esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 4 Cross-Border Risk Transmission Model in the Global Shipping Network
### Construction of a Coupled Global Air and Sea Transport Network
#### 4.1.3 Shipping Network  
The shipping network data used in this paper are sourced from a third-party data provider, retrieving global vessel berthing data from January to June 2023. The raw data consist of a total of 3,279,155 records. By arranging the vessel data in chronological order, we obtained route information. Since this study examines cross-border risk propagation on a global scale, and each country contains multiple port routes, the city with the highest port throughput in each country is used to represent the trade relations between different countries. The number of voyages is then counted, resulting in a total of 6774 merged voyage data entries. Based on this, a global freight network is constructed with cities as nodes, international shipping routes between cities of different countries as edges, and the number of voyages as the edge weight.  
This paper processes the collected global maritime data using Python, with the importing and exporting countries and regions in the raw data as nodes and the freight value between each node as edges, constructing a global maritime complex network comprising a total of 241 nodes.  
In the constructed global maritime network, 33 nodes have a node degree of 100 or more, with 22 nodes having a degree of 200 or above. The node representing Sao Paulo has a degree of 270, making it the highest-degree node in the network. Calculations show that the average network degree \(<k>\) of the global maritime network is 54.64, indicating dense connections between nodes, and overall, the network exhibits a relatively dense state. The node representing Shanghai has a degree of 41, slightly below the average network degree, but still ranks among the top 17.8% of all nodes in terms of node degree, indicating relatively dense connections with other nodes.  
The average clustering coefficient of the maritime network constructed in this paper reached 0.77, indicating a high degree of clustering among network nodes, forming a complex network with nodes closely connected to each other, consistent with the conclusion reflected by the average node degree of the network. The clustering coefficient of Shanghai's node is 0.79, higher than the average clustering coefficient of the maritime network, which indirectly reflects that the nodes connected to Shanghai are relatively \"active\", with a higher probability of being connected to other neighboring nodes.  
In the maritime network constructed in this paper, the closeness centrality of Shanghai's node is 0.012, second only to the New York node's 0.247. This indicates that the distance between Shanghai's node and other nodes in the network is relatively short, facilitating quicker information dissemination.  
The following Figure 12 illustrates the interconnections between city nodes in maritime networks across different countries, generated using SCImago Graphica Beta 1.0.49 software:  
Figure 12: Maritime network diagram.
# Nano-Fibrous Networks from Co-Assembly of Amphiphilic Peptide and Polyelectrolyte
## 1 Introduction
In a comprehensive study, various polyelectrolytes such as \(\lambda\)-carrageenan or poly(acrylic acid) were studied together with heparin, alginate or hyaluronic acid in order to vary the aggregation strength with the PA, and the resulting membrane structure was discussed accordingly [22]. Bulk gels with lamellar microstructure were formed by vortexing solutions of chitosan and C\({}_{16}\)V\({}_{3}\)A\({}_{3}\)E\({}_{3}\)-OH with potential applications for protein delivery in regenerative medicine and wound healing [23]. Finally, poly(sodium 4-styrenesulfonate) (PSSNa) hydrogels were reported by [PERSON] et al. with sulfonate groups chosen to promote hydrogel mineralization, as well as binding sites for basic proteins. This dual functionality enables to retain and control the release of charged proteins with potential application in 3D environments for cell differentiation [24]. In the race to the production of high performance materials, controlling their structure/morphology at the lowest scale has become vital. The nanofabrication process becomes more complex when a percolating porosity is required in applications, such as in filtration membranes. In that case, one strategy to introduce a porous volume is to use nano-building blocks which will leave a space between them after assembly [25; 26; 27; 28; 29; 30; 31; 32; 33]. Here, the outstanding properties of PA molecules to self-assemble into nanofibers are combined to the mechanical properties of polyelectrolytes to yield porous composite materials. The objective of this work is to design new membrane materials via the mixture of a charged PA and a polyelectrolyte. These two precursors being water-soluble, a waterborne membrane production strategy is developed which fits with the objective of minimizing the environmental impact of membrane preparation. In this work, C\({}_{16}\)-V\({}_{3}\)A\({}_{3}\)K\({}_{3}\)G-NH\({}_{2}\) PA was synthesized and co-assembled in water with PSSNa into porous nanofibrous network. As stated above, mixtures of PA and polyelectrolytes generally produce spontaneous gels, which makes difficult to shape the membrane. Thereby, the ratio between PA and the polyelectrolytes was studied to optimize the material structure. Porous films were elaborated by tape casting and spray coating. Whereas self-supported membranes were mechanically weak, co-assemblies supported onto commercial ultrafiltration membranes could sustain water pressure up to 3 bars while a moderate permeability was measured confirming the existence of a percolated network. The produced membrane material falls into the ultrafiltration range with a pore radius of about 7.6 nm.
# Two-Way Linear Probing Revisited
## 2 Background and History
### Worst-Case Performance  
The focal point of this article, however, is the worst-case search time which is proportional to the length of the longest probe sequence over all keys (llps, for short). Many results have been established regarding the worst-case performance of open addressing.  
The worst-case performance of linear probing with the fcfs policy was analyzed by [PERSON] [9]. He proved that the maximum cluster size, and hence the llps needed to insert (or search for) a key, is asymptotic to \((\alpha-1-\log\alpha)^{-1}\log n\), in probability. As we mentioned above, this bound holds for linear probing with _any_ replacement strategy. [PERSON] and [PERSON] [43] studied the threshold of emergence of a giant cluster in linear probing. They showed that, when the number of keys \(m=n-\omega(\sqrt{n})\), the size of the largest cluster is \(o(n)\), w.h.p.; however, when \(m=n-o(\sqrt{n})\), a giant cluster of size \(\Theta(n)\) emerges, w.h.p.  
[PERSON] [44] proved that, with uniform probing and the fcfs replacement strategy, the expected llps is asymptotic to \(\log_{1/\alpha}n-\log_{1/\alpha}\log_{1/\alpha}n+O(1)\) for \(\alpha\)-full tables. However, [PERSON] and [PERSON] [21; 40] showed that, if random probing is combined with the lcfs policy, then the expected llps is at most \((1+o(1))\Gamma^{-1}(\alpha n)=O(\log n/\log\log n)\), where \(\Gamma\) is the gamma function.  
On the other hand, the robin hood strategy with random probing leads to a more striking performance. [PERSON] [22] first proved that the expected llps is \(O(\log n)\). However, [PERSON], [PERSON], and [PERSON] [45] tightened the bounds and revealed that the llps is indeed \(\log_{2}\log n\pm\Theta(1)\), w.h.p., thus achieving a double logarithmic worst-case insertion and search times for the first time in open addressing hashing. Unfortunately, one cannot ignore the assumption in random probing about the availability of an infinite collection of hash functions that are sufficiently independent and behave like truly uniform hash functions in practice. On the other side of the spectrum, we already know that the robin hood policy does not affect the maximum unsuccessful search time in linear probing. However, robin hood may be promising with double probing.
# Stomatal and Non-Stomatal Turbulent Deposition Flux of Ozone to a Managed Peatland
###### Abstract  
Ozone is a key trace gas in the troposphere; because it is a greenhouse gas, it is very reactive, and it is potentially toxic to humans, fauna, and vegetation. The main sink processes for ozone are chemical reactions and the turbulent deposition flux to the earth's surface. The deposition process itself is rather complex: The interactions between co-varying drivers such as the tropospheric ozone concentration, turbulence, and chemical reactions are not well understood. In the case of ozone deposition to vegetation, another aspect that must be studied is the role of stomatal regulation for a wide range of conditions. Therefore, we measured turbulent deposition fluxes of ozone with the eddy covariance technique during the peak of the growing season in 2014 over a managed, rewetted peatland in NW Germany. The deposition flux was large during the day (up to \(-15\) nmol m\({}^{-2}\) s\({}^{-1}\)) and relatively small during the night (between \(-1\) and \(-2\) nmol m\({}^{-2}\) s\({}^{-1}\)). Flux partitioning by applying the surface resistance analogy and further analysis showed that the stomatal uptake was smaller than non-stomatal deposition. The correction of stomatal conductance with the gross primary production (GPP) improved the estimation of day- and nighttime stomatal deposition fluxes. Statistical analysis confirmed that the friction velocity (u*) was the single most important driver of non-stomatal ozone deposition and that relationships with other environmental drivers are not linear and highly variable. Further research is needed to develop a better process understanding of non-stomatal ozone deposition, to quantify the role of surface deposition to the ozone budget of the atmospheric boundary layer, and to estimate uncertainties associated with the partitioning of ozone deposition into stomatal and non-stomatal fluxes.  
ozone deposition; turbulence; eddy covariance; peatland; greenhouse gases +
Footnote †: journal:
## 2 Optically Transparent Antennas  
An optically transparent antenna acts similar to a normal conventional antenna, comprising ground, substrate, and radiating patch layers. Glass, quartz, acryl, sapphire, and Plexiglass are considered rigid substrate materials. Flexible includes PEN, PDMS, polyamide, PI, PET, and other materials, including water, Rogers 6002, PVC, and quarter glass, as mentioned in [67]. Top and bottom copper comprise either transparent conductive oxide material (TCOM) or metal mesh. Depending on the application, the substrate and radiating elements are chosen. These antennas are suitable for wireless communication, especially 5G, automotives, MIMO communication [68], and wireless access points, or may be combined with a satellite's solar panels [69] or in glass of a building for efficient 5G and IoT communication [27].  
OTAs are designed using conductive films or metal meshing, as illustrated in Figure 3. The metal mesh allows the meshing of a radiating patch with mesh made up of copper, aluminum, gold, and silver having geometrical shapes of square, diamond, and hexagonal. Achieving transparency through conductive films requires the layering of conductive film materials. Moreover, the combination of the metal with conductive mesh also results in transparent antennas; details of such materials are mentioned in Figure 3.
## Case Study: Product Development
### Modeling
#### 6.2.2 Activity Detection  
The activity detection means recognition of the subject's posture and/or action. There are five different states that should be distinguished, such as sitting, lying, standing, walking and falling.  
As the most dangerous case, reliable fall detection is the most vital. The \(SMV\) defined by (1) is a suitable measure to detect a fall. The tumble causes changes in the \(SMV\) with distinctive positive and negative acceleration peaks corresponding to its beginning and the final contact with the floor, as shown in Figure 3. However, in some cases, e.g., a rapid onset of the walk could create similar \(SMV\) changes. Nevertheless, it is possible to avoid false alarms by monitoring also the accelerometer measures in the \(x\)-, \(y\)- and \(z\)-axes or by an additional localization and posture checking.  
The information about a dynamic posture, such as walking, is determined on the basis of the three-axial accelerometer and magnetometer along with the PDR algorithm, used also for the localization method described in the previous subsection. Whenever the designed system detects a step, the system interprets such activity as walking.  
The subject's static postures, such as lying, sitting and standing, can be recognized and distinguished by means of the three-axial accelerometer and magnetometer. The lying position can be easily distinguished from the other postures because in this position, gravity affects mostly the acceleration \(z\)-component, whereas when standing and sitting, the \(y\) direction is the dominant acceleration component caused by gravity. Therefore, a suitable discriminator can determine when the \(z\)-component exceeds the other two components. The most difficult is to distinguish between  
Figure 3: Exemplary \(SMV\) characteristics for the fall test.  
sitting and standing. These postures cause similar acceleration with just small acceleration deviations resulting from slight movements of the body. Therefore, the system cannot differentiate between characteristic features of sitting and standing; however, it can detect the change of posture. Analyzing the accelerations in the \(x\), \(y\) and \(z\) directions along with the \(SMV\) vector makes it possible to find out the threshold levels to distinguish between sitting down from standing up. Moreover, using appropriate threshold levels makes it possible to distinguish the actions of lying down on a bed from a sitting position and also the action of getting up from the lying down position.
###### Abstract
[30] proposed conservative Q-Learning (CQL) for determining the initial drug dose and evaluating the final drug dosage using clinical experience of professionals. However, RL, as a popular approach to cope with the sequence problem, is particularly good at optimizing specific goals, but when optimizing medicine dosage, a multi-objective problem occurs. If simply tumor suppression or elimination is desired, the medication concentration in the patient's body will be high, causing the normal cell population to be similarly removed and the immune system to be compromised, perhaps leading to fungal or viral infections. Severe organ damage in the patient's body is another risk associated with high medication concentrations. In order to achieve a balance between multiple objectives in optimization, we should not only focus on eliminating tumors as the end goal, but also consider the variables that may have an impact on the patient's quality of life while undergoing treatment [31; 32]. Patients should therefore receive a multi-objective optimized treatment plan [33]. In this paper, we proposed a Multi-Objective Deep Q-Network based on Multi-Indicator Experience Replay (MIER-MO-DQN) to address the problem of exact medicine dose regulation. First, we provide a mixed evaluation model based on the integration principle, which, by adding two different nonlinear decision techniques on the conventional multi-objective decision methods, enables multi-objective deep reinforcement learning to make more logical decisions. Second, in contrast to the conventional experience replay, we design a novel experience replay for multi-objective deep reinforcement learning that takes into account more indications and establishes a multi-objective format in order to increase the network's stability and speed of convergence. Third, while still preserving the level of health that the patient considers acceptable, our algorithm is capable of choosing the most effective treatment plan out of a variety of drug doses for patients with various physical conditions. Our method performs better when compared to existing multi-objective deep reinforcement learning methods. This paper is organized as follows, with Section 2 detailing the mathematical model of cancer chemotherapy. In Section 3, the detailed MIER-MO-DQN implementation and modeling problem are presented. The experimental results are presented in Section 4, along with a discussion of the suggested model's robustness. Section 5 concludes our study.
## 5 Experimental Results and Analysis
### Additional Comparison  
To better demonstrate the performance of our proposed MDPCA-DBN model, we compared its performance with eight state-of-the-art intrusion detection techniques, namely, a Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm (SCDNN) [30], Self-taught Learning Technique (STL) [53], Deep Neural Network (DNN) [54], Gaussian-Bernoulli RBM [38], Recurrent Neural Networks (RNN-IDS) [55], a Multiclass Cascade of Artificial Neural Network (CASCADE-ANN) [56], EM Clustering [27] and Decision Tree (DT) [27]. Table 13 demonstrates the comparison results of the proposed model with other models in terms of accuracy, DR (detection rate) and FPR (false positive rate) on the NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21), and UNSW-NB15 datasets. As shown in Table 13, the proposed method achieved the best performance in terms of accuracy, detection rate and false positive rate on the NSL-KDD (KDDTest +) and NSL-KDD (KDDTest-21) datasets. It can be seen from the results that the proposed method achieved the highest accuracy of 90.21% and the highest detection rate of 96.22% on the UNSW-NB15 dataset, but the FPR is slightly worse. The CASCADE-ANN method proposed by [PERSON] et al. [56] achieved a better false positive rate (with 4.05% more), but its accuracy and DR are worse than the proposed method.  
Simulation results show that the proposed method outperformed on the NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21), and UNSW-NB15 datasets in terms of Accuracy, DR and FPR except for CASCADE-ANN (FPR on UNSW-NB15 dataset). The results clearly demonstrate that the detection performances of the proposed model are more effective. However, because deep learning requires two stages of pre-training and fine-tuning, the proposed method requires more computation time than other shallow classification methods.
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
## 5 Conclusions  
This paper first proposes a QC-LDPC code, co-processing method, and VLSI architecture for Wi-Fi 6 chips. Through field splicing and block splicing, the strategy and architecture are compatible with all possible protocol modes (>10\({}^{6}\)). The corresponding implementation result shows the co-processor enables a maximum throughput of 16 Gbps, a maximum latency of less than 1 us, a hardware complexity of 136 kGE, and can flexibly scale to future 8-space streams and 16-space streams.  
**Author Contributions:** Conceptualization, B.W.; methodology, Y.W.; implementation, Y.W.; writing-original, Y.W.; writing-review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The RTL codes of the co-processor can be found at: [[https://pan.baidu.com/s/11K9](https://pan.baidu.com/s/11K9) gRxO0 Ve-5 RnJf02 hgg6Q?pwd=t2g4.w]([https://pan.baidu.com/s/11K9](https://pan.baidu.com/s/11K9) gRxO0 Ve-5 RnJf02 hgg6Q?pwd=t2g4.w) (access on 1 January 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Area (mm\({}^{2}\))** & **Complexity (kGE\({}^{1}\))** & **Percentage (\%)** \\ \hline Block Stitching & 0.079 & 61.91 & 45.52 \\ Input Buffer & 0.046 & 35.73 & 26.28 \\ Output Buffer & 0.037 & 28.63 & 21.05 \\ Global Control & 0.005 & 4.01 & 2.95 \\ Preprocessor & 0.167 & 130.28 & 95.80 \\ Global Control2 & 0.001 & 1.01 & 0.74 \\ Input Buffer2 & 0.005 & 3.88 & 2.85 \\ Bit Width Converter & 0.002 & 1.32 & 0.97 \\ Postprocessor & 0.008 & 6.21 & 4.56 \\ Total & 0.175 & 136.49 & 100 \\ \hline \hline \end{tabular} \({}^{1}\) One gate equivalent (GE) corresponds to the area of a two-input drive-one NAND gate of size 1.28 um\({}^{2}\).  
\end{table}
Table 3: Hardware complexity of the proposed implementation.  
Figure 9: Postprocessor Architecture.
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Other Considerations  
Predictions need to be perfectly identical when compressing and decompressing, because, otherwise, the decoder will rely on false data. Floating point operations cannot guarantee cross compiler, cross processor, and cross-operating system this hard constraint. It became even more important to have fixed rules when support for streaming instructions like SSE and AVX was added. It was decided that fixed point arithmetic will be used across all operations. Even setting initial values for lookup operation tables like stretching, squashing or logarithm was done by interpolation of initial integer values or by numerical integration. Components described here use fixed-point values with varying point position. The representation can be on 16 bit or 32-bit integers. For example, representing the weights of the context mixing algorithm in 16 bit is useful when using vector instructions, since more values fit into operands. Some exceptions to this rule were made for the sake of maximum compression for the wav model and the ordinary least squares algorithm used in image compression.  
Another unintuitive part is that the update part of each model takes place right before the prediction part. The first prediction is by default 0.5 since it relies on no information. Afterward, each time the predictor is queried, it does the update with the known bit and then computes a prediction. This is done as an optimization since the accessed memory locations during the update might still be loaded in the cache and the prediction might need the same locations.