esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A time dependent, incompressible, laminar flow of a Cu-Al\({}_{2}\)O\({}_{3}\)/H\({}_{2}\)O hybrid nanofluid has been inspected in a rectangular channel whose permeable walls have a capability of dilating and squeezing. Al\({}_{2}\)O\({}_{3}\)/H\({}_{2}\)O has been taken as a nanofluid in which the influence of three different shapes, platelet-, cylinder- and brick-, of Cu nanocomposites have been further examined. Moreover, the graphical aid has been provided to capture the deviations in velocity and temperature with the rising or falling of various embedded parameters. The graphs describing the phenomena of local rate of heat transfer under the influence of various parameters are also included.  
The following conclusions have been drawn from the present study:  
* An upsurge in temperature has been detected for the case of injection/squeezing against the increasing \(\Phi_{2}\). For injection/dilation case (Figure 13), a significant drop in temperature has been perceived below the central line of the channel with growing Cu volume fraction \(\Phi_{2}\). However, it is again experiences a rise in the other part of the channel.
* The platelet shaped nanostructures prominently possess the higher temperature values as compared to cylinder- and brick- shaped nanostructures.
* Shape factor remains unsuccessful to produce a striking change in the velocity.
* The growing absolute values of \(\mathcal{A}\), \(\alpha\) and \(\mathcal{R}\) considerably transmit the heat at the upper wall of the channel. Besides, a similar behavior has been noticed for increasing values of \(\Phi_{2}\) except when suction accompanied with contraction.
* \(\mathrm{Nu}_{\mathrm{l}}\) mostly remains on the higher side for increasing Cu nanocomposite volume fraction \(\Phi_{2}\).
* In most of the cases, platelet shaped nanocomposites prove a better heat transfer ability as compared to the other shapes of nanoparticles.  
**Author Contributions:** [PERSON] and N.A. developed the problem and its MATHEMATICA code. U.K. in collaboration with [PERSON], did the literature review, developed and implemented the computer code, and interpreted the subsequently obtained results. [PERSON] did the editing and removed the grammatical mistakes. [PERSON] in consultation of rest of the Authors, reconfirmed the credibility of obtained solutions. All authors have read and approved the final manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments and Results
### Absorption Losses in MHz Radio Bands at Different Conductivities of Water  
The results shown in Figure 10 show the range of radio frequencies whose losses are under 3 dB.  
The results in Figure 10 show the absorption losses for MHz radios in various water conductivities. All of the results are summarized in the Table 5.  
Figure 8: Absorption loss in 2.4 GHz in freshwater conductivity.  
Figure 9: Absorption loss in 2.4 GHz in river water and seawater conductivities.  
Thus, for absorption losses to be less than 3 dBs at water conductivity of 0.001 S, the radios ranging from \(1\times 10^{8}\) Hz or greater can be used. Similarly, for absorption losses to be less than 3 dB at water conductivity of 0.01 S, the radios ranging from \(1\times 10^{7}\) Hz or greater can be used. In addition, for absorption losses less to be than 3 dB at water conductivity of 0.001 S, the radio ranging from \(1\times 10^{6}\) Hz or greater can be used.
## 4 Conclusions  
In this paper, in order to realize the simultaneous enhancement of \"oscillation power and frequency\" of the planar Gunn diode, we propose the multi-2 DEG-channel self-parallel diode (SPD) for the first time, and present a microscopic analysis on electron movements and RF characteristics of SPD. A trench anode is etched through 2 DEG channels and breaks the longer 2 DEG channel into several shorter channels. As a result, one regular Gunn diode is equal to several diodes in parallel. Therefore we name such diode a self-parallel Gunn diode (SPD).  
In a symmetric multi-2 DEG-channel SPD, dipole domain forms and travels in each channel without interference; therefore, the oscillation power is nearly multiplied as compared with the regular diode. We also analyze the complicated physical mechanism of the asymmetric multi-2 DEG-channel SPD, where L\({}_{2}=n\) L\({}_{1}=n\)L (where \(n\) is an positive integer and \(n\geq 2\)). Based on the simulation results, \(n\) circles of the electron domain in shorter channels approximately equal one circle of the electron domain in the longer channels, which results in \(n\) current peaks in the current waveforms and \(n\) harmonic components. The highest output power and efficiency are achieved on the \(n\)th harmonic, and its frequency is determined by L\({}_{1}\).  
Asymmetric SPD can not only generate several harmonic components, but also offers an easy transfer between two different fundamental frequencies. In addition, based on SPD structure, we can do further studies on the coupling mechanism of more complex electronic domains, which will provide theoretical guidance for designing and optimizing Gunn diodes.  
Y.W. and L.Y.; methodology, Y.W., and and L.-A.Y.; software, Y.W.; validation, Y.W., L.-A.L. and J.-P.A.; formal analysis, Y.W.; investigation, Y.W.; resources, Y.H.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, L.-A.Y., L.-A.L.; visualization, J.-P.A.; supervision, L.-A.L., Y.H.; project administration, J.-P.A.; funding acquisition, J.-P.A., L.-A.Y. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the National Key Research and Development Program (Grant No. 2017 YFB043000), the National Natural Science Foundation of China (Grant No. 61274092, and 61674117) and the China Postdoctoral Science Foundation (Grant No. 2019M653554)  
The authors declare no conflict of interest.  
[PERSON]; [PERSON] High-Power Solid-State Terahertz Sources; 2015 SPIE Newsroom.  
Figure 7: The frequency spectrums for the single-2 DEG-channel SPD (a) and triple-2 DEG-channel SPD (b) respectively.
## 8 RAN Decomposition Scheme
### Lessons Learned  
The advent of C-RAN helps in addressing various challenges of conventional DRAN. However, the employed CPRI in the C-RAN imposes stringent requirements on the transport network for wireless signal transmission. Consequently, it will be demanding for the CPRI-based link to support hundreds of Gbps capacities being envisaged by the 5G and beyond networks. To attend to the issue, the conventional RAN functional block demands further partitioning into modules. The split should be logical to ensure that the functional modules can be appropriately and flexibly assigned to either CU or DU, as the case may be and based on the specified use cases. In this context, the RAN FSOn scheme is a viable technique for alleviating the network requirements. Note that, based on the adopted split point, the RAN FSOn exhibits some trade-offs concerning the complexity, latency, bandwidth, and JP support on the transport networks. This results in relatively different QoS requirements and variations in the transport requirements for different deployment scenarios. Consequently, the adopted split point can considerably affect the transport design and the RAN architecture. Therefore, the MNOs and MVNOs have to weigh the trade-offs with the intention of choosing an applicable split option that could best serve the projected use cases [23; 425].  
Generally, in the FWA deployment, the split options from 1 to 6 can be effectively and suitably supported with transport solutions that are capable of offering data rates within the range of 1 Gbps per cell. Consequently, PtMP-PON schemes such as CWDM-PON, DWDM-PON, and TWDM-PON can be employed without any significant effect on the latency. This is due to the fact that a unique wavelength can be allotted to each cell for effective communication. Conversely, for Options 7 and 8, high capacity PtP optical links are required for the transport network [23]. In addition, the cells that are offering capacity, which is higher than 10 Gbps wireless speed using massive MIMO technology are expectedto be deployed spot-by-spot instead of dense deployment. Regarding this, it will be more viable to shift the demanding MIMO processing function away from the antenna site to the CU, by employing the C-RAN architecture. This implementation will give comparatively simplified DU with low-footprint and low-cost [393].
# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 3 Our Method
### Regression Network  
Motivated by Fast R-CNN [6] where a regression network is incorporated to refine the bounding box of the predicted object, we introduce a regression network for the temporal boundary refinement of the proposals, which is illustrated in Figure 5. Further, we train the regression network via multi-task leaning. It is also used as a window-level classifier to predicate the proposal candidates into proposals.  
Given a proposal candidate \(p\), which is composed of segments \(\left\{s_{i}\right\}_{s}^{e}\) with features \(f_{RGB}=\left\{fri_{i}\right\}_{s}^{e}\) and \(f_{OF}=\left\{fo_{i}\right\}_{s}^{e}\), where \(s\) and \(e\) are the indices of staring and end segments of the proposal candidate \(p\), \(fr_{i}\) and \(fo_{i}\) are the pool5 features of segment \(s_{i}\), respectively, as extracted by the trained RGB and OF Res3D. The representation of the proposal candidate \(p\) is given by:  
\[Concatenation(AveL2(F_{RGB}),AveL2(F_{OF})), \tag{2}\]  
where \(AveL2(*)\) represents the operation of averaging and L2-normalization. The regression network takes the representation of proposal candidate as input, and outputs the confidence score, indicating whether the input proposal candidate is an action proposal, and also outputs the temporal boundary regression offsets, which are defined as follows:  
\[o_{s}=s_{p}-s_{g}, \tag{3}\]  
\[o_{e}=e_{p}-e_{g}, \tag{4}\]  
where \(s_{p}\) and \(e_{p}\) are the starting and ending segment indices of the proposal; \(s_{g}\) and \(e_{g}\) are the starting and ending segment indices of the matched ground truth. We employ multi-task loss \(L\) to jointly train the classifier and temporal boundary regression.  
\[L=L_{cls}+\lambda L_{reg}, \tag{5}\]  
where \(L_{cls}\) is a standard binary softmax cross-entropy loss for action/background classification. \(L_{reg}\) is the temporal regression loss, which is defined as:  
\[L_{reg}=\frac{1}{N_{p}}\sum_{i=1}^{N_{p}}l_{i}(|o_{s,i}|+|o_{e,i}|), \tag{6}\]  
where \(l_{i}\) is the label, \(1\) for positive (action) sample and \(0\) for negative (background) sample. \(N_{p}\) is the number of positive samples. In other words, we only regress the boundary of positive samples. We utilize L1 norm to make the loss be more robust to outliers. The strategy to construct training samples is described in Section 4.2.  
Additionally, the predication score of the proposal is also used to evaluate the proposal and to refine the action categorization score in the classification step.  
Figure 5: The illustration of boundary refinement.
# A New Image Oversampling Method Based on Influence Functions and Weights
## 4 Results and Discussion
### Generate Image Quality Analysis  
Figure 6 shows the oversampling effect of different methods in the CIFAR-10 image dataset, which we analyze in comparison with the BANG, GAMO, and DeepSmote methods. The BANG method generates a very blurry image with misalignments between pixels in the image. The GAMO method eliminates pixel misalignments in the image. However, it generates a more blurry image, which makes it difficult for the eye to recognize the category of each image, which makes it difficult for the classifier to learn the image features. DeepSMOTE effectively improves the quality of image synthesis because of its efficient encoding/decoding structure with an enhanced loss function. However, compared to our method, some of the images in the DeepSMOTE method are blurred, and our images are more explicit in synthesized images and more similar to the original images.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 3 Multi-Agent Planning and Control
### Soft Locally Interactive Structure  
Multi-agent tracking exhibits a more subtle \"soft\" form of locally interactive structure. Suppose the interest-point locations are actually known beforehand (in practice, the agents must explore the grid and share their observations to determine these locations). The optimal policy (that visits the most interest-points) solves a more difficult version of the Travelling Salesman Problem. It requires an exhaustive search of all possible paths over the grid, enumerating the length of each path, and selecting the path of shortest length. There are exponentially many such paths in the size of the grid and number of agents. The following argument based on \"soft\" locally interactive structure yields an \"approximately\" optimal policy without the exponential complexity of an exhaustive search.  
There are two scenarios to consider: one where the normalized distribution of agents matches the distribution of interest-points, and the other where the two distributions differ. In the first scenario, agents visit the interest-points in their immediate vicinity to maximize the total number of visitations. In the second, some (or all) of the agents must first re-deploy to areas of the grid that are under-served to restore the equilibrium between both distributions, so they can resume visiting interest-points in their immediate vicinity. Thus, as long as the system remains in scenario one, the tracking problem is locally interactive; the interest-points in the immediate vicinity of each agent take precedence over the interest-points further away. Only when the distributions diverge, does remote activity affect the optimal behavior of any given agent. The overall effect is a soft locally interactive structure, where the system is mostly in the equilibrium of scenario one. This situation is depicted in Figure 3.  
The performance of \(\pi^{*}(f(s))\) in (13) depends in part on the compression loss (i.e., the loss of useful information) which occurs when the compression functions \(\{f_{i}:i\in\mathcal{N}\}\) do not perfectly conform to the locally interactive-structure in \(\mathcal{S}\). It is difficult to quantify this loss without solving the multi-robot TSP problem alluded to earlier.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 4 Simulation Results and Analysis  
For inspecting the efficacy and reliability of the MPFDR algorithm, simulations were conducted using MATLAB 2018b on a machine running Windows 10. The machine has an Intel Xeon E5-2620 v4 CPU and 32 GB memory. The parameters setup for the simulation of the proposed algorithm is reported in Table 1. In this section, a comprehensive comparison is carried out based on the simulation results. In terms of performance analysis, the following three types of comparative performance indicators are adopted: the network lifetime (NL), average hops (AH), average energy (AE), and packet loss ratio (PLR). Among them, as mentioned in definition 4, NL can show the effect of the algorithm in balancing node energy to maximize the WSN lifetime. PLR may be evaluated as the ratio of packets that should have been received by the sink but were not. It can effectively indicate transmission reliability. AH can indicate the network delay. It may be measured as the number of nodes in which a packet passes from a source node to a sink. AE denotes the average energy consumption of the entire network in every transmission period, which can measure the energy economy.
# Opportunities for Early Detection and Prediction of
###### Abstract  
Industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems, which control critical infrastructure such as power plants and water treatment facilities, have unique characteristics that make them vulnerable to ransomware attacks. These systems are often outdated and run on proprietary software, making them difficult to protect with traditional cybersecurity measures. The limited visibility into these systems and the lack of effective threat intelligence pose significant challenges to the early detection and prediction of ransomware attacks. Ransomware attacks on ICS and SCADA systems have become a growing concern in recent years. These attacks can cause significant disruptions to critical infrastructure and result in significant financial losses. Despite the increasing threat, the prediction of ransomware attacks on ICS remains a significant challenge for the cybersecurity community. This is due to the unique characteristics of these systems, including the use of proprietary software and limited visibility into their operations. In this review paper, we will examine the challenges associated with predicting ransomware attacks on industrial systems and the existing approaches for mitigating these risks. We will also discuss the need for a multi-disciplinary approach that involves a close collaboration between the cybersecurity and ICS communities. We aim to provide a comprehensive overview of the current state of ransomware prediction on industrial systems and to identify opportunities for future research and development in this area.  
ransomware; industrial control systems; SCADA; ransomware detection and prevention; attack likelihood prediction; situation awareness; security assessment +
Footnote †: journal: Future Internet  
protocols, which can increase the complexity of the systems and make it more difficult to secure them. Among the malware types, ransomware has been utilized in recent years to extort victims by locking their personal and operational data in exchange for a ransom [9]. There is no doubt that ransomware attacks are on the rise as well as some other types of cyberattacks. Therefore, the ways to properly address the problem are important.  
Not only are PCs and mobile devices targeted by ransomware attacks, but also Industrial Internet of Things (IoT) and SCADA systems [10]. The issue is exacerbated in cyber-physical and industrial systems such as SCADA due to the adoption of security measures and procedures from the traditional systems [11]. These measures and procedures do not fully conform or fit with the SCADA environment and context due to their special nature and the data exchanged between each of the critical components. These aspects are highly dependent on the system context, operations, and data-processing flow. Such aspects can be investigated through several factors that contribute to deterring the momentum of ransomware attacks in industrial control systems (ICS).
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 2 Cavitation Mechanism and Flow Dynamics in Cavitating Jets
### Cavitating Jet Flow Dynamics
The PIV results showed a periodic change in the jet velocity along the jet axis and the cavitation cloud goes through the following phases: emergence, expansion, development, compression, redevelopment, compression, and collapse. In addition, the bubble cloud length increases for higher pump pressure. They also found that the acceleration of the acoustic shock wave of the jet is between 0 and 6000 m/s\({}^{2}\).
## 2 Methodology
### Annual Simulations
#### 2.3.2 Daylight Simulations  
Daylight simulations are performed using the Three-Phase Method [15] in Radiance. It is an alternative to the expensive Daylight Coefficient method though it builds on top of it by splitting the flux transfer path into multiple phases. A very good implementation of this method is available in Ladybug-Honeybee [16] with workflows for the annual daylight simulations. The Three-Phase Method supports point in time parametric simulations with complex fenestration systems such as Electrochromic glass. It usually requires generation  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Glare Perception** & **DGP Value** \\ \hline Imperceptible & \textless{}0.36 \\ Perceptible & 0.36–0.4 \\ Disturbing & 0.4–0.45 \\ Intolerable & \textgreater{}0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Significance of DGP values.  
of four types of matrices to be generated before the illuminance values are calculated at the workspaces.
* The View (V) matrix contains the flux transfer from the points on the workspace where illuminance is to be calculated to the Electrochromic glass.
* The Transmission (T) matrix contains the flux transfer through the Electrochromic glass.
* The Daylight (D) matrix contains the flux transfer from exterior of Electrochromic glass to the sky.
* The Sky (S) represents the descritized sky vector with 145 patches based on Tregenza sky model(ref) The resultant matrix is given by  
\[E=V*T*D*S\]  
where \(E\) is an illuminance matrix for the points on the workspaces. The matrices \(V\) (\(12\times 145\)) and \(D\) (145 \(\times\) 145) are generated with radiance-based workflows involving _rfluxmtx_ or _contrib._\(V\) is based on the location of the points on the workspace and \(D\) are daylight coefficients w.r.t to EC glass and the sky, respectively. The \(T\) matrix is a BSDF of the different EC glass states obtained from the Radiance's _genBSDF_. A detailed description of the workflow can be found in a tutorial by [PERSON] [17]. These simulations also generate a lot of data, as they are run for 8760 h in a year for three points in each of the workspaces, even though this is less expensive than the glare simulations and can be performed overnight on a normal computer.
###### Abstract  
As a new type of unmanned autonomous marine observation platform, underwater gliders (UGs) offer advantages such as low energy consumption and long operational ranges. However, during the gliding process, the complex marine environment often leads to abrupt changes in short-term control parameters, complicating the data and rendering them more challenging to predict. This typically poses difficulties in adjusting control parameters based on human experience, thereby significantly reducing UG control efficiency. To address this issue, this paper proposes a novel method termed DFFormer, aimed at enhancing the predictive accuracy of the rudder angles during UG motion. The proposed method integrates discrete wavelet transform (DWT) for rudder angle signal decomposition and employs a fast Fourier transform-based attention mechanism (FFT-Attention) to effectively capture and analyze its frequency- and time-domain characteristics. Notably, the method leverages a Transformer architecture to process the decomposed signals through multiple parallel pathways, substantially improving the capability to forecast the complex and variable control parameters of UGs. The effectiveness and practicality of the proposed method are demonstrated through actual sea trials. The experimental results indicate that the proposed method surpasses traditional approaches in terms of accuracy and computational efficiency, exhibiting superior performance in predicting UG control parameters and, to some extent, enhancing their heading-keeping ability.  
underwater glider; control parameters predicting discrete wavelet transform; fast Fourier transform +
Footnote †: journal: Marine Science and Engineering  
_Article_