esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gas Chromatography (GC) Analysis  
The MRPs of the concentrated volatile extracts were analyzed using GC techniques [1], namely GC-FID/MS (flame ionization detection/mass spectrometry) analysis for PE membrane extract and GC-MS analysis for RAS extract. The GC-FID was performed using a 6890N GC equipped with a DB-WAX column (60 m \(\times\) 0.25 mm i.d., film thickness 0.25 \(\mu\)m) (Agilent Technologies, Santa Clara, CA, USA). The PE membrane extract (1 \(\mu\)L) was injected at a split ratio of 1:10. The temperatures of both the GC injector and detector were set at 250 \({}^{\circ}\)C. The column temperature was initially set at 40 \({}^{\circ}\)C for 1 min, raised to 200 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and then maintained at that temperature for 37 min. The helium carrier gas was set to a linear velocity of 32 cm/s. Subsequently, compound identification was performed using a 7890A GC-5975C MS (Agilent Technologies) under the same GC conditions described above. For MS detection, the ion source and interface were maintained at 230 \({}^{\circ}\)C. The ionization energy was 70 eV, and the mass acquisition range was _m/z_ 33-350.  
The RAS extract was analyzed using a 7890B GC-5977A MS equipped with a DB-WAX column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\mu\)m) (Agilent Technologies). The column was initially set at 40 \({}^{\circ}\)C for 1 min, raised to 200 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and maintained at this temperature for 17 min. The other GC parameters and MS conditions were set as described above.  
Compounds were identified by comparing linear retention indices (RIs) using a homologous series of _n_-alkanes (C\({}_{7}\)-C\({}_{30}\)) and mass spectral fragmentation patterns with MS data from the National Institute of Standards and Technology MS Library (Version 2008 or 2014). Co-injected authentic standards were also used for confirmation of compound identification. The weight intensity of the identified peak was calibrated to the FID or MS response of the internal standard, and the compound content was expressed as \(\mu\)g/100 g. All assays were performed in triplicate.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 3 System Design and Algorithm
### Geographic Protocol Design  
When communicating through a UAV, a problem may occur if a communication protocol that does not consider the characteristics of the UAV is used. In particular, communication between UAVs can cause problems with data transfer beyond the network range, because the locations of drones change frequently. If the data to be sent are delivered through broadcasting, they can be delivered quickly at once, but this is not safe because it has a negative effect on the network bandwidth and anyone can receive the data. Conversely, if we communicate by setting a route, data can be transmitted safely and quickly, but a large overhead occurs when maintaining or re-establishing a routing table. A routing system suitable for UAV communication should safely transmit data, consider UAV mobility, and set a low network load. The protocol proposed in this paper reliably delivers data through broadcasting and forwarding. Our system reduces the network load and increases the reliability by adding routing rules, and it also considers the mobility characteristics of UAVs.  
The network area is designed using the current location of the UAV so that the UAVs in the cluster can transmit and receive data through an optimal route. Longitude, latitude, and altitude information is obtained through a GPS sensor and the information is entered into the private A-class IP address of the connected network interface controller (NIC). Therefore,  
Figure 3: The overall overview of the proposed system.  
the IP addresses of all UAVs contain their own physical address information. UAVs in the swarm recognize each other's location addresses to maintain the topology, so they can access the IP without the need for a separate routing table or re-establishment. Therefore, it is possible to reduce the overhead that occurs when maintaining or re-establishing a routing table required for data transmission.  
All UAV addresses in the swarm are passed to the main UAV server, and the server builds a UAV squadron simulation environment using the location information. When a data transmission request comes from a UAV squadron, the server identifies the source and destination and calculates the FR angle through the GLAN algorithm in the simulation. The server delivers the calculated FR angle back to the swarm, and all UAVs in the cluster set routing rules according to the information received, to determine whether to forward or discard the received broadcast data. In particular, we use geolocation information to identify network addresses between UAVs in the swarm, to reduce the need to maintain routing tables and establish forwarding areas to enable efficient communication. Through this process, even if the UAV location changes and data are broadcasted, the network load can be reduced and reliable data can arrive safely.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 2 System Model  
### Co-Channel Networks with Dual Connectivity  
For HetNets, the wireless radio resources not only determine the quality of the network but also determine whether the bandwidth requirements of UEs are satisfied. In the limited wireless radio resources, how to effectively improve the spectrum utilization is very important; therefore, co-channel HetNet is currently the most efficient approach, meaning that Macro BS and pico BS co-use the same frequency. However, the challenge of the co-channel network is the interference between each base station. Based on the different downlink transmission power of the base station in HetNets, the low power pico BS with the distance closer to the Macro BS will suffer the greater interference, and lead to the decrease of the signal coverage of the pico BS, as shown in Figure 1[25].  
In this paper, we combine the dual connectivity technology with CRE and ABS by integrating resources from two base stations to enhance the data transfer rate of each UE. For example, in 3 GPP co-channel network scenario [1] as shown in Figure 2, it can be observed that UE 1 and UE 3 are close to the pico BS and Macro BS, respectively, and can receive better CQI than the UE 2. On the other hand, UE 2 is located at the signal junction of the base station and has a worse CQI due to the greater interference. In order to enhance the data transfer rate, UE 2 with dual connectivity can access the resources from Macro BS in non-ABS situations and can also access the resources from pico BS in ABS situations (see part (a) in Figure 2) where non-ABS means the ABS mechanism is not enabled.  
Figure 1: Pico base station (BS) coverage area in the presence of a Macro BS.  
We learned that the difference between the dual connectivity and the original single connectivity (SC) is that some UEs, such as UE 2, can access radio resources from the Macro BS and the pico BS simultaneously. In order to facilitate the management, as shown in Figure 2b, we define two categories of the user equipment, namely Macro user equipment (MUE) and Pico user equipment (PUE). The characteristics of each category are described as follows.  
* **Macro user equipment (MUE):** Only access the radio resources from the Macro BS, and cannot access the radio resources from the pico BS. Therefore, in our proposed mechanisms, we will give the MUE a higher priority than the PUE in the resource allocation of the Macro BS.
* **Pico user equipment (PUE):** Besides accessing the radio resources from the pico BS, a UE may also access the radio resources from the Macro BS.
## 2 USV System Structure
### Control Module  
The control module can collect sensor information in real-time, control the USV's movement, manage the mission device, and realize the independent work of the USV. The control module consisted of an industrial personal computer (IPC) and a bottom control box which was equivalent to the brain of the USV. The environmental information, the ontology information, and the target information collected by the sensors were transmitted to the control module. The control algorithms pre-embedded in the IPC processed this information and then different control instructions were calculated and transmitted to other modules.  
The key of the control module was the IPC interfaced with a serial extension board (Digital Signal Processor) and a network switch. The general IPC from YanHua's APAX series was used as a hardware platform and the embedded control system based on Linux was developed. Since data could be collected, processed, and stored independently by the IPC, the loss of the data could be effectively avoided in the case of communication interruption or indirect/incomplete transmission.
# Applying Reinforcement Learning to Protect Deep Neural Networks from Soft Errors
## 2 Related Work
### Adopting RL to Analyze Behaviors of DNNs  
Reinforcement Learning (RL) is an interactive optimization process in which an agent makes decisions and receives feedback in the form of rewards from the environment. To ensure that an agent can make appropriate decisions in varying situations, Reinforcement Learning (RL) typically relies on a quality function (Q-function) or a value function (V-function) to quantify the impact of the agent's decisions--such as specific states or state-action pairs--on the environment. By iteratively approximating these functions, an RL-based agent can optimize its decision-making process. As environments become increasingly complex, the evaluation of the decisions is often implemented using various neural network architectures. For example, Deep Q-Learning (DQN) employs neural networks--specifically policy and target networks--to approximate Q-values in environments with discrete and low-dimensional action spaces [53]. In contrast, the Deep Deterministic Policy Gradient (DDPG) uses actor and critic networks to optimize the agent's actions in continuous and high-dimensional action spaces [54]. Due to their learning-enabled nature, DRL approaches are widely used for optimization-related tasks such as communication and networking in Internet of Things [55] and anomaly detection [56]. In particular, given the extensive state spaces associated with neural networks, DRL-based methods offer a promising approach for understanding and analyzing their behaviors and performance by formulating them as optimization problems. For example, DRL-based agents are widely employed to optimize network pruning by identifying and removing redundant components within a DNN. In such cases, the agent's actions involve pruning specific parts of the target DNN--which serves as the environment to be compressed--while the states represent the DNN's performance in response to these actions. One example is the use of a DRL-based agent for layer-wise pruning using the DDPG algorithm [57]. Building on this idea, a multi-agent design is proposed to perform channel-wise pruning within the DNN [58]. Although most existing approaches leverage DRL-based methods to optimize DNN architectures by targeting coarse-grained structures (e.g., layers or channels), only a few focus on using DRL to address vulnerabilities at a fine-grained level, such as individual parameters.  
As mentioned above, this work proposes an RL-based method to protect DNNs from soft errors by addressing and identifying vulnerable bits during functional development phase. After reviewing related work on fault injection techniques, we propose a functional fault injection service featuring a simulated memory hierarchy model. With the adoption of layer-wise resiliency analysis, the DQN-based agent protects vulnerable bits from soft errors by generating bit masks within a specific layer. To enhance the efficiency of protection and reduce the time cost to deploy the agent across DNNs, we utilize resiliency analysis as prior knowledge to adopt transfer learning.
## 6 Discussion  
The results show that the dependencies of the coupling coefficient vs. air-gap length of the coils having both circular and square geometries have similar pattern. The structures of the coils with cores having higher ferromagnetic volume (ferrite) show superiority considering the coupling coefficient. The results of the investigation of the impact of longitudinal coil displacement show that the coil structures which have cores with a higher volume of ferrite show higher sensitivity to the displacement, and from a particular range of the displacement, even produce lower values of the coupling coefficient. The research of the impact of the number of turns on the coupling coefficient shows that an increased number of turns causes higher values of the coupling coefficient only in the lower range of the number of turns. From a particular point, the increase in the number of turns does not cause growth of the coupling coefficient. The highest sensitivity is exhibited by the structures with backings having ferromagnetic bars. The impact of the width of the ferromagnetic bars on the coupling coefficient shows nearly linear dependencies in the investigated range. Predictably, the higher coupling coefficient values are produced by the coils having higher volume of the ferromagnetic backings. In all the considered cases, the coil systems having square geometry showed noticeable superiority over the circular ones with respect to the coupling coefficient value.  
Figure 19: Coupling coefficient vs. width of the ferromagnetic bars.  
Figure 18: Geometry of the square coils: (a) width of the Fe bars 2 cm; (b) width of the Fe bars 4 cm.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
Deliberate errors come from malicious threats using GNSS jammers or spoofers, which usually do not hold static, so they cannot be easily traced. Finding a solution for intentional disturbances is one of the major concerns in the upcoming era of growing autonomous navigation systems (ANS). Jammers, as a kind of signal blocker for the GNSS, are radio frequency transmitters, which interfere or, in the worst cases, block signal reception. Since they present a serious risk to public safety, several national laws prohibit their use. On the contrary, however, marketing and selling such jammers over the internet is not prohibited nor regulated. The fact that people with some electro-technical skills could build a GNSS jammer by themselves is of great concern. It is a known fact that GNSS geodetic receivers from different manufacturers follow specific internal filtering processes that can mitigate effects, for example multipath [16]. While studying the effects of jamming, different approaches in the detection and mitigation of the GNSS multipath with a combination of signal-to-noise-ratio (SNR) measurements can be used as the reference [17]. Manufacturers of receivers have recently been putting huge efforts into research to overcome the problems of the intentional degradation of signals. Although they have their own specific anti-jamming solutions,they all include antenna-based and receiver-based techniques into their solutions. They produce receivers with signal-degradation mitigations or even equip them with additional sensors, for example, inertial measurement units (IMUs). Chips within instruments incorporate spectrum analysers, surface acoustic wave (SAW) and more sophisticated thin-film bulk acoustic resonators (FBAR). Adaptive notch filters are an effective solution used to mitigate jamming and extend the capabilities of GNSS operations. These filters are capable of tracking frequency variations of a jamming signal, pulse blanking or adaptive beamforming based on the multi-antenna solution [18; 19]. The use of controlled reception pattern antennas (CRPA) is beneficial since they provide significant anti-jamming protection [20]. Despite significant improvements, local interference from nearby receivers remains, meaning there are severe issues to be resolved in the future [21]. As a result of previous studies carried out at the University of Ljubljana and those from the literature, the main objective of this research is to investigate the impact of L1/E1 carrier frequency (1575.42 MHz) jammers on geodetic receivers from different manufacturers and generations. In order to achieve this, receivers were tested simultaneously in the field in real jamming scenarios. Among the many techniques for analysing the impact of a jammer, the authors limited themselves to changes in the observed carrier-to-noise density (C/N\({}_{0}\)) values of the received signals and differences in coordinates acquired from GNSS processing while jamming. The reason was that quality positioning is one of the major concerns in geodesy.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 3 Methods
### Field Dependency Extraction  
Dependencies between fields are important in building valid packets, which often means that the value of one field is affected by the value or attribute of another field. For example, in the Modbus TCP protocol, the value of the header field \"Length\" is determined by the actual length of the \"Data\" field. Wireshark protocol dissector files encompass a series of field parsing rules, including the definition of inter-field dependencies. During the parsing process, it is imperative to analyze the execution conditions and iteration counts of the program to ascertain these dependencies, facilitating the addition of appropriate attributes to pertinent fields.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attribute** & **Static Array** & **Buffer Read** & **Proto Tree** \\ \hline name & ✓ & - & - \\ identifier & ✓ & - & ✓ \\ optional value & ✓ & - & - \\ data type & ✓ & ✓ & - \\ length & - & ✓ & ✓ \\ bit-length & - & ✓ & - \\ variable name & - & ✓ & - \\ endian & - & ✓ & ✓ \\ offset & - & ✓ & ✓ \\ dependencies & - & - & ✓ \\ \hline \end{tabular}
\end{table}
Table 1: The attribute information of the field, and where it can be extracted. “✓” means the attribute can be extracted.
## 2 Methodology
### Enhanced Sampling Algorithms
#### 2.2.2 Under-Sampling Algorithm  
ENN (edited nearest neighbors) is an under-sampling algorithm used for handling imbalanced datasets [47]. It improves the quality of the dataset and enhances the predictive accuracy of classifiers by removing samples inconsistent with the majority class between their neighbors. It can effectively reduce the risk of overfitting and purify the dataset by removing possible noise samples (especially potential erroneous samples synthesized by over-sampling). And by combining with different over-sampling algorithms, the optimal benefits of the 'over-sampling + under-sampling' processing flow can be comprehensively discussed. The specific process of the ENN algorithm is as follows (Figure 4): Firstly, for each sample, calculate its Euclidean distance to other samples with Equation (3). Then, if it is found that the class of the sample is different to the most frequently occurring class among its \(K\) nearest neighbors, it will be marked as noise or a potentially misclassified sample and removed. This process is iterated until no more noise samples are found. After processing with the ENN algorithm, the resulting dataset can be represented as:  
\[S_{ENN}=\{x_{i}|y_{i}=node(K_{i}),\ i=1,2,\ldots,n\} \tag{7}\]  
where \(S_{ENN}\) represents the dataset after under-sampling processing, \(x_{i}\) represents the \(i\)-th sample, \(y_{i}\) represents the true label of the \(i\)-th sample, \(K_{i}\) represents the set of classes of the \(K\) nearest neighbor samples for the \(i\)-th sample, and \(mode(K_{i})\) represents the most frequently occurring class in \(K_{i}\).
## 3 Results
### Multi Variable Crashworthiness and Weight Optimization of Vehicle Body
#### 3.2.4 Optimization Results and Discussion  
The Pareto solution set obtained through the application of the AI-PSO algorithm, immune algorithm, and PSO algorithm is illustrated in Figure 14. The hybrid optimization algorithm AI-PSO achieved a solution closer to the Pareto front, indicating the availability of an improved optimization scheme for the roll crashworthiness and weight of vans. Upon several generations, the PSO algorithm attained an optimal solution, which stabilized, and the group's optimal fitness no longer increased along with the iteration numbers. The hybrid algorithm maintained its population diversity and particle speed throughout the iteration process. When near a local extreme point, the algorithm slightly perturbed the optimal local extreme value, thereby escaping the local extreme point and searching again. Consequently, this effectively addressed the issue of the PSO algorithm oscillating near local optimal solutions. The hybrid algorithm exhibited strong global search ability, with a more accurate and uniform Pareto solution set than that of the PSO algorithm. While the PSO algorithm delivered 29 solutions, the hybrid and immune algorithms delivered 60 solutions. Based on the global search outcomes of the PSO algorithm, the hybrid algorithm restricted the subsequent antibody mutation range, thus requiring 1/3 the computation time of the immune algorithm under similar iteration numbers. Overall, the AI-PSO hybrid algorithm effectively enhanced the precision, the Pareto solution set uniformity, and the computational efficiency of both the PSO algorithm and the immune algorithm.  
None of the solutions in the Pareto solution set were able to fully satisfy the requirements for optimizing the van rollover crashworthiness and weight. However, the method with the shortest distance could be utilized to determine the best compromise solution, as shown in Table 7. The hybrid algorithm reduced the total van component weight by 21.572 kg while increasing the specific energy absorption of the key structure by 9.65%. Thus, the hybrid optimization algorithm minimized the vehicle's weight and improved its van rollover crashworthiness. At this point, the top cover's displacement was less than 127 mm, and the maximum angular velocity of the body center of mass was less than 4.6 rad/s, fulfilling the design requirements.  
Figure 14: Rollover crashworthiness and weight optimization results for the van.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling
### Multi-Type Ground Support Vehicle Scheduling Model  
The sets, parameters and variables of the ground support vehicle scheduling model are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Notation** & **Meaning** \\ \hline \multirow{3}{*}{sets} & \(F^{v}\) & the set of flights requiring service from vehicle type \(v\) \\ \cline{2-3}  & \(V\) & the set of vehicle types, \(V\in\left\{1,2,3,4,5,6\right\}\), represents ferry vehicles, passenger stairs, fuel trucks, water trucks, baggage belt loaders, and baggage tractors, respectively. \\ \hline \multirow{6}{*}{parameters} & \(T^{o}\) & the service time for vehicle type \(v\). \\ \cline{2-3}  & \(s_{ij}^{p}\) & the driving distance for the vehicle from the location of flight \(i\) to the location of flight \(j\). \\ \cline{2-3}  & \(N_{v}\) & the time taken for the ferry vehicles to travel back and forth between the parking position \(g\) and the terminal. \\ \cline{2-3}  & \(c\) & the number of vehicle type \(v\). \\ \cline{2-3}  & \(M\) & positive infinity. \\ \cline{2-3}  & \(\left[RT_{i}^{p},DT_{i}^{p}\right]\) & the time window during which vehicle type \(v\) can service flight \(i\). \\ \hline \(C^{4}\) & the capacity of the water truck. \\ \hline \(d_{i}\) & the water demand for flight \(i\). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sets, parameters and variables of the ground support vehicle scheduling model.