esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The control of transmission rates is currently a major topic in network research, as it plays a significant role in determining network performance. Traditional network design principles suggest that network nodes should only be responsible for forwarding data, while the sending node should manage control. However, sending nodes often lack information about network resources and must use slow-start algorithms to increase the transmission rate, potentially leading to wasted bandwidth and network congestion. Furthermore, incorrect judgments about network congestion by sending nodes may further reduce network throughput. The emergence of new Internet architectures, such as information-centric networks (ICNn), has empowered network nodes with more capabilities, including computation and caching. This paper proposes a method for transmission rate control that actively avoids congestion through network node bandwidth allocation. The sending, network, and receiving nodes each calculate the available transmission rate, and the sending node negotiates with the other nodes through a rate negotiation message to obtain the maximum transmission rate possible given the current state of the network. The network nodes notify the sending node to adjust the transmission rate to adapt to changes in the network through a rate adjustment message. Simulation experiments show that the proposed method is better than traditional methods in reducing network congestion, providing a stable transmission rate, increasing the network throughput capacity, and improving performance in high-latency and high-bandwidth networks. Additionally, the proposed transmission rate control method is fairer than traditional methods.  
bandwidth allocation; congestion reduction; network transmission; rate control Article
# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 4 Influence of Machining Parameters on Surface Quality
### Analysis of the Effect of Grinding Parameters
From Figure 11c, it can be seen that the number of defects and the total defect area are in a fluctuating state when the cut depth is increased from 5 to 20 \(\upmu\)m. This is due to the fact that at small grinding depths, the increase in grinding depth mainly leads to an increase in the number of flanking abrasive grains involved in the grinding, which does not significantly affect the machined surface quality formed through end-face grinding. After the grinding depth exceeds 15 \(\upmu\)m, the number of defects and the total area continue rising until 25 \(\upmu\)m. This is because an increased cut depth is less favorable for chip discharging, and the broken silicon carbide particles will cause a secondary cutting of the machined surface, which leads to a decrease in the machined surface quality and larger values of \(A_{\mathrm{t}}\) and \(N_{\mathrm{d}}\). As the amplitude of ultrasonic vibration increases, it can be noticed from Figure 11d that the values of both \(A_{\mathrm{t}}\) and \(N_{\mathrm{d}}\) show an overall significant decrease. Increasing the amplitude of the ultrasonic vibration increases the dynamic fracture toughness of the SiC particles and the critical depth at which brittle-plasticity transition occurs, so the SiC particles are more likely to be plastically removed or broken into small particles, thereby improving the surface quality. Figure 11: Influences of grinding parameters on \(A_{\mathrm{t}}\) and \(N_{\mathrm{d}}\): (**a**) spindle speed; (**b**) feed rate; (**c**) cutting depth; (**d**) ultrasonic vibration amplitude.
# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 3 Results
### Fitting Models and Prediction Result Analysis
#### 3.2.2 LSTM Model  
After multiple attempts, the following LSTM network structure settings were used for CO\({}_{2}\) concentration prediction:  
* Input layer: After continuous debugging of the size of the time window, it was found that the error is the smallest when the number of time windows is three. Therefore, the neuron dimension of three has been selected for our input layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Optimal Model** & **AIC** & **BIC** & \(p\)**-Value** \\ \hline T1 & ARIMA (3, 1, 1) & 1401.47 & 1417.06 & 0.74 \\ T2 & ARIMA (4, 1, 3) & 1433.24 & 1458.19 & 0.62 \\ T3 & ARIMA (1, 1, 1) & 1186.36 & 1195.72 & 0.93 \\ T4 & ARIMA (5, 1, 4) & 1446.43 & 1477.61 & 0.57 \\ T5 & ARIMA (5, 1, 3) & 1427.32 & 1455.38 & 0.36 \\ T6 & ARIMA (3, 1, 3) & 1405.53 & 1427.36 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal ARIMA model parameters and effectiveness test results.  
Figure 7: ARIMA model residual Q detection results of T1\(-\)T6 datasets (**a–f**). The solid red straight line represents the normal distribution, and the solid blue dot denotes the residual values predicted by the model.  
* Output layer: Since our goal is to forecast CO\({}_{2}\) concentration for future moments, the number of neurons in the output layer is therefore determined to be one.
* Hidden layer: In this research, we utilized a sequential model in Keras, which is comprised of one LSTM layer and one dense layer. The neuron dimension of the first hidden layer is 64, and the second hidden layer has a neuron dimension of 1.  
Therefore, this research chose three as the number of neurons in the input layer, namely, \(X_{t}=f\) (\(X_{t-1}\), \(X_{t-2}\), \(X_{t-3}\)). This is utilizing the preceding three data points to forecast the next one data point. The other parameters are configured as follows: The learning rate is set to 0.002. The maximum number of iterations (epoch) is set to 200, and the number of samples passed to the model for training each time (batch size) is 32. This network architecture exhibits the lowest RMSE and is well suited for predicting CO\({}_{2}\) concentration.  
In summary, the final model structure of the LSTM neural network is as follows:  
The neuron dimension of the input layer is three, and the neuron dimension of the output layer is one. It consists of a LSTM layer and a dense layer. The neuron dimension of the LSTM layer is 64, and the neuron dimension of the dense layer is 1. The optimizer chosen is [PERSON]. The learning rate is set to 0.002. The epoch is set to 200, and the batch size is 32. This model has the smallest error, a high fitting degree, and high prediction accuracy, so this structure is used for prediction.
# Deep Model Poisoning Attack on Federated Learning
## 3 Attack Methodology
### Problem Definition  
We consider that our attack method consists a total of \(N\) clients working in a federated learning system. We follow the notations and definitions of federated learning, as defined in [1]. At each round \(t\), the server randomly selects \(C\cdot N\) clients (\(C\leq 1\)) to participate training process. Each selected client \(i\) receives global model \(\mathbf{G}^{t}\) at round \(t\) and fine-tunes global model to obtain model update \(\mathbf{\Delta}_{i}^{t+1}\). The malicious client \(m\) holds a clean dataset \(\mathcal{D}_{m}=\{(x_{j}^{m},y_{j}^{m})\}_{j=1}^{|\mathcal{D}_{m}|}\) and a poisoning dataset \(\mathcal{D}_{p}=\{(x_{j}^{m},\tau)\}_{j=1}^{|\mathcal{D}_{p}|}\), where \(\tau\) is the attacker's target label. The attacker utilizes \(\mathcal{D}_{m}\) for benign training to maintain high performance of local model in main task, meanwhile fine-tuning the neural network with \(\mathcal{D}_{p}\) for adversarial task. The malicious client alternately refines main task and adversarial task using \(\mathcal{D}_{m}\) and \(\mathcal{D}_{p}\), respectively, which is called alternating minimization (see Figure 2 for detail).
# Carbon Storage Potential and Carbon Dioxide Emissions from Mineral-Fertilized and Manured Soil
## 5 Conclusions  
Compared to the mineral fertilization, the multi-year organic fertilization of loamy sandy soils increased the organic carbon content in the topsoil by approx. 86.9% and 30.0-55.9% in the deeper soil horizons. However, using exclusive manure fertilization, about 15.5-28.3% lower grain yields were obtained than with mineral fertilization. Thus, soil manuring proportionally reduced the photosynthetic CO\({}_{2}\) assimilation compared to mineral fertilization. Moreover, CO\({}_{2}\)-C soil emissions from the long-term manured soils can significantly exceed that of the soils receiving mineral fertilizers. The CO\({}_{2}\)-C soil emission measurements showed that between 2975.0 and 3933.1 kg CO\({}_{2}\)-C ha\({}^{-1}\) were released into the atmosphere in the mineral fertilization system, and between 5149.2 and 5365.0 kg CO\({}_{2}\)-C ha\({}^{-1}\) were released in the organic fertilization system. The long-term effect of the fertilization was an almost two-fold increase in the content of the SOC and TN in the manure-treated soil (Ca + M). Insignificant changes were noted in the content of these elements in the mineral-fertilized soil (CaNPK). The results from this experiment suggest that only long-term fertilization with manure might increase the carbon accumulation in the sandy soil. However, it consequentially causes an undesirable increase in CO\({}_{2}\) emissions into the atmosphere and a decrease in plant yields. Therefore, the organic fertilization of sandy soils with a low carbon accumulation potential, although it certainly improves the soil health (increases the SOC content), may hinder the achievement of climate-neutral agriculture and interferes with the productive function of the soil as a result of reducing plant yields.  
Conceptualization, T.S.; methodology, T.S. and M.S.; validation, T.S. and M.S.; formal analysis, T.S., H.E.A., A.K.S., B.S. and M.S.; investigation, T.S. and M.S.; data curation, M.S.; writing--original draft preparation, T.S. and M.S.; writing--review and editing, H.E.A., A.K.S. and B.S.; visualization, T.S. and M.S.; supervision, M.S.; project administration, T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Science Centre, Poland, grant number NN 305 060640, and by Warsaw University of Life Sciences within the System of Financial Support for Scientists and Research Teams, No. 853-2-80-45-700500-S22004. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data described in this study are available on request from the corresponding authors.  
Special thanks to the staff of the Division of Agricultural and Environmental Chemistry of Warsaw University of Life Sciences for their professional support in the field and laboratory measurements and the staff of the Experimental Station in Skierniewicz for their general support for the conducted research. The authors declare no conflict of interest.
## 4 Conclusions  
The development of biodegradable and edible materials to replace conventional plastics is gaining attention as public awareness of health and environmental problems is growing. In this study, we created gelatin-based, biodegradable, and edible synthetic polymer films using simplified and cost-effective film-forming methods. The prepared films were tested by various means such as chemical analysis, transparency moisture permeability, mechanical strength, hardness, and wettability. FT-IR analysis was performed for its chemical compositional study. FT-IR data revealed that addition of papaya and soy protein to gelatin solution did not change the original chemistry of the gelatin films, as no significant change was observed in FT-IR spectra after addition of papaya and soy protein to gelatin films. However, the transparency of gelatin films was enhanced by around 10% by addition of papaya, and the same was reduced by 20% when soy protein was mixed in gelatin together with papaya. This could be due to surface imperfections generated which may scatter the light, causing overall transparency reduction. Tensile strength analysis revealed that the G/PP-2 film exhibited the highest Young's modulus of elasticity, which is comparable to commercial plastic films, indicating gelatin-based films can easily replace conventional plastic packaging films. Moisture permeability values were found to be around 50 g/m\({}^{2}\).day in ambient conditions, which is in accordance with food grading packaging standards. Furthermore, the films of gelatin and gelatin-papaya showed excellent bendability, as they retained the WVTR after 2000 bending cycles, whereas films of G/PP/SP-2 showed fast degradation (around 80% as compared to their initial performance)  
Figure 9: Effect of gelatin, papaya, and soy protein on water vapor transmission rate (WVTR) of edible films.  
after 2000 bending cycles. This could be due to their brittle nature. The lab scale production cost (calculated from raw material usage) of gelatin-papaya films is less than 0.5 USD/m\({}^{2}\). Therefore, overall, gelatin and gelatin mixed with papaya exhibited the best performance in most tests and can be perfect choices for large-scale production.  
Conceptualization, I.A.C.; methodology, J.A., B.B. and A.B.; writing, I.A.C. and J.A.; validation, I.A.C., A.D.C. and A.A.S. ([PERSON]); investigation, J.A., I.A.C. and A.A.S. ([PERSON]); supervision, A.D.C., A.A.S. ([PERSON]) and I.A.C.; funding acquisition, S.A., M.M.G., I.A.C. and A.A.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
I.A.C. and A.A.S. acknowledge the seed funding support from NED University of Engineering and Technology, Karachi, Pakistan. The APC was funded by Dr. [PERSON], King Saud University, Saudi Arabia.  
Not Applicable.  
Data Availability Statement: Data available upon request from the corresponding authors.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 4 FlexBFT Design
### Back-Up Mode Protocol
Their purpose is to opportunistically output TXs in a segment of good network conditions after entering back-up mode, minimizing the overall latency of back-up mode. First, continuous snw-ABC runs as normal; if pace synchronization is activated due to Byzantine faults, timeouts, etc., snw-ABC is not restarted. In another case, when the pessimistic path (ABC implemented with dumbo) is outputting, pace synchronization is activated to achieve agreement in the optimistic path, which ensures that all correct nodes have the same output on the optimistic path. Additionally, the optimistic path records all TXs output in this round. **Persistence of back-up mode. ** When both modules complete running in parallel, each optimistic path has output batches of transactions \(Blocks_{o}\), while the pessimistic path has a large batch of \(Block_{p}\) to be output. At this point, to prevent duplicate submissions of TXs for each node, output \(Block_{p}-\{TXinBlocks_{o}\}\) is implemented. If \(Blocks_{o}\) is not empty, the next round returns to the fast mode protocol; otherwise, the back-up mode protocol continues running. Simply put, if the optimistic path akin to fast mode cannot operate and output transactions while in back-up mode, it indicates that the network conditions have not improved. In such a scenario, reverting to fast mode would be futile, as there is a high likelihood that fast mode will also be unable to make progress. It would be preferable to persist in back-up mode. Conversely, whenever the optimistic path manages to output, FlexBFT perceives this as an opportunity to return to fast mode and initiates the next epoch in fast mode to conduct a general and fast consensus process.
## 5 Discussion
### Accuracy Verification Results with Respect to ICESat-2  
The assessments of the vertical accuracy of the six DEMs using high-precision ICESat-2 altimetry points show that SRTM1 is significantly better than other DEMs (Figure 2), while the statistics accuracy of the AW3D30 DEM is closest to that of SRTM1 DEM, and the result is in agreement with other studies [24]. In addition, the 30 m DEMs, except ASTER GDEM2, have higher accuracies than that of the 90 m DEMs, which could be attributed to the higher spatial resolution.  
As described in Section 4.2, a close relationship between DEM errors and terrain height was found (Figure 3), as has been suggested by [17] and [27]. This is due to the fact that the DEM surface is more erroneous in high-altitude areas with rugged terrains. Furthermore, the RMSE shows an increasing trend with the growing height, indicating that relief increases the uncertainty of height measurement. According the performances of different DEMs, the SRTM1 DEM is suggested to apply when the terrain height is \(<\)400 m, but when the height goes beyond 400 m, AW3D30 DEM is a suitable candidate data.  
The comparison between height accuracy and terrain slope angle shows that the larger the slope inclination angle, the lower the vertical accuracy (Figure 5), which has proven in previous studies [16; 17; 24]. The assessment results also show that the accuracies of DEMs are less than 2 m in flat areas (slope angle less than 2\({}^{\circ}\)). Furthermore, the InSAR DEMs have better quality than optical DEMs in flat areas. However, the errors of all the six DEMs increase rapidly when the slope value is \(>\)2\({}^{\circ}\), which is consistent with the results in [27]. Thus, as the areas where the slope is \(<\)6\({}^{\circ}\), InSAR DEM should be selected and utilized. However, in concordance with [PERSON] el al. [61], we did not find a clear relationship between DEM errors and terrain aspect (Figure 6). Other authors, however (e.g., [55]), did find a statistical correlation, which can be attributed by the horizontal shifts between different datasets rather than the influence of the terrain aspect on DEM accuracy.  
In addition, the vertical accuracy of these DEMs across different land cover types were assessed in Section 4.4. The assessment results show that the areas with ground features have higher elevation residuals (e.g., forest and grassland), and the InSAR DEMs show better quality in the areas with artificial surface than the optical DEM. Similarly, [PERSON] et al. [62] and [PERSON] et al. [63] also found that both SRTM (90 m and 30 m variants) and ASTER GDEM2 did show higher errors in areas with forest canopy cover. Thus, in the future work, how to accurately rectify the DEM deviation caused by forest canopy height is an important study to obtain the \"bare-earth\" elevation [3; 64; 65].
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 5 Temperature-Humidity Coupled Model for Sludge Treatment
### On the Existence, Uniqueness, and Regularity of the Solution: Variational Formulation
#### 5.7.1 Existence and Uniqueness  
We define the paired bilinear operator \(\mathcal{A}:X\times X\to\mathbb{R}\) as [61; 62; 63]:\[\mathcal{A}((T(\mathbf{x},t),W(\mathbf{x},t)),(\varphi,\psi))\] \[=\int_{\Omega}k\
abla T(\mathbf{x},t)\cdot\
abla\varphi\,d\mathbf{x} +\int_{\Omega}\rho C_{p}(\mathbf{u}\cdot\
abla T(\mathbf{x},t))\varphi\,d \mathbf{x}\] \[\qquad\qquad\qquad\qquad+\int_{\Omega}k_{e}(T)\big{(}p_{v}(T)-p_{v, \text{amb}}\big{)}\
abla\psi\,d\mathbf{x}. \tag{18}\]  
It is simple to verify that the operator \(\mathcal{A}\) is continuous, i.e., there exists \(C>0\) such that:  
\[|\mathcal{A}((T(\mathbf{x},t),W(\mathbf{x},t)),(\varphi,\psi))|\] \[\leq C(\|T(\mathbf{x},t)\|_{H^{1}}+\|W(\mathbf{x},t)\|_{H^{1}})( \|\varphi\|_{H^{1}}+\|\psi\|_{H^{1}}). \tag{19}\]  
Moreover, \(\mathcal{A}\) is coercive under the assumptions of uniform positivity of the coefficients (\(k(x)\geq k_{0}>0\)):  
\[\mathcal{A}((T(\mathbf{x},t),W(\mathbf{x},t)),(T(\mathbf{x},t), W(\mathbf{x},t)))\] \[\qquad\geq\alpha(\|T(\mathbf{x},t)\|_{H^{1}}^{2}+\|W(\mathbf{x}, t)\|_{H^{1}}^{2}), \tag{20}\]  
where \(\alpha>0\). The functional associated with the source and coupling terms is:  
\[F((\varphi,\psi))=\int_{\Omega}Q\varphi\,d\mathbf{x}+\lambda\int_{\Omega} \frac{\partial W}{\partial t}\varphi\,d\mathbf{x}+\int_{\Omega}\frac{ \partial W}{\partial t}\psi\,d\mathbf{x}. \tag{21}\]  
Under the assumptions of regularity (\(Q\in L^{2}(\Omega)\), \(\frac{\partial W}{\partial t}\in L^{2}(0,T;H^{1}(\Omega)^{\prime})\), \(F\) is continuous. Then, the Lax-Milgram theorem guarantees the existence and uniqueness of \((T,W)\in L^{2}(0,T;X)\).
## 3 Results
### Structural Characterization of OGABs by HPaec-Pad  
After the analysis of Mw of OGABs by HPSEC, the fraction of LMW with Mw \(\leq\) 5.5 kDa was obtained after 6 h of hydrolysis (OGAB-6h). To get an idea of the distribution of the degree of polymerization (DP) in this fraction, an analysis by HPAEC-PAD was performed. Figure 4 shows the HPAEC-PAD spectrum of the OGABs-6h fraction. Taking into account the Mw of OGAB-6h (5500 g/mol) and the Mw of a sodium form of mannuronic acid (216 g/mol) we determined the maximum degree of polymerization (DP) in our fraction which varied from 2 to 24. The purification of the various DPs then analysis by LC-MS spectrometry are necessary to confirm the degree of polymerization of all the LMWs obtained in the fraction OGAB-6h.  
Figure 4: Distribution of degree of polymerization (DP) in the OGAB-6h fraction by high performance anion exchange chromatography with pulsed amperometric detection (HPaEC-PAD).  
Figure 3: Profile of reducing sugars of hydrolyzed alginate oligosaccharides (OGABs). Each value was the mean of 3 repetitions \(\pm\) standard error.
## 5 Theoretical Analyses of Hard Projectile Perforation on SC Walls
### Rear Steel Plate Effect  
For the SC walls with 200 mm thick concrete panels, the penetration responses, i.e., energy consumption and rear steel plate contribution, were explored analytically by taking into account steel plates with different thickness. In Figure 18a, all five curves have the tendency to converge at increasing striking velocity. Under different striking velocities, the absorbed energy of SC walls with a 200-mm thick concrete panel and different thickness steel plates is shown in Figure 18b, where SC wall consumption energy increases with increasing striking velocity. For the SC walls with a 200 mm thick concrete panel, energy  
Figure 16: Comparison of analytical model and simulation. (**a**) Projectile displacement history; (**b**) projectile velocities history; (**c**) projectile deceleration history.  
Figure 15: Penetration resistant force for five stages.  
consumed by the rear steel plates is depicted in Figure 18c. The absorbed energy increases with increasing rear steel plate thickness. Under penetration impact of different projectile striking velocities, their energy absorption shows a tendency of linear increase.  
The effect of concrete panel thickness on energy consumption during perforation was studied. Figure 19a shows the absorbed energy of SC walls with different concrete panels. Figure 19b shows the equivalent concrete panel thickness determined for various SC wall perforation with the same ballistic limit.  
\[h_{\text{eq}}=h_{c}+3.06h_{t} \tag{19}\]  
To evaluate the penetration resistance of SC walls with front concrete panels and rear steel plates, the concept of equivalent thickness of a concrete panel was considered. It was assumed that the equivalent concrete panel thickness had the same ballistic limit as the corresponding SC walls. After data fitting, Equation (19) was derived for the description of equivalent concrete panel thickness in terms of \(h_{\text{c}}\) and \(h_{t}\) of SC walls. This may help researchers to estimate the ballistic limit of SC walls.  
Figure 17: Model validation in terms of residual velocity. **(a)** Different thickness combinations; **(b)** different projectile impact velocity.  
Figure 18: Results of analytical model for perforation on SC walls with \(h_{\text{c}}=200\) mm. **(a)** Residual velocity; **(b)** absorbed energy by SC walls; **(c)** absorbed energy by steel plate.