esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions and Future Work  
The design of a WSN is influenced by many factors including hardware constraints, transmission media, energy consumption, topology, scalability and fault tolerance. The significance of these factors increases in environments with several hundreds or thousands of sensor nodes. Additionally, the protocols and algorithms adopted must be efficient and scalable as well.  
When targeting novel WSN applications, like real-time intruder tracking, information from different sources must be collected and processed as quickly as possible, to provide the sink with accurate information about the intruder location at all times. If, additionally, the sink needs to move along the  
Figure 13: Varying the sink speed: (**a**) number of routing/injected packets, (**b**) normalized routing load.  
WSN deployment scenario in an attempt to pursue and seize the intruder, the degree of complexity increases further, and the adopted routing protocol becomes critical.  
In this paper, we introduced a comprehensive solution to achieve this goal when relying on cheap, binary detection sensors and the IEEE 802.15.4 technology. Our solution combines a fast intruder tracking algorithm with an efficient routing protocol (MRLG), to obtain the best performance. In particular, experimental results showed that, even at high mobility levels for both the intruder and the sink (fast running speeds), the proposed strategy allows the tracking error to be typically maintained below 10 m, even for highly irregular intruder mobility patterns. Additionally, we compare the proposed MRLG routing protocol against CTP, a known tree-based protocol, to further emphasize on the benefits of our proposal. Overall, we consider that the proposed solution is validated by the results obtained.  
As future work, we plan to focus on mobile sink trajectory optimizations using more sophisticated scenarios that account for physical obstacles, combined with different sensor deployment strategies. We also plan to deploy and test the solution proposed in this paper in a real testbed, to further validate the high efficiency levels reported here.
## 3 Materials and Methods
### Anomaly Detection System  
We implemented a comprehensive anomaly detection system using the anomalib library to identify potential sensor attacks through machine learning-based analysis. The system uses the EfficientAD model, which demonstrated superior performance for detecting anomalies in autonomous driving sensor data.
## 5 Conclusions  
The unconfined compressive strength and the microstructure of a clayey soil stabilized with lime or cement and industrial waste from the natural stone industry were studied. The experimental results show a general positive effect of using this by-product as a ternary element in soil stabilization. The main conclusions drawn from this study can be summarized as follows:  
* A clear positive effect was observed on the unconfined compressive strength of the stabilized soil when adding limestone powder as a ternary element together with lime or cement.
* The strengthening effect of the limestone powder waste was higher when using lime as a commercial binder.
* The addition of limestone powder resulted in a more compact structure with lower values of total porosity.
* Limestone powder waste can be used for soil stabilization in a sulfate aggressive medium, as no harmful effect was observed when samples with cement or lime, with or without limestone powder, were made with a sulfate solution of 3000 mg SO4\({}^{2-}\) per liter up to the maximum age studied (6 months).
* Limestone powder waste from the natural stone industry enhanced the stabilization effect of lime and cement for soil stabilization.  
Future lines of research in this field include an increase in the percentage of industrial waste employed for soil stabilization, studying the influence of the type of soil on the results obtained and the construction of full-scale embankment models to study the real behavior of the mixing soil.  
Conceptualization, J.L.P., J.C. and I.S.; methodology, J.L.P., J.C. and I.S.; writing--original draft preparation, J.L.P.; writing--review and editing, J.C. and I.S.; funding acquisition, J.L.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish Ministry of Universities under project number PRX21/00554 and the University of Alicante under project number GRE17-11 and developed within the framework of the project INNVA1/2021/8 of the Agencia Valenciana de Innovacion.  
Not applicable.  
Not available on request.  
**Acknowledgments:** We would like to thank Calcinor S.A., Cementos Tudela Veguin, S.A. and Romanense de Marmoles S.L. for the supply of lime, cement, and industrial waste, respectively.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber-Physical Attacks
## 4 Case Study of a Cyber-Physical Transportation Network  
A case study of a cyber-physical transportation network, subjected to cyber-physical attacks, is presented in order to demonstrate the application of the proposed vulnerability assessment approach. The case study also highlights the usefulness of the approach by comparing the results with other approaches that adopt a centrality-based ranking (e.g., node degree), as described in Section 2. The topology of the transportation network adopted in this case study takes the form of an undirected graph G with a set of eight nodes (i.e., N = 8) that represent the IoT-enabled transportation infrastructure (e.g., Node 3 represents an IoT-enabled bridge) which was subjected to cyber-physical attack. A set of 14 weighted edges (i.e., E = 14) exist that represent the distances (i.e., \(\mathrm{d}_{\bar{\mathrm{q}}}\)) between the interconnectednodes N, as shown in Figure 4. For the purposes of this case study, we consider that the ZigBee devices form the IoT-based WSN [79]. ZigBee technology is a wireless technology, offering many advantages to users (e.g., low cost, flexible deployment) embedded in IoT devices as a communication protocol. It builds on the IEEE 802.15.4 standard [80], and has been identified through experimental studies as a reliable technology for various civil engineering digital services (e.g., wireless structural monitoring, traffic monitoring, etc.) [81]. The ZigBee protocol stack operates under the following layers, namely: application (i.e., data transmission and security services); network (i.e., routing, security, and configuration of new devices); MAC (i.e., interface between physical and network layer); and physical (i.e., functions related to ZigBee hardware). Every individual protocol stack layer suffers from inherent vulnerabilities (e.g., insecure key transport) that threaten the operation of the IoT-based WSN [82].
# Physics-Guided Neural Network-Based Feedforward Control for Seamless Pipe Manufacturing Process
## 3 Results
### Neural Network Training
#### 3.1.2 Physics-Guided Neural Network Training
A custom loss function, defined with Equation (43), is used during training, with a slight modification to the regularization term:  
\[V_{\text{reg}}(\theta)=\lambda_{\text{NN}}\sum_{w\in\mathcal{W}_{\text{NN}}} \|w\|^{2}. \tag{54}\]  
Here, \(\mathcal{W}_{\text{NN}}\) denotes the set of weights in the neural network correction branch. The \(L_{2}\) regularization is applied solely to these weights, while leaving the physical model layer parameters fixed. We set \(\lambda_{\text{NN}}=1\times 10^{-5}\); this small value was chosen empirically to provide sufficient regularization to prevent overfitting, while allowing the correction branch to effectively learn the residual dynamics. Figure 16 shows the convergence of the training and validation loss curves, which demonstrates the effective generalization of the PGNN. For comparison, a standalone neural network with two hidden layers of 32 neurons each (and the same activation functions) was also implemented; this network lacks the physics-based constraints and requires a larger dataset to achieve similar performance. The identified physical model, obtained using least squares system identification, serves as a baseline. While it achieves reasonable accuracy, the estimated parameters often lack physical interpretability, limiting their application under new conditions. In contrast, the standalone neural network achieves similar accuracy but requires larger  
Figure 15: Piston speed during the generation of the training dataset for the PGNN (only the portion corresponding to positive piston velocities is shown). datasets and lacks physical interpretability, making it less robust in scenarios with limited data availability. Figure 17 highlights the PGNN's decomposition into contributions from the physical model (orange line) and the neural network correction (green line). The total PGNN output (red line) closely matches the actual system behavior (blue line), demonstrating the synergy between the physics-based model and the neural network correction. A performance comparison of the PGNN, standalone neural network, and physical models is shown in Figure 18, with the quantitative results summarized in Table 6. The PGNN achieves the lowest MSE among the models. By embedding physical constraints and leveraging data-driven corrections, the PGNN outperforms the standalone approaches in scenarios with an incomplete physical understanding or limited data. Figure 12:0 Training and validation loss: convergence of the PGNN during training. Figure 12:1 PGNN decomposition with actual \(u\): the figure illustrates the contributions of the physical model (\(u_{\text{Phy}}\)) and neural network correction (\(u_{\text{NN}}\)) in predicting the system output. The total PGNN output (\(u_{\text{PGNN}}\)) closely matches the actual system output (\(u_{\text{Actual}}\)).
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from 1,3-PDO  
In microbial cell factories without knockdown of the 1,3-PDO production pathway (such as engineered _K. pneumoniae_[30; 89] and _L. reuteri_[39]), the production of 3-HP via the glycerol pathway is often accompanied by a massive production of 1,3-PDO, exceeding 20 g/L. However, up to four oxidoreductase are involved in the synthesis of 1,3-PDO in _K. pneumoniae_, and it is challenging to completely block the production of 1,3-PDO by gene knockout [96]. Some researchers have even tried to coproduce 1,3-PDO and 3-HP to balance the reducing power [89]. Therefore, it is attractive to further convert 1,3-PDO from fermentation broth into 3-HP. Furthermore, pure 1,3-PDO obtained from the microbial process has a market price of only USD 1/kg [48], which makes 1,3-PDO an economic substrate for 3-HP production. Wild-type _G. oxydans_ can naturally catalyze the conversion of 1,3-PDO to 3-HP. [PERSON] et al. [14] balanced the enzymatic activities of aldehyde dehydrogenase and alcohol dehydrogenase in the 1,3-PDO production pathway by mixing two mutants of _G. oxydans_ in a 1:2 ratio, and the resulting artificial consortium converted 40 g/L of 1,3-PDO into 45.8 g/L of 3-HP by whole-cell catalysis. Similarly, endogenous enzymes that convert 1,3-PDO to 3-HP were also identified in _H. bluephagenesis_. An enhanced 1,3-PDO synthesis pathway was constructed by optimizing the expression of endogenous aldehyde dehydrogenase (Aldhb) and endogenous alcohol dehydrogenase (AdhP). The resulting engineered _H. bluephagenesis_ produced 154 g/L of 3-HP at the rate of 2.4 g/(L-h) in fed-batch fermentation [27], which was very close to the industrial production requirement. The biosynthesis of 3-HP from 1,3-PDO is a promising strategy but has been less studied, and the construction of a 1,3-PDO pathway in common hosts will likely be an effective way to increase the 3-HP titer.
## 2 Materials and Methods
### Custom PD-AFO Design and Production
#### 2.1.2 3D Design  
The custom PD-AFOs are designed in Blender (www.blender.org, Blender Foundation, accessed on 10 December 2021) by matching the AFO surface to the subject's foot and leg geometry in weight-bearing (Figure 3, left). Anatomical congruence is pursued at the foot plantar surface and at the calf strap to ensure comfort and proper fixation of the AFO to the leg. Since the AFO can bend during walking, attention is taken to avoid any possible contact/sliding between AFO and foot bones that may cause discomfort and pain.  
Blender is a flexible mesh modeling software that allows easy editing and postural correction of the 3D scanned limb. Some control points are manually adapted to ensure the correct alignment of the AFO to the patient's leg. The modeling process is based on Blender's Modifiers, nondestructive geometrical operations that allow a semi-implicit modeling workflow. They can be used to ensure a real-time anatomical adaptation and dimensioning of the AFO. Blender allows advanced manual and real-time control of  
Figure 3: (**Left**) and (**middle**), morphology-based design of the custom AFO with respect to the 3D model of the foot and leg. (**Right**), the AFO is manufactured via selective laser sintering.  
Figure 2: The plantar aspect of the patient’s feet is 3D scanned in weight-bearing via the Kinect-based 3D scanning device. Foot plantar surface and leg geometry are acquired separately and merged into one 3D image.  
freeform geometries. This allows the operator to constantly refine the device through both parametric and manual editing to provide a design fully tailored to the patient's geometry. The main AFO linear dimensions and thickness are parametrically associated with the subject's foot length and with the body weight.
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 5 Discussion
### Digital Twin  
In our proposal, digital twins were not explicitly included; however, there is a potential synergy with external digital twins through the edge layer, enabling real-time interaction. The edge tier acts as a gateway, acquiring information from control devices and smart sensors. The proposed system focuses on integrating data for bottleneck detection, sourced from sensors, actuators in PLCs (as demonstrated in the Siemens and Schneider PLC experimentation), and other IoT devices, such as autonomous sensors. The communication aspects of the proposal involved the use of advanced technologies, including industrial Ethernet, fieldbus, and wireless options, such as 5G or Bluetooth. The edge tier, based on Edge computing, ensures the protection of physical integration heterogeneity, accommodates diverse communication protocols, and provides security and privacy measures [67].  
Within the context of integration with the digital twin, physical resources, such as sensors and actuators, will send real-time data to the digital model. The Figure 17 shows a graphical example of integration from the proposed system to the digital twin.  
Figure 17: Graphical example of integration of the proposed system in the digital twin.  
The digital twin offers the opportunity to leverage historical production data. An additional benefit is the real-time control and readjustment of sensors and actuators based on the digital model simulation, enabling optimization without disrupting the actual production line. This capability is particularly valuable for addressing indicators of bottleneck issues, such as throughput.  
The integration of the proposed IIoT system with the digital twin can be achieved using the various communication protocols discussed in this manuscript. To ensure proper operation, the digital twin must be precisely aligned with the real machine, ensuring that measurements sent by the integration are correctly synchronized with other components. Challenges in achieving synchronization, particularly in IoT networks with entities with variable resources, are discussed in [69]. These challenges include synchronizing hierarchical component structures and addressing broadcast-response delays, especially in cloud environments.  
A possible future line of inquiry could be the reuse of the edge tier to operate on a total transmission of the real machine to a digital twin of its own, which would generate an added value by improving predictions by being able to make changes without damaging the real machine.  
Regarding an incremental design, one way will be to follow an incremental approach, according to the typology in [70], from twin components to achieve a twin system that is equivalent to the production line.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Environmental Conditions
#### 4.1.5 Speed of Sound  
Speed of sound is defined as the distance travelled via sound waves in a unit of time. This parameter plays a significant role in designing the UAV prototype. Some of the major uses are:  
* Useful in separating the flight regimes into two distinct areas with distinct flow conduct.
* Assists in converting compressible flow geometry to one that can be measured using simpler, incompressible methods.
* Efficient air travel and the maximum practical flight speed will be restricted.
* Provides a hint to the designer about how to drive this boundary higher. For example, the speed of sound at Mars' surface is 240 m/s\({}^{2}\), and [94] this is comparatively lower than the Earth's (343 m/s\({}^{2}\)).
## 2 Materials and Methods
### Batch Column Experiment of Nutrient Precipitation and Chemical Analysis  
The experiments consisted of five treatments, i.e., PMCP as control, TN, HTN, TNPK, and HTNPK. Into each glass column was placed a filter and 5 g of different blended biochar pellets. Then, 50 mL deionized water was added. Successive precipitations were performed for nutrient extraction, and the 50 mL of deionized water was replaced over a period of 77 days. The collected water was filtered using Whatman No. 2 filter paper. It was then analyzed for NH\({}_{4}\)-N and NO\({}_{3}\)-N, and the rest of the water samples were stored in a refrigerator until the analysis of PO\({}_{4}\)-P, K, and SiO\({}_{2}\) were undertaken using a UV spectrophotometer (ST-Ammonium, C-Mac, Korea). Total carbon (TC) and total organic carbon (TOC) contents were measured using a TOC analyzer (Elementar Vario EL II, Hanau, Germany). Total P, K, and Si in different types of the blended biochar pellets were analyzed using inductively-coupled plasma atomic emission spectrometry (ICP-AES, IntegraXL, GBC LTd., Braeside, Australia) after digesting the samples with nitric and hydrochloric acids.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PH** & **EC** & **TC** & **TOC** & **TIC** & **TN** \\  & **(dS m\({}^{-1}\))** & & & **-g kg\({}^{-1}\).** & & \\ \hline Biochar & 9.78 (1:20) & 16.53 & 575.5 & 533.0 & 42.5 & 2.0 \\ Pig manure compost & 8.77 (1:5) & 3.40 & 288.8 & 258.6 & 30.2 & 29.1 \\ \hline \hline \end{tabular}  
* \({}^{1}\)) TC; Total carbon, TOC; Total organic carbon, TIC; Total inorganic carbon, and TN; Total nitrogen. The values are represented mean of triplicates samples.  
\end{table}
Table 1: Physicochemical properties of biochar and pig compost used \({}^{1}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatments \({}^{*}\)** & **T-N** & **T-P** & **K** & **SiO\({}_{2}\)** \\ \hline TN & 84.0 & 35.4 & 13.5 & 119.5 \\ HTN & 102.0 & 29.5 & 11.8 & 125.5 \\ TNPK & 75.2 & 32.8 & 57.2 & 108.6 \\ HTNPK & 89.5 & 35.6 & 39.0 & 96.9 \\ PMCP & 29.1 & 79.4 & 20.8 & 67.2 \\ \hline \hline \end{tabular}  
* \({}^{*}\)TN, uploaded with urea solution at room temperature, HTN; urea solution heated at 60 \({}^{\circ}\)C, TNPK; N, P and K solutions at room temperature, HTNPK; N, P and K solutions heated at 60 \({}^{\circ}\)C and PMCP; pig manure compost pellets as control. The values are represented as means of triplicate samples.  
\end{table}
Table 2: The chemical properties of different biochar pellets before used experiments.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 6 Conclusions  
A method for expressing the temporal continuity of network topology in a space information system is proposed in this paper. In addition to the basics of link establishment, the concept of link quality is introduced, which results in extending the traditional link establishment relationship from a binary function to a time-dependent function. Using the method in this paper, the change trend of link quality in the network topology of a space information system can be grasped in real time, and prerequisites for route planning can be provided more accurately. In addition, since the orbital dynamics of the space information system are not affected by perturbation and other factors in the short term,the link quality of the space information system network topology can also be predicted through the method in this paper, which is convenient for the further reconstruction and optimization of the network topology.  
The limitation of this method is that it is closely linked to the constellation configuration. Therefore, the method does not take into account the long-term effects of orbital perturbation, etc. If the entire constellation is not orbitally maintained, the credibility of the conclusions obtained by this method will be reduced or even invalid. In addition, this method does not consider the constraints such as energy demand and thermal control demand for actual chain construction. In the subsequent practical application, the above constraints should be taken into account in the calculation of the adjacency matrix. This is also where the methodology of this paper needs to be further improved. Furthermore, this study will be strengthened in the following two aspects: one is increasing the network topology expression function of the method in the terrestrial inter-satellite link operation and management system; the other one is the implementation of the methods in on-board autonomous routing planning.  
Conceptualization, M.H., X.S. and X.C.; methodology, M.H., X.S., X.C. and F.Z.; software, X.C. and F.Z.; validation, M.H., X.S. and B.L. ([PERSON]); formal analysis, M.H. and J.Z.; writing--original draft preparation, M.H. and X.S.; writing--review and editing, X.C., F.Z., B.L. ([PERSON]) and S.C.; supervision, B.L. ([PERSON]) and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number [42074025].  
All data generated or analyzed during this research are included in this article.  
The authors are grateful to [PERSON], the author of ref. [15], for his support and assistance.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
LEOLow earth orbit  
SDNSoftware-defined network  
FSAFinite state automation  
OISLsOptical inter-satellite links  
ISLInter-satellite link