esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Path Planning Model  
#### 3.3.1 Model Assumptions  
(1) The calculation of distance employs spherical distance and does not account for factors such as urban road networks and building density. (2) The analysis area is treated as a two-dimensional homogeneous space, without considering the impact of urban facilities. (3) The position of the parcel locker is fixed, meaning that it is determined before the courier delivers the parcel. (4) The trajectory data is derived from the user's walking data; by setting a speed threshold, transportation methods such as cycling and driving are excluded. The extraction of walking trajectories does not consider indoor activities, such as data loss from GPS signals within buildings like the teaching building, and determines whether the location is an activity hotspot based on the user's dwell time in signal-deprived buildings within the trajectory data.
# Supply Chain Data Analytics for Digital Twins:
## 2 Theoretical Background
### Applications  
The AI and ML methods mentioned in the previous Section have found applications in multiple fields where, in conjunction with Digital Twin, they can make a difference. These fields include, but they are not limited to, asset health, fault detection, energy and supply chain optimization.  
Assisted evaluation of asset health is crucial across various systems and applications. In the construction sector, buildings often have lifespans exceeding 50 years, and based on a Digital Twin using Building Information Modeling (BIM) with building data, analytics can detect health problems in components like pumps [15]. For oil companies, real-time monitoring and analytics is essential in asset management due to fluctuating prices. Analytics can provide critical insights into the status and condition of their static and rotating equipment [16]. Additionally, asset monitoring can extend turbine lifespans by predicting blade failures using system data, which is vital in harsh environments where defects are prevalent [17].  
Another field with similar applications is that of Real-Time Fault Detection and Diagnosis, where machine learning is evaluated. Promising research is notably focusing on Future Fault Detection and Diagnosis (FDD), better refined techniques for unbalanced datasets and temporal series data, and implementations of Explainable Artificial Intelligence (XAI) tailored to industrial fault detection for enhancing interpretability and trustworthiness. [18]  
As described in [19], Smart Grid is another field of high importance where common applications of analytics include fault classification and identification, which involves detecting, classifying, and localizing faults in microgrids, ensuring the protection of loads from voltage and frequency fluctuations. Additionally, in preventive maintenance machine learning techniques, such as SVM, LSTM, and hybrid models, are used to analyze operations data and diagnostics data to predict equipment failures.  
In supply chain optimization, multiple data sources can become available for analytics and optimization, including customer, shipping, delivery, order, sale, store, and product data [20]. These data sources offer multiple applications in scheduling, inventory management, and demand forecasting. Techniques, such as neural networks and regression analysis, are frequently used, along with time-series forecasting, clustering, and support vector machines [21]. The implementation of AI techniques in supply chain management is estimated to reduce costs by 10 to 25% across various aspects [22].
## 3 Proposed Architecture: Multi-Model IDS Based on Traffic Load
### Performance Evaluation
#### 3.8.1 Confusion Matrix  
Evaluating the performance of ML algorithms by using confusion matrices can provide significant insights into the performance and bias of different algorithms. Table 7 presents the confusion matrix.  
In order to determine the accuracy of the model or the correct rate, it is necessary to determine how accurate the model is for the given classification task.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
where the following apply:  
1. TN denotes correct prediction for the benign class.
2. TP indicates correct prediction for the malicious class.
3. FP implies incorrect prediction for the benign class. It is predicted as malicious, but it is actually benign.
4. FN represents incorrect prediction for the malicious class. It is predicted as benign, but in reality, it is malicious.  
According to the definition of precision, it is the ratio of true positives to all positives.  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
Recall can be defined as the ratio between the true positives and the sum of the false negatives plus the true positives.  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
F1-score indicates the degree of similarity between a predicted and true set based on the balance between precision and recall.  
\[F1\text{-}Score=2\times\frac{Precision\times Recall}{Precision+Recall}=\frac{2 TP}{2 TP+FP+FN} \tag{5}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Class/Prediction** & **Benign** & **Malicious** \\ \hline Benign & TN & FP \\ Malicious & FN & TP \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for binary classification.
## 4 Results and Discussion
### Detailed Analysis of Model Performance
#### 4.7.3 Four Sensors with Non-Overlapping Data  
Increasing the sensor count to four, TCN achieves its highest test accuracy of **0.9770**. LSTM follows with a test accuracy of **0.9752**, and GRU achieves **0.9738**. Traditional models like RF and XGBoost show improved performance due to the additional sensor data, with test accuracies of **0.9753** and **0.9735**, respectively. KNN and DT lag behind, indicating limitations in scaling with more sensors.
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## 4 Experimental Results
### Experimental Setup  
The databases considered in the experiments are the MySQL, PostgreSQL, Oracle, and Microsoft SQL servers. The training set \(\mathcal{T}\) and two test sets \(\mathcal{S}\) and \(\mathcal{U}\) of the experiments can be found in the Kaggle platform [30]. There are 98,275 SQL queries (i.e., \(L=98,275\)) in the training set \(\mathcal{T}\). Among the queries for training, there are 55,915 malicious queries and 42,360 legal queries. The test set \(\mathcal{S}\) contains 24,707 queries. The number of malicious and legal queries in the test set are 11,573 and 13,134, respectively. The test set \(\mathcal{U}\) consists of queries with length exceeding 1000 characters. There are 500 queries in the test set \(\mathcal{U}\), where 100 queries are legal, and the others are malicious. The queries in the test set \(\mathcal{S}\) and \(\mathcal{U}\) are different from the ones in the training set \(\mathcal{T}\).  
Note that the length of queries in sets \(\mathcal{T}\) and \(\mathcal{S}\) are below 1000 characters. However, for the NLP techniques based on self-attentions, it may be necessary to find relationship among tokens far apart in an input query. Therefore, the inclusion of the test set \(\mathcal{U}\) containing lengthy queries is beneficial for the observation of the effectiveness of self attention operations. In this way, the robustness of neural network models to the attacks with lengthy queries can be evaluated. A summary of the datasets considered in the experiments can be found in Table 1.  
For the proposed model, the vocabulary size (i.e., the size of the set \(\mathcal{V}\) of vocabularies) for the SQL-specific tokenizer is 158. There are three semantic labels for the vocabularies: command, expression, and symbol. The number of vocabularies having semantic labels as command, expression, and symbol are 139, 14, and 5, respectively. The maximum number of tokens from an input query was set to be \(N=512\). The dimensions of numerical vectors produced by mappings \(f_{1}\) and \(f_{2}\) are \(M_{1}=10\) and \(M_{2}=1\), respectively. Therefore, \(M=M_{1}+M_{2}=11\). The dimension of the encoded representation matrix \(\mathbf{U}\) in (2) is then \(N\times M=512\times 11\). The number of heads for the self attention operations is \(h=4\). The threshold value \(\eta\) was set to be \(\eta=0.5\) for determining whether the query is malicious. All the training operations were carried out by an NVIDIA GPU Geforce RTX 3080 Ti with a memory size of 64 GB. Furthermore, the software platform used for the training operations is TensorFlow 2.15.
# Spectrophotometric Determination of Biopolymers in Alabama Benchmark Soils
## 2 Materials and Methods
### Soil Analysis
#### 2.2.2 Determination of Soil Chemical Properties  
Soil organic matter (SOM) was measured gravimetrically by the loss-on-ignition (LOI) method [30]. Total nitrogen (TN) and soil organic carbon (SOC) were measured by dry combustion [31] using the CN-928 Series Macro Determinator (LECO Corporation; Saint Joseph, MI, USA). The ratio between SOC and TN was used to compute the C:N ratio. The concentration of soil-available phosphorus was measured spectrophotometrically after the extraction of soils with 0.03 M NH\({}_{4}\)F and 0.025 M HCl in a ratio of 1:7 [32]. Active carbon was determined by oxidizing with KMnO\({}_{4}\)[33]. Air-dried soil was incubated with 0.02 M KMnO\({}_{4}\) and allowed to stand, and the absorbance of the diluted supernatant was measured at 550 nm using a GENESYS(tm) 150 UV-Visible spectrophotometer (Thermo Scientific(tm) Madison, WI, USA), against standards (0.005-0.02 M).  
Soil pH and electrical conductivity (EC) were measured using the HI5522 two-channel meter (Hanna instruments, Woonsocket, RI, USA). Soil pH was determined potentiometrically in a 1:2.5 soil:water suspension (\(w/v\)) and soil:0.01 M CaCl\({}_{2}\) (\(w/v\)). The EC was measured in a 1:5 soil:water suspension (\(w/v\)).  
The soil cation exchange capacity (CEC) and effective cation exchange capacity (ECEC) were determined after the exchange with ammonium acetate (NH\({}_{4}\)OAc) and measuring Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), Na\({}^{+}\), H\({}^{+}\), Al\({}^{3+}\), and Mn\({}^{2+}\) in the extracts using inductively coupled plasma optical emission spectrophotometry (ICP-OES). Soil potentially mineralizable nitrogen (PMN) was determined by extracting the soil with 0.2 M KCl and quantified using the cadmium reduction method [34]. Exchangeable acidity was determined by extracting the soil with a neutral salt solution, (potassium chloride and potassium fluoride) and the exchanged hydrogen ions and aluminum ions then titrated with a standard solution of sodium hydroxide [35].
## 7 Conclusions  
Based on IR-UWB through-wall radar, this paper proposed a method for human activity recognition using mobile edge devices and feature fusion. The method first uses an exponential weighting method to suppress static clutter and the clutter caused by wall multipath effects, generating time-distance feature maps. Then, the Hilbert transform and short-time Fourier transform are applied to the time-distance feature maps to generate time-Doppler feature maps. The signal preprocessing method we use is simple and efficient, laying a solid foundation for deploying the algorithm on mobile edge devices. Secondly, we use Ghost convolution to construct a TG2 feature extraction module suitable for radar spectrograms. Experiments verified that the TG2 Net, formed by adding a head layer to this module, had high recognition accuracy for both types of radar spectrogram features. Moreover, compared with existing lightweight networks, TG2 Net has extremely low parameters and FLOPs. Finally, the TG2 module is used to extract the two types of spectrogram features separately, and the AFF is improved to CAF using CA, extending the nonlinear fusion to a spatial distribution. Experiments showed that TG2-CAFNet was superior to the current mainstream models in terms of its lightweight nature and recognition accuracy when performing through-wall radar human activity recognition. Despite the satisfactory performance of TG2-CAFNet, there are still some limitations in both the method and the dataset used in this study. One of the main limitations lies in the reliance on the spatial features of radar spectrograms for action recognition. This means that to recognize an action, the action must be completed before generating the spectrogram, which limits the method's real-time capabilities. Furthermore, the dataset used in this study mainly contains relatively isolated and standardized actions, whereas actions in real life are continuously evolving. TG2-CAFNet only handles isolated action segments, neglecting the temporal dependencies of these actions, which limits its ability to dynamically capture continuous movements. Additionally, when multiple individuals perform actions simultaneously, their spectrograms may overlap, leading to interference and reduced recognition performance. To overcome these limitations, future research could focus on the following aspects:  
* Improving real-time recognition: developing algorithms that can recognize actions incrementally as they occur, rather than waiting for the entire action to be completed, to enhance the method's applicability in real-time scenarios.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Evaluation Criteria--Path Quality Metrics
#### 4.4.2 Path Smoothness  
Path quality is also assessed based on its smoothness, which has a direct impact on both energy and time efficiency [45]. A smoother trajectory leads to reduced energy consumption and shorter mission durations while also being advantageous for the vehicle's mechanical integrity [46]. Although smoothness is less critical for UAVs with high maneuverability, such as multicopters, it plays a crucial role for fixed-wing aircraft with more limited maneuvering capabilities. This consideration is particularly important for HALE UAVs given their limited energy reserves. [PERSON] et al. define the smoothness of a discrete path using the following equation:  
\[\text{SMOO}=\sum_{n=1}^{N_{w}-1}\text{min}\big{(}\alpha(Sg_{n},Sg_{n+1}),\beta (Sg_{n},Sg_{n+1})\big{)}. \tag{26}\]  
In this equation, \(\alpha(Sg_{n},Sg_{n+1})\) and \(\beta(Sg_{n},Sg_{n+1})\) represent the angles between the \(n\)-th path segment \(Sg_{n}\) and its subsequent segment \(Sg_{n+1}\). The parameter \(N_{w}\) denotes the total number of waypoints considered. Since the study focuses on three-dimensional path planning, the smoothness metric has been adapted for 3D paths. In its 3D formulation, smoothness is ultimately expressed as follows:  
\[\begin{split}\text{SMOO}=1-\frac{1}{\pi(N-2)}\sum_{n=1}^{N_{w}- 2}\big{|}\bigtriangleup_{min}(\overline{\mathbf{w}_{n}\mathbf{w}_{n+1}}, \overline{\mathbf{w}_{n+1}\mathbf{w}_{n+2}})\big{|}\\ \mathbf{w}_{n},\mathbf{w}_{n+1},\mathbf{w}_{n+2}\in\mathbf{W}, \end{split} \tag{27}\]  
where \(\mathbf{w}_{n}\) denotes \(n\)-th waypoint of the waypoint vector \(\mathbf{W}\), \(N_{w}\) is the number of waypoints in \(\mathbf{W}\), and \(\bigtriangleup_{min}(\overline{\mathbf{w}_{n}\mathbf{w}_{n+1}},\overline{ \mathbf{w}_{n+1}\mathbf{w}_{n+2}})\) is the smallest angle between the path segments \(\overline{\mathbf{w}_{n}\mathbf{w}_{n+1}}\) and \(\overline{\mathbf{w}_{n+1}\mathbf{w}_{n+2}}\), \(\bigtriangleup_{min}(\cdot)\in\langle-\pi,\pi\rangle\). Similar to LEN, the SMOO metric can be applied to any path represented as a one-dimensional array of waypoints. However, for paths with densely spaced waypoints, down-sampling must be performed before calculating SMOO.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### Second Scenario  
In this scenario, the jammer node is deployed in the second position to analyze the self-healing and clustering protocols. Figure 10 shows how the network reacts to the jamming attack. The clusters in green and pink colors adapt to the jammer keeping the same shape. However, the blue and yellow clusters reshape to overcome a strong jammer presence. Consequently, the coverage results are 93.30% for green and pink clusters and 66.67% for blue and yellow clusters.  
The first steps of the algorithm try to reach the CH in one hop using the maximum power of transmission. Figure 10 represents the jammer effect on each cluster. The green and pink clusters have only one isolated node. On the other hand, the blue and yellow clusters have five isolated nodes in different positions. Then, the algorithm calculates the optimum transmission power to ensure all nodes reach the CH. The result of this calculation is a transmission power of 17.88 dBm.  
The information transmitted for FP and PA strategies is the same, owing to the use of the same transmission power. The amount of information transmitted reaches 687,024 kb. Similar to the previous jammer position, the transmitter nodes deploy their batteries faster than the CH owing to the difference between the reception and transmission power. However, the PA strategy notably increases the residual energy by 423.77 J.  
The cooperation strategy generates the same paths detected from the previous jammer position. The nodes that provide cooperation have less load owing to the decrease in the isolated nodes in green and pink clusters. However, relay nodes in blue and yellow clusters present the same load as the previous scenario. Consequently, the amount of information transmitted is the same for the CP strategy. Then, the PAC strategy marginally increases the amount of information transmitted to 120,720 kb. Additionally, the residual energy only  
Figure 10: Second scenario: where the network adjusts to the presence of the jammer in the new position. Therefore, not all clusters present the same structure. The number of isolated nodes is higher in the blue and yellow clusters.  
differs by 26.95 J. Finally, FCPA surpasses all the protocols in the amount of information transmitted with 924,288 kb, while the energy residual is 396.97 J.
# Possibilities of Powering Military Equipment Based on Renewable Energy Sources
## 2 Literature Review
### Application of Solar-Wind Power Plants  
Mobile solutions are an interesting and dynamically developing sort of renewable energy system. In this group, there are some promising hybrid solutions enabling the simultaneous electricity generation using both solar and wind energy, which significantly increases their reliability in various weather conditions. The mobility of such devices additionally extends the scope of their applications, dominated by power supply, including military communication systems [32] or small residential installations located in places without power grid access. Mobile solar-wind power plants are generally solar-wind electricity generators of a design and characteristics enabling them to be easily moved by available transportation means. The components of such sets are featured with a high integration degree of the functional system elements (Figure 2), which means that they consist of a small number of parts that can be easily connected by a person with little experience.  
A solar-wind electricity generation system (hybrid generator) that generates electricity consists of mechanical-electrical components with their control systems and electronic circuits connected to them. They convert the generated current to a form that allows charging of batteries using an inverter to convert direct current into alternating current with characteristics matching the devices powered from it. Each system also includes cables for connecting the components, a set of electricity accumulators, and systems for distributing the generated direct and alternating current. The key parts of each set are mechanical and electrical devices that enable the conversion of energy obtained from external factors into electricity [31]. Due to the specifics of each set, these generator components determine the basic features of the entire hybrid system (i.e., the dimensions, efficiency, characteristics of the generated current, operational stability, the possibility of mobile use etc.). Each solar-wind power generation system (Figure 3) consists of four key functional blocks:wind electricity (BEW), light electricity (BES), generated energy accumulation (BAE), and distribution of generated electricity (BDE).
## 2 Materials and Methods
### Transcriptomic Analysis  
Library preparation and transcriptome sequencing of FaHMGROE, FaHMGRR, and FaHMGRC strawberries were conducted by the Beijing Novogene Technology Corporation (Beijing, China). Differentially expressed gene (DEG) analysis was performed using the DESeq R package (1.18.0) with the following criteria: false discovery rate (FDR) \(<\) 0.05, \(\mid\) fold change\(\mid\)\(\geq\) 2, and adjusted \(p\)-value \(<\) 0.05 [31]. Principal component analysis (PCA) was performed with the gmodels R package. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed with the GOseq R 3.12 software package and KOBAS 3.0 software, respectively [32]. A weighted gene correlation network (WGCNA) was constructed based on a hierarchical clustering tree. All treatments included three biological replicates. To validate transcriptomic data, ten DEGs were selected for confirmation via qRT-PCR.
## 4 Discussion
### Consistency of GPP and VIUPD  
In this study, we evaluated the utility of VIUPD-derived VCI using the USDM, different time scale SPI, PDSI, Z-index, NDVI-derived VCI, VUA derived SMCI, NSIDC derived SMCI, VUA derived TCI and MODIS derived TCI. The results indicated that the VIUPD-derived VCI performed well for drought monitoring. However, the good performance of VIUPD-derived VCI must derive from the good performance of VIUPD. To evaluate the sensitivity of VIUPD to drought impact and to further validate the utility of VIUPD-derived VCI, we analyzed the consistency of GPP and VIUPD.  
Figure 10 showed the inter-annual and seasonal patterns of VIUPD and GPP. In this study, we only chose the months from April to October as the study period. For the 9 sites on the whole, the VIUPD showed similar trends with GPP. According to the US National Drought Mitigation Center [36], the US-ARM site experienced a severe drought in 2006. Both of GPP and VIUPD of 2006 are much lower than that of 2005. Similar information can be found in other sites, for example, the US-Aud suffered drought ever year before 2005, accordingly the GPP and VIUPD are lower than which of 2005 and 2006.  
Figure 10: Seasonal patterns of VIUPD and GPP. Subfigures show the seasonal patterns of VIUPD and GPP in the flux tower sites of US-ARM, US-Aud, US-FFe, US-FR2, US-KFS, US-FR3, US-Kon, US-R03 and US-Var. The red points stand for the GPP and the blue points stand for the VIUPD.  
Figure 10 shows that although the pattern of VIUPD may differ slightly from that of GPP, VIUPD remained consistent with the GPP during the growing season for both drought and non-drought years.
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 2 Materials and Methods
### Hypotheses  
The process of merging the images described in this paper is based on the following hypotheses:  
1. The spectral quality of the Sentinel 2 data is superior to that of the other sources, similar to how [PERSON] and [PERSON] propose [12] for the sensor carried by Landsat compared to other multispectral sensors.
2. Since orthopho and drone images come from different sensors and were taken on different days, homogenization of reflectance values is required. It is assumed that the histogram matching technique is a sufficient approximation for such homogenization.
3. The reflectance values of high-resolution images (orthophoto and drone) must be corrected to be assimilated into the reflectance values provided by Sentinel 2 images.
4. The RGI bands of high-resolution images can approximate the B4, B3, and B8 bands of Sentinel 2.
5. Reflectance correction is based on the fact that, in each of the bands studied, the average of the pixels of the images of higher spatial resolution that are located below a pixel of Sentinel 2 must coincide with the value of that pixel.
6. The correction is linear and depends exclusively on the positions of the pixels in each image.