esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Packet Capture  
_Session Feature Buffer Pool_  
To deploy SwiftSession in a real-world environment and achieve the incremental classification of flows, we designed a structure named Session Feature Buffer Pool to temporarily store the features of the arrived data packets of a flow.  
As shown in Figure 1, the flow buffer pool contains several cells. \(Cell_{i}\) corresponds to the flow \(flow_{i}\) in the current network environment where data packets have been captured but not yet identified. It stores the features \(f_{j}\) extracted from the \(j\) captured data packets of the current flow, including the sequential feature \(f_{j}^{t}\) and the statistical feature \(f_{j}^{s}\). Whenever a new data packet of this flow was captured, we calculated the new feature \(f_{j+1}\) incrementally, which is the combination of the previously captured data packets and the current one. The descriptions in the following sections illustrate that such calculation is feasible and consumes a relatively low number of computational resources. Then, we adaptively select the corresponding ML model in the classifier cluster to identify the new flow feature and report a classification accuracy \(p_{j+1}\). We set a parameter, the probability threshold \(\tau\), to control the strictness of the method during deployment. The larger the value of \(\tau\), the more inclined the method is to obtain more data packets to ensure a higher recognition  
Figure 1: The architecture of SwiftSession.  
accuracy. Conversely, the smaller the value of the \(\tau\), the more inclined the method is to adopt a more aggressive reporting strategy for a faster classification speed. In practical applications, the threshold \(\tau\) can be adjusted to respond to different scenarios flexibly.  
When the classification accuracy \(p_{j+1}\) reported by the classifier cluster is less than \(\tau\), the method temporarily stores the new feature \(f_{j+1}\) in the flow feature buffer pool and waits for the capture of the next data packet belonging to this flow. When the reported accuracy \(p_{j+1}\) is greater than or equal to \(\tau\), the method releases the memory occupied by this cell and reports the classification of the flow (which can be protocol, application, behavior, etc. Experiments in Section 5 proved that our method can classify traffic at multiple granularities).  
Regarding memory release, we set a maximum duration and a maximum number of data packets to clear the memory occupied by invalid flows. In the examples in Section 5, the maximum duration is 300 s, and the maximum number of data packets is 10.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results
#### 5.3.1 Network Lifetime Evaluation  
In this series of experiments, the performance of the proposed ERCP algorithm was evaluated in terms of the network's lifetime in comparison to HMBCR [10], ECRP [11], and EPSO-CEO [24] for both homogeneous and heterogeneous networks under different traffic rates. This evaluation was carried out in terms of how long the network remained operational. Figures 1 and 2 both illustrate the relationship between the average traffic rate and the variance in the lifetime of the network for homogeneous and heterogeneous networks, respectively. The figures demonstrate that the ERCP method, which was proposed, enhances the network's lifetime in comparison to the HUMBCR algorithm, the ECRP algorithm, and the EPSO-CEO algorithm for networks that are either homogeneous or heterogeneous. Some of the reasons why such outcomes are justified are as follows:  
The proposed ERCP algorithm balances network energy use among sensor nodes at the cluster head selection level due to the suggested energy measure. It also considers link quality when choosing a cluster head to cut down on energy wastage from retransmitting failed packets. HUMBCR, ECRP, and EPSO-CEO algorithms waste resources by retransmitting failed packets during cluster head selection because they are unaware of the reliability of data transfer from member nodes to cluster heads. That is to say, the obtained results further validate the benefits of the proposed quality metric. At the same time, it can achieve effective load balance among cluster heads through the proposed load metric. Therefore, the obtained results further validate the load metric's benefits in network lifetime enhancement.  
At the routing level, the proposed ERCP algorithm utilizes the node's energy metric, which leverages node traffic load and residual energy to balance network energy usage across sensor nodes effectively. It prevents lost links to save energy wasted by retransmitting dropped packets as much as possible. On the other hand, HUMBCR, ECRP, and EPSO-CEO algorithms depend on residual energy to balance energy consumption, which  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Deployment strategy & Uniformly random \\ Num sensor nodes & 300 \\ Maximum number of retransmissions & 4 \\ Packet size & 50 byte \\ Buffer size & 128 Kbyte \\ Frequency & 868 MHz \\ Path loss exponent & 3 \\ Minimum radio range & 150 m \\ Data rate & 20 Kbps \\ Shadow fading variance & 3 \\ Reference distance & 1 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation environment parameters.  
is inadequate according to this work's observations. They attempt to route data along the shortest path, but a lack of knowledge regarding data transmission reliability wastes energy by retransmitting missed packets.
## 2 Materials and Methods
### Sampling and Measurement  
#### 2.4.1 Soil Temperature and Water Content  
The soil temperature was measured uninterruptedly in a 24-h cycle. The probe of an RC-4 geothermometer (Jingchuang Electric Co., Ltd., Yongkang, China) was placed at a 15 cm depth in the soil near the root system of the cabbage, the temperature data were recorded every 5 min, and each plot was repeated three times. The base temperature for cabbage growth was 10 \({}^{\circ}\)C [22], and temperatures >10 \({}^{\circ}\)C were summed up as effective cumulative temperature, while temperatures \(\leq\)10 \({}^{\circ}\)C were treated as 0. Finally, the effective cumulative soil temperature was calculated for the whole reproductive period. The soil water content was measured at different fertility stages of the cabbage. Eighteen points were randomly selected for each treatment, and the soil around the root system at 0-5 cm and 10-15 cm depth was taken with a ring knife for fresh soil weighing and brought back to the laboratory for drying and weighing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & **Type of Mulch** & **Main Ingredient** & **Mulch Thickness/mm** \\ \hline WCM & White common mulch & Polyethylene & 0.010 \\ BCM & Black common mulch & Polyethylene & 0.010 \\  & mulch & Polyethylene & 0.010 \\  & White & Poly(adipic acid)-butylene glycol & \\ WBM & biodegradable & p-benzenedicarboxylate + & 0.010 \\  & mulch & poly(lactic acid) & \\ BBM & Black & Poly(adipic acid)-butylene glycol & \\  & biodegradation & p-benzenedicarboxylate + & 0.010 \\  & mulch & poly(lactic acid) & \\ BPM & Permeable & Polyethylene & 0.010 \\  & membrane mulch & Polyethylene & 0.010 \\ WBCM & Black-and-white & Polyethylene & 0.010 \\  & composite mulch & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information parameters of different types of plastic mulch mulches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reproductive Stage** & **Irrigation Time** & **Irrigation Amount/m\({}^{3}\)** \\ \hline Seeding stage & 5-30 & 160 \\  & 6-7 & 72 \\ \hline Rosette stage & 6-14 & 68 \\  & 6-22 & 62 \\ \hline Heading stage & 7-4 & 106 \\  & 7-12 & 42 \\ \hline Total & & 510 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Irrigation time and irrigation amount of cabbage.
## 2 NDN Background and Related Work
### Related Work
#### 2.3.2 Anchor-Based Approach
To reduce handover latency in real-time communication scenarios, a scheme proposed in [21] utilizes the producer's future location prediction to ensure seamless producer mobility. When a producer detects a handoff event by monitoring the signal strength from the current PoA, it sends a special Input to the current PoA, which then predicts the best PoA candidate the producer is expected to connect to after the handoff. Once the handoff is completed and the producer successfully connects to the new PoA, the consumer's Input is redirected toward the new PoA from the old PoA. However, using a redirection strategy can result in path stretching, leading to increased latency. Furthermore, when the prediction of a producer's future location goes wrong, the new PoA broadcasts the producer's reachable prefix throughout the network, resulting in significant signaling overhead and congestion.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 4 Experiments and Results
### SOC Estimates  
Using the feature vectors derived from the U-Net encoder, datasets containing the latent features and ground truth SOC values for all samples in CHLSOC and a subset of samples in the LUCAS 2015 and 2018 datasets relevant to our study (i.e., those for which the LC class was _cropland_ and _grassland_) were constructed. For each entry in the dataset, the geographical coordinates were kept. In addition, we evaluated the impact of adding elevation above sea level to the features, to account for the fact that LUCAS is restricted to lower altitudes and to provide additional information to the model.  
Based on these, we evaluated 10 different machine learning algorithms available within the scikit-learn library [54], aiming to identify the algorithm that exhibited the optimal balance between accuracy and generalization. The candidates included: linear regression, ridge regression, ElasticNet regression, K-Nearest Neighbors regression, extremely randomized trees regression, support vector regression, decision tree regression, random forest regression, gradient boosting regression, and neural network regression.  
The models were evaluated using 10-fold cross-validation. The results of these experiments are shown in Tables 3 and 4. As the data in the tables show, adding elevation led to a modest improvement for nearly all of the algorithms tested, except for the MLP, the performance of which deteriorated significantly when elevation was added. Among the algorithms evaluated, extremely randomized trees performed best. We, therefore, decided to conduct further evaluation based on this methodology alone.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **MAE** & **MSE** & **R\({}^{2}\)** \\ \hline Linear Regression & 2.0078 & 13.4416 & 0.2202 \\ \hline Ridge & 2.0078 & 13.4416 & 0.2202 \\ \hline ElasticNet & 2.019 & 13.7272 & 0.2041 \\ \hline K-Nearest Neighbors & 1.8316 & 13.2385 & 0.2289 \\ \hline Extremely Randomized Trees & 1.1018 & 6.2549 & 0.6384 \\ \hline Support Vector Machine & 1.8117 & 14.5302 & 0.1586 \\ \hline Decision Tree & 1.3366 & 10.28 & 0.3954 \\ \hline Random Forest & 1.1619 & 6.6169 & 0.6173 \\ \hline Gradient Boosting & 1.4796 & 9.303 & 0.4635 \\ \hline Multi-Layer Perceptron & 3.1267 & 452.4427 & \(-\)23.4217 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of 10-fold cross-validation with all 3 datasets with elevation.
## 4 Research Needs and Trends in Wind Farm Design and Optimization
### Sub-Modeling of Adverse Factors
Literature works commonly consider a flat square area and assume, with the exception of a few works [55,219], that the wind field characteristics are identical throughout the entire site. A flat square area assumption certainly holds for offshore sites, but it is very unrealistic for onshore sites, where terrain is rarely flat, has non-uniform roughness and irregular boundaries. The reason behind ignoring the topography complexity is due to the fact that it is hard to obtain reliable estimations of its effects though simple models and cheap computational routines. Thus, additional assumptions are commonly considered when optimizing onshore wind farms (e.g., it is commonly assumed that WT wakes follow the terrain shape and are not affected by the presence of the ground). However, as previously mentioned, the use of commercial software (see Section 4.4) and the concept of preprocessing might help to overcome some of these limitations. Therefore, the description of how wakes, turbulence, noise and electromagnetic fields evolve in the domain while considering the local topography remains an important problem to address. In addition, common approaches in the literature neglect the effects of topography in the design of the civil engineering and electrical infrastructure. In complex terrain, the total length (and therefore the total cost) of the auxiliary roads or the MVAC collection lines can be significantly different from that for flat sites. This fact limits the applicability of the traditional Euclidean minimum spanning tree and the Steiner tree approaches and induces the necessity for the development of new robust approaches. The uncertainty and risk modeling of wind farms has been scarcely treated in the scope of the WFDO problem as pointed out in Section 3.2.5. Two important unaddressed problems in the topic have been identified: (1) only a small set of factors that contribute to the overall uncertainty were included in the optimization frameworks of the literature works; (2) The treatment of uncertainty is based on the assumption in which the stochastic variation of certain variables can be described using predefined statistical distributions. The assumptions, however, are not validated and require additional research. Environmental and/or social impacts within the WFDO procedures have been neglected in most works as pointed out in Section 3.2.4. Although their inclusion into an optimization framework demands significant efforts in both the modeling and optimization stages, they should not be discarded as they usually influence the viability of wind projects.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Performance Evaluation for 5G Communications  
As a full-fledged waveform to hide, we considered the 5G physical layer communications where a 5G user equipment (UE) transmits a 5G uplink signal to a base station (gNodeB) in the presence of the perturbation from the CJ. MATLAB's 5G toolbox was used to generate 5G signals that included the transport (uplink shared channel, UL-SCH) and physical channel. The transport block was segmented after the cyclic redundancy check (CRC) addition and low-density parity-check (LDPC) coding was used as forward error correction. The output codewords were QPSK-modulated as an example. Next, the generated resource grid was OFDM-modulated with inverse fast Fourier transform and cyclic prefix (CP) addition operations where the subcarrier spacing was 15 kHz. The target code rate was set to \(\frac{820}{1024}\) and the output I/Q samples were stored after the signal passed through the channel. The eavesdropper attempted to distinguish the received signals from noise, whereas the receiver attempted to decode the received signals by removing the CP and performing FFT, channel equalization, QPSK demodulation, LDPC, and CRC decoding operations.
## 3 Proposed GLLCSA
### Gllcsa  
We combined all the above strategies, and the pseudo-code for the whole GLLCSA framework is shown in Algorithm 1. Figure 1 is the flowchart of GLLCSA. In the proposed GLLCSA, almost all parameters are affected by the GLLCSA group size. However, the new tactic will not increase the complexity of the original algorithm too much. GLLCSA adopts a gradient search strategy, which includes the Newton method to change the gradient descent rate, and it can coordinate the rationality of each object and improve the authenticity of the algorithm simulation.  
```
Step 1. Initialization  Randomly initialize the location of \(N\)-group crows in the search space  Assess the location of crows  Initialize the memory of crows  Specify the best and worst solutions \(x_{best}^{m}\) and \(x_{worst}^{m}\) Step 2. Main loop while(\(iteration<iteration_{max}\)) for\(i=1\):N  Randomly choose a crow to follow  Define perception probability if\(r_{j}\geq AP_{j}iteration\)  Calculate the position using Equation (1) else for\(d=1\):\(D\) if\(FES/iteration_{max}\) \(x^{d}=\) a random position of search space else  Select randomly \(r1\
eq r2\
eq r3\
eq r4\
eq n\) in the range of [\(1\), \(N\)]  Calculate the position using Equation (19) endif endfor endif Local escaping operator  Calculate the position using Equation (20)  Update the positions \(x_{best}^{i}\) and \(x_{worst}^{i}\)  Create the new position \(x_{new}\) using Equation (30) if\(x_{new}\) better than \(x^{i}\) \(x^{i}=x_{new}\) endif endfor  Inspect the feasibility of the new positions  Assess the new location of the crows  Update the memory of each crow  endwhile Step 3. Return\(g\_best\)
```  
**Algorithm 1**Pseudo-code of GLLCSA  
According to Figure 1, the time complexity of GLLCSA relates to the following factors: initialization, fitness evaluation, individual location update, ranking, gradient search mechanism, local operator, and levy flight mutation. On account of the time consumption of fitness, an evaluation is decided by the concrete optimization problem. The computational complexity analysis centers on the other six aspects. Assuming the population and initialization of the solutions with \(N\) agents, the computational complexity of the sorting mechanism is \(O(N)+O(N\times logN)\). The computational complexity of the individual location update is \(O(G\times N\times D)\), where \(G\) is the highest iteration number and \(D\) is the dimension. The complexity of the \(GSR\), \(LEO\), and \(LM\) update stages is \(O(G\times N\times D)\). Therefore, the computational complexity of \(GLLCSA\) is \(O(N+N^{*}logN)+O(G^{*}D(2^{*}N+1))\), which is the same as the original \(CSA\).
## 3 Results and Discussion
### Verifying of CFX  
The verification of the CFX model's results has been evaluated in three y* = 1.62, \(-\)1.0, and \(-\)0.29 cross-sections of the branch channel. Table 1 shows the statistical indexes for comparing the numerical model's results and the experimental model's results in different y* cross-sections.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **y* = -0.29** & **y* = -1.0** & **y* = -1.62** \\ \hline RMSE & 0.01 & 0.012 & 0.017 \\ MAPE (\%) & 2.0 & 5.2 & 6.95 \\ R2 & 0.95 & 0.88 & 0.68 \\ SI & 0.025 & 0.061 & 0.082 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical index for the verification outcomes of the numerical model.  
The mean relative error MAPE has been obtained to be 5% in this comparison therefore Figure 5 shows a good consistency existing between the results of the CFX model and the outcomes of the experimental model. In three cross-sections \(y^{*}=-1.62\), \(-1.0\), and \(-0.29\), the mean relative error MAPE has been obtained to be approximately 2%, 5.2%, and 6.95%, respectively, concerning Table 1. The value of RMSE is equal to 0.012, 0.01, and 0.017 for cross-section \(y^{*}=-1.62\), \(-1.0\), and \(-0.29\), respectively, and the amount of R\({}^{2}\) for three \(y^{*}=-1.62\), \(-1.0\), and \(-0.29\) cross-section is 0.88, 0.68, and 0.95, respectively, in the table. In \(y^{*}=-1.0\) and \(y^{*}=-1.62\) in the branch channel downstream, the values of the longitudinal velocity increase in the direction against the flow and the velocities become negative due to the presence of the recirculation zone in the separation zone. In depths of \(z^{*}=0.0\) to \(z^{*}=0.2\) and in the contraction zone the velocities are maximum V\({}_{\rm max}\) and the contraction zone is denser than the flow surface in this area since the flow lines are denser. The amount of SI for three \(y^{*}=-1.62\), \(-1.0\), and \(-0.29\) cross-section is 0.025, 0.061, and 0.082, respectively.  
Figure 5: Verification graphs between the results of the CFX model and the [PERSON] and his colleagues’ experimental model in: (**a**) \(y^{*}=-0.29\), (**b**) \(y^{*}=-1.00\), and (**c**) \(y^{*}=-1.62\).  
The profiles of the flow longitudinal velocity in y* = -2.00 cross section of the branch channel were drawn in 0.838 discharge in the numerical model and the experimental model was drawn to examine the matter more carefully (Figure 6). Contours (a) and (b) present the longitudinal velocities of the flow in the generated experimental model and numerical model respectively in Figure 5 Concerning the Figure 6, the values of the longitudinal velocity increase along the opposite direction of the stream and cause severe fluctuations in the stream longitudinal velocity in y* = -2.00 since the recirculation zone has completely developed in the separation zone. Concerning the figure, the longitudinal velocities reach the maximum value V\({}_{\max}\) in x* = 0.4 to x* = 0.7 area and in the depth of z* = 0.0 to z*0.2 and so the intensity of the velocity fluctuation is greater in this area in comparison with the flow surface and the other areas of the intake channel.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 6 Simulation Results and Discussions  
The performance of the proposed PS-CARA scheme is evaluated by upgrading the system-level simulator designed in MATLAB [40]. This simulator is designed by taking care of the assumptions given for the PS scenario [34]. The key assumptions considered during simulations are: (1) user can associate to one BS at one time, that is either with MBS or mPC, (2) only uplink interference scenario is modeled, (3) frequency division duplexing (FDD) is considered, (4) urban macro scenario is considered because this scenario is compulsory for both general purpose scenario and public safety scenario [34], and (5) all users and mPC are deployed outside only in the coverage area of MBS. In Table 3, we summarized the parameters used to perform the simulations.  
We compare the performance of the proposed PS-CARA scheme with the conventional static resource allocation (C-SRA) scheme, which ignores the context information during resource allocation.  
Figure 3: Dynamic resource allocation for access, backhaul, and sidehaul links.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Values**} \\ \hline Layout & Hexagonal with 7 Cell Sites (3 cells per site), Urban Macro \\ mPC/MBS & 4 \\ Carrier Frequency & 2 GHz \\ Bandwidth, RB & 10 MHz, 50 \\ Inter-site Distance & 500 m \\ UL Resource Partitioning (BH-SHAL) & PS Scenario (R/4:R/2:R/4), Non-PS Scenario (R/2:R/4:R/4) \\ Antenna Configurations & \(1\times 1\) \\ Mobility Pattern & Random Walking Model \\ UE/mPC Speed & 3 Km/h \\ BS/UE Transmission Power & MBS: 43 dBm, mPC: 30 dBm, UE: 23 dBm \\ \(P_{b},\alpha\) & \(-\)80 dBm, 0.7 \\ No. of UEs & 8/MBS, 2/mPC (PS UE: 60\%, Non-PS UE: 40\%) \\ Schedulers & Round Robin (PS UEs), MCALOHA/MCALOHA-ES (Others) \\ Pathloss Models & Urban (MBS), WINNER + B1 (mPC) \\ Fading Model & PedB \\ Priority Modeling & 3 GPP Access Class Barring Models \\ Thermal Noise & \(-\)174 dBm/Hz \\ Traffic Models & Full Buffer \\ Simulation Time & 20 Drops, 500 Subframes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Figure 4: Propose PS-CARA scheme for mPC in public safety scenario.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 5 ALOHA Software, Strengths, Limitations, and Need for Improvements  
Following the analysis presented in the Section 4, it is essential to delve deeper into the evaluation of ALOHA software within the broader landscape of risk assessment tools for industrial safety management. ALOHA (Areal Locations of Hazardous Atmospheres) is widely used for modelling hazardous material releases, with a particular focus on ammonia. However, while the software demonstrates strengths in specific operational contexts, its limitations and the potential for technological advancements warrant further examination.