esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Collision System for Small Civil Aircraft
## 2 Anti-Collision Systems  
We distinguish two types of systems--Sense and Avoid (SAA) and See and Avoid (SEA). The See and Avoid principle may be considered a primary method for collision avoiding, in the case of manned aircraft. Each time circumstances allow, pilots are bound to monitor and search for potential conflicts in operations, particularly in the areas where flights are not controlled by an air traffic control (ATC) point.
# Fractional Order PI-Based Control Applied to the Traction System of an Electric Vehicle (EV)
## 7 Results of the Simulation
As can be seen in Figure 16, there is better performance when the FO-PI controller is tuned for the specific task that the system is executed. For this simulation, the speed reference has an initial value of zero then at 10 s of simulation time a step of 25 km/h is applied, followed by a decrease, at 100 s of simulation, of the reference until zero at 140 s of simulation. Finally a ramp is applied to elevate the reference from zero (150 s of simulation) to 15 km/h (250 s of simulation). As shown, the maximum peak is lower in the Figure 14a that is the case when the system is under the FO-PI controller tuned for step response. However the system follows the ramp in the better way in Figure 14b that is the case when the FO-PI controller was tuned for tracking response. Those results can be seen more clearly when the ITAE of the speed error is analyzed as shown in Figure 17. Figure 18 shows the motor torque under FO-PI controllers, tuned respectively for step and ramp response, as can be seen the motor torque under the FO-PI controller tuned for step response has a higher amplitude value that the one obtained with the same controller but tuned for ramp response, that is because the proportional and integral gains are higher than the case of step response. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multicolumn{2}{c}{**Second-Order Model**} & \multicolumn{2}{c}{**Control Parameters**} \\ \cline{2-6}  & \(\omega_{n}\) & \(\xi\) & \(\lambda\) & \(K_{p}\) & \(K_{i}\) \\ \hline Step & 2.1314 & 6.0171 & 0.15 & 3 & 1.5 \\ \hline Ramp & 4.1964 & 3.3135 & 0.25 & 2 & 1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model and control parameters for step and ramp response. Figure 15: Motor torque with the system under a PI controller. Figure 16: Car speed with the system under a Fractional Order PI controller. Figure 17: ITAE of the car speed with the system under FO-PI controller.
# Quantitative Characterization of Complex Systems--An Information Theoretic Approach
## 1 Introduction
Further, the approach illustrated enables the identification of driving and reacting components/sub-systems of a complex system. A parallel can be drawn between information entropy and system complexity, where, as a system evolves or changes its state, the entropy will also change. This helps to identify entropy in a complex system as a function of the systems components, and their interactions. Supporting this relationship is the theorization that entropy is a special case applicable to system of systems based on combination of change in information and system disorder, where the increase in information leads to an increase in disorder, also known as entropy [9]. Further, information in a system can be contemplated as an enabler for sub-systems to interact among themselves [9]. System components (in this case, sub-systems of a complex system) and their interactions take information as an anchor that drives a change in a system, thus supporting the statement \"Entropy in a complex system is a function of the system components and their interactions\". To test the applicability of the parallel drawn to information entropy, in this paper a research question is formulated:  
Q1. Can information entropy help in quantitatively characterizing a complex system? To address this question, first a two-stage examination method is identified to introduce a structured thought regarding the application of information theory to different systems. Then, a simulation-based case study is introduced with tailored information entropy-based metrics developed to aid in portraying the application of information entropy in complex networks, systems with low structural rigidity (i.e., unpredictable sub-system interactions), and high information gain. The reminder of this paper is structured as follows. Section 2 expands on the agnostic two-stage examination framework used. Section 3 provides a background on the concept of entropy and its interpretations, along with the foundation of entropy from information theory justifying the approach adopted in answering the research question. Section 4 provides an overview and definition of complex networks, followed by the case study illustrating entropy-based metric development and its application to complex networks in Section 5. Finally, the discussion and conclusions on the applicability of information theory to understand and characterize complex systems are identified in Section 6.
## 2 Results and Discussion
### Optimization of QuEChERS
#### 2.3.1 Selection of Raw Sample Treatment Method  
Xinjiang jujube has a high content of sugar because the temperature in which it grows is significantly different between day and night. It is difficult to stir dried jujube like other fruit and vegetables. Thus, in this study, we removed the date stone once we obtained the fresh jujube. Different treatment methods (directly homogenized via an ultra-turnx and freeze-drying grinding under liquid nitrogen) were optimized. Though the obtained freeze-dried powder has a higher contact area, it easily clusters when exposed to air or water, which complicates homogenization through shaking by hand or apparatus. Thus, we chose the direct-homogenization method.  
Initially, QuEChERS extraction was targeted at the matrix of water-rich vegetables and fruits. However, when the samples have a water content below 80%, it is recommended to add additional water. Therefore, water was added to jujube samples, and different sample/water ratios from 1:0 to 1:4 were investigated. As seen in Figure 1, with the increase in the ratio, the extraction recoveries (ERs%) of all _Alternaria_ toxins increased. This could be explained by the high content of water helping to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & **Precursor** & **Molecular** & **Cone** & **Production** & **Collision** & **Retention** \\  & **Ion (\(m\)/z)** & **Ion** & **Voltage (V)** & **(\(m\)/z)** & **Energy (eV)** & **Time (min)** \\ \hline \hline
**Tenuazonic acid (TeA)** & 198.92 & [M + H] + & 35 & \
\begin{tabular}{c} 124.8 \\ 152.9 \\ 184.9 \\ 213.0 \\ 226.8 \\ \end{tabular} &
\begin{tabular}{c} 15 \\ 3.54 \\ 3.52 \\ 4.12 \\ 3.54 \\ \end{tabular} \\
1 & \
\begin{tabular}{c} 3.54 \\ 3.54 \\ 3.52 \\ 272.9 \\ \end{tabular} &
\begin{tabular}{c} 3.54 \\ 3.52 \\ 26 \\ 3.54 \\ \end{tabular} \\ \hline \hline \end{tabular}  
* The underlined production was the most abundant and thus used for quantification purposes.  
\end{table}
Table 1: Optimized tandem mass spectrometry (MS/MS) instrumental parameters for _Alternaria_ toxins.  
swell the matrix and weaken the interactions of analytes with matrix components [35], which led to an efficient extraction. Thus, 2.5 g jujube and 10 mL water were selected to conduct the experiment.
## 4 Experiments and Results
### Benchmarking
#### 4.2.2 Generation Benchmark  
The generation benchmark tested the ability to generate GDPR-compliant data categories for different processing activities, under different prompts, by comparing the generated responses with entries in a reference database. Benchmark scores were based on the percentage of generated data categories that matched those in the database, although that reference was not an exhaustive ground truth. As a result, the overall performance of the models was moderate, as shown in Table 5, with scores ranging from one to three points. Qwen2-7B and GPT-4 scored the highest. This highlights that Qwen2-7B is adept at generating structured lists of data categories, making it suitable for generating records of processing activities for GDPR compliance. Zephyr-7b-beta and orca_mini_v7_b, however, scored lower. A major reason for the lower scores was the tendency of the models to use broader categorizations, often grouping several specific categories under a single term, such as combining \"first name\" and \"last name\" into a broader category such as \"identificatory data\". This reflects a balance between capturing overarching themes and losing granularity, which can impact compliance when specific distinctions are legally significant in GDPR documentation.  
The benchmark evaluation highlighted individual aspects of model performance. To provide a comprehensive comparison, we then calculated utility scores that integrated the benchmark scores with the answer evaluation.
## 2 System Description and Simulation Setup
### Digital Processing  
Figure 5a shows a schematic of an NN equalizer [18]. It is a two-layer NN, performing regression, with L neurons in the hidden layer and eight neurons in the output layer (one symbol). A hyperbolic tangent function is used as an activation function in the hidden layer, while a linear function is used in the output layer. The number of inputs of the NN depends on the optical module. If the n-stage SD/SC Rx is being analyzed, there are five symbols (40 samples) in the input. Otherwise, the number of input symbols are five per PD, which means 200 samples for the two-stage SD Rx. It is important to highlight that the middle symbol of the input is the one being equalized (symbol #3). In this way, the NN can take into account the symbols' neighbors' interference. The input window is shifted by a symbol period. The n-stage SD/SC Rx (Figure 4a), together with the NN, is called in this work the hybrid n-stage SD/SC. The two-stage SD RX (Figure 4b), together with the NN, is the hybrid two-stage SD.  
The weights of NN, group delay, and phase of CD mitigation of the optical modules are jointly trained when these variables are available in the simulation. The algorithm to update them is the stochastic gradient descent with adaptive moment estimation [19]. The loss function considered is the cross-entropy between the transmitted and received bits. During an entire simulation (\(2^{18}\) symbols), all the trainable variables are kept constant and they are only updated when the next simulation starts. All the variables are updated at the same time after 600 iterations of the training process.  
Figure 5b shows an example of a decided 8-bit sequence with seven memory MLSEs. The MLSE \"m\" memory is an algorithm with \(2^{m}\) trellis states. The probability density function of the received sequence bits are first estimated, and then used to make the decision [9].
## 5 Simulation Results
In particular, the IRS element number 256 achieves the most significant SE with UE 4 and 5, respectively. However, because of the low-rank issue, performance suffers when there are more than 6 users. Generally speaking, it is anticipated that the IRS will be stationed near to the BS. Additionally, the LOS route is included in the Rician fading channel between the BS and the IRS. Because of the increased spatial correlation of channels in this system setting, IRS-aided multi-user systems struggle with a shortage of rank. Low-rank issues are also brought on by channel sparsity of the short mmWave wavelength. Figure 7 demonstrates that the lack of rank causes the performance curve to appear as the user grows. As a result, numerous IRSs are employed in systems with more users, or a robust scattering environment is required. Furthermore, Figure 7 indicates that in the proposed systems, the SE performs almost identically when there are 10 users. The perfect channel state information at the transmitter (CSIT) [27] curve is shown in a black line. A similar trend is compared to the other results. Figure 8 shows the performance of the actual transmitted and processed data throughout the network with the different SNR values. For this analysis, the three different modulation techniques--BPSK, QPSK, and 16 QAM--are considered. Also, the analytical results are compared with the modulations; it can be seen that the simulation results follow a similar trend to the analytical results. In addition, the performance is measured by changing IRS element numbers 64 and 256, respectively. It can be seen from Figure 8 that the throughput of the system is gradually increased with the IRS elements and the change in modulation orders. After an SNR of 15 dB, most of the modulation techniques reach the highest throughput values. With the IRS element number of 256 as well as BPSK modulation, the throughput is maximized. In contrast, the lowest throughput is achieved with an IRS element number of 64 and a QAM modulation of 16. Figure 6: SE versus SNR results of the proposed and other methods for the IRS-based wireless system in the metaverse [22; 25; 26].
# Influence of Medium Frequency Light/Dark Cycles on the Cultivation of _Auxenochlorella pyrenoidosa_
## 3 Results
### Intermitt Light
#### 3.2.1 Intermittent and Continuous Light with the Same Irradiance  
In the light regime as shown in Figure 2A, the total energy incident on the culture surface of intermittent light is less than the continuous light because of the ratio of light/dark (L/D). For example, at the light intensity of 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\)and the L/D = 1:1, the total energy incident on the culture surface of intermittent light is only half of the continuous light. It obviously indicates that the total energy incident on the culture surface increased with the increase in L/D.  
Based on Figure 3, the light intensity less than or equal to 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\), where the light intensity corresponds to the light-limited region. Therefore, the intermittent light leads to a decrease in the biomass productivity because the light energy support to photosynthesis by intermittent light is less than that of continuous light. The results obviously indicate that the biomass productivity increased with the increase in L/D, which agreed well with previous studies [16; 17]. As shown in Figure 4A, one-sixth reduction of the total energy support led to a 10.9% reduction of biomass productivity. Similarly, the reduction in biomass productivity was 7.8% at the light intensity of 220 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) (Figure 4B), which means that the greater the light intensity, the smaller the gap of biomass productivity between continuous light and intermittent light when the light intensity is conducted in the light-limited region.  
At the light intensity of 420 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) (Figure 4C), the intermittent light with L/D of 1:5, 1:2, 1:1, 2:1, and 5:1 corresponded to the light intensity of continuous light of about 70, 140, 210, 280, and 350 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\), respectively. Due to the intermittent light reducing the effect of photo-inhibition, photo-oxidation, and mutual shading, more cells receive enough light to perform photosynthesis. Therefore, the intermittent light achieved a larger biomass productivity than continuous light when the light intensity is in excess of that necessary for photosynthesis. In addition, with the increase in L/D, the biomass productivity increased first, and then decreased for the excessive light intensity. This means that there is an optimal value of the light/dark ratio, which led to an improvement in the biomass yield of intermittent light when compared with the continuous light.  
Figure 4: _Cont._In summary, Figure 4 obviously indicates that the effect of the L/D cycle on the biomass production of intermittent light is only slight. The biomass productivity of medium frequency intermittent light is mainly related to the light intensity and the ratio of light to dark.
## 4 Application layer
### IoT Connectivity
LTE Cat 4, with a maximum downlink rate of 150 Mbps and an uplink rate of 50 Mbps, has a better intel high data rate market by simultaneously communicating a greater volume of data, whereas the LTE Cat 1 IoT solution presents its advantages with its amazing cost performance in the medium-rate market [84]. The LTE Cat 1, along with the LTE Cat 4, also relies on the same existing 4G LTE network, which means adopting LTE Cat 1 communication technology will cost no extra deployment investment on the network operator's side. Moreover, taking advantage of the technological maturity and global coverage of the 4G network, LTE Cat 1 has a strong and reliable network foundation to empower various IoT applications and scenarios [84]. Tables 9 and 10 summarize the main features of the short- and long-range IoT technologies.
## 1 Introduction
MIROS addresses the following challenges: achieving real-time scheduling with low data memory cost; improving utilization efficiency of memory resources; managing energy resource efficiently; simplifying application programming complexity; improving application reprogramming performance; improving node reliability, and developing a new OS debugging approach. The structure of this article is as follows: In Section 2, the design and implementation of the MIROS hybrid real-time scheduler is presented. With this hybrid scheduling, the number of threads in the MIROS can be decreased. As a result, the real-time scheduling can be achieved with low data memory cost. In Section 3, three kinds of improved MIROS dynamic allocation mechanisms are proposed. These mechanisms are improved on the basis of the current WSN dynamic allocators. With these mechanisms, the allocators become more flexible or can support memory defragmentation. In Section 4, the design and implementation of the new MIROS mid-layer software Efficient Mid-layer Software for User-Friendly Application Development Environment (EMIDE) is discussed. The same as the Embedded Java Virtual Machine (EJVM) and dynamic loading mechanisms (DLM), EMIDE can simplify the application programming process and improve application reprogramming performance. However, it is designed to be both memory and energy efficient, and is thus suitable for use even on severely resource-constrained WSN nodes. In Sections 5 and 6, the works about using the multi-core hardware platform to reduce energy consumption and improve node reliability are investigated, respectively. In Sections 7 and 8, the MIROS inter-process communication (IPC), the new OS debugging method and the ongoing studies are presented.
# Curve of Spee and Second Mandibular Premolar Agenesis--Present Knowledge and Future Perspectives
###### Abstract  
Background: We investigated the relationship between the mandibular Curve of Spee (COS) and a persisting primary second mandibular molar (ppM2) due to an agenesis of the second mandibular premolar, using a digital software technique. Methods: Digital dental casts were obtained from 200 patients at the Department of Orthodontics in Innsbruck and Vienna, Austria. Patients (age-, gender-, and malocclusion-matched) were equally divided into two groups (n = 100) according to the existence of a ppM2. COS depth, overjet, overbite, and angle-classification were measured digitally using the OnyxCeph3 TM (version 3.2.147) software. ANOVA and Kruskal-Wallis tests were used to analyze relationships. For statistical analyses, \(p<0.05\) was considered as statistically significant, \(p<0.01\) as highly significant. Results were visualized with box plots and bar charts. Results: The deepest COS was present in patients with a ppM2. Furthermore, a positive correlation was shown between COS depth and angle-class II, between COS depth and age, as well as between COS depth and overbite. No gender differences could be observed. Conclusions: In our study population the COS depth was dependent on whether there is a ppM2 due to an agenesis of a second mandibular premolar or not, as well as on the malocclusion in sagittal direction.  
curve of Spee; tooth agenesis; persisting primary tooth; orthodontics; overbite; overjet; age; gender
# A Universal Malicious Documents Static Detection Framework Based on Feature Generalization
## 6 Experiments and Results
### Experimental Results
#### 6.3.2 Comparison with Antivirus Software
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Software** & **ACC (\%)** & **TPR (\%)** & **FPR (\%)** & **F1-Score** \\ \hline \multirow{4}{*}{PDF} & Kaspersky & 97.47 & 96.59 & 0.0102 & 0.9827 \\  & McAfee & 89.92 & 86.43 & 0.0407 & 0.9271 \\  & NOD32 & 95.22 & 93.57 & 0.0204 & 0.9667 \\  & Ours & 99.37 & 99.40 & 0.78 & 0.9956 \\ \hline \multirow{4}{*}{DOC} & Kaspersky & 46.17 & 26.74 & 0 & 0.4219 \\  & McAfee & 84.46 & 78.86 & 0.0086 & 0.8818 \\  & NOD32 & 55.54 & 39.51 & 0.0344 & 0.5663 \\  & Ours & 97.40 & 97.23 & 0.82 & 0.9845 \\ \hline \multirow{4}{*}{DOCX} & Kaspersky & 94.33 & 80.45 & 0 & 0.8917 \\  & McAfee & 96.30 & 87.25 & 0 & 0.9319 \\  & NOD32 & 96.44 & 87.85 & 0.0540 & 0.9346 \\  & Ours & 99.40 & 97.36 & 0.27 & 0.9834 \\ \hline \multirow{4}{*}{XLS} & Kaspersky & 42.03 & 6.06 & 0 & 0.1143 \\  & McAfee & 86.63 & 78.33 & 0 & 0.8785 \\  & NOD32 & 48.81 & 17.05 & 0 & 0.2913 \\  & Ours & 94.66 & 93.64 & 1.17 & 0.9635 \\ \hline \multirow{4}{*}{GIF} & Kaspersky & 53.74 & 37.93 & 0.0349 & 0.5500 \\  & McAfee & 27.13 & 2.23 & 0.0233 & 0.0436 \\  & NOD32 & 26.34 & 1.15 & 0.0058 & 0.0228 \\  & Ours & 99.88 & 99.89 & 0.17 & 0.9992 \\ \hline \multirow{4}{*}{JPEG} & Kaspersky & 76.50 & 49.10 & 0.0107 & 0.6586 \\  & McAfee & 78.40 & 53.33 & 0.1017 & 0.6951 \\  & NOD32 & 58.99 & 11.15 & 0 & 0.2007 \\  & Ours & 96.76 & 94.54 & 1.34 & 0.9638 \\ \hline \multirow{4}{*}{PNG} & Kaspersky & 81.60 & 63.32 & 0 & 0.7754 \\  & McAfee & 50.48 & 1.27 & 0 & 0.0250 \\  & NOD32 & 49.86 & 0.02 & 0 & 0.0005 \\  & Ours & 98.50 & 97.64 & 0.615 & 0.9850 \\ \hline \multirow{4}{*}{RTF} & Kaspersky & 99.28 & 98.64 & 0.0894 & 0.9927 \\  & McAfee & 94.83 & 89.63 & 0.0298 & 0.9452 \\ \cline{1-1}  & NOD32 & 96.30 & 92.59 & 0.0298 & 0.9614 \\ \cline{1-1}  & Ours & 98.68 & 97.59 & 0.28 & 0.9864 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The experimental results of the detection of different types of malicious documents by different antivirus software. As shown in Figure 4, in most cases, the F1-score of the proposed static detection framework for malicious documents was higher than those of the compared antivirus software. This proves that the proposed framework achieved good performance in the detection of various types of documents.
## 2 Analysis of the Impact of Cold Wave Events on Wind Power Output
### Cold Wave Event Criteria  
The impact of cold wave events on wind power generation was preliminarily analyzed in the preceding section. While cold wave events are typically associated with a significant temperature decline, wind speed variations exhibit complex patterns, making wind power forecasting based solely on wind speed inherently challenging. During cold wave events, a decrease in wind speed correlates with reduced wind power output, and such predictable variations have a limited impact on forecasting accuracy. However, anomalous scenarios in which wind power output decreases despite increasing wind speeds--likely due to mechanical failures induced by icing--significantly degrade forecasting accuracy. To enable precise assessment of cold event impacts on wind farms, it is imperative to establish multidimensional cold wave event recognition criteria that quantify operational impacts on wind power output.