esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Pruning of the Transferred YOLOv5s Model
Thus, the most appropriate pruning rate can be considered as a balance between maximizing \(A_{D}\) and minimizing MS. To describe such a balance, we designed the following objective function \(F_{x}\) related to the pruning rate, \(A_{D}\), and MS:  
\[F_{x}=A_{D}(pr)+\left[MS_{ini}-MS(pr)\right]\,, \tag{13}\]  
where \(pr\) indicates a variable of the pruning rate; \(A_{D}(pr)\) and \(MS(pr)\) denote the \(A_{D}\) and MS corresponding to \(pr\), respectively; \(MS_{ini}\) is the MS when unpruned. According to the MS of the transferred YOLOv5s in Figure 6d, \(MS_{ini}=27.221\). The maximum value of \(F_{x}\) is the most appropriate balance between ad and \(A_{D}\) and MS. As illustrated in Figure 6e, it is clear that the maximum extreme value of \(F_{x}\) is obtained when the pruning rate is equal to 75%. This also means that setting the pruning rate to 75% can establish a relatively reasonable balance between maximizing \(A_{D}\) and minimizing MS, whereas the others suffer from either too much loss in \(A_{D}\) or too little reduction in MS. Figure 6f further shows the change in the number of channels before and after pruning. Except for one input and three output layers, all layers have a proportional decrease in the number of channels, and these reductions are significant. Figure 6: Results of network pruning in the transferred model: (**a**) our network pruning strategy; (**b**) the visualization for the initial output of the specific layer in our model; (**c**) the visualization for the channel removal in the specific layer under descending \(L_{2}\) norm value; (**d**) influence on network pruning on the model performance; (**e**) the impact of different pruning rates on \(F_{x}\) in the validating set; (**f**) changes in the number of channels before and after pruning.
## 2 Data and Methods
### Bootstrapping  
We implemented bootstrapping techniques to complement our asymptotic theory approach with regards to estimating parameter uncertainty. We again assume that we are have experimental data for a dynamical system from an underlying observation process in Equation (4) where \(\widetilde{\mathcal{E}}_{i}\) are _i.i.d._ with mean zero and constant variance \(\sigma^{2}_{0}\) and \(\theta_{0}\) is the \"true value\" hypothesized to exist in statistical treatments of data [10]. The random variable also has realizations  
\[y_{i}=f(t_{i},\theta_{0})+\widetilde{\epsilon}_{i}. \tag{13}\]  
We use the following algorithm [10] to compute the bootstrapping estimate \(\hat{\theta}_{BOOT}\) of \(\theta_{0}\) and its empirical distribution.  
First estimate \(\hat{\theta}^{0}\) from the entire sample \(\{y_{i}\}_{i=1}^{n}\) using OLS.
2. Using this estimate, define the standardized residuals \[\tau_{i}=\sqrt{\frac{n}{n-p}}\big{(}y_{i}-f(t_{i},\hat{\theta}^{0})\big{)}\] (14) for \(i=1,\ldots,n\), where \(n\) is the number of data points, and \(p\) are the number of model parameters. Set \(m=0\), which will represent the total number of artificial samples we will create.
3. Create a bootstrapping sample of size \(n\) using random sampling with replacement from the data (realizations) \(\{\tau_{1},\ldots,\tau_{n}\}\) to form a bootstrapping sample \(\{\tau_{1}^{m},\ldots,\tau_{n}^{m}\}\).
4. Create bootstrap sample points \[y_{i}^{m}=f(t_{i},\hat{\theta}^{0})+\tau_{i}^{m}\] (15) for \(i=1,\ldots,n\).
5. Obtain a new estimate \(\hat{\theta}^{m+1}\) from the bootstrapping sample \(\{y_{i}^{m}\}\) using OLS.
6. Set \(m=m+1\) and repeat steps 3-5 until \(m\geq 1000\) (this can be any large value, but for these experiments we used \(M=1000\)). We then calculate mean, standard error, and confidence intervals using the formulas: \[\hat{\theta}_{BOOT} = \frac{1}{M}\sum_{m=1}^{M}\hat{\theta}^{m},\] (16) \[Var(\theta_{BOOT}) = \frac{1}{M-1}\sum_{m=1}^{M}(\hat{\theta}^{m}-\hat{\theta}_{BOOT} )^{T}(\hat{\theta}^{m}-\hat{\theta}_{BOOT}),\] (17) \[SE_{k}(\hat{\theta}_{BOOT}) = \sqrt{Var(\theta_{BOOT})_{kk}},\] (18)  
where \(\theta_{BOOT}\) denotes the bootstrapping estimator. We present the results of these techniques as standard errors about the mean of the parameter estimates, as well as the parameter distributions created. This procedure is performed for each replicate in our experiments.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 4 Case Study
### Infrastructure Sharing Methodology  
The required coverage area was divided into \(R=323\) elements in a strategically placed and evenly spaced grid, and the Atoll network simulation tool was used to model three different mobile telecommunication base transceiver stations (BTSs) to ascertain the level of network coverage. Figure 8 shows the network coverage with all three BTSs in place and powered on. Each square box represents 15.62 hectares, and the total network coverage area was 5044.71 hectares. Use of the tool allowed calibration of the matrix of receiver signal strengths \(r_{k,i}\), which were then stored in a \(T\)-by-\(N\) matrix. In the initial configuration, 81.42% of the grid cells received'strong' network signal strength. In the initial network configuration, for three independent BTSs, it will therefore cost 21,074,000 \(\times\) 3 = 63,222,000 NGN in total. Using the ICT tool developed, the colocation problem was then solved with constraints in place to achieve (i) \(\geq\) 77.5% network coverage and also (ii) \(\geq\) 75.0% network coverage, where coverage is the percentage of cells that receive'strong' signals. The results obtained are described in the next section, while Figures 9 and 10 display coverage after colocation onto two masts and one mast, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S/N** & **Items** & **Unit** & **Cost (NGN)** \\ \hline
1 & Land & 1 & 200,000 \\
2 & Diesel generator & 2 & 6,000,000 \\
3 & Diesel & 18,250 & 4,161,000 \\  & consumption/year & & \\
4 & Battery & 24 & 4,320,000 \\
5 & BTS radio & 1 & 1,500,000 \\
6 & BTS antenna & 3 & 360,000 \\
7 & Mast & 1 & 4,000,000 \\
8 & Duplexer & 1 & 145,000 \\
9 & Rectifier & 1 & 48,000 \\
10 & Power amplifier & 1 & 190,000 \\
11 & Converter & 1 & 25,000 \\
12 & Air conditioner & 1 & 125,000 \\
13 & Diesel storage tank & 1 & 150,000 \\  & **Total** & & **21,224,000** \\ \hline \hline \end{tabular}
\end{table}
Table 4: BTS cost used for the case study.  
Figure 7: Measurement taken from mast 1 at 2000 m distance.  
Figure 8: Area coverage with three BTSs on.  
Figure 9: Area coverage with two BTSs on.
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 6 Conclusions and Future Work  
We have presented a novel deep learning approach for image steganography. We show that the conventional image steganography methods mostly do not serve with good payload capacity. The proposed approach, StegNet, creates an end-to-end mapping from the cover image, hidden image to embedded image and from embedded image to decoded image. It has achieved superior performance than traditional methods and yet remains quite robust.  
As seen in Figure 13, there is still some noise generated at non-texture-rich areas, i.e., plain white or plain black parts. The variance loss adopted by StegNet might not be the optimal solution to loss distribution.  
In addition to the idea of \"trading accuracy for capacity\", the embedded image does not need to be even visually similar to the cover image. The only requirement to the embedded image is to pass the third-party supervision and the hidden image should be successfully decoded after the transmission is complete, and therefore the embedded image can look similar to anything that is inside the cover image dataset while can look nothing related to anything that is inside the hidden image dataset. Some of the state of the art generative models in neural networks can help achieve it, i.e., Variational Autoencoders [29; 50], Generative Adversarial Networks [35; 51; 52], etc.  
Some work is needed for non-equal sized images steganography since \"1:1\" image steganography is huge for traditional judgment; however, the ability of neural networks still remains to be discovered. Whether it is possible to generate approaches for even better capacity, or with a better visual quality for even safer from detections. Some other work is needed for non-image hidden information steganography, i.e., text information, binary data. In addition to changing the hidden information type, the cover information type may also vary from text information to even videos. Furthermore, some work is needed for applying StegNet on lossy-compressed image file formats or third-party spatial translations, i.e., cropping, resizing, stretching, etc.  
Conceptualization, Y.Y. and X.L.; Data Curation: Y.Y.; Formal Analysis, Y.Y. and X.L.; Funding Acquisition, P.W. and X.L.; Investigation, Y.Y., X.L. and P.W.; Methodology, Y.Y. and X.L.; Project Administration, P.W. and X.L.; Resources, Y.Y.; Software, Y.Y.; Supervision, P.W.; Validation, Y.Y.; Visualization, Y.Y.; Writing--Original Draft Preparation, Y.Y.; Writing--Review & Editing, X.L. and Y.Y.  
This work was supported by the Shanghai Innovation Action Plan Project under grant number 16511101200.  
The authors declare no conflict of interest.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.11 Data-Driven Tree Search (DDTS) Algorithm & Convolutional Neural Networks (CNNs)  
Aiming to enhance the decision-making process when solving the Container Loading Problem (CLP), the Data-Driven Tree Search (DDTS) algorithm integrates machine learning principles with traditional tree search methods. Notably, Convolutional Neural Networks (CNNs) play a pivotal role in this innovative approach [38].  
The algorithm is trained on a dataset derived from diverse container packing scenarios, allowing it to learn and generalise patterns from past instances. The integration of CNNs facilitates the algorithm in learning intricate representations directly from raw data, alleviating the necessity for explicit feature engineering. The tree search mechanism within DDTS is characterised by an iterative exploration of potential packing configurations. This exploration is guided by learned heuristics derived from the CNN-processed training dataset. The adaptability inherent in this decision-making process contributes substantively to the algorithm's efficacy, resulting in an improved ability to identify feasible solutions efficiently [38].
# Self\(-\)Mode\(-\)Locked \(2-\)um GaSb\(-\)Based Optically Pumped Semiconductor Disk Laser
## 4 Conclusions  
In conclusion, we have presented analytical considerations of self\(-\)mode\(-\)locking semiconductor disk laser operation in different cavity lengths configuration by the DDE model. For self\(-\)mode\(-\)locking, the cavity length cannot be increased unboundedly for a low repetition rate. It needs to be configured according to the gain, loss, and thermal limits of the actual device. Furthermore, we demonstrated the self\(-\)mode\(-\)locked operation of GaSb\(-\)based SDLs based on an optimized long cavity length of \(\sim\)365 mm and obtained stable self\(-\)mode\(-\)locking at a center wavelength of 2034.5 nm. During mode\(-\)locked operation, the pulse duration of GaSb\(-\)based SDLs was 255.48 ps with a repetition rate of 404 MHz. The maximum average output power was 173 mW. Further optimization of the epitaxial structure of the SML GaSb\(-\)based SDLs and compensation of the intracavity nonlinear dispersions are expected to generate shorter pulses and higher single\(-\)pulse energy.  
Conceptualization, J.F.; methodology, J.F., J.S. and X.Z.; software, J.F. and B.M.; validation, J.F.; formal analysis, J.F., B.M. and X.Z.; investigation, J.F. and J.S.; resources, J.S. and Y.Z.; data curation, J.F. and B.M.; writing\(-\)original draft preparation, J.F.; writing\(-\)review and editing, B.M. and C.T.; visualization, J.F.; supervision, C.T.; project administration, Y.Z. and C.T.; funding acquisition, C.T., Z.N. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 61790584, 62025506, 62174159, 62061136010), Key R&D Program of Shanxi Province (grant number 202102030201004), and the K.C. Wong Education Foundation.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
[PERSON] [22] developed a reactive inverse proportional navigation (PN) algorithm by increasing Line of Slight (LOS) rate, but the algorithm is inefficient and defective in some scenarios. [PERSON] [23] applied swarm intelligence techniques to collision avoidance between UAVs. [PERSON] [24] [PERSON] [25] [PERSON] [26] introduced an automatic air collision avoidance system (Auto ACAS) that coordinates maneuver trajectories between fighters. Although Auto-ACAS applies to fighters, it provides a good concept for the collision avoidance of fixed-wing UAVs. Traffic Collision Avoidance System (TCAS) is designed as the final safeguard to resolve midair collisions (MACs) and evidently decrease near midair collisions (NMACs) in the aviation densities of up to 24 aviation vehicles within a 5 NM radius [27]. There is great difference between the proposed methodology and the TCAS. Firstly, TCAS only suggests maneuvers in vertical direction to the pilot, while the proposed methodology includes automatic maneuver implementation in all directions. Secondly, The TCAS performs well in solving one-on-one threats between traditional aircrafts, but in some complex situations, TCAS would issue improper maneuvers while a new secondary encounter is induced to the nearby (remotely piloted aircraft) RPA of previous operations [28]. While the proposed methodology is designed for multiple threats in formation and clusters. The coordination of maneuvers is among all participants in a conflict. Thirdly, the logics are quite different. In TCAS, an RA is produced in real time, the direction choosing and change of vertical speed depends on the current relative situation, while in proposed methodology, the maneuver style (MS) is pre-set to a group of specific categories, the aircrafts choose from the specified maneuver styles. Aiming to solve the defects of traditional CA, this paper presents a novel collision avoidance algorithm for fixed-wing UAVs based on maneuver coordination and planned trajectory prediction, which takes full advantage of the maneuverability of fixed-wing UAVs and generates the collision avoidance maneuver based on the coordination of planned trajectories. The proposed algorithm is classified as the mixed style, which has features that refer to both tactical and strategic domains addressed in the literature analysis. When solving collision, the proposed algorithm plans collision-free safety routes (planned trajectories) for UAVs, and in module of maneuver evaluation and maneuver activation, it draws lessons from methods of Point of closed Approach (PCA), and Collision Cone Approach (CCA). This paper is organized as follows: Section 2 summarizes the operational principle; Section 3 describes the module of maneuver generation and presents the module of trajectory prediction; Section 4 presents the module of treat management; Section 5 presents the module of maneuver evaluation; Section 6 presents the module of maneuver activation; Section 7 presents a couple of scenario simulations and their results; Finally, the conclusions and future work are summarized in Section 8.
## 3 Results and Discussion
### Base Case Analysis
Therefore, CO\({}_{2}\)-WAG can effectively slow down the rate of CO\({}_{2}\) gas extraction compared to CO\({}_{2}\) flooding. Currently, the treatment and separation of output gas to recover methane and reinjected carbon dioxide are priorities for many oilfields. Gas treatment stations in oilfields have a certain upper limit of gas that can be processed per day. In the CO\({}_{2}\) flooding method, the gas output rate may be too high, especially in the final stages of development. It may lead to a risk that a large amount of methane gas and carbon dioxide cannot be captured and recovered in time, causing a great loss of economic benefits and more emissions of greenhouse gases. In contrast, according to the preceding analysis, the CO\({}_{2}\)-WAG method can solve the aforementioned issues by efficiently extracting crude oil and reducing the gas production rate, thereby allowing the gas treatment station sufficient time to capture and recover methane gas and reinject carbon dioxide.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 3 Data Processing  
It is difficult to deal with unstructured data with a huge amount of data and no typical characteristics. Network traffic is a continuously generated data flow. Before extracting features, the necessary step is to divide the data flow, obtain several small fragments, and further process them. This section first analyzes the relevant content of the TLS Protocol, takes the communication message as the analysis object, marks each message, and then describes the communication process between the client and the server. Then, it analyzes the characteristics of domain names of various application categories, extracts representative domain names, and provides relevant domain name data for exception analysis for further manual analysis of exceptions. In the fourth section, we introduce the process of fine-grained efficient dynamic fingerprint extraction.  
Figure 1: Huf algorithm.  
Frequent project set mining is an algorithm for discovering relationships between independent items. An improved frequent project set mining algorithm applied to fingerprint extraction is proposed, which has three characteristics: first, the method can process dynamic data, dynamically extract feature fingerprints from continuously generated network data, find the \"breakpoint\" that divides network traffic, and extract the entire application fingerprint; second, in data analysis, the sequential dependence of messages is the key content of application behavior, and the application fingerprint is established through the \(N\)-gram model; third, the utility of the message is used as a screening criterion.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 4 The LoRaWAN Protocol  
LoRaWAN is a MAC protocol, built to use the LoRa physical layer. It is designed mainly for sensor networks, wherein sensors exchange packets with the server with a low data rate and relatively long time intervals (one transmission per hour or even days). This section describes the LoRaWAN V1.0 specification [23], as released in January 2015.
## 3 Methodologies
### Wavelet Transforms  
Wavelet transform (WT), as a linear representation, splits original signals into different frequency components and time energy distribution. According to the different time and scale parameters, wavelet transform is generally divided into two kinds of decomposition formats: the discrete wavelet transformation (DWT) and the continuous wavelet transformation (CWT). As an effective signal processing technique, the WT decomposition is especially effective to decompose those nonlinear and nonstationary signals, which have different scales or singularities or short-lived transient components [21]. Wavelet function captures the time-frequency dependency using a multi-resolution and multi-level decomposition. The continuous wavelet transformation \(W(a,b)\) is expressed as the inner products of the time-shifted wavelet function \(\Psi_{a,b}(t)\) and the decomposed signal \(f(t)\). Continuous wavelet transform is expressed as follows:  
\[W_{f}(a,b)=\langle f(t),\Psi_{a,b}(t)\rangle=\frac{1}{\sqrt{a}}\int_{-\infty}^{ +\infty}f(t)\Psi\left(\frac{t-b}{a}\right)dt \tag{4}\]  
where \(\Psi_{a,b}(t)\) is the mother wavelet, and the two parameters a and b respectively denote the time-scale and time-shift factor. The variable \(W_{f}(a,b)\) is the wavelet transform coefficient that represents the similarity and correlation between the signal \(f(t)\) and wavelet \(\Psi(t)\). According to the characteristics of ocean turbulence, a continuous wavelet transform is performed to extract scale properties and their correlations of energy cascade and intermittency in different scales.
## 2 Methodology
### Models Trained from Scratch  
Normally, because of the training of the Artificial Intelligence (AI) models, the datasets are divided into training and test data; KITTI is also divided into a training set and a test set, but the official KITTI website has a validation tool with its own validation set. As the KITTI test set has been used to measure the performance of the models that have been trained from scratch with our auto-generated labels, the comparison with the results shown in each paper is not fair because it is obtained by submitting their models to the official benchmark tool with a specific test tested by authors. Therefore, to make a proper comparison, the pre-trained models in OpenPCD have been taken to measure their performance with the KITTI test set, whose results are slightly different Table 1. The main reason for the performance differences is not only the set used in the validation, is that an AOS has been calculated by recalling 40 positions, as recently recommended on the official KITTI website [33].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Source**} & \multicolumn{3}{c}{**BEV**} & \multicolumn{3}{c}{**3D Detection**} \\  & & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline \multirow{3}{*}{PointPillars} & [PERSON] et al. [14] & 88.35 & 86.10 & 79.83 & 79.05 & 74.99 & 68.30 \\  & OpenPCDet & 92.03 & 88.05 & 86.66 & 87.70 & 78.39 & 75.18 \\  & OpenPCDet (Auto-label) & 93.55 & 84.70 & 81.56 & 85.06 & 75.00 & 71.43 \\ \hline \multirow{3}{*}{Voxel R-CNN} & [PERSON] et al. [15] & - & - & - & 90.90 & 81.62 & 77.06 \\  & OpenPCDet & 93.55 & 91.18 & 88.92 & 92.15 & 85.01 & 82.48 \\  & OpenPCDet (Auto-label) & 95.78 & 90.99 & 88.77 & 92.54 & 83.28 & 82.21 \\ \hline \multirow{3}{*}{PointRCNN} & [PERSON] et al. [13] & - & - & - & 85.94 & 75.76 & 68.32 \\  & OpenPCDet & 94.93 & 89.12 & 86.83 & 91.49 & 80.65 & 78.13 \\  & OpenPCDet (Auto-label) & 95.57 & 90.96 & 88.80 & 92.19 & 82.89 & 80.33 \\ \hline \multirow{3}{*}{PV-RCNN} & [PERSON] et al. [29] & - & - & - & - & 81.88 & - \\  & OpenPCDet & 93.02 & 90.32 & 88.53 & 92.10 & 84.36 & 82.48 \\ \cline{1-1}  & OpenPCDet (Auto-label) & 92.50 & 86.93 & 85.83 & 90.09 & 78.25 & 75.38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the accuracy of the models between using the KITTI dataset and KITTI with auto-generated labels by YOLOv5.