esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Static Traffic Assignment (STA)
#### 3.1.1 User Equilibrium (\(Ue\)): Definition and Formulation of the Problem
Note that, from (7), it is possible to abstract away the routes simply by adding flow conservation constraints. In other words, constraints (3), (5), and (6) can be replaced by the following constraints for each vertex \(s\):  
\[ \begin{split}\begin{cases}\sum_{a\in A_{s}^{+}}r_{a}=\sum_{a^{ \prime}\in A_{s}}r_{a^{\prime}}\\ \sum_{a\in A_{s}^{+}}r_{a}=\sum_{d\in D}q^{od}\\ \sum_{a\in A_{s}^{-}}r_{a}=\sum_{o\in O}q^{od}\end{cases},\\ \end{cases}\end{split}  \tag{8}\]  
With \(A_{s}^{+}\) and \(A_{s}^{-}\) denoting, respectively, the sets of outgoing and incoming arcs of vertex \(s\), and with constraint (8), it is evident that the solution obtained from the problem no longer provides the users' routes. It substitutes them with the number of users per road segment. Thus, for a static traffic assignment problem, the \(UE\) problem aims to find the optimal flow rates on road segments. The solution allows for multiple possible route configurations [18]. To show the approach, let us consider the illustrative example given in Section 2.4 Figure 2. The \(UE\) problem given in Equations (3) and (4) consists of finding \(r_{1}\) and \(r_{2}\) such that \(q_{1}+q_{2}=q^{od}\) and \(t_{1}=t_{2}\). The alternative problem formulation is  
\[ \begin{split}\min&\int_{0}^{r_{1}}\bigg{(}10\bigg{(}1+2 \Big{(}\frac{z_{1}}{1800}\Big{)}^{2}\bigg{)}\bigg{)}dz+\int_{0}^{r_{2}}\bigg{(}2 0\bigg{(}1+2\Big{(}\frac{z_{2}}{3600}\Big{)}^{2}\bigg{)}\bigg{)}dz\\ \text{s.t.}& r_{1}+r_{2}=3000\text{ vph}\\ & r_{1}\geq 0,r_{2}\geq 0\end{split}  \tag{9}\]  
With only these two roads (\(r_{2}=3000-r_{1}\)), it is possible to obtain the analytical solution to problem (9) by searching where the derivative of the function equals zero:  
\[ \begin{split} 10\bigg{(}1+2\Big{(}\frac{r_{1}}{1800}\Big{)}^{2} \bigg{)}-20\Bigg{(}1+2\bigg{(}\frac{3000-r_{1}}{3600}\bigg{)}^{2}\bigg{)}& =0\\ r_{1}&=600\Big{(}\sqrt{59}-5\Big{)}\\ r_{1}&=1609\text{ vph}\end{split}  \tag{10}\]  
We draw the reader's attention to the first line of Equation (10). This expression is obtained by taking the derivative of the function to be minimized. It is worth mentioning that setting the derivative function to zero is equivalent to expressing \(c_{1}-c_{2}=0\). If there are only two available roads, solving the problem analytically is straightforward. However, numerical approaches, such as gradient descent-based methods, are needed to find a solution when dealing with multiple road segments.
## 4 Results
### Comprehensive Simulation Analysis  
#### 4.2.1 Comprehensive Simulation Scenario Setting  
In the natural system, there is no single policy change, and according to Figures 6-8, it is found that the change of a single factor has a limited effect on the improvement of indicators such as the supply of PB and the reduction of carbon emissions. Therefore, it is necessary to consider the impact of multiple policies on the PBIP system to simulate the objective facts. The research results show that continuously increasing policy incentives cannot bring the most valuable promotional effect. To maximize the effect of policy promotion, it is necessary to set reasonable policy combination parameters for comprehensive simulation. According to the above statistical analysis, it was found that LP, PRRP, FSP, and CPMP had a relatively small impact on the system when increased by 10% and 20% and only had a significant effect when they were increased to 30%. Therefore, the comprehensive simulation value of LP, PRRP, FSP, and CPMP was set to 30%. The promotional effect is stronger when PTP is increased by 10%, but it becomes limited when PTP is further increased. Therefore, PTP is increased by 10%. The promotional effect of FP on the entire system is most significant, and when FP increases to 20% or more, it has a substantial impact on the system. The simulation scheme for FP is set to increase by 20% and 30%. The improvement of R&DSP will have a sustained positive impact on the entire system, resulting in increases of 10%, 20%, and 30% in R&DSP. This study sets six scenario control schemes for comprehensive simulation, as shown in Table 4.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Scenario** & **LP** & **PRRP** & **FP** & **FSP** & **PTP** & **R\&DSP** & **CPMP** \\ \hline Scenario 1 & \(\uparrow\)30\% & \(\uparrow\)30\% & \(\uparrow\)20\% & \(\uparrow\)30\% & \(\uparrow\)10\% & \(\uparrow\)10\% & \(\uparrow\)30\% \\ Scenario 2 & \(\uparrow\
## 2 Methods
### Path Optimization
#### 2.4.2 Path Cost Calculation  
After completing the collision detection, the cost of all collision-free trajectories is calculated. Since the cost of each path point has already been calculated during the expansion of the path points, the total path cost is the sum of the costs of all path points on that path, as shown in Equation (15):  
\[C_{path}=\sum_{i=0}^{n}c(x_{i})^{*} \tag{15}\]  
\(c(x_{i})^{*}\) represents the cost of the \(i\)-th path point in the improved FMT* algorithm. This cost is determined by the distance from the path point to the root node and the distance from the path point to the singularity region. Using this equation, the path with the minimum cost can be obtained.
## 2 Results and Discussion
### Effect of PAW Treatment on the Main VOCs in Rocket Leaves
#### 2.1.1 Glucosinolate Hydrolysis Products (GHPs)
It was previously reported that different factors could affect the yield and abundance of GHPs from rocket leaves. These may include pH, the solvent used for extraction, the method for leaf homogenization, liquid or headspace extraction, and sample state (e.g., fresh or dried). As GHPs' profiles change rapidly, it is difficult to compare absolute quantities between various studies [2]. One study reported that the amount of erucin and erucin nitrile produced during this hydrolysis is pH dependant [22]. [PERSON] et al. [23] also reported a low amount of nitrile compounds detected in rocket leaves after processing, which was suggested to be a cause of the acidity of hydrolysis conditions. Another study on the hydrolysis of the GSLs to their nitriles using beneficial bacteria confirmed that aerobic and anaerobic conditions favored the production of erucin nitrile, but for Enterobacteriaceae in aerobic conditions, only trace amounts of erucin nitrile were produced [18]. This may explain the reciprocal transformation among erucin and erucin nitrile during glucoerucin hydrolysis in PAW treated samples. However, this effect did not seem to be time-dependent. Despite the low content in terms of the overall volatile profile, the compound 1-butene 4-isothiocyanate was reported to have descriptions of typical pungency in rocket at high intensities [17]. The compound also showed a significant increase in both PAW-5 and -20 samples. Generally, along with their contribution to the rocket aroma, ITCs are suggested to have cytotoxic activity against the most common cancer types [21]. Our data infer that the retention of ITCs in PAW-2 and PAW-10 samples, or their significant increase in PAW-5 and PAW-20, will have important implications for health benefits to the consumer. Cold  
Figure 2: Enzymatic hydrolysis of glucoerucin by myrosinase enzyme intro erucin and erucin nitrile. plasma treatment has also previously been reported to increase total ITC content in green mustard seeds [24]. Since ITCs can survive during PAW processing, this may suggest that PAW processing can enhance this property of rocket leaves.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming  
### FreeRTOS Memory Management Strategy Optimization  
Given the specialized functionality of this design--with infrequent task thread creation/deletion and constrained microcontroller memory--the system implements FreeRTOS's heap_1 allocation scheme [21; 22; 23; 24]. Compared to commercial measurement instruments, the heap_1 strategy demonstrates superior operational efficiency on the resource-limited STM32F103 microcontroller (STMicroelectronics, Paris, France): the absence of dynamic memory release prevents fragmentation risks while ensuring long-term stability [25]. Performance metrics confirm that allocation operations (requiring only pointer arithmetic and critical section protection) average \(<2\) us. Residual memory is monitored in real time via xPortGetFreeHeapSize(). Experimental results indicate \(\leq 82\%\) memory utilization under a full load, with reserved capacity accommodating extreme scenarios while balancing real-time performance and memory efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **ATG332D (BeiDou)** & **u-blox NEO-M8N (GPS)** & **Improvement** \\ \hline Cold start sensitivity & \(-148\) dBm & \(-146\) dBm & \(+2\) dB \\ Multipath rejection ratio & \(62\%\) & \(45\%\) & \(+17\%\) \\ Satellite-based enhanced channel & BDSBAS & SBAS & unique GEO correction \\ \hline \hline \end{tabular}
\end{table}
Table 2: Module performance comparison table.  
Figure 5: Hardware physical image.
## 4 Numerical Analysis and Results
### Case Study I
#### 4.1.3 Service 3  
The next service is different from the previous two. In this case, the delay tolerance is 6 hours and the data volume is 32 TB. Service 3(\(S_{3}\)) has consistent attributes but different characteristics. This service is not that low in data volume, compared to the others. These services are delay-sensitive and, therefore, cannot be delayed for more than 6 hours. In this case, the user has all three options to disseminate data. The controller will first look for all the network options, including WLAN, UMTS, and whether there are buses available to carry data within the given timeframe. The same procedure will be followed for all attributes, as defined in Tables 9-11:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attributes** & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Bandwidth** & \begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & \begin{tabular}{c} **Delivery** \\ **Probability** \\ \end{tabular} & \begin{tabular}{c} **Criteria** \\ **Weight** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & 1 & 7 & 1 & 5 & 0.42274576 \\ \hline \begin{tabular}{c} **Bandwidth** \\ **Tolerance** \\ \end{tabular} & 1/7 & 1 & 1/7 & 2 & 0.08567345 \\ \hline \
\begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & 1 & 7 & 1 & 7 & 0.45678945 \\ \hline
\begin{tabular}{c} **Delivery** \\ **probability** \\ \end{tabular} & 1/5 & 1/2 & 1/7 & 1 & 0.06435676 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Normalized score table for all the attributes with the weight factor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attributes** & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Bandwidth** & \begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & \begin{tabular}{c} **Delivery** \\ **Probability** \\ \end{tabular} & \begin{tabular}{c} **Criteria** \\ **Weight** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & 1 & 7 & 1 & 5 & 0.42274576 & 0.4163 \\ \hline \begin{tabular}{c} **Bandwidth** \\ **Tolerance** \\ \end{tabular} & 1/7 & 1 & 1/7 & 2 & 0.08567345 & 0.0782 \\ \hline \
\begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & 1 & 7 & 1 & 7 & 0.45678945 & 0.4455 \\ \hline
\begin{tabular}{c} **Delivery** \\ **probability** \\ \end{tabular} & 1/5 & 1/2 & 1/7 & 1 & 0.06435676 & 0.0599 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Normalized score table with priority vector.  
\(\lambda_{max}=4.234869383\); CI = 0.078289794; CR = 0.08698866 \(<\) 0.1.  
This pairwise matrix also passes a consistency check, which means that priority is selected correctly. We have given importance to different attributes as per different services. Next, we calculated criteria weight for all the attributes and then, added priority vector to all of the attributes as per different services. Based on these calculations, next, we will rank our network for different services.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Field Data Collection  
Three collection modes of field land cover samples have been designed at the initial phase of the Carbon Special Program and the Ecological Decade Project to collect as many field samples as possible. A total of 35,792 field samples have been collected in this area. For each field sample, the predominant land cover class, the geographical position and the field photos were recorded.  
The first mode was designed to collect the field samples located around 2 km from the main road and the distance of two neighbor field samples was less than 3 km. Based on this mode, 16,721 field samples were collected in 2011 and 2012 by the mapping group. Meanwhile, the independent validation group collected 4546 field validation samples in 2011 by the same mode, which were only used to assess the accuracy of the CLC-SW2010 product. All field samples collected by the first mode are shown by solid red dots in Figure 4.  
Figure 3: The temporal distribution (DOY, day of year) of Landsat TM images and HJ-CCD images for each tile in Southwestern China. The X-axis was the scenes (path-row) of the Landsat TM images and the HJ-CCD images.  
The second mode was used to collect the field samples in the predefined plots, including forest plots, grassland plots, shrub plots and cropland plots. More than 10 field samples were randomly distributed in each sample plot. These field investigation tasks were conducted by scientific survey teams from the Carbon Special Program, including forest survey teams, grassland survey teams, remote sensing survey teams, _etc._ A total of 6772 field samples (shown by solid green dots in Figure 4) were provided to the mapping group, 1378 of which were not provided until the accuracy assessment was completed.  
The purposes of the third mode for collecting field samples were neither to train rule sets nor to evaluate the accuracy, but rather to discover the classification errors in the preliminary classification results. In this mode, about 20 field verification points were randomly chosen by the stratified sampling method for each county-level administrative division. Supported by local governments, a total of 7553 verification points (shown by solid blue dots in Figure 4) were collected by hundreds of staff from the local environmental authorities.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Zero Signal  
In the case of white noise, all three methods (TRMAT, SVD, and MDL) explicitly indicate that the rank is equal to zero. The noise level plays no role in this particular scenario.  
In the case of red noise, it is recommended that a criterion without an additional check for stationarity of the residual be used for the stationary signal. In this case, for \(\phi=0.7\), the three methods, TRMAT_AR, SVD_AR, and MDL_AR, almost always yield a rank estimate equal to 0.
# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 3 System Overview  
A wireless sensor was used (Shimmer Research, Dublin, Ireland), each consisting of a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. Due to the efficiency of accelerometers in activity recognition, the dataset used in this research was collected using a single tri-axial accelerometer [31; 32; 33]. It is a sensing device used to measure acceleration in three orthogonal directions simultaneously. However, gyroscope and magnetometer were excluded since prior studies indicate that accelerometers provide higher overall accuracy [16]. In addition, the ferromagnetic materials that are commonly available in domestic environments can affect magnetometers. The sensor was configured to collect acceleration data with a sampling frequency of 30 Hz (range \(\pm\) 2 g), which has been shown to be sufficient for recognizing similar activities [30; 31]. In addition, a previous study demonstrated that the type and intensity of human activities can be recognized using signals with a sampling rate equal to 10 Hz [34].  
Sensors are placed on the wrist and upper arm when recognizing upper-limb activities, both of which were examined in this research. However, due to the type of motion being recognized, certain activities, such as EE, EF, and IR, lack upper-arm movements. This meant that the sensor placed on the upper arm could not detect any motion. Accordingly, the wrist was chosen as the sensor position.  
In terms of axis orientation, the Y-axis was in parallel with the wrist, pointing toward the fingers and across the X-axis. In addition, the Z-axis pointed away from the backside of the wrist, as shown in Figure 2.  
Figure 1: Physical activities used to rehabilitate spinal cord injuries (SCI) patients: (**a**) Shoulder flexion; (**b**) Shoulder abduction; (**c**) Internal rotation; (**d**) External rotation; (**e**) Extension; (**f**) Elbow flexion; (**g**) Elbow extension [28].
## 2 System Model and Assumptions  
In this paper, we investigate a downlink MU-PhC-MIMO VLC system, the specific block diagram of which is fully described in Figure 1. The transmitting end comprises a device, which has \(N\) LED arrays. The entire system caters to \(K\) users, each equipped with a single PD. We use the identifier \(\text{User}_{k}\) to represent the \(k\)-th user, where \(k=1,\cdots,K\).
## 3 Applications of Multimodal Fluorescent-Magnetic Nanoparticles
### Cancer Therapy
For example, agents based on magnetic nanoparticles covalently bound to the surface of photosensitizer Chlorine6 showed excellent spectroscopic and functional properties for NIR fluorescent imaging and photodynamic therapy, as well as in drug delivery and MRI due to the presence of the magnetic core nanoparticles [73]. [PERSON] et al. prepared highly biocompatible luminescent superparamagnetic nanocomposites that consisted of Fe\({}_{3}\)O\({}_{4}\)/SiO\({}_{2}-\)QDs. This material acted as a high-efficiency RF absorber and showed optical ability for cell labelling, i.e., characteristics that make it an interesting material for use as a hyperthermia agent in pancreatic cancer therapy, among other medical applications [17]. Another example of a fluorescent magnetic nanomaterial the showed interesting properties to be used in hyperthermia in the combination of magnetic nanoparticles and IR-emitting QDs was synthesised by [PERSON] et al. The nanocomposite allows monitoring of the distribution of the nanoparticles in vivo, which is important for MRI and optical imaging based tomography and also presents potential for magnetic hyperthermia treatments [56]. In another study, magnetic nanoemulsions loaded with citrate-coated maghemite nanoparticles and chloroaluminumphthalocyanine have been used as agents for in vitro combined magnetic hyperthermia and photodynamic therapy (at 670 nm wavelength) in the treatment of different cancer cell line models. It was found that the use of magnetic hyperthermia resulted in only 15% of the cell viability reduction, while the use of only photodynamic therapy treatment yielded a52% decrease in the cell viability. Most importantly, the total reduction of the cell viability about 70% was achieved by combining the magnetic hyperthermia and photodynamic therapy treatments [96]. Finally, it was demonstrated recently that multifunctional nanomaterials comprising of magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles and photosensitizer conjugated hyaluronic acid can be used for enhanced tumor diagnosis and therapy. These nanocomposites showed enhanced water solubility, heat and singlet oxygen generation properties. In addition, the combination of magnetic hyperthermia and photodynamic therapy treatment resulted in synergistically enhanced tumor growth inhibition efficacy. The multifunctional nanomaterial was used for in vivo detection of tumors implanted in mice using MRI and optical imaging [91].
## 5 Vulnerabilities of SDN to LDDoS Attacks
### LDDoS Attack against Infrastructure Layer
#### 5.3.2 Packet Buffer Overflow  
When the switch receives a new packet, it buffers the packet before forwarding only the header to the controller through a Packet_In message. The SDN controller determines the best path for evaluating the packets and sends them back to the switch as an output packet. The OpenFlow agent (OFA) receives the packet and adds new flow rules to the packet buffer flow and the flow table depending on the controller's response. OpenFlow switches contain a low-capacity packet buffer flow [74]. Because of this limitation, LDDoS attackers may keep sending new packets to induce the OpenFlow switch to send the Packet_In message, which will consume and fill the packet's buffer flow.
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## 3 The Proposed Approach
### Overview of the Proposed Model  
The proposed lightweight neural network model is able to accurately identify malicious SQL codes without imposing heavy computation and storage overhead. As shown in Figure 3, the proposed model can be separated into two major portions: input embedding and detection. The embedding operations can be viewed as the pre-processing step of the model. Its goal is to map the SQL statements into numerical matrices. These matrices capture semantic meaning and context, allowing the proposed model to process the SQL statements.  
Based on the matrices delivered by input embedding operations, the detection model then produces the final results. The goal of detection model is to extract both local and global features from the input numerical matrices so that detection operations can be accurately carried out. Both CNN and multi-head attention operations are employed in the model. Detailed descriptions of the neural networks for input embedding and detection operations are revealed in the following two subsections.  
Figure 2: The deployment of the proposed lightweight model in a low cost computer containing the web server for SQL injection detection. The proposed model is able to perform accurate detection with low latency even when the computer is limited in computation and storage capacity.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 3 Results
### Laboratory Validation  
The validation with controlled conditions consisted of an experiment using the same device presented in Figure 2. It contained around 50 moths and the microphone was connected to the Raspberry Pi running the detection algorithm with a running network connection. The first minutes were used to adjust and set the amplification gain to reach the threshold. When set, the ambient noise (speech, AC System, impulsive noise) did not activate an event. When the moths (one or several) flew near the microphone, the detection algorithm of the system detected it and sent the event information to the server. This happened several times in the 1 h validation session. There was no pheromone trap inside the plastic container.