esa-sceva/satcom-chunk-collection huggingface dataset:
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 2 Contention Window Adaptation Mechanism  
### The Backoff Scheme in Shared Networks  
The well-known wireless network IEEE 802.11 family, especially the IEEE-802.11e, provides a QoS compliant medium access control based on traditional CSMA/CA. A common aspect of any kind of CSMA/CA variant is the exponential backoff procedure. Actually, most of the QoS facilities in wireless networks are provided by means of fine-tuning -in one way or in another- the behavior of the exponential backoff scheme (see Related Work section for more information).  
The traditional exponential backoff scheme consists of generating a random backoff interval to minimize the probability of collision with other contending stations. For every packet transmission, the backoff interval is obtained from the slot time and a value called backoff counter. The backoff counter is uniformly chosen in the range (0, CW), where CW is called _Contention Window_. CW value is related to the number of failed transmission attempts for a packet. Initially, CW is set equal to a value \(CW_{min}\), called the minimum contention window. For each unsuccessful transmission, CW is doubled up to a maximum value \(CW_{max}=2^{m}CW_{min}\). On the other hand, the backoff counter is decremented as long as the channel is sensed idle. If a transmission is detected on the channel, the counter is frozen, and reactivated when the channel is sensed idle again. When the counter reaches 0, the station transmits. A successful packet reception is marked by the transmission of a positive acknowledgment (ACK) by the destination station after another fixed period of time (_Short Interframe Space_ (SIFS)). The failure in the reception of an ACK frame at the transmitter is assumed as a collision at the receiver. On the other hand, upon a successful transmission, the backoff algorithm reduces the contention window to \(CW=CW_{min}\). The backoff procedure guarantees a fair play (i.e., the channel is equally shared among all station) and, at the same time, it tries to avoid collisions.  
Using the exponential backoff mechanism is one of the methods of providing QoS in multiple access networks. Depending on the values of the backoff parameters, a mobile terminal is giving more channel access opportunities. This is the background idea of the IEEE 802.11e in its contention-based medium access function, called _Enhanced Distributed Channel Access_ (EDCA) [1]. 802.11e defines different types of user data named Access Categories (AC): Voice, Video, Best Effort, and Background, and defines \(CW_{min}\) and \(CW_{max}\) values of contention window depending on the type of data. These values are fixed and cannot be dynamically changed to maintain the QoS requirements against network changes or to response to dynamic application requirements.
## 3 Results and Discussion
### Encapsulation Efficiency of Spray-Dried Anthraquinone-Loaded Casein Micelles
Secondly, during spray-drying, the remaining aloin after decomposition has not  
Figure 1: Encapsulation efficiencies (EE%) of anthraquinone-loaded casein micelles (CMAQP, CMFDP, CMWLAG) (**a**) and percentage of surface attachment of anthraquinones (**b**). (Values are from triplicates; the mean and standard deviation are shown. Values significantly different of all spray-dried powders (CMAQP, CMFDP, CMWLAG) when analyzed by two-way ANOVA with [PERSON]’s test analysis indicated with asterisks: *_p_ < 0.01, **_p_ < 0.001, ***_p_ < 0.0003, ***_p_ < 0.0001.). been encapsulated inside the casein micelles; instead started to get attached to the surface of the powders due to its steric hindrance and hydrophilic nature. Hence, aloin shows zero EE%; however, 50% of surface attachment percentage as per Figure 1b while in powdered form. The increase in encapsulation efficiencies of aloe-emodin and rhein from zero to 12% and 8%, respectively, was due to the cluster deformation of casein micelles during spray-drying that could expose the hydrophobic core that facilitated the attachment of aloe-emodin and rhein. However, the least encapsulation efficiency in CMWLAG compared to CMAQP, CMFDP was since, during nanoencapsulation, the pH of the WLAG encapsulated casein micelle (CMWLAG) solution decreased to pH 4.6. The aggregation of casein micelles occurred at their isoelectric point (pl 4.6) and precipitated. These aggregated and fractal structures of CM as a result of acidification, have lower densities [31]. So casein micelles (CMWLAG) maintain weaker, hollow and branched structures [32] that can then easily be broken down by spray-drying [33]. As reported previously, this type of structure is unsuitable for maintaining encapsulated anthraquinones' stability in dried form. Thus, an increase in the free anthraquinones attaching to the hydrophilic terminal instead of attaching to the casein micelles' hydrophobic core (surface attachment as per Figure 1b) decreased the encapsulation efficiencies.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 5 Experimental Results and Discussion
### Image Preprocessing Contrast Experiment  
To verify the effect of the improved Wallis filter, the mosaic results without preprocessing, using the classic Wallis filter and using the improved Wallis filter, were compared in this paper. Two ScanSAR images of GF-3, which corrected the intrinsic periodic scalloping in the azimuth and the effect of the roll angle error on the range, were selected to implement this experiment. We divided every stitching result into three parts for comparison. As shown in Figure 8a, the intensity of the original images was not consistent. There was an obvious stitching seam due to the fault in the azimuthal intensity distribution (the azimuth is along the vertical direction in this image). The result using the classic Wallis filter is presented in Figure 8b, where the seam still exists even if the intensity distribution in azimuth of the middle part is balanced. Therefore, the limitation of the classic Wallis filter is that it had difficulty handling the situation where the means and standard deviations  
Figure 7: Workflow of the proposed method.  
were very similar but the trends in the intensity along the seam were opposites. Figure 8c provides the result based on the improved Wallis filter. The azimuthal intensity of all three parts is consistent, and the connection between the two images is smooth, which means that the mosaic seam was successfully eliminated. In addition, for two images with the same trends in the intensity along the seam, both the classic Wallis filter and the improved Wallis filter could achieve good results. Therefore, the effect of the mosaic results can be markedly improved by the improved Wallis filter, which is more robust than the classic filter.
# Design and Optimization of Lattice Structures: A Review
## 3 Design and Optimization of Uniform Lattice Structures  
According to Section 2.1, the lattice structures are classified into uniform lattice structures and non-uniform lattice structures in this paper. In this section, the design and optimization of uniform lattice structures are mainly described from three aspects: (1) unit cell structure design, (2) mathematical algorithm, and (3) unit cell topology optimization. For the definition of lattice structure, lattice structure is formed by arrangement of unit cells in space. Different types of unit cells have their unique characteristics, which can reflect and determine the performance of the whole lattice structures. Therefore, design and optimization of unit cells are the basic research elements. In the third section of the paper, the design and optimization of uniform lattice structures are mainly realized and achieved through the design and optimization of single unit cells. Three aspects are described: (1) Uniform lattice structures formed based on the design of unit cell shape; (2) uniform lattice structures formed based on mathematical algorithm; (3) uniform lattice structures formed based on the optimization of unit cell topology.
## 3 Experimental Results and Discussion
### Stiffness Degradation  
Stiffness degradation is an important facet for the analysis of mechanical properties of replaceable links. The calculation formula is as follows:  
\[K_{\text{i}}=\frac{\left|P_{\text{i}}{}^{+}\right|+\left|P_{\text{i}}{}^{-} \right|}{\left|\Delta_{\text{i}}{}^{+}\right|+\left|\Delta_{\text{i}}{}^{-} \right|} \tag{6}\]  
where, \(K_{\text{i}}\) is the stiffness at \(i\) loading cycle; \(P_{\text{i}}{}^{+}\) and \(P_{\text{i}}{}^{-}\) are, respectively, the maximum load at the positive and negative directions at \(i\) loading cycle; and, \(\Delta_{\text{i}}{}^{+}\) and \(\Delta_{\text{i}}{}^{-}\) are, respectively, the maximum displacement at both the positive and negative directions at \(i\) loading cycle. The stiffness degradation amplitude of all specimens is shown in Table 7.  
The following conclusions about stiffness degradation of link can be obtained from Table 7:  
(1) Energy dissipation link cross section comparison:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **RSL-1** & **RSL-2** & **RSL-3** & **RSL-4** & **RSL-5** & **RSL-6** & **RSL-7** & **RSL-8** \\ \hline SDM & 65.46\% & 60.06\% & 62.04\% & 61.83\% & 64.31\% & 58.72\% & 60.01\% & 57.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Stiffness degradation amplitude.  
By comparing RSL-4, RSL-7, with RSL-8, the stiffness degradation amplitude of link with larger section size is relatively lower, which indicates that the increase of section size results in a reduction of stiffness degradation amplitude and plastic deformation capacity of link.  
(2) Energy dissipation link length comparison:  
By comparing RSL-1, RSL-3, RSL-4, with RSL-6, with the increase of link length and the reduction of stiffness degradation, it can be seen that the plastic deformation capacity of the shear links is better than that of bending links.  
(3) Energy dissipation link stiffener spacing comparison:  
When the cross-section size, length, and yield type of the link are the same, the stiffening spacing of the link has little influence on the stiffness degradation amplitude. The stiffness degradation amplitude increases with increasing stiffening distance of the link.
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### Network Model Architecture for Strauberry Ripeness Detection
#### 2.3.2 DySample
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**FAST R-CNN** & **Backbone** & **Params** & **AP** & **AP\({}_{\textbf{50}}\)** & **AP\({}_{\textbf{75}}\)** & **AP\({}_{\textbf{5}}\)** & **AP\({}_{\textbf{M}}\)** & **AP\({}_{\textbf{L}}\)** \\ \hline Nearest & R50 & 46.8 M & 37.5 & 58.2 & 40.8 & 21.9 & 41.1 & 48.9 \\ Deconv & R50 & +2.4 M & 37.3 & 57.8 & 40.3 & 21.3 & 41.1 & 48.0 \\ PiexShuffle & R50 & +9.4 M & 37.5 & 58.5 & 40.4 & 21.5 & 41.5 & 48.3 \\ CARAFE & R50 & +0.3 M & 38.6 & 59.9 & **42.2** & **23.3** & 42.2 & 49.7 \\ IndexNet & R50 & +8.4 M & 37.6 & 58.4 & 40.9 & 21.5 & 41.3 & 49.2 \\ A2U & R50 & +38.9 K & 37.3 & 58.7 & 40.0 & 21.7 & 41.1 & 48.5 \\ FADE & R50 & +0.2 M & 38.5 & 59.6 & 41.8 & 23.1 & 42.2 & 49.3 \\ SAPA-B & R50 & +0.1 M & 37.8 & 59.2 & 40.6 & 22.4 & 41.4 & 49.1 \\ DySample-S & R50 & +4.1 K & 38.5 & 59.5 & 42.1 & 22.5 & 42.1 & **50.2** \\ DySample-S+ & R50 & +8.2 K & 38.6 & 59.8 & 42.1 & 22.5 & 42.1 & 50.0 \\ DySample & R50 & +32.7 K & 38.6 & 59.9 & 42.0 & 22.9 & 42.1 & **50.2** \\ DySample+ & R50 & +65.5 K & **38.7** & **60.0** & **42.2** & 22.5 & **42.4** & **50.2** \\ \hline Nearest & R101 & 65.8 M & 39.4 & 60.1 & 43.1 & 22.4 & 43.7 & 51.1 \\ DySample+ & R101 & +65.5 K & **40.5** & **61.6** & **43.8** & **24.2** & **44.5** & **52.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Object detection results with Faster R-CNN on MS COCO. Best performance is in **boldface** and second best is underlined. Figure 8: YOLOv8 network structure diagram with CA + DySample.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 2 Related Work
### Algorithm Complexity Analysis  
According to the above algorithm description, the algorithm RATP (Algorithm 1) pseudo code as follows:  
Figure 1: The metric of each node.  
```
Input: Historical trajectory information of nodes

Output: optimal path

1:\(\theta\): the average speed of vehicle since long time movement;

2:\(\delta_{\mathrm{n}}\): vehicles' random and independent processes when the vehicles move at infinity;

3:\(\lambda\): the mobile coordination factor;

4: the node mobility model:

\[\mathrm{v_{t+Att}}=((\lambda-1)\mathrm{v_{t}}+(1-\lambda)\theta+\sqrt{1-\lambda^{ 2}}\delta_{\mathrm{n}})\Delta t;\]

5: each data point \(\mathrm{v_{i}}\) can be obtained by transformation the data point \(\lambda_{\mathrm{i}}\) through \(\Lambda\lambda_{\mathrm{j}}+\mu\);

6: the trajectory probability model:

\[\mathrm{p}(\mathrm{V}|\lambda)=\prod\
olimits_{\mathrm{j}=1}^{\mathrm{k}}\sum \
olimits_{\mathrm{j}=1}^{\mathrm{k}}\sigma_{\mathrm{j}}\varphi(\mathrm{v_{j} }|n_{\mathrm{j}}+\Lambda\lambda_{\mathrm{j}},\psi_{\mathrm{j}});\]

7: use the EM algorithm to optimize the parameter \(\sigma,\;\mu,\;\Lambda,\;\psi\);

8: get the speed of the next moment.

9: the vehicle position:

10: if the vehicle driving direction has changed do

Equation (15);

11: else:

Equation (16);

12: end if

13: the distance of the vehicle moving in \(t+\Delta t\) time: Equation (18);

14: calculate the metric value of the candidate node based on the predicted location information:

\[\varsigma_{j}=\tau_{a}\frac{d}{d_{0}}+\tau_{b}\frac{4\kappa}{\pi}+\tau_{c}(1- \frac{n}{N});\]

15: the candidate node with the smallest metric be selected as the next-hop forwarding node;

16: end;
```  
**Algorithm 1**Routing Algorithm based on Trajectory Prediction--RATP  
The complexity of the algorithm is the resource required by the algorithm after it is written into an executable program. The resources include time resources and memory resources. The same problem can be solved by different algorithms, and the quality of an algorithm will affect the efficiency of the algorithm and even the program. The purpose of algorithm analysis is to select appropriate algorithm and improve algorithm. The evaluation of an algorithm is mainly considered from the time complexity and space complexity. The RATP algorithm uses the Gaussian process to calculate the probability of the node's future time velocity, and uses the candidate node metric to calculate the next-hop data forwarding node. The complexity of the algorithm is \(O(n+\log n)\).
## 2 Software-Defined GNSS Receiver
### Carrier-to-Noise Density Ratio (C/N\({}_{0}\)) Estimation  
The Carrier-to-Noise density ratio (C/N\({}_{0}\)) for the GPS receivers is often calculated based on the Narrow-band Wide-band Power Ratio (NWPR) of the received signal [21]. If this C/N\({}_{0}\) estimation technique is used, the NH code (in case of D1 navigation signal) must have to be wiped off before the narrowband power is calculated. Otherwise, the narrowband power calculation will be erroneous due to the presence of bit transition within the 20 ms bit boundary. In case of D2 navigation signal, the narrowband power computation may not be accurate enough since the data bit duration for D2 signal is only 2 ms, which is 10 times less than GPS L1 C/A or BeiDou D1 data bit duration. In view of this particular issue, a new C/N\({}_{0}\) estimation technique based on the Signal-to-Noise Power Ratio (SNPR) is implemented in the FGI-GSRx software receiver. The implementation scheme is unique in the sense that the noise power is computed from the tracking channel itself through a +2 chips distant correlator from the prompt correlator. The properties of the Gold codes (both GPS L1 and BeiDou B1 signals are Gold codes) suggests that any auto-correlation with the same Gold codes outside \(\pm\)1 chip delay from the prompt correlator should either be zero or very close to zero due to the limiting length of the Gold codes itself. A fair choice of +2 chips early correlation index is preferred here, as this correlation index (+2 chips with respect to prompt correlator) will have no impact from the multipath which usually comes as a delay in the late side of the correlation. The implementation scheme for SNPR-based C/N\({}_{0}\) estimation technique is mentioned in the following.  
* An estimate of the noise power \(\mu_{N}\) is obtained first by correlating the incoming signal with the locally generated PRN (Pseudo Random Noise) code with a correlator which is +2 chips early from the prompt correlation index.
* The signal power \(\mu_{S,N}\) is computed from the prompt correlation between the incoming signal and the locally generated PRN code after each code integration period \(T_{coh}\) (_i.e._, 1 ms for GPS L1 or BeiDou B1).  
* Signal-to-Noise Power Ratio (_SNPR_), after each code epoch period, can be written as follows: \[\textit{SNPR}_{i}=\frac{\mu_{S_{i}}}{\mu_{N_{i}}}=\frac{\mu_{S_{i,N_{i}}}-\mu_{N_{ i}}}{\mu_{N_{i}}}\] (1)
* Finally, Carrier-to-Noise-density ratio, C/N\({}_{0}\) can be derived as: \[C/\left.N_{0_{0}}\right|_{\textit{abs}-\textit{He}}=\left|0\log_{10}\left( \frac{\textit{SNPR}_{i}}{T_{\text{coh}}}\right)\right|\] (2)  
Figure 6 shows the C/N\({}_{0}\) of the tracked BeiDou satellites. As shown in the figure, PRN 14 has the highest C/N\({}_{0}\) (\(\sim\)48 dB-Hz) and PRN 5 has the lowest C/N\({}_{0}\) (\(\sim\)39 dB-Hz) depending on their elevation angles with respect to the receiver at the time of data collection. The performance evaluation of SNPR-based C/N\({}_{0}\) estimation technique with respect to the traditional NWPR-based technique for BeiDou B1 signal is presented in [23].
## 3 Proposed Model
### Discriminator Architecture  
The discriminator architecture developed in this study is identical to that of the encoder, with the addition of two fully-connected layers. The first of these layers is connected to global pooling (GlobaMaxPooling2D), with 100 connection points used for feature extraction. This layer corresponds to the feature similarity between the original and generated images to optimize the model loss. The second fully-connected layer is represented by one neuron, which reflects the output of determining the falsification of  
Figure 1: Generator architecture.  
an image. The primary difference between the proposed architecture and that of WGAN is that the sigmoid layer is replaced by the LeakyReLU layer, as shown in Figure 2. In addition, we adopted the loss function used in Skip-GANomaly, which combines three loss values: adversarial, contextual, and latent. Adversarial loss is used to increase the reconstruction ability regarding normal images, contextual loss guides the model to learn contextual information and sufficiently capture the data distribution, and latent loss helps generate realistic and contextually similar images. During the training phase, the model is able to correctly reconstruct normal samples and incur a high loss for the reconstruction of abnormal samples, thereby improving the efficiency of anomaly detection.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 2 Related Work  
### Wired Network Environment  
Studies related to malicious traffic detection using ML and feature selection in wired networks are as follows. [PERSON] et al. [13] conducted a best detection model study using feature selection for anomaly detection with supervised ML. A total of 149 samples were collected and analyzed, including 81 clean and 68 malware samples. Six types of supervised ML and one type of unsupervised ML were used in this study. The accuracy of each algorithm was as follows: random forest (RF), 73.15%; decision table, 76.51%; Bayesian network, 68.45%; multilayer perceptron (MLP), 77.18%; lazyIBK, 74.49%; logitboost, 76.51%; and EM, 54.36%. Among them, the MLP algorithm was proposed as an effective detection approach.  
To improve the accuracy of an ML classifier and detect malicious traffic, [PERSON] et al. [14] presented a feature selection model that uses WMI_AUC and RFS. Two datasets were used for an experimental evaluation, namely, HIT Trace 1 and NIMS, developed in separate networks. Furthermore, 11 types of ML classifiers were used, i.e., Bays Net, naive Bayes, SMO, AdaBoost, Bagging, OneR, PART, Hoeffding, C4.6, RF, and random tree. The experimental results showed that decision tree ML classifiers, namely, RF and C4.5, performed efficiently when testing with features selected using WMI_AUC and RFS.  
[PERSON] et al. [15] presented an online sequential semi-supervised ML approach based on a network entropy estimation, co-clustering, information gain ratio, and extra trees algorithms for DDoS detection. Three datasets (NSL-KDD, UNBISCX12, and UNSW-NB15) were used to evaluate this approach, and the results showed the accuracy and false-positive rates. The accuracy for each dataset was 98.32% for NSL-KDD, 99.88% for UNBISCX12, and 93.71% for UNSW-NB15. The false-positive rates were 0.33%, 0.35%, and 0.46%, respectively.  
[PERSON] et al. [16] and [PERSON] et al. [17] proposed ML-based IoT network anomaly detection models that can be applied to an IoT gateway or device in a wired network. Specifically, [PERSON] et al. [16] proposed a real-time lightweight model to classify traffic using random undersampling and word embedding algorithms, whereas [PERSON] et al. [17] proposed a framework with an ML-based botnet anomaly detection IoT device and sequential extraction architecture. In the experiment, most of the relevant features were selected and configured for each attack class.  
As mentioned above, several studies have used ML and feature selection in wired environments, and each has shown significant results. However, because GTP traffic is not analyzed, their application is limited for detection in 5G core networks.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 2 Methodology
### Design the Solution  
A modular design approach designs the overall solution of the project. Different schematics and layouts are depicted to complete the hardware requirements. Anchors, such as fixed objects, and tags, such as movable objects, are deployed.  
The DWM1000 is an IEEE802.15.4-2011 UWB consistent remote handset module dependent on Decawave's DW1000 IC. It enhances the accuracy of locating the object within 10 cm dia area of interest, and it includes high information rate interchanges of up to 6.8 Mbps as well as a phenomenal correspondences scope of up to 300 m on account of rational beneficiary strategies. The DecaWave Module DWM1000 is upgraded for incomparable indoor exactness areas, and Real Time Location Systems (RTLS) and Wireless Sensor Networks (WSN) are necessary for information interchanges. It is utilized for a vast scope, containing an assortment of business sectors. The key highlights are [11; 12]:  
* Integration of DW1000 IC, powering the executives, radio wire, clock control to disentangle plane reconciliation
* Allows simple joining with a wide scope of MCUs
* Supports 110 kbps, 850 kbps and 6.8 Mbps information rates
* Frequency of 3.5 GHz-6.5 GHz--channels 1, 2, 3, 4, 5, 7
* 23 mm \(\times\) 13 mm \(\times\) 2.9 mm 24-pin side castellations
* Supports ToF and TDoA area planes  
The high-level block diagram of DWM1000 is explained in detail in Figure 1(b), where different components receive and send signals from tags and anchors devices.