esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations  
This study employs driving simulation technology, which is essential for investigating the complex driving environments of tunnel and interchange connecting sections. These scenarios require high levels of experimental control to effectively isolate and analyze specific behaviors and variables. A simulator provides an ideal setting for such precise manipulation and repeatability of conditions, which would be challenging and potentially hazardous to replicate in real-world environments [53]. However, while simulators ensure safety and detailed control, they inherently lack some real-world complexities and may amplify specific driver behaviors, potentially leading to variations in error rates compared to actual scenarios. These controlled conditions are crucial for conducting safe and effective preliminary investigations into core behavioral patterns in these challenging environments.  
Furthermore, the selection of participants may impact the applicability of our findings. Our study involved exclusively younger drivers, aged 25-33, limiting the generalizability to more experienced or older drivers. The literature suggests that experienced drivers typically exhibit quicker reaction times and more proactive driving behaviors, influencing their decision-making regarding speed control and following distances [54]. This demographic limitation raises concerns about the representativeness of other drivers.  
Additionally, the within-subjects experimental design, where drivers encountered multiple scenarios in randomized orders, could also introduce biases [55]. While randomization helps mitigate some effects, repeated exposure to the simulator may lead to increased familiarity with the experimental setup, potentially altering drivers' behaviors over successive trials and leading to skewed results.  
To build on our findings and enhance the generalizability of the results, integrating field experiments into future research could validate our findings and uncover additional influential factors not apparent in simulator settings. Diversifying the demographics of study participants would also help broaden the applicability of our results. Implementing methods to counteract simulator familiarity effects, such as varying the complexity of scenarios, should be considered to mitigate potential biases and ensure the robustness of outcomes.
## 4 Results and Discussion
### Effect of Vibration on the Shear Stress
#### 4.2.4 Effect of Different Vibration Amplitude  
Figure 11 compares the analytical and simulated effective value of the shear stress without and with different vibration applied, whose amplitude is, separately, 0.25, 0.3 and 0.35 g. It is shown that the predictions agree well with the simulations. When vibration is applied, the shear stress is reduced. The value is further reduced when the amplitude of applied vibration is growing. For example, when the amplitude of applied vibration is 0.3 and 0.35 g at 200 Hz, the effective value of the analytical shear stress is 0.51 and 0.48 MPa, respectively. Compared with that under the no-vibration field (0.89 MPa), the shear stress is decreased separately by 42.7% and 46.1%. The reason for this is that increasing vibration amplitude will decrease the pressure drop of the melt inside the extrusion liquefier, leading to the reduction in shear stress according to Equation (A5). Therefore, increasing the amplitude of applied vibration can further reduce the shear stress inside the extrusion liquefier of ME, helping to improve the continuity and uniformity of the extrudate, and thereby enhance the forming quality of ME products.  
Figure 10: The shear stress at the internal wall of the extrusion liquefier without and with 0.35 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 (No vibration) & 0.89 & - & 0.88 & - \\
100 & 0.55 & 38.2 & 0.54 & 38.6 \\
200 & 0.48 & 46.1 & 0.46 & 47.7 \\
300 & 0.40 & 55.1 & 0.39 & 55.7 \\
400 & 0.35 & 60.7 & 0.34 & 61.4 \\
500 & 0.31 & 65.2 & 0.30 & 65.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effective value of the shear stress without and with 0.35 g vibration applied at different frequencies.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications
### Use Cases
#### 3.1.1 Interference Detection
In that sense, using ML for interference detection is expected to decrease the probability of false detection by 44% compared to traditional methods [1]. An ML model based on an autoencoder is proposed for interference detection, assuming the interference is an anomaly in the system. The autoencoder will be composed of an encoder neural network and a decoder neural network, stacked sequentially. The encoder compresses the data blocks and reduces the dimensionality of the input. The data is passed to the decoder, which increases the dimensions and restores the original dimension. If the input signal does not include any interference, the reconstructed signal will be very close to the input signal. On the contrary, if the input signal comprises interferences that modify the original statistics, the reconstructed signal will be noticeably different (see Figure 3).
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 2 LoRaWAN Outline  
This section provides a brief description of the LoRaWAN protocol. It includes the architecture, spectrum utilization, modulation characteristics, and transmission power control (TPC) strategies.
## 6 Implementation and Validation
### Implementation
#### 6.1.3 Working Memory  
We have implemented our working memory with a centralized scheme. This scheme was chosen because it is the easiest to apply and the most effective to maintain consistency with updates from various processes.  
Each of the processes maintains a copy of the working memory, which has a graph structure described in Section 4. These processes are subscribed to a ROS topic in which any update is published in this working memory. When a process receives an update in working memory, it replaces the local copy with the new version received. This operation is not expensive since the changes are sporadic, and the graph does not contain a lot of information. Perceptual and geometric information, which may provide a large amount of data, or be updated at high frequency, are not stored in the graph, but its source is referenced, as we will describe later.  
There is a centralized process that contains the master copy. Each time it is updated, it publishes the entire graph so that all processes can update their local copies.  
The key to this scheme is that the processes do not perform updates on their local copies. Still, use services (synchronous communication with confirmation) to request the modification to the process that maintains the master copy. In such a way, a local update in a process is effective after receiving the modified graph with the requested update.  
The exceptional cases, as we introduced previously, are large or updated at high-frequency data. Specifically:  
* **Geometric transforms**. Geometric relations are stored in the graph as arcs connecting two nodes. The type of these relations is tf, and each one contains a translation and rotation. ROS uses a centralized system of geometric transforms. Any node that set geometric relations to publish them in the topic /tf and /tf_static. Any node requiring this information subscribes to these topics. This system supports updates to high frequencies. A geometric relation in ROS connects to frame (axes of reference in a 3D space) with another frame. When creating/updating an arc of type tf, instead of storing the geometric information in the graph, it is published to the ROS geometric transforms systems, in /tf.
* **Information from sensors**. Instead of storing the sensor information in the graph, we store the reference to the information source. This information is potentially large and of high frequency.  
If we stored it in the graph, we would spend a lot of bandwidth in maintaining the coherence of the graph in replicas where this information is probably not used. If the information that a node representing a sensor has is required, the sensor's information is consulted at that moment, or the last collected reading is delivered.
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion
### Heat Transfer  
Figure 13 and Table 7 illustrate the effect of micropile spacing and surface temperature boundary condition on the heat exchange potential, at the peak of the tenth heat injection and extraction seasons. It can be noted that the heat exchange in earlier cycles was within about 10% of the values shown. The values indicated in Table 7 are the total heat flow averaged over the length of the pile. These absolute values of heat exchange should be treated as illustrative only, as the full heat transfer process (e.g., fluid flow within and the heat exchange pipes, etc.) has not been modeled. For comparison, the values for R/D = 30 in Table 7 are about half of the heat exchange values suggested by [PERSON] (2006) [2]. The relative effects created by the variations considered are of interest, however.  
When the \"inside\" surface temperature condition was applied, heat injection was reduced and heat extraction was enhanced, with respect to that for the \"outside\" condition, due to the general warming imposed in the ground around the micropiles, directly below the overlying structure, see Figure 8.  
The effect of pile-pile thermal interference is apparent, with total heat exchange and the difference between \"outside\" and \"inside\" conditions reducing as R/D reduced. Heat exchange became practically null for MP-15 when R = 0.8 m. In practice, micropiles tend to be used in groups with close absolute spacing (even if the relative spacing is large), so the conditions that lead to the apparent loss of heat transfer efficiency will be the subject of further study, to define how to optimize the distribution of TA micropiles within foundations.
## 5 System Development and Design
### The Node Extraction Module  
The algorithm principle of the target node extraction module is based on the content from Section 3.4, and the algorithm is integrated into the node extraction module. An example of this module is shown in Figure 12. Area 1 displays the target node input by the user, Area 2 shows the source data related to the target node, and Area 3 presents the relationship graph of the target node. Using this functionality, users can intuitively view the information contained within the node in the knowledge graph, including physical requirements, functional requirements, performance requirements, design constraints,  
Figure 10: (**a**) SRs representation knowledge graph, (**b**) example of other requirements for the “Automatic Fare Collection System” node.  
and interface requirements, thereby gaining a deeper understanding of the node's overall requirements and relationships. The figure illustrates the relationship graph of the \"Automatic Fare Collection System\", where designers can clearly understand the complete set of requirements associated with this node, including its physical requirements, such as the physical components like \"Transmission Channel\", \"Maintenance and Testing System\", \"Central Clearing System\", and \"Transmission Channel\". The algorithm performs a layer-by-layer search for relevant neighboring nodes of the target node. It first retrieves the neighboring nodes related to the \"Automatic Fare Collection System\" and then gradually explores the child nodes of these neighbors. This process continues until no more child nodes exist. The target node extraction module provides designers with complete information about the target node while preserving the consistency of the requirement relationships in the original graph.  
Figure 11: Example of the SR knowledge graph requirement representation module.  
Figure 12: Example of the node extraction module for the automatic fare collection system.
## 2 Related Theory
### Particle Swarm Optimization Algorithm  
PSO is a widespread metaheuristic algorithm that aims to find an optimal or near-optimal solution within a given solution space by simulating the foraging behavior of a bird flock [21]. Each individual is continuously updated based on their position and velocity information about the optimal position. In each evolutionary process, each particle updates the velocity and position information at the next moment using Equations (1) and (2) to approximate the optimal or suboptimal solution in the solution space.  
\[v_{id}^{t+1}=\omega v_{id}^{t}+c_{1}r_{1}\big{(}pbest_{id}-x_{id}^{t}\big{)}+c_ {2}r_{2}\big{(}gbest_{d}-x_{id}^{t}\big{)} \tag{1}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1} \tag{2}\]  
In PSO, \(v_{id}^{t}\) and \(x_{id}^{t}\) represent the d-dimensional velocity and the position components of the particle at moment \(t\), respectively. The particle's d-dimensional individual optimal component of the ith particle is denoted by \(pbest_{id}\), while \(gbest_{d}\) represents the d-th dimensional component of the population optimum. The learning factors \(c_{1}\) and \(c_{2}\) usually take a fixed value of 2, while the inertia weight \(\omega\) controls the particle's momentum. To increase the algorithm's randomness, \(r_{1}\) and \(r_{2}\) are set to random numbers between 0 and 1.  
In addition, when the velocity evolved by the particle swarm optimization algorithm exceeds \(V_{max}\) or falls below \(V_{min}\), it is often necessary to limit the velocity by clamping it to the maximum or minimum velocity. It is commonly used in PSO algorithms, as shown in Equation (3).  
\[v_{id}^{t+1}=\left\{\begin{array}{ll}V_{max},&if\;v_{id}^{t+1}>V_{max}.\\ V_{min},&if\;v_{id}^{t+1}<V_{min}.\end{array}\right. \tag{3}\]
## 2 Materials and Methods
### Microbial Community Analysis  
#### 2.5.1 Viability of Methanogens Incubated with Sodium  
The dominance of methanogens in the two-stage anaerobic digester was identified recently by molecular quantification tools, such as quantitative polymerase chain reaction (qPCR) [14]. However, this technique does not necessarily differentiate between viable and dead cells since both viable and dead cells were reported to be amplified, which would lead to an overestimation of actual viable cells [32].  
Propidium monoazide (PMA) can, however, be introduced to intercalate extracellular DNA to prevent them from being amplified by polymerase chain reaction (PCR) [33]. Therefore, combining PMA cross-linking with qPCR would allow for the quantification of membrane-intact cells only, excluding extracellular DNA embedded within the sample matrix and membrane-compromised cells [34; 35].  
In this study, PMA-qPCR was utilized to investigate the viability of methanogens at different sodium concentrations. PMA (Biotin, Boston, MA, USA) was dissolved in 20% dimethyl sulfoxide to make 2 mM stock solution and stored in \(-\)20 \({}^{\circ}\)C before use. 25 \(\mu\)L of 2 mM PMA was added to 500 \(\mu\)L of sample to prepare 0.1 \(\mu\)M of PMA in 2 mL microfuge tubes [33; 35]. Samples were kept in the dark for 5 min at room temperature with shaking. Samples were then placed approximately 30 cm from a 500 W halogen light source (Plusline 240 V, fixed in Halolite QVF 135, Philips N.V., Amsterdam, the Netherlands) and laid horizontally on ice, with shaking for 10 min. The photo-activated samples were washed twice by centrifugation (5000 \(\times\)\(g\), 10 min) and then resuspended in 500 \(\mu\)L of 1 \(\times\) PBS buffer (pH = 7.40).  
DNA from PMA-intercalated sludge samples was extracted by an automated nucleic acid extractor (MagNA Pure) according to the manufacturer's protocol (Roche Diagnostics GmbH, Mannheim, Germany). qPCR was performed following [PERSON] et al. [36], with primer/probe sets targeting 16s rRNA genes specific for _Archaea, Methanosaetaceae, Methanosarcinaceae, Methanomicrobiales, Methanobacteriales_ and _Methanococcusaeae_.
## 3 Settlement Calculation with Error Corrections and Resampling
### GNSS Absolute Settlement Calculation over Time  
To accurately assess a slab track's surface settlement using the GNSS system, the initial position of the GNSS rover was taken into consideration. By utilizing the GNSS rover's initial position as a reference point, this approach facilitated the determination of track settlement based on the GNSS system with enhanced precision and reliability. The calculation method employed for the slab track settlement system was as follows:  
\[S_{GNSS}(t)=\,\mid U_{t}-U_{0}\,\mid, \tag{5}\]  
where \(S_{GNSS}\) is the absolute settlement observed by GNSS rover, \(U_{0}\) and \(Ut\) are the initial z-position and the current z-position on the ENU coordinate of GNSS rover, respectively.
# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 3 Results and Discussion  
### Characterization  
#### 3.1.1 Extraction Efficiency  
The extraction step operation is checked with a quartz crystal microbalance (QCM) device and reveals a deposit mass of 47 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{cm}^{2}\) and a negative variation mass of that deposit of 0.5 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{cm}^{2}\) after the extraction step, which represents about 1% of the total mass.
## 3 Method
### Timeliness  
In our proposed scheme, the Content Administrator (CA) is responsible for the production and management of the digital content property rights and the identity verification of the Licensee (L); the Content Administrator (CA) is also responsible for the issuance of a time-sensitive playback license, and the Licensee's playback key identification code cannot permanently occupy the playback of digital content. The Licensee must obtain the decryption key through the authorization key. However, the authorization key contains the digital content ID and the authorization period. If the authorization period expires,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase** & **Item** & **Signature** & **Sender** & **Receiver** & **Signature Verification** \\ \hline Authentication and issuing invoice phase & \((r_{L-M},s_{L-M})\) & L & CA & \(x_{L-M}\stackrel{{?}}{{=}}r_{L-M}\)mod\(n\) \\ \cline{2-5}  (direct authorization) & \((r_{M-L},s_{M-L})\) & CA & L & \(x_{M-L}\stackrel{{?}}{{=}}r_{M-L}\)mod\(n\) \\ \hline Authentication and issuing invoice phase & \((r_{L-A},s_{L-A})\) & L & P & \(x_{L-A}\stackrel{{?}}{{=}}r_{L-A}\)mod\(n\) \\ \cline{2-5} Authentication and issuing invoice phase & \((r_{A-M},s_{A-M})\) & P & CA & \(x_{A-M}\stackrel{{?}}{{=}}r_{A-M}\)mod\(n\) \\ \cline{2-5} (proxy authorization) & \((r_{M-A},s_{M-A})\) & CA & P & \(x_{M-A}\stackrel{{?}}{{=}}r_{M-A}\)mod\(n\) \\ \cline{2-5}  & \((r_{A-L},s_{A-L})\) & P & L & \(x_{A-L}\stackrel{{?}}{{=}}r_{A-L}\)mod\(n\) \\ \hline Payment verification and browsing phase & \((r_{L-C},s_{L-C})\) & L & CA & \(x_{L-C}\stackrel{{?}}{{=}}r_{L-C}\)mod\(n\) \\ \cline{2-5}  (direct authorization) & \((r_{C-L},s_{C-L})\) & CA & L & \(x_{C-L}\stackrel{{?}}{{=}}r_{C-L}\)mod\(n\) \\ \hline \hline \multirow{4}{*}{Payment verification and browsing phase (proxy authorization)} & \((r_{L-P},s_{L-P})\) & L & P & \(x_{L-P}\stackrel{{?}}{{=}}r_{L-P}\)mod\(n\) \\ \cline{2-5}  & \((r_{P-C},s_{P-C})\) & P & CA & \(x_{P-C}\stackrel{{?}}{{=}}r_{P-C}\)mod\(n\) \\ \cline{2-5}  & \((r_{C-P},s_{C-P})\) & CA & P & \(x_{C-P}\stackrel{{?}}{{=}}r_{C-P}\)mod\(n\) \\ \cline{2-5}  & \((r_{P-L},s_{P-L})\) & P & L & \(x_{P-L}\stackrel{{?}}{{=}}r_{P-L}\)mod\(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-repudiation of the proposed scheme.  
the Licensee will be unable to obtain the decryption key; that is, it cannot perform digital content playback. Thus, we do not worry about the leakage of digital property rights.
## 3 Sharing ML Models as Cyber Threat Intelligence
### Sharing ML Models as Threat Intelligence  
Rather than sharing IoCs whose effectiveness may deteriorate over time, our goal is to share ML models and guarantee interoperability across the members of a community. We therefore defined a new MISP object template that enables not only to embed the ML model but also other associated artifacts, such as examples of false positives and false negatives including adversarial examples. The MISP object template in Listing 3 is a reduced version of our meta-model to represent and share a machine learning model for IDS threat detection. Our MISP object template has attributes to describe the input feature vectors and the output labels, to attach sample code to load, pre-process raw data into input feature vectors, and to evaluate and update the ML model. The template also includes an optional attribute to represent the version of the ML model, and an attribute to hold a digital signature of the model so that the recipient can guarantee its authenticity. However, note that this MISP object template does not specify the kind of machine learning model (e.g., decision tree, SVM, neural network), but only the type of format in which it is serialized. For the sake of simplicity, our proof-of-concept is built on a Python-based framework that leverages Scikit-Learn (cfr. [[https://scikit-learn.org/](https://scikit-learn.org/)]([https://scikit-learn.org/](https://scikit-learn.org/)), accessed on 24 February 2021) for traditional machine learning models, and Tensorflow 2 (cfr. [[https://www.tensorflow.org](https://www.tensorflow.org)]([https://www.tensorflow.org](https://www.tensorflow.org)), accessed on 24 February 2021) and Keras (cfr. [[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)), accessed on 24 February 2021) for deep learning-based models. The encoding formats for the ML models mentioned in the MISP object template in Listing 3 correspond to these frameworks. To collaboratively improve shared ML models, we also need a way to share examples where the ML model misclassifies an input sample. Listing 4 describes a MISP object template specifically for IDS threat detection to collect PCAP traces that are incorrectly labeled as a false positive or false negative, with the ability to include the correct label. Note that the ML labels are not fixed and the user can provide custom labels beyond the same defaults (i.e., 'true' and 'false') provided, for example, to upload and share multiclass classification models that can distinguish different threats. Once the MISP object templates are registered within MISP, they can be instantiated by adding objects to an event, as illustrated in Figure 1, either through the web interface of MISP, or by making use of MISP's REST APIs to submit events with attributes and objects.