esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
When trained and tested on the same image type, all classifiers performed comparably well given a large enough training dataset and an optimized size and compilation of the feature vector (Figures 11 and 12). However, when trained on one image type and tested on other image types, large differences with respect to the generalization ability and therefore the transferability could be observed between the different classifiers. SVM showed the best transferability for all VHR and MR image types. RT seemed almost as transferable as SVM, whereas KNN showed a rather unstable behavior when transferred between image types. The worst performance was observed for NB, which showed an almost random classification performance on several image types and clearly lacked generalization ability, indicating the need for a training dataset that closely describes the statistical distribution of the specific image type and image scene to be classified. The results of this experiment showed clear differences in the generalization ability of the different classifiers. However, in order to further strengthen the results, an extended number of image scenes per image type should be considered. Also the effects of techniques to enhance the transferability of classifiers like retraining, domain adaptation or multi-task learning should be evaluated in the context of an object-based image analysis. Image segmentation clearly affected the succeeding classification stage and optimal operating points for all classifiers coincided with independently assessed optimal segmentation parameter values. SVM, RT and KNN proved to be less sensitive to differences in the segmentation input with respect to NB. Quantitative segmentation accuracy assessment and parameter tuning are therefore important steps to be undertaken when following an object-based image analysis approach, especially when parametric classifiers such as NB are used. Performance evaluations on the QB image showed for all classifiers comparably low sensitivity values (Figures 4 and 8), which could be an indication that the classification scheme of the reference dataset is not well adjusted. Since a large variety of roof materials is usually present in the built environment, a more refined description of the landuse/landcover class \"buildings\" depending on the roof material may be a valuable solution to increase the sensitivity of all classifiers. In this context, the generally large variability of spectral signatures within the built environment and regional differences in predominant roof materials may also lead to limitations for the transferability of trained learning machines and should be taken into account when approaching global classification tasks. Using different training datasets between regions that show specific roof material compositions (e.g., tiled roofs in large parts of Central Europe, matched and metal roofs in rural South East Asia) should be further evaluated especially for the VHR image analysis.
## 3 Proposed Methodology
### Gap Loss  
To optimize feature embeddings, different loss formulas, such as contrastive [27] and triplet [32] are used. Similar works relevant to data metric learning consider effective negative exploitation [27]. The model is trained to pull similar features together while pushing dissimilar features apart. Considering a set of triplets of input histograms \(p_{i}^{Q}\), it includes an anchor point in \(Q\), a true connection in \(S\) that is known to be similar to the anchor, and a false connection in \(S\) that is known to be dissimilar, indexed by \(i,p,n\), respectively. We select anchor points and a mining group per scene. A positive matching \(p\) is the matching with minimum Euclidean distance and shorter than the threshold value, whereas the negative matching \(n\) is any of non-positive matching for the keypoint in the anchor point cloud. In addition, we use the pairwise loss for the mined quadruplet (\(p_{i}\), \(n_{i}\), \(p_{j}\), \(n_{j}\)) and form a gap loss. Given the number of keypoints in these two clouds \(M\) and \(N\), the ground-truth correspondences matrix \(M\in\left\{0,1\right\}^{(M+1)\times(N+1)}\) are generated by projecting the keypoints to the other point cloud. Specifically, for each keypoint in the point cloud, it is either connected with a matched keypoint or with the non-matched keypoint (the so-called \"dustbin\") of the other point cloud. Consequently, the matching scores between any keypoint and the dustbin are filled in the last row and column of correspondences matrix \(M\). For a generalized graph matching, we create the predicted soft score assignment matrix \(\overline{P}\in\left[0,1\right]^{(M+1)\times(N+1)}\) using the Sinkhorn algorithm [53]. The loss is then calculated as:  
\[Loss=\sum_{i=1}^{M}\text{log}\left(\sum_{n=1}^{N+1}[(z_{i})_{{}_{+}}+1]\right) +\sum_{j=1}^{N}\text{log}\left(\sum_{m=1}^{M+1}[(z_{j})_{{}_{+}}+1]\right) \tag{8}\]where \(z_{i}=-\log r_{i}+\log n_{i}+\eta\), \(z_{j}=-\log c_{j}+\log n_{j}+\eta\), and \(\left(z_{i}\right)_{+}=\max(z_{i},0)\). Specifically, \(r_{i}=\sum\limits_{(i,n)\in\mathcal{P}}\overline{P}_{in}M_{in}\) represents the true connection assignment value for keypoint \(p_{i}\) in point cloud \(Q\) and \(\mathcal{P}\) is a set of all positive pairs in extracted features in a minibatch. Meanwhile, \(n_{i}=\sum\limits_{(i,n)\in\mathcal{N}}\overline{P}_{in}M_{in}\) refers to the false connection assignment value for \(p_{i}\) and \(\mathcal{N}\) is a negative subset of features in a minibatch that will be used for negative mining. The gap loss aims to expand the margin between the positive match with all the other negative matches at least a margin away \(\eta\). The second terms in the loss function for keypoint \(p_{j}\) in point cloud \(S\) are computed in a similar manner.
# An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design
## 2 Improved NSGA-II Methodology with Dual-Sorting Approach  
### Workflow of the Dual-Sorting NSGA-II Method  
In the classical NSGA-II method, the shield solutions in the population are sorted and ranked based on the dominance to find the Pareto front. However, it does not effectively differentiate solutions with the same rank in terms of their distance from the Pareto front. To address this limitation, we introduce a dual-sorting approach, as illustrated in Figure 1:  
* The initial parent population \(P_{t+1}\) is generated using traditional \(R\)-based sorting;
* A modified parent population \(P^{\prime}_{t+1}\) is then created using a new sorting method based on the fitness value \(O\), which accounts for the concave characteristics of the Pareto front;
* This population undergoes crossover and mutation to produce the offspring population \(Q_{t+1}\);
* The combined parent population \(P_{t+1}\) and offspring population \(Q_{t+1}\) form the next-generation population \(C_{t+1}\).  
For a detailed explanation of the rank number \(O\) and its associated crowding distance \(I_{o}\), refer to Section 2.2.
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations
#### 4.3.7 Limitations of Data Analysis in Maritime IoT  
The effectiveness and accuracy of data analysis significantly depend on the quality of the data being analyzed. Poor quality data, which might be incomplete, inaccurate, outdated, or irrelevant, can lead to erroneous insights and faulty decision-making. For instance, inaccurate data from a weather sensor can lead to wrong predictions about sea conditions, potentially resulting in hazardous navigation decisions. Therefore, it is crucial to ensure the collection of high-quality, accurate, and relevant data for analysis in maritime IoT applications [91].  
Complexity  
Data generated from various IoT devices in the maritime industry can be immense in volume, high in velocity, and diverse in variety. Analyzing such big data requires sophisticated analytics capabilities, which can handle the complexity of different data types and structures, and the speed at which data is generated and processed. This adds a layer of complexity and requires a high level of expertise in data science and analytics, which might not be readily available in many maritime organizations [92].  
As with any technology dealing with data, data security and privacy are significant concerns in maritime IoT. Sensitive data, including operational, financial, and personal data, needs to be protected from potential breaches. Any compromise on data security can have serious consequences, including financial losses, reputational damage, and even legal implications. Moreover, maritime organizations must ensure compliance with international and regional data protection regulations, adding another layer of complexity to data analysis in maritime IoT [93].  
Cost  
Implementing advanced data analytics can be costly. The investment needed for the necessary hardware, software, and infrastructure can be substantial. Additionally, there is the cost of hiring or training staff with expertise in data analysis. While these investments can lead to significant benefits in the long run, they might be prohibitive for smaller maritime organizations or for those with tight budgets [94].  
Interoperability  
In the maritime industry, data can come from various sources and in different formats, each with its own standards. The lack of standardization and interoperability between these systems can pose significant challenges in integrating and analyzing this data. Without effective integration, the full potential of data analysis cannot be realized, as valuable insights might be hidden in the unconnected, siloed data. This necessitates the adoption of common standards and protocols to ensure seamless data integration and maximize the benefits of data analysis in maritime IoT [95].
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 3 Overview of the LLDN Superframe  
Each superframe in IEEE 802.15.4e LLDN contains a beacon slot, management time slots and _macLLDNnumTimeSlots_ of equal duration as shown in Figure 1. In the beginning beacon slot of each superframe, the PAN coordinator broadcasts the beacon frame, and the associated nodes start synchronization with the superframe structure. The beacon frame also helps the nodes for re-synchronization, which have gone due to entering the power saving mode. The time slots in the superframe can be assigned to one or more nodes. However, each slot is assigned to exactly one node and called the slot owner. The slot owner can transmit the data without any explicit addressing in the data frame. When more than one node is associated with one shared slot, it is known as the shared group time slot.  
The nodes in a group shared slot follow the CSMA/CA mechanism with a simple eight-bit address inside the data frame to transmit the data to the coordinator. Management time slots are used to transmit management data in both the uplink and downlink. Apart from this, the presence of the bidirectional time slot gives scope for communication between the coordinator and nodes. However, the direction of communication is broadcast through the beacon. As shown in Figure 1, one slot is reserved to broadcast a separate group acknowledgment (GACK) by the coordinator, and only the failed nodes use the retransmission slots in the superframe. Note that a node does not know about the number of failure nodes. However, with the information received from the GACK that contains one bitmap to indicate the successful and failed transmissions, a node can choose the suitable retransmission slot in the same order as the transmission slot. Based on this information, only the collided nodes attached to one shared slot can find the suitable retransmission slots and access the channel through CSMA/CA.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 4 Conclusions  
In this paper, we have proposed a novel modulation scheme, i.e., single pulse variable modulation (SPVD), which can be efficiently used in body-centric nanonetworks that operate in the THz band. SPVD can achieve higher energy efficiency, compared to TS-OOK. It can also achieve a lower error rate as a consequence of the diminished molecular absorption noise. On the other hand, slightly more time is needed to transmit the same number of bits compared to TS-OOK, thus leading to lower data rate. However, this is not a primary issue, as the type of applications that are to be used with body-centric nanonetworks do not require that high of a data rate. Moreover, the modulation order of the proposed scheme can be chosen to be low in order to avoid heavily deteriorating the data rate, effectively turning this issue into a trade-off between energy efficiency and data rate. Finally, future research directions on this topic include the performance analysis, in terms of bit error rate, capacity, and collision probability, for both single and multi-user systems.  
The conceptualization of the modulation was done by [PERSON], [PERSON], and [PERSON]; [PERSON], [PERSON], and [PERSON] performed the theoretical analysis and the simulations. [PERSON], [PERSON], and [PERSON] validated the theoretical analysis and simulation results. [PERSON], [PERSON], and [PERSON] supervised the process. All authors analyzed the results, and contributed to writing and reviewing the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Study and Investigation on 5G Technology: A Systematic Review
## 4 5G Technologies
### Machine Learning Techniques for 5G
* ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices. Figure 6: Pictorial representation of machine learning (ML) in 5G. State-of-the-Art of Approaches  
In [79], author's firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication. In [68], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results. In [15], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers' best acceptance policies and satisfiability (tenants). They also suggested novel N3 AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.
# An Automatic Transformer from Sequential to Parallel Java Code
## 5 Control Flow Graph Analysis
### Define and Evaluate the Two Parallel Paths  
The last step of our approach consists of evaluating the instructions that compose the two paths, each of which could be assigned to a thread. The first path will be from the node containing the instruction we want to run in parallel and the following instruction, while the other path is from the latter to the data-dependent statement. Algorithm 6 shows the instructions executed by the approach. The paths are computed as Dijkstra shortest path algorithm, using the API provided by the third-party library JGraphT. By construction, the shortest path is the only path between two given nodes. The length of a path represents the number of nodes (instructions) within it; therefore, we check the lengths of both paths by filtering out paths with short lengths and paths with differences between their lengths that are bigger than a threshold. The function returns true if the paths satisfy these two requirements, and false otherwise.  
Figure 1 shows the CFG of the method in Listing 4; node 96 should be executed in parallel, while node 100 is the data-dependent one; the first path is from node 96 to node 98, while the second path is from node 98 to node 100. The length of the first path is 17, while the length of the second path is 42 (this is the count of the instructions constituting the path that considers the instructions of the called methods). When the main thread reaches the instruction at line 100, it will wait until the other thread is finished. Since both paths have a sufficiently large number of instructions, the method call is refactored to execute in parallel.  
Listing 6 shows the code of the method refactored for parallel execution by using CompletableFuture, as it allows running the method at line 96 asynchronously (using runasync()). The instruction at line 99, future.join(), is the waiting point for the main thread until the task defined at line 96 finishes.  
Figure 2: Control flow graphs of the code with conditions shown in Listing 5.  
```
functionComparePaths(\(node_{1},node_{2}\)) \(m\gets node_{1}.getStatement().getMethodCall()\) \(cfg_{m}\gets m.getCfg()\) \(lastNode\gets cfg_{m}.getLastNode()\) \(nextNode\gets lastNode.getNext()\) \(path_{1}\gets DijkstraShortestPath.findPathBetween(node_{1},lastNode)\) \(path_{2}\gets DijkstraShortestPath.findPathBetween(nextNode,node_{2})\) if\(path_{1}.getLength()\geq 5\wedge path_{2}.getLength()\geq 5\)then if\(|path1.length()-path_{2}.length()|<10\)then returntrue endif endif endif returnfalse endfunction
```  
Listing 6: Method start() updated with CompletableFuture, executing the method called at line 96, where variable future is initialized, and for line 99, where future.join() waits for its completion.  
```
publicvoidstart(){
94 CompletableFuture<Void>future;
95 if(reenetJUL){
96 future=CompletableFuture.runAsync(()->resetJULLevels());
97}
98 propagateExistingLoggerLevels();
99 future.join();
100 isStarted=true; }
```  
**Algorithm 6** The algorithm used to define and compare two parallel paths.
# High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
## 3 HATS for Molecules
### THz Reference for Molecular Photoionization
The alignment-dependent high harmonic yields in molecular frame \(S_{\text{HHG}}\left(\Omega,\theta\right)\) can be expressed by using the QRS theory [76; 88]  
\[S_{\text{HHG}}\left(\Omega,\theta\right)\propto\Omega^{4}W^{2}\left(\Omega \right)\sigma_{\text{PICS}}\left(\Omega,\theta\right)N\left(\theta\right), \tag{3}\]  
where \(\Omega\) is the harmonic frequency, \(W(\Omega)\) is the amplitude of the flux of the returning electrons, \(\sigma_{\text{PICS}}(\Omega,\theta)\) is the differential photoionization cross section of energy \(\hbar\Omega\) under angle \(\theta\), and \(N(\theta)\) is the angular ionization probability. For the THz wave generated from the laser-aligned molecules, the alignment dependent THz amplitude is proportional to the cycle-averaged electric current density \(J_{R}(\theta,t)\)[81; 89; 38],  
\[E_{\text{THz}}(\theta)\propto\frac{d\langle J_{R}(\theta,t)\rangle}{dt}= \langle e\upsilon_{d}(t)n(\theta,t)\rangle, \tag{4}\]  
Figure 7: The reconstruction of the single molecular response: (**a**) the polar and azimuthal angles about the polarization axis of the aligning and the generation pulses, as well as the corresponding coordinate transformation; and (**b,c**) the experimental results and fittings of the relative amplitudes of the 21 st harmonic (the aligned yields divided by the random pointing yields), which are functions of the aligned angles (\(\alpha^{\prime}\)) and the time delay between the aligning and the generation pulses. where \(v_{d}(\theta)\) represents the drift velocity of the electron released at \(t\), and \(n(\theta,t)\) is the angle-dependent ionization rates at \(t\). It should be noted that the cycle average of \(n(\theta,t)\) is proportional to the angle-dependent ionization probability \(N(\theta)\). By replacing \(N(\theta)\) with the alignment dependent amplitude \(E_{\rm THz}(\theta)\), the angle-dependent differential PICS can be written in the form of [82]  
\[\sigma_{\rm PICS}(\Omega,\theta)\propto\frac{S_{\rm HHG}(\Omega,\theta)}{ \sqrt{S_{\rm THz}(\theta)}}. \tag{5}\]  
Since alignment dependent yields of HATS are obtained simultaneously, the angle-dependent differential PICS can be tracked within one run of experiment in an all-optical way. For comparison, the angle-dependent differential PICS obtained from the HATS detection are presented with the calculation results in Figure 8. When gradually increasing the momentum of the returning electrons from the 21 st to the 25 th harmonics, the corresponding minimum angles of PICS also increase (green arrows), which is caused by the antisymmetric \(p_{z}\) orbital participation within the HOMO of nitrogen molecule [90]. The experimental [21] and theoretical [88] angle-resolved PICSs are in good agreement, indicating a promising potential application in revealing molecular structures.
# The Stability of New Single-Layer Combined Lattice Shell Based on Aluminum Alloy Honeycomb Panels
###### Abstract  
This article proposes a new type of single-layer combined lattice shell (NSCLS); which is based on aluminum alloy honeycomb panels. Six models with initial geometric defect were designed and precision made using numerical control equipment. The stability of these models was tested. The results showed that the stable bearing capacity of NSCLS was approximately 16% higher than that of a lattice shell with the same span without a reinforcing plate. At the same time; the properties of the NSCLS were sensitive to defects. When defects were present; its stable bearing capacity was decreased by 12.3% when compared with the defect-free model. The model with random defects following a truncated Gaussian distribution could be used to simulate the distribution of defects in the NSCLS. The average difference between the results of the nonlinear analysis and the experimental results was 5.7%. By calculating and analyzing nearly 20,000 NSCLS; the suggested values of initial geometric defect were presented. The results of this paper could provide a theoretical basis for making and revising the design codes for this new combined lattice shell structure.  
honeycomb sandwich structure; single-layer combined lattice shell; stability test; random defect mode method; defect-sensitive structure +
Footnote †: journal: Applied Sciences