esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Flight Parameter Preprocessing
### Interpolated Data for Different Sampling Rate  
The sampling rate refers to the frequency at which the flight reference recorder collects flight data per second. Different flight parameters have different sampling rates. For instance, parameters such as fuel load and engine RPM change slowly and do not require a high sampling rate. Moreover, increasing the sampling rate would exponentially increase the data volume. However, for subsequent training of deep learning models using sequential models, complete data are required. This poses a challenge because it leads to missing flight parameter data within the same time interval, and blindly increasing the sampling rate cannot solve this issue. To ensure data continuity, interpolation methods are typically used to fill in missing values for flight parameters with different sampling rates [24]. The filled data is presented in Table 3. The data originates from a flight of a small fixed-wing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flight Parameter Name** & **Unit** & **Flight Parameter Name** & **Unit** \\ \hline Deflection Angle of Left Horizontal Stabilizer & rad & Pitch Angle & rad \\ Deflection Angle of Right Horizontal Stabilizer & rad & Roll Angle & rad \\ Deflection Angle of Left Aileron & rad & Yaw Angle & rad \\ Deflection Angle of Right Aileron & rad & Axial Angular Acceleration & rad/s\({}^{2}\) \\ Rudder Deflection Angle & rad & Normal Angular Acceleration & rad/s\({}^{2}\) \\ Left Leading Edge Slats Deflection Angle & rad & Lateral Angular Acceleration & rad/s\({}^{2}\) \\ Right Leading-Edge Slats Deflection Angle & rad & Normal Load Factor & g \\ Pitch Angular Velocity & rad/s & Axial Load Factor & g \\ Roll Angular Velocity & rad/s & Lateral Load Factor & g \\ Yaw Angular Velocity & rad/s & Mach Number & Ma \\ Dynamic Pressure & MPa & Atmospheric Airspeed & m/s \\ Total Aircraft Fuel Quantity & kg & Sideslip Angle & rad \\ Elevation & m & Attack Angle & rad \\ Low-Pressure Turbine Speed of Right Engine & m/s & Low-Pressure Turbine Speed of Left Engine & m/s \\ High-Pressure Turbine Speed of Left Engine & m/s & High-Pressure Turbine Speed of Right Engine & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight parameters selected in the paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ground Speed \(v_{g}\)(m/s)** & **Flight Stage** \\ \hline \(V_{g}=0\ v_{g}=0\) & Ground run \\ \(v_{g}\leq 80\) & Taxi, take-off, approach landing, descent \\ \(v_{g}>80\) & Climbing, heading out, aerobatics, cruising. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correspondence between ground speed and flight phase.  
UAV used for testing flight data, sourced from the Aircraft XX Institute of China. These data were provided to us after undergoing certain de-identification procedures.
## 3 Calculation Results and Analysis
### Concentrated Light Intensity and Spectral Analysis along the Optical Axis
When the cell is far away from the focal plane (at Z = 114 mm), the non-uniformity begins to improve and the par falls back to 9.1. The maximum PAR of the short, medium and long bands and their positions on the optical axis are shown in Table 2. The maximum PAR of the short, medium and long bands is at Z = 110.5 mm, 111.4 mm and 112.3 mm. The maximum PAR of the full-wave band is at Z = 111.8 mm, which is the result of the synthesis of the three bands. The non-uniformity of focusing is strong near the focal plane. On the contrary, there is a better uniform spot away from the focal plane. The output characteristics of the triple-junction cell placed near the focal plane will be negatively affected by the non-uniformity. Improving the distance from the cells to the lens can improve the output performance. \begin{table}
\begin{tabular}{c c c} \hline  & **Maximum PAR** &
\begin{tabular}{c} **Optical Axis Position of** \\ **Maximum Par (Z)** \\ \end{tabular} \\ \hline short wavelengths (300–700 nm) & 55.5 & 110.5 mm \\ middle wavelengths (700–900 nm) & 67.4 & 111.4 mm \\ long wavelengths (900–1700 nm) & 74.8 & 112.3 mm \\ all wavelengths (300–1700 nm) & 64.1 & 111.8 mm \\ \hline \end{tabular}
\end{table} 
Table 2: The optical axis position of the maximum peak-to-average ratio (PAR) of different wavelengths.
# Internet of Things in Marine Environment Monitoring: A Review
## 4 New Technologies for Marine Environment Monitoring and Protection
### Network Topology Control  
In wireless sensor networks (WSNs), network topology control capability is a key factor in the performance of the entire network. A reasonable WSN topology control structure can effectively improve the efficiency of network communication protocols and the overall performance of the network. A good network topology helps extend the overall life cycle of the network. Therefore, WSN topology control optimization technology is the key to determining the overall performance of the network, including the network coverage and connectivity. The node selection policy changes the state of the node itself and avoids the communication link redundancy among nodes to form a performance-optimized network structure. Table 2 summarizes the advantages and disadvantages of different topology control algorithms for underwater wireless senor networks (UWSNs) ([PERSON] et al., 2017).  
Even though this classification of topology control algorithms is for underwater wireless sensor networks, it can be well applied to the IoT-based marine environment monitoring in general. Please note that UWSN is not the focus of this paper. For comprehensive reviews of underwater sensor networks and applications, please refer to references [80; 81]. It is also interesting to check out the concept of Internet of Underwater Things (IoUT) and its potential applications [82].  
The rest of this section provides a review of some examples of interesting approaches proposed and developed in the literature on network topology control. [PERSON] et al. [83] used power control to achieve reliable data delivery based on the sea surface movement that affects the surface signal reflection and the strength of the received signal at a node. [PERSON] et al. [84] proposed an approach to reduce link interference and achieved high throughput by using a correlation matrix to describe the source-destination relationship and conflict relationships among the links. Power control is used to achieve a Signal to Noise Ratio (SNR) larger than the decoding threshold.  
[PERSON] et al. [85] proposed a cycle difference set-based protocol to determine the number and positions of active and sleep intervals in one cycle to guarantee that both the transmitter and receiver are awake for communication. [PERSON] et al. [86] developed an optimization model to investigate the performance of the on-the-fly adjustment of the sleep interval in duty-cycled UWSNs to achieve a balanced energy consumption.  
[PERSON] et al. [87] presented a method to incorporate the AUV resurfacing time in the Vol function and AUV path planning algorithm. [PERSON] et al. [88] designed the distributed topology control (DTC) and centralized topology control (CTC) depth adjustment-based topology control algorithms for disconnected and void nodes repositioning to improve network connectivity and data routing.
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 1 Introduction
Yet, due to the lack of reliable connectivity to data centers through terrestrial networks, e.g., in remote areas, some tasks have to be forwarded to data centers via the visible LEO satellite. In addition to the cloud-edge-end hierarchical architecture we are investigating, there is a rising trend in integrated continuum architectures. For instance, [PERSON] et al. in [24] introduced the meta-operating system reference architecture (RAMOS) to tackle the data surge resulting from IoT proliferation, aiming to establish a dynamic, distributed, and trusted continuum for future data-intensive applications at the edge. Yet, creating a continuum from IoT to the edge and cloud still poses an ongoing challenge [25]. Therefore, we aim to propose a joint computing offloading and resource allocation method in the LEO satellite edge network, based on the parameterized deep Q-network (P-DQN) reinforcement learning (RL), to capture the dynamics in network conditions. The main contributions are listed as follows:  
* To better simulate the real LEO network, the dynamic and changeable LEO satellite scenario is defined. The wireless channel with time-varying characteristics is modeled, the communication and computing models under three different offloading strategies are constructed, and the service latency model is obtained. * The joint computing offloading and resource allocation problem in the LEO satellite edge network is built. Constraints on offloading decisions on processed tasks,on remaining available computing resources, and on power control on both LEO satellites and the cloud server are respectively inferred, followed by the optimization problem formulation. * For the highly dynamic LEO satellite edge network and the discrete-continuous hybrid action space, an MDP model with parameterized actions is constructed to capture the dynamics in computing offloading, resource allocation, and power control, and the P-DQN RL method is used to maximize the number of accessed tasks. The rest is organized as follows. In Section 2, a brief summary of existing works on computation offloading and resource allocation in LEO satellite networks is provided. In Section 3, the system model is proposed and the optimization problem is established. In Section 4, the problem is further characterized by the parameterized Markov process and solved by the P-DQN. In Section 5, simulation experiments are conducted to verify the algorithm performance. Finally, Section 6 concludes the work and provides an outlook on future endeavors.
## 5 Experiment
### Accuracy of Malware Detection  
The proposed ViT model is compared with well-known CNN-based app family detection models to evaluate its performance.  
The input image size for all models was 224 \(\times\) 224. This is the image size commonly used in image classification models. There is no significant change in detection performance for images above a specific size. The batch size was set to 256 and the epoch to 80. Epoch 80 has been shown to perform well in multiple tests. For image-based malware detection models, CNN-based models are mainly used. ResNet [40], DenseNet [41], MobileNet [42], etc., are popular because they have the advantages of a high performance, stable learning, lightweight models, and applicability to various fields. The our proposed model with the best validation loss during training was used for evaluation.  
The structure and parameters of our proposed model are shown in Table 4. Considering the size of the test data, the model was set to 12 for Layers and 8 for Heads. Even if the size of the model parameter was increased, there would be no significant performance improvement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Layers** & **Hidden Size D** & **MLP Size** & **Heads** & **Patch Size** \\ \hline our proposed model & 12 & 128 & 2048 & 8 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Configuration of Proposed VIT Model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Macro Average** & **Weighted Average** \\ \hline \(Macro\_precision=\frac{\sum Precision_{i}}{n}\) & (17) & \(Weighted\_precision=\sum w_{i}\times Precision_{i}\), \(w_{i}=\frac{n_{i}}{N}\) \\ \(Macro\_Recall=\frac{\sum Recall_{i}}{n}\) & (19) & \(Weighted\_Recall=\sum w_{i}\times Recall_{i}\), \(w_{i}=\frac{n_{i}}{N}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics for evaluating the performance.  
Table 5 shows the results of the evaluation of the models on the AndroZoo dataset.  
Our proposed model achieved an accuracy of 0.8027, which is significantly high in comparison to other deep learning models such as ResNet18, ResNet50, DenseNet121, and MobileNetV2.  
The comparison of the deep learning models trained on the CICMalDroid2020 dataset is shown in Table 6.  
The CNN model proposed by the [PERSON] et al. [7], which used CNN models to interpret detection results by generating heatmap images, was used as a comparative CNN model. Our proposed model has a better performance than the CNN model. Our proposed model achieved an accuracy of 0.8681, which is the third best performance after ResNet18 and DenseNet121, and there was no significant difference in performance. ResNet18 performed well because it can generalize to a relatively small number of data.  
The following Figure 3 is the Receiver Operating Characteristic (ROC) curves of each model for the AndroZoo dataset. The AUC value on the macro average, which reflects the model's performance, ViT leads with 0.90, closely followed by ResNet18 and DenseNet121, each at 0.89.
## 2 Methodology
### Second Step: Evolutionary Algorithm to Find a Sub Optimal Order  
The second step consists to define an algorithm able to find a sub-optimal order to reach the different locations. The cost function is the shortest distance. The principle of the proposed algorithm is depicted in Figure 5. It is a genetic algorithm based on three mutations.  
Figure 4: New target location defined by the intersection of communication ranges.  
Figure 5: Principle of the evolutionary algorithm to generate a new population.  
The algorithm starts with the creation of a set of initial solutions (list of node numbers whose order is randomly determined): that forms the initial population. This population is next divided in groups: in each group, the best solution, that means the one with the route having the minimal Euclidean distance, is kept and called the survivor. The other routes of the group are replaced by copies of the survivor with mutations: on a random interval, the node orders are either reversed, shifted or swapped, see the details in Figure 6. All the routes are then randomly remixed to form the new population. This process is repeated until a stop criteria is satisfied (number of iterations). The advantage of such an algorithm is to be relatively simple and easy to program. The main parameters are the initial size chosen for the population and the number of iterations to stop the algorithm. A compromise has obviously to be reached between the calculation cost and the opportunity to find a good solution.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 4 Discussion
### Four CRISPR Systems Are Used as an Anti-Herpesviral Strategy  
Across all studies, a total of four CRISPR systems are employed, namely CRISPR-Cas9, CRISPR-CasX, CRISPR interference (CRISPRRi), and CRISPR activation (CRISPRRa), each differing in their structure and/or mode of action. Different mechanisms of the antiviral effect during lytic viral replication following the double-stranded DNA cleavage by the Cas9 endonuclease have been proposed: (1) disrupted viral genome packaging and consequent reduced virion production and transmission, (2) reduced production of viral proteins encoded by the targeted essential viral genes, and (3) induced genome instability, as a result of mutation induction following NHEJ, leading to the production of altered proteins with abnormal functionality [45]. The CRISPR-CasX system also implies RNA-guided double-stranded cleavage by an endonuclease CasX. However, the smaller size and higher specificity of CasX compared to Cas9 could be advantageous for vector packaging and clinical implementation [33]. Given that the CRISPR and CRISPR systems are based on transient transcriptional modulation without the induction of double-stranded breaks, these two CRISPR systems could be a safer option as they do not cleave the DNA, therefore circumventing the chance of mutation induction.
# LoRa 2.4 GHz Communication Link and Range
## 3 LoRa in the 2.4 GHz Band
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Frequency & \(f\) & 2.4 & GHz \\ Spreading factor & \(SF\) & 5–12 & - \\ Bandwidth & \(BW\) & 203/406/812/1625 & kHz \\ Code rate & \(R_{C}\) & 4/5 & - \\ Transmission power & \(P_{TX}\) & 12.5 & dBm \\ Transmitter antenna gain & \(G_{TX}\) & 2 & dBi \\ Transmitter cable losses & \(L_{TX}\) & 2 & dB \\ Fading margin & \(L_{m}\) & 0 & dB \\ Receiver antenna gain & \(G_{RX}\) & 2 & dBi \\ Receiver cable losses & \(L_{RX}\) & 2 & dB \\ Base station height & \(h_{b}\) & 20 & m \\ Mobile station height & \(h_{m}\) & 2 & m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used for path loss modeling. in which \(\
u_{\omega}\sim CN(0,\sigma^{2}/K)\). Figure 1 presents two received LoRa signals that constitute eight preamble (upchirp) symbols at 2.4 GHz with a bandwidth equal to 812 kHz. The short signal was transmitted at an SF of 9, while the longer signal was transmitted at an SF of 10. Figure 1a,b shows the combined received signals in the time domain and in the spectrogram (i.e., time and frequency) domain, respectively. Figure 1c,d shows the cross-correlation functions (6) when the received signals have been cross-correlated with base chirps of the SF equal to 10 and 9, respectively. It is clear that the two signals can be distinguished correctly, even though they interfere with each other. The unique orthogonality property of the ZC sequence allows the LoRa communication system to provide a multiple access technique in the 2.4 GHz frequency band.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 7 Powdered Products: Case Studies
### Dairy Products
[67] evaluated the behavior of other dairy products, such as powdered cheese, and showed that cheeses have unique characteristics influenced by cheese ripening and the addition of different powdered dairy ingredients (buttermilk and sodium caseinate). Their findings demonstrated that the dispersibility of cheese increased with the maturation degree, while lower degree of maturation had the best rehydration results due to an enhanced interaction of food with water [67]. The characterization of fermented powdered dairy products is not only defined by their chemical composition and physical properties, but also by microbiological factors. Lactic acid bacteria (with probiotic properties) are naturally present in fermented dairy products as part of their production (as starter culture) and can be intentionally added to improve probiotic function, and ensuring the survival of these bacteria during the drying process and storage is a challenge [44; 72]. To minimize the loss of lactic acid bacteria, [PERSON] and [PERSON] [44] described some solutions. One option is pretreatment in the drying process, drying the bacteria in an aqueous solution to encapsulate the bacteria and increase their viability. This process is also noted to prevent enzymatic browning processes and the contact of bacteria with oxygen [44]. [PERSON] et al. [66] investigated the physical properties of powdered yoghurt made by spray-drying, highlighting particles that had little water absorption power (not very hygroscopic) and were spherical and porous. As for instant properties, they presented low wettability and solubility [66].
## 5 Performance Evaluations
### Evaluation Metrics
False injection ratio (FIR), False execution ratio (FER), Average query delivery delay (AQDD), Network load (NL) are used to evaluate the performance of convex hull based geocasting. However, since our aim is to evaluate the performance of discussed mobility handling techniques in a tree-based network, we are mostly interested in ER and CO which are directly related with mobility handling. The other metrics, which are not much affected by mobility, were extensively evaluated for a wide range of scenarios in our previous paper [6]. The effects of location estimate errors on geocasting can also be found in [6]. Here, to see the effectiveness of the geocasting based on convex hulls (called GeoCHT at the remaining of the paper), we provide a brief comparison of convex hull based geocasting with GEAR protocol in the next section. After that, we discuss the performance of mobility handling techniques, global and local updates, used by our protocol.
## Appendix A Samples and Response Rates for Main Variables by Country  
Table 11 shows the sample size and response rates by countries, for public contracts (PUBPRO), for eco-innovation of different environmental benefits (ECIIMP), and for categories of eco-innovation (ECOCAT). In all, there were 98,809 observations (questionnaires) collected. Spain and Bulgaria collected answers from most companies. However, Spain did not survey innovation with environmental benefits and public-sector procurements. In addition, the data for ECOCAT were missing in the Czech Republic, Germany, Spain, and Norway. Germany also ignored the public procurement data in its survey. Taking those countries into consideration, the response rate for PUBPRO, ECOIMP, and ECOCAT are 63%, 26.81%, and 8.76%, respectively.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## Materials and Methods
### Fluorescence and Confocal Microscopy  
In fluorescence (FM) and confocal laser scanning microscopy (CLSM) experiments, 1x10\({}^{4}\) cells were seeded on micro coverglasses. Treated samples were exposed to 0.4, 0.7, or 1 \(\upmu\)M solution of Ru(bpy)\({}_{3}\)\({}^{2+}\) in specific cell medium and analyzed after 24, 48, and 96 h, while control samples were growth in medium without fluorophore. Then, all samples were washed three times in PBS, in order to remove all of the fluorophore excess, and fixed using 4% paraformaldehyde/PBS. After washing, slides were dried, mounted with Fluor Gel (EMS) and observed at 488 nm wavelength with an Olympus BX50 fluorescence microscope equipped with a DC500 camera (Leica). Emission bandwidths of 515-580 nm (for intrinsic fluorescence) and above 590 nm (long passband filter for Ru(bpy)\({}_{3}\)\({}^{2+}\)) were monitored. The images of the same samples were acquired with a CLSM (Zeiss LSM700) and analyzed using the ZEN 2011 software [23]. The fluorescence signal intensity of the cells in the serial sections were evaluated using ImageJ software (NIH, Bethesda, MD; available at [[http://rsb.info.nih.gov/ij/index.html](http://rsb.info.nih.gov/ij/index.html)]([http://rsb.info.nih.gov/ij/index.html](http://rsb.info.nih.gov/ij/index.html))).
## 1 Introduction
Then, to achieve the optimal positioning accuracy with the lowest contour error, an improved artificial bee colony (IABC) algorithm is adopted to concurrently provide the auto-tuning capabilities for numerous parameters of the ACC2 PID control system. Thus, numerous control parameters of the proposed controllers are dynamically optimized and on-line tuned by the IABC algorithm to simultaneously minimize the tracking error in each axis and contour errors of the biaxial motions. Compared with the conventional 2 PID controllers and cross-coupled control scheme with constant control gains, the proposed ACC2 PID controller depicts the superiority without the need of trivial trials for the control parameters tuning. In this regard, stable control without fragility during the control process can be achieved. Experimental results are carried out to verify the accuracy and effectiveness of the proposed ACC2 PID control system. The remainder of this study is organized as follows: the working principle and dynamics model of an LMs-driven X-Y-Y stage are expressed in Section 2. The determination of the contour error is introduced in Section 3. In Section 4, the IABC algorithm is developed as the auto-tuning scheme for the ACC2 PID control parameters with tracking performance optimization. The details of the contour tracking control by using the proposed ACC2 PID control strategy are given in Section 5. Experimentations are discussed in Section 6. The conclusion is shown in Section 7.