esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Simulation Results
#### 4.4.5 F-1 Score Results for Models  
Table 7 shows the values of the model average F-1 score results under different machine learning models. These simulations are computed and collected over the five-simulation environment to collect these vehicles over the network environment. The F-1 score depicts the performance of the model accuracy measurement over the dataset. Figure 17 shows the F-1 score values for all ML models over the dataset for network attack density and attack computations. The GBT and RF show the highest level of the F-1 score across all levels of simulation parameters. According to the results, the MLPC shows lower or equivalent metrics compared to GBT and RF. On the 40% attack density values, the MLPC shows lower results compared to other results in the simulation environments. The SVM and LR show comparatively good results over the 40% attack density values for the proposed and network models. At the 70% attack density, the models outperform with better results compared to their current score and results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Average Recall Metric**} & \multicolumn{5}{c}{**ML Algorithms**} \\ \cline{2-6}  & **GBT** & **LR** & **MLPC** & **RF** & **SVM** \\ \hline
10\% & 0.96 & 0.95 & 0.97 & 0.98 & 0.96 \\ \hline
20\% & 0.97 & 0.955 & 0.92 & 0.98 & 0.87 \\ \hline
30\% & 0.98 & 0.93 & 0.98 & 0.99 & 0.88 \\ \hline
40\% & 0.97 & 0.94 & 0.83 & 0.98 & 0.93 \\ \hline
50\% & 0.92 & 0.93 & 0.78 & 0.95 & 0.89 \\ \hline
60\% & 0.91 & 0.96 & 0.89 & 0.99 & 0.92 \\ \hline
70\% & 0.96 & 0.74 & 0.82 & 0.87 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 6: Model recall results under different networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Average Precision Metric**} & \multicolumn{5}{c}{**ML Algorithms**} \\ \cline{2-5}  & **GBT** & **LR** & **MLPC** & **RF** & **SVM** \\ \hline
10\% & 0.986 & 0.975 & 0.864 & 0.989 & 0.864 \\ \hline
20\% & 0.987 & 0.897 & 0.874 & 0.978 & 0.875 \\ \hline
30\% & 0.987 & 0.864 & 0.861 & 0.990 & 0.869 \\ \hline
40\% & 0.982 & 0.787 & 0.750 & 0.979 & 0.921 \\ \hline
50\% & 0.942 & 0.821 & 0.874 &Figure 16: Comparison of model recall values over the network performance.  
Figure 17: F1 Score values for multiple models for network attacks.
## 3 Results
### Model Validation
#### 3.2.1 Comparison with MOD17 Products  
To evaluate our model performance in comparison with existing satellite-based products, we produced time series of MOD17. At first, we compared in situ GPP and the MOD17 1 km 8-day GPP products. From the scatter plot in Figure 8, we can observe that the slope of the predicted-observed relationship of the MOD17 product is higher than 1 (1.62) indicating that the MODIS model is underestimating the ground GPP.  
Figure 8: Scatter plot with fitted regression line showing the correlation between the predicted GPP MOD17 predictions (x-axis) and the observed GPP values (y-axis).  
Figure 7: Scatter plot with fitted regression line showing the correlation between the predicted GPP values (x-axis) obtained by our model and the observed GPP values (y-axis).  
Then, we compared both our model and the MOD17-based time series with in situ measurements of GPP. Both the MOD17 and our GPP products showed a general agreement with the observed data, especially during the fall and winter months when low primary productivity was observed (October 2020 to February 2020). For the months with a peak of GPP (April 2021 to June 2021), our simulated GPP performed better than the MOD17 product, which was underestimating the observed GPP measurements (Figure 9).  
A comparison of the two models in terms of MAE, RMSE, and Sest is presented in Table 7. While having a high correlation with flux-derived GPP, the MOD17 products presented higher prediction standard errors due to the high underestimation of the GPP during the peak biomass suggesting that the calibration of the MOD17 products should be performed together with the investigation of the sampling procedure that can ensure that collected ground measurements are adequately representative and sufficient to validate the target low resolution EO product.
## 3 Self-Configuration Management (SCM)  
The introduction of a self-configuration management paradigm that addresses software flaws is the main goal of our suggested approach. The SCM model, which includes the network, radio, and system fault models, will be used to do this. We are currently trying to finish the suggested plan and expand on prior work [12]. In essence, the SCM method strives to develop a dual-integrated approach that combines IOT technology with our suggested network. To link and exchange software packets among selected network nodes and systems over the internet, it will be necessary to integrate sensors, software, and monitoring values from many fields. Table 1 provides the notations and their explanations.  
\begin{table}
\begin{tabular}{l l} \hline
**Symbol** & **Explanation** \\ \hline S & Set of sensor nodes in sensor networks \\ S\({}_{\text{i}}\) & A sensor node distributed on \(\text{Pi}(\text{x}_{\text{i}},\text{y}_{\text{i}})\) \\ N & Number of deployed sensor nodes \\ P\({}_{\text{R}}\) & Likelihood of faulty sensor nodes in sensor networks \\ S\({}_{\text{1}}\) & Set of sensor nodes experiencing error-free deadlocks in sensor networks, S\({}_{\text{i}}\in\) S \\ S\({}_{\text{2}}\) & Set of sensor nodes where deadlocks occur when sensor networks fail, S\({}_{\text{2}}\in\) S \\ S\({}_{\text{3}}\) & Set of sensor nodes experiencing random failures in sensor networks. S\({}_{\text{3}}\subset\) S \\ N\({}_{\text{CONF}}\)- & The number of faulty sensor nodes that were configured, N\({}_{\text{CONF}}\). \(<\) N \\ S\({}_{\text{F}}\) & Set of faulty sensor nodes in sensor networks, S\({}_{\text{F}}\subset\) S \\ S\({}_{\text{H}}\) & Set of error-free sensor nodes in sensor networks, S\({}_{\text{H}}\subset\) S \\ C\({}_{\text{1}}\) & Table of clustering of s i which contains all the information about each sensor node and its neighbors. \\ S\({}_{\text{H}}\) & Failure status of sensor node S\({}_{\text{i}}\). \\ D\({}_{\text{T}}\) & Distance range will be covered by a predefined transfer rate \\ C\({}_{\text{1}}\) & The cluster supervisor is in charge of the configuration mode and is usually the head of the group. \\ C (lps) & Channel throughput capacity (bandwidth), measured in bits per second. \\ B & The frequency bandwidth in Hertz, and \\ M & the number of levels a single signal can take on. \\ SNR & Desired signal-to-noise ratio at the receiving node. \\ NR & Receiver signal-to-noise ratio. \\ Gant & Transmit antenna gain. \\ E\({}_{\text{T}}\) & Consumed energy in bit transfers. \\ E\({}_{\text{noncode}}\) & Encoder power consumption. \\ N\({}_{\text{b}}\) & Total number of bits transmitted. \\ E\({}_{\text{tb}}\) & Energy consumption when transmitting a single bit from a node. \\ E\({}_{\text{tb}}\) & Energy consumption when a single bit is received by a node. \\ \hline \end{tabular}
\end{table}
Table 1: The notations used in modeling the proposed distributed SCM technique.
## 3 Results
### DEM-Based Shadow Coarse Detection  
DEM-based shadow coarse detection first needs to determine the location of the quadrangle points of the coarse shadow mask on the DEM and calculate the solar azimuth and elevation angle at the moment of image acquisition. We choose Image 1 and Image 2 to perform shadow detection with a 5 m, 12.5 m, and 30 m DEM. Figure 10 illustrates the results. The white part of the shadow mask is shadow, and the black part is the background. We can see that the DEM rough shadow mask is able to recognize most of the shadows in the image. It has an effective localization on the main shadow areas. However, because the DEM is not accurate enough to reach the same resolution as the image, the rough shadow mask cannot have a close fit with the real shadow.  
Because of the lower resolution, the 30 m DEM shadow mask is rougher than the 12.5 m, but overall, it does not affect the effectiveness in locating shadow areas. The 5 m DEM is generated by combining the 12.5 m DEM with forward and backward panchromatic stereo images. Its shadow mask has more refined shadow regions with more complete edges, and it fits to the original image better. However, a high-resolution DEM is difficult to obtain, and the actual cost of detecting shadows with them is high.  
The rough shadow mask may have problems with shadow edges fitting inaccurately and with positional offsets. Although its direct detection of shadow cannot reach ideally high accuracy, it can be used to locate the main shadow areas to avoid the misdetection of non-shadowed dark ground objects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Producer’s** & **Users’ Accuracy** & **Committed Error** & **Omitted Error** & **Overall Accuracy** \\ \hline Shadow & \(P_{s}=\frac{\text{TP}}{\text{TP}+\text{FN}}\) & \(U_{s}=\frac{\text{TP}}{\text{TP}+\text{FP}}\) & \(E_{c}=\frac{\text{FP}}{\text{FP}+\text{TN}}\) & \(E_{o}=\frac{\text{FN}}{\text{FN}+\text{TP}}\) & \(\tau=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{IN}+\text{FP}+\text{FN}}\) \\ \hline Non-shadow & \(P_{n}=\frac{\text{TN}}{\text{TP}+\text{TN}}\) & \(U_{n}=\frac{\text{TN}}{\text{FN}+\text{TN}}\) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation indicators for shadow detection.
## 2 Materials and Methods
### Trace Element Analysis  
Quantitative determination of Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, V, and Zn, was performed using an inductively coupled plasma tandem mass spectrometer (ICP-QQMS, 8900 Agilent, Santa Clara, CA, USA), equipped with a collision/reaction cell (CRC) and connected to an SP4 autosampler, after acid microwave digestion of the whole body (B), gaster (G), body without the gaster (B-G), and honey samples. The samples were analysed twice, and the ICP measures were replicated three times for each sample.  
All the samples were subjected to freeze-drying under vacuum conditions at a temperature of \(-60\)\({}^{\circ}\)C for 72 h to allow for the elimination of water without the use of high temperatures, which could alter subsequent analyses for some elements, such as Hg. Once the samples were freeze-dried, they were placed in a desicator until acid digestion was carried out. The samples were weighed up to a maximum of about 0.5 g, and the acid digestion was carried out by adding to the samples 10 mL of sub-boiling distilled 67-69% ultrapure NORMATOM HNO\({}_{3}\) (VWR Chemicals, Radnor, PA, USA). The mixture was warmed to 170 \({}^{\circ}\)C in a microwave digestion system (MARS 6 Microwave Digestion System, CEM Corporation, Matthews, NC, USA) for 15 min (20 min of heating to a temperature of 170 \({}^{\circ}\)C). After digestion, the mixture was cooled, and ultrapure water was added to reach a volume of 25 mL. Finally, two microliters of supra-pure HCl were added to stabilise Hg.  
A commercially produced standard solution (ICP multielement standard solution CertiPUR(r), Rochester, MI, USA; Merck Chemicals and Reagents; 1000 mg L\({}^{-1}\)) in nitric acid and an internal standard solution (Internal Standard Mix for ICP-MS systems; 100 mg L\({}^{-1}\); 6-Li, Sc, Ge, Rh, In, Tb, Lu, Bi; Agilent Technologies Italia s.p.a.) were used to prepare appropriate elemental calibration standards. An ordinary least-squares regression model (OLSR) was used for the calibrations. Linearity between the intensity and concentration (R\({}^{2}\) > 0.999) was observed in the range of 1-1000 ppt for Hg and the range of 1-50,000 ppt for the other metals. To quantify the metals in the different kind of samples, the digested solutions were diluted at 1:10 and 1:50. The instrumental detection limits, based on the calibration curve method, were estimated to be 166.14 ppt for Al, 7.09 ppt for As, 0.73 ppt for Cd, 0.42 ppt for Co, 11.36 ppt for Cr, 34.55 ppt for Cu, 325.02 ppt for Fe, 2.30 ppt for Hg, 27.04 ppt for Mn, 29.95 ppt for Ni, 8.58 ppt for Pb, 3.23 ppt for V, and 948.39 ppt for Zn.  
Analytical quality control included analysis of a digestion reagent blank with each batch of 12 samples. All laboratory glassware was soaked in 10% HNO\({}_{3}\) for 24 h and then rinsed before use with ultrapure water.  
All concentrations are expressed on a dry weight (dw) basis.
# The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms
## 4 Regarding 2-Dimensional Attainment Surfaces
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Problem** & **Intersections** & \(\boldsymbol{\mathcal{V}}\)**vs. \(\boldsymbol{\mathcal{O}}\)** & \(\boldsymbol{\mathcal{V}}\)**vs. \(\boldsymbol{\mathcal{S}}\)** & \(\boldsymbol{\mathcal{S}}\)**vs. \(\boldsymbol{\mathcal{O}}\)** \\ \hline \multirow{4}{*}{WFG1} & Rotational & \((0.0,99.9)\) & \((0.2,96.9)\) & \((28.6,65.8)\) \\  & Inward & \((0.0,100.0)\) & \((0.2,97.6)\) & \((\mathbf{38.4},\mathbf{55.6})\) \\  & Outward & \((0.0,99.9)\) & \((0.2,97.5)\) & \((26.6,68.0)\) \\  & ASSIL & \((0.0,99.9)\) & \((0.2,97.5)\) & \((27.2,67.2)\) \\ \hline \multirow{4}{*}{WFG2} & Rotational & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,65.5)\) \\  & Inward & \((0.0,100.0)\) & \((0.0,100.0)\) & \((0.0,86.6)\) \\  & Outward & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,63.9)\) \\  & ASSIL & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,73.1)\) \\ \hline \multirow{4}{*}{WFG3} & Rotational & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,88.8)\) \\  & Inward & \((0.0,100.0)\) & \((0.0,100.0)\) & \((0.0,87.3)\) \\ \cline{1-1}  & Outward & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,89.2)\) \\ \cline{1-1}  & ASSIL & \((0.0,99.9)\) & \((0.0,99.9)\) & \((0.0,88.8)\) \\ \hline \multirow{4}{*}{WFG4} & Rotational & \((0.0,99.9)\) & \((0.0,99.9)\) & \((99.9,0.0)\) \\ \cline{1-1}  & Inward & \((0.0,100.0)\) & \((0.0,100.0)\) & \((100.0,0.0)\) \\ \cline{1-1}  & Outward & \((0.0,99.9)\) & \((0.0,99.9)\) & \((99.9,0.0)\) \\ \cline{1-1}  & ASSIL & \((0.0,99.9)\) & \((0.0,99.9)\) & \((99.9,0.0)\) \\ \hline \multirow{4}{*}{WFG5} & Rotational & \((16.9,20.0)\) & \((0.1,62.7)\) & \((52.4,2.0)\) \\ \cline{1-1}  & Inward & \((10.1,21.0)\) & \((0.2,59.8)\) & \((39.9,0.5)\) \\ \cline{1-1}  & Outward & \((18.6,23.4)\) & \((0.2,64.4)\) & \((55.0,2.9)\) \\ \cline{1-1}  & ASSIL & \((16.2,19.1)\) & \((0.2,61.1)\) & \((50.3,1.7)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Intersection line comparison between VEPSO (\(\mathcal{V}\)), SMPSO (\(\mathcal{S}\)), and OMOPSO (\(\mathcal{O}\)); blue indicates performance of 5% worse than the competing algorithm, and red indicates performance of 5% better than the competing algorithm. Variations in the results between the different intersection line generation approaches can be seen. ZDT1, ZDT3, ZDT4, and ZDT6 all show variations in the results greater than 5% for each of the comparisons. WFG1, WFG2, WFG5, WFG6, and WFG9 all show variations in the results greater than 5% for at least one of the comparisons. The variations are indicative of the bias towards certain attainment surface shapes shown by the various intersection line generation approaches.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## System Model
### Path Loss Model  
Due to the naturally lossy intra-body environment, the data transmission from the in-body region to the on-body area suffers considerable power attenuation [1]. Moreover, the positions of the on-body relay nodes and the coordinator affect the communication link quality and transmission distance. The path loss (PL) model can be formulated as a function of the distance \(d\) between an implanted sensor to the on-body device [10]:  
\[PL_{dB}(d)=PL_{dB}\Big{(}d_{ref}\Big{)}+10n\log_{10}\Bigg{(}\frac{d}{d_{ref}} \Bigg{)}+s_{dB},d\geq d_{ref} \tag{1}\]  
where \(d_{ref}\) is the reference distance; \(PL_{dB}\Big{(}d_{ref}\Big{)}\) denotes the corresponding PL value; and \(n\) and \(s_{dB}\) represent the PL exponent and the shadow fading parameter, respectively.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Materials
Simulink has been chosen to test the developed algorithms and furthermore, the use of Stateflow has allowed to create blocks that evolve with an event driven logic and therefore are disconnected from the sampling time of the system scheme. The KF developed for the GNSS/PDR integration considers a state in which the estimated position refers to a North-East local reference frame and represents the distance in meters from the starting point, i.e., the origin of the North-East plane itself. On the other hand, the GNSS readings are originally expressed in latitude and longitude and do not directly provide information consistent with those adopted in the definition of the state variables. A routine, that given two consecutive GNSS readings provides their distance in meters and the angle with respect to the North, is needed to determine the direction of the motion occurred. This conversion is carried out by applying the Haversine formula [9] and it has been implemented in Simulink following the diagram in Figure 3, where the GNSS_detectedsignal indicates the presence or absence of satellite data. An Android software application for smartphone has been created to emulate the Simulink schemes through the Simulink Support for Android devices. The developed GNSS/PDR navigation system works with information coming from the low-cost MEMS sensors mounted on smartphone and its implementation as a state machine allows to execute the calculations necessary for the correct operation between one step and another, creating a control logic with a not excessive computational cost able to be managed by the processor of mobile devices. Figure 3: Simulink diagram of the GNSS/PDR integration. Figure 2: Simulink diagram of the proposed GNSS/PDR system.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Model Training  
The models--Logistic Regression, Random Forest, KNN, and a CNN--were trained and evaluated on the same post-SMOTE dataset using key metrics, with the results summarized in Table 2 for reproducibility:  
* **Logistic Regression**: The model is configured with random_state = 7 to ensure consistent results, max_iter = 5000 to allow sufficient iterations for convergence, solver = 'bfbfgs' for efficient optimization using limited-memory BFGS, and class_weight = 'balanced' to automatically adjust for class imbalances in the dataset.
* **CNN**: The architecture consists of dense layers with 120, 80, 40, and 20 neurons, followed by a single-neuron output layer. It utilizes a batch size of 10 and the Adam optimizer for adaptive learning rate adjustments and is trained for 100 epochs to ensure convergence and minimize loss.
* **Random Forest**: The model uses n_estimators = 100 decision trees, with random_state = 42 sets to ensure the reproducibility of results across different runs by maintaining the same random state in the training process.
* **KNN**: The model is initialized with default hyperparameters, and random_state = 42 ensures consistent results in the training and evaluation process by controlling the randomization.  
While further tuning could enhance accuracy, the focus remains on interpretability through XAI techniques--essential for informed security decisions, detecting vulnerabilities, and ensuring robust protection in future 6G networks.
###### Abstract  
The precise orbit determination (POD) accuracy of the Chinese BeiDou Navigation Satellite System (BDS) is still not comparable to that of the Global Positioning System because of the unfavorable geometry of the BDS and the uneven distribution of BDS ground monitoring stations. Fortunately, low Earth orbit (LEO) satellites, serving as fast moving stations, can efficiently improve BDS geometry. Nearly all studies on Global Navigation Satellite System POD enhancement using large LEO constellations are based on simulations and their results are usually overly optimistic. The receivers mounted on a spacecraft or an LEO satellite are usually different from geodetic receivers and the observation conditions in space are more challenging than those on the ground. The noise level of spaceborne observations needs to be carefully calibrated. Moreover, spaceborne observational errors caused by space weather events, i.e., solar geomagnetic storms, are usually ignored. Accordingly, in this study, the actual spaceborne observation noises are first analyzed and then used in subsequent observation simulations. Then, the observation residuals from the actual-processed LEO POD during a solar storm on 8 September 2017 are extracted and added to the simulated spaceborne observations. The effect of the observational errors on the BDS POD augmented with different LEO constellation configurations is analyzed. The results indicate that the noise levels from the Swarm-A, GRACE-A, and Sentinel-3A satellites are different and that the carrier-phase measurement noise ranges from 2 mm to 6 mm. Such different noise levels for LEO spaceborne observations cause considerable differences in the BDS POD solutions. Experiments calculating the augmented BDS POD for different LEO constellations considering spaceborne observational errors extracted from the solar storm indicate that these errors have a significant influence on the accuracy of the BDS POD. The 3D root mean squares of the BDS GEO, IGSO, and MEO satellite orbits are 1.30 m, 1.16 m, and 1.02 m, respectively, with a Walker 2/1/0 LEO constellation, and increase to 1.57 m, 1.72 m, and 1.32 m, respectively, with a Walker 12/3/1 constellation. When the number of LEO satellites increases to 60, the precision of the BDS POD improves significantly to 0.89 m, 0.77 m, and 0.69 m for the GEO, IGSO, and MEO satellites, respectively. While 12 satellites are sufficient to enhance the BDS POD to the sub-decimeter level, up to 60 satellites can effectively reduce the influence of large spaceborne observational errors, i.e., from solar storms.  
LEO constellation; BDS; precise orbit determination; measurement noise; spaceborne observational errors 2021133189. [[https://doi.org/10.3390/rs13163189](https://doi.org/10.3390/rs13163189)]([https://doi.org/10.3390/rs13163189](https://doi.org/10.3390/rs13163189))
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Full Communicating Sensor-Energy Consumption Results
#### 6.3.1 Consumed Energy: Scenario 1  
Figure 15 presents the power consumption amount by each task of the communicating sensor. As indicated before, the main energy consumers are the microcontroller unit (\(E_{MCU}\) = 0.061 mJ), the sensor unit (\(E_{m}\) = 0.26 mJ) and the RF unit (\(E_{T\tau}\) = 0.59 mJ). It is noted that the transceiver part is the main energy consumer in the sensor node. In this case, the LoRa frame must be retransmitted by the sensor node. As indicated in [34], if the downlink message is lost for any reason, the LoRa specifications recommends to transmit packet up to eight times.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Time Duration (ms)** & **Consumed Power (mW)** \\ \hline Sensor (_BMA220_) & 25 & 10.5 \\ Data transmission (_SX1272_) & 6.5 & 92.4 \\ MCU _STM32L073_ (4 MHz) & 33.5 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of sensor node tasks.  
Figure 14: Proposed LoRaWAN (Class A) scenarios.  
Figure 16 depicts the battery life as a function of measurement period, with a capacity of 950 mAh and a supply voltage of 3.3 V, the sensor node autonomy is estimated at 5 years, 1 month and 24 days (61.8 months) when the measurement period is equal to 30 s.