3.4142\Omega^{2}\,s^{2}+2.6131\Omega^{3}\,s+ \Omega^{4}, \tag{15}\]is used, where parameter \(\Omega\) is the geometric mean root of the characteristic polynomial. This parameter determines the desired transient time of the closed-loop system.  
Note, that the controller design is of a secondary meaning for this study that is focused on the data exchange between the satellites, and the other control schemes can be used for improving the formation dynamics. For example, in the case of essential parametric uncertainty, the Implicit Reference Model adaptive control [86; 87] or the sliding mode control of [88; 89; 90] can be also employed.