esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments and Results  
We performed several experiments to evaluate the proposed multitask NMT model on different translation tasks and POS tagging task. The proposed model is reviewed on the translation task from MSA to AD and from MSA to English. The experiments were conducted on two different types of Arabic dialects: Levantine and Maghrebi, Levantine is a spoken variety of widely used in Jordan, Syria, Palestine and Lebanon. Maghrebi Arabic is a spoken dialect used in Morocco, Algeria and Tunisia. The Multi-task Learning Based-Neural Machine translation Models will be used to handle the low-resource languages. Moreover, 10,000 sentence pairs was used for MSA-ENG translation task and 20,000 sentence pairs was used for the ENG-GER translation task.
## 3 Approach
### Digital Twin Interactions  
The DT proposed at the beginning of this chapter takes the shopfloor data, the worker's information, and process description from the database to inform the user about the status of the production line and to give rotation suggestions based on previous recorded live data. Based on the premises mentioned before, the following flow of events is proposed to send a rotation suggestion (Figure 3):* The complex event processing (CEP) component of the DT detects when a rotation of the workers has been triggered. The CEP is a method for tracking and analyzing information flows about things that happen and extracting a conclusion from them. In this case, it analyzes the process information flow received by the DCF. The frequency at which the DCF receives information from the shopfloor depends on how the information is updated in the system, by pulling the data or listening to events. This configuration may vary according to how the middleware and DCF are programmed in the specific system. Then, the CEP in every instance checks the status.
* Regular information flow: the process workflow is normal, and they are producing; therefore, the DT must be recording data.
* Interrupted information flow: digital twin has stopped receiving signals from the production line. This can be due to two circumstances: the shopfloor had a problem that caused it to stop production. In that case, DT informs the user through different notifications in the dashboard. The other scenario contemplates that a break has occurred, so a suggestion of rotation is needed.
* In this last case, a rotation request is sent. This request contains the ID of the rotation requested as well as the current position of the workers in the workstations. It also includes the historical record of which operator has been working in which station during the entire shift (assumption 3).
* The human scheduling engine sends a proposed rotation based on the data processed by the human skills modeling engine before it was triggered.
* The digital twin displays the proposed rotation to the user through the UI. It can be accepted or modified since it is a proposition, and the user has the right to choose. However, the modification of the suggested rotation is subject to the following conditions.
* The system does not allow modifying the rotation of workers to keep the worker in the same workstation continuously during the shift.
* The worker proposed for a job can only be changed in the case of real need (e.g., illness) and can only be replaced by another from the general list of workers and not by one of the workers proposed for another station.
* Once the position of one worker is modified, the user needs to fill a report indicating the causes of the change.
* Then, the final rotation is confirmed to the DCF and CEP as a rotation confirmed.
* Finally, the data processing with the new workers positioned at their new workstations begins. This cycle is repeated throughout the work shift.  
Figure 10 shows the proposed workflow.  
Figure 10: Sequence diagram of a rotation request.
## 3 Proposed Method  
To enhance the embedding performance, and against the KPA attack proposed in [26], a novel RDH-ED method is proposed. The framework of the method is outlined in Figure 6, which consists of three primary stages: A. Image Encryption. The cover image is encrypted by the image owner using the encryption key, yielding the encrypted image; B. Data-Hiding. Upon receipt of the encrypted image, the data hider employs the data-hiding key, to encrypt the data and embed it; C. Data Extraction and Image Recovery. Based on the type of key retained, the receiver can either extract the embedded information, decrypt the original image, or extract the data and decrypt the original image concurrently.
## 5 Conclusions  
This paper has introduced a \"double-boomerang\" watermarking technique for dynamic QoS and error control management of multimedia content in wireless IP networks. We employed the transmitted video-sequences to monitor the QoS in a videoconference call. Every video-sequence of each user involved in the communication describes a boomerang trajectory and the QoS management process is equally distributed between the network's nodes. Our technique can be also used for quality control in UMTS environments, in particular it aims to infer with the outer loop power control for quality measure. The performance of the proposed method has been analyzed by wide simulation trials, focusing on multimedia communications environments, with typical UMTS operative settings taking into account the near-far effect. The obtained outcomes evidence the sensitivity of the proposed method and show the capability of this network-aware processing of multimedia content to react to the alterations suffered by the data through the communication channel.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### Protocol Format
#### 2.4.3 HTTP Protocol  
HTTP is a stateless request-response protocol widely used for data exchange between web browsers and web servers. It is used to transfer web pages, images, videos, and other resources.  
HTTP Request: Consists of a request line, request headers, and a request body. The request line includes the request method (such as GET, POST), the target URL, and the protocol version (such as HTTP/1.1).  
HTTP Response: Consists of a status line, response headers, and a response body. The status line contains a status code (such as 200 OK), the protocol version (such as HTTP/1.1), and the response status description. The response headers provide metadata such as cache control, content type, and server information.
###### Abstract
An increasing number of researchers are attempting to model human skeletons using hypergraphs and employ hypergraph neural networks to obtain the feature representations of human actions, thereby facilitating more efficient human action recognition. To address the above issues, this paper proposes a framework to explore high-order skeleton correlations with physical and non-physical connections for action recognition. Specifically, the contributions of this paper are as follows:  
* Firstly, this paper proposes a spatio-temporal hypergraph modeling method of human skeleton correlation. This method focuses on the inherent physical connections and non-physically connected relationships among human skeletons. For different actions, different weights are assigned to different vertex in the hypergraph, which highlights the regions that have a significant impact on human action. * Secondly, this paper proposes an adaptive multi-channel spatio-temporal hypergraph neural network (AMC-STHGNN). The network captures the high-order correlation among human skeletons, and the features of different data channels are adaptively fused, making full use of the complementarity and diversity among three types of features. * Finally, our proposed method is tested on two public datasets and shows a superior performance, effectively enhancing the ability of environmental perception and action analysis. The rest of this paper is organized as follows. We first introduce related work in Section 2, including action recognition based on skeleton and hypergraph neural networks. Then, in Section 3, we review the basic theory of hypergraphs and hypergraph neural networks. Section 4 provides detailed implementation details of the proposed method. The experimental results and discussions of the proposed method are presented in Section 5. Finally, a summary of the entire paper is given in Section 6.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Positional Encoding
#### 2.7.1 Temporal Information  
The temporal order of features at the input of the network is identified through 1D trained positions expressed by a vector added to the input of the model. This time information is essential to correctly understand the order of the timesteps and prevents the Transformer from mixing them up. As shown in Figure 8, the trained Transformer model clustered 1 day in the vicinity with the most similar properties. The days that are far apart in time have mostly a negative value of similarity in positional encoding of the decoder block. The encoder finds the strongest similarity in several days \"around\".
# Dynamic Response and Geogrid Strain Analysis of GRS Retaining Wall
## 4 Geogrid Strain Calculation Method
### Test Verification  
In order to verify the rationality of the strain calculation method of the geogrid under external load proposed in this paper, a set of model tests under static load conditions were conducted. After the filling was completed, the loading was started from 10 kN step by step until \(F\) = 140 kN, and the load was increased by 10 kN at each level and continued for 15 min, until the retaining wall was damaged. The lateral displacement of static load, with equal amplitude loading and equal frequency loading \(F\) = 140 kN, is shown in Figure 16.  
Two groups of tests with static and equal amplitude loading were selected among them, the strains of the geogrid under each level of loading were calculated, the calculated values of geogrid strain were compared with the measured values and the results are shown in Figure 17. From the figure, it can be seen that the calculated  
Figure 16: Lateral displacement at \(F\) = 140 kN.  
good agreement, and it is feasible to calculate the geogrid strain under external loading by applying the calculation method in this paper.  
This method can be used for the calculation of geogrid strain in general reinforced soil structures, which provides a new method for calculating the design value of geogrid tension in the relevant codes, and has certain guiding significance for improving the design calculation method of GRS retaining walls. However, there is room for improvement. The method proposed in this paper is only reliable under low-amplitude dynamic load and static load. However, for high-amplitude dynamic load, the embedded locking effect between the geogrid and soil particles is weakened, which will lead to larger displacement of the facing panels and deformation of the geogrid in the soil by pulling. How to consider these factors in the calculation analysis needs to be further studied.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Ratio of Route Re-Discoveries Packets  
Figure 12 illustrates the comparison of ESR protocol with other protocols in terms of a ratio of route re-discoveries under different nodes density topologies. It is observed that in the presence of malicious nodes, a network incurred a high number of packets for route discoveries and re-transmissionnns. Nevertheless, based on Figure 12, it is seen that ESR protocol decreased the packets of route re-discoveries in the evaluation of other solutions specifically when the network size increased. ESR protocol had attained an average of 38% reduction in the number of packets for route re-discoveries under the scenario of different nodes density topologies. The reason behind this was that ESR measured the  
Figure 11: Network overhead under varying nodes density topologies.  
Figure 10: Communication cost in varying nodes density topologies.  
network congestion over the elected cluster heads and based on network conditions ESR updated the routing flags of cluster heads. Accordingly, ESR protocol re-adjusted the routing paths from normal nodes to their associated cluster head by flooding the route discoveries packets within a precise region rather than the entire network field.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
###### Abstract  
The purpose of this study was to develop methods to localize forest windstorm damages, assess their severity and estimate the total damaged area using space-borne SAR data. The development of the methods is the first step towards an operational system for near-real-time windstorm damage monitoring, with a latency of only a few days after the storm event in the best case. Windstorm detection using SAR data is not trivial, particularly at C-band. It can be expected that a large-area and severe windstorm damage may affect backscatter similar to clear cutting operation, that is, decrease the backscatter intensity, while a small area damage may increase the backscatter of the neighboring area, due to various scattering mechanisms. The remaining debris and temporal variation in the weather conditions and possible freeze-thaw transitions also affect observed backscatter changes. Three candidate windstorm detection methods were suggested, based on the improved k-nn method, multinomial logistic regression and support vector machine classification. The approaches use multitemporal ESA Sentinel-1 C-band SAR data and were evaluated in Southern Finland using wind damage data from the summer 2017, together with 27 Sentinel-1 scenes acquired in 2017 and other geo-referenced data. The stands correctly predicted severity category corresponded to 79% of the number of the stands in the validation data, and already 75% when only one Sentinel-1 scene after the damage was used. Thus, the damaged forests can potentially be localized with proposed tools within less than one week after the storm damage. In this study, the achieved latency was only two days. Our preliminary results also indicate that the damages can be localized even without separate training data.  
b
###### Abstract  
Hysteresis current controllers (HCCs) are extensively used in low-frequency power inverters and active power filters. Very little is known about their strengths and weaknesses when applied at supraharmonic frequencies. The major concern regarding the use of HCCs is their variable and uncontrollable switching frequencies. This results in difficulties in filter design, high switching losses, and the possibility of resonance conditions with power system elements. In this article, investigations are conducted on the application of HCCs in a hybrid filter for a 6 kHz matrix converter (MC)-coupled supraharmonic load. The effects of the MC-coupled load and VSC switches on the HCC's switching frequency are analyzed. A novel mathematical model for obtaining a fixed-frequency HCC is presented. The model is verified in open- and closed-loop HCC control configurations for a three-phase hybrid active power filter (HAPF). Fast Fourier transform (FFT) analysis of the supply current after the implementation of the novel HCC-HAPF showed 2.31% of the 50 Hz fundamental. The analysis is verified in a MATLAB/Simulink environment.  
hybrid active filter; hysteresis current controller; matrix converter; supraharmonic frequency
# Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks
## 1 Introduction
In contrast to VLC systems, FSO systems are aimed at outdoor long-range applications, such as ground-to-air/satellite communications, mobile fronthaul networks, and inter-satellite links [6]. Although FSO links are severely impacted by outdoor weather conditions, such as fog, dust, and rain, the technology offers several advantages, including straightforward installation, free licensing, cost-effectiveness, immunity to EMI, and optical-fiber-link bandwidth [18; 19]. Furthermore, OWC technologies, e.g., VLC, and RF-based communications, e.g., mm-wave or terahertz, may be seamlessly integrated in B5G networks because they are complementary access network technologies. This work reports the implementation of a hybrid network architecture based on A-RoF, FSO, mm-wave, fiber-wireless (FiWi), and VLC technologies. Figure 1 illustrates the plurality of applications of the proposed hybrid FiWi system in a B5G network. The CO is physically connected to the network core and DU by means of fiber-based backhaul and midhaul links. In addition, the fronthaul, which may be composed of fiber-optic or FSO links, connects the DU to multiple VLC access points or mm-wave femtocells in indoor environments and fourth-generation (4G)/5G BSs.
# Selection of Key Frames for 3D Reconstruction in Real Time
## 3 Materials and Methods
### Feature Selection  
After recording and analyzing the inertial data output of several videos, we determined the sample rate and that inertial data were referenced to video frames. As seen in Table 1, each frame was associated with 16 to 17 inertial data entries according to 3D acceleration and angular velocity. Let \(\overset{\rightarrow}{S_{n}}=\left[S_{n}^{x},S_{n}^{y},S_{n}^{z}\right]^{T}\) and represent a 3D signal of acceleration a or angular velocity \(\omega\) addressed to a frame with \(i\leq 17\) inertial signals and n \(\leq\) i. Then, the Euclidean norm of \(\overset{\rightarrow}{S_{n}}\) is defined as:  
\[S_{n}=||S_{n}||=\sqrt{S_{n}^{x2}+S_{n}^{y2}+S_{n}^{z2}} \tag{5}\]  
Due to the norm, the signals become unbiased in case of negative numbers. Thereupon, we defined the following features.  
1. Signal Energy.  
The signal energy [21] is the sum of the squared signal norm \(\overset{\rightarrow}{S_{n}}\) and was normalized by the number of signals per frame as:  
\[E_{S}=\frac{1}{N}\sum_{i=1}^{N}{S_{n}}^{2} \tag{6}\]  
where \(N\) denotes the number of signals per frame.  
Mean Signal Norm.  
The mean signal norm denotes the mean over all signals per frame of acceleration and angular velocity, respectively, as:  
\[\overline{S}=\frac{1}{N}\sum_{i=1}^{N}S_{n} \tag{7}\]  
where \(N\) denotes the number of signals addressed to the frame.  
Min Signal Norm.  
The min signal norm denotes the min norm of all signals per frame of acceleration and angular velocity, respectively, as:  
\[S_{min}=\underset{n}{\text{min}}(S_{n}) \tag{8}\]  
Max Signal Norm.  
The max signal norm denotes the max norm of all signals per frame of acceleration and angular velocity, respectively, as:  
\[S_{max}=\underset{n}{\text{max}}(S_{n}) \tag{9}\]  
Signal Variance.  
The signal variance is the variance of the signal norms per frame of acceleration and angular velocity, respectively, as:  
\[\sigma_{S}^{2}=\frac{1}{N-1}\sum_{i=1}^{N}(S_{n}-\overline{S})^{2} \tag{10}\]  
where \(N\) is the number of signals addressed to the frame. The signal variance detects the differences between the signals per frame.
## 3 Results and Considerations
### Further Study  
The resistivity obtained by ERT is a site-specific value that comprehensively reflects the physicochemical properties inside the landfill and the type of landfill waste, as expressed in [PERSON]'s law, and, thus, has limitations that make it inapplicable for interpretation at other sites. As a future development of ERT monitoring, the effectiveness of ERT monitoring in unknown landfill sites is expected to increase when combined with AI-driven networks. The use of AI in the inverse analysis process is expected not only to increase the resolution and inference speed of geophysical surveys but also to detect the features and trends related to time-dependent physics [27,28]. The time-lapse ERT used in this study is also consistent with the slope stability assessment [28]. Using a similar approach, such as [PERSON]'s slope estimator, suggests that time-lapse ERT will effectively detect water infiltration paths and estimate the time required to wash out pollutants. Since resistivity electrical survey is widely used as a leak detection system for waste disposal sites in Japan, the utilization of AI and ERT is expected to reduce costs by using fewer electrodes, shortening data acquisition time, and understanding real-time impervious performance [29].
## 3 Experimental Section  
### Optical Setup  
An experimental MPM setup was used for the entire FCS experiments. The schematic diagram of the experimental multiphoton setup is shown in Figure 2. Two-photon excitation was achieved by a mode locked femtosecond pulsed Ti: Sapphire laser (Tsunami laser, Spectra Physics, Santa Clara, CA, USA) pumped by Millenia laser (532 nm, Spectra Physics, Santa Clara, CA, USA532 nm) and the laser intensity was modulated by a Pockels cell (350-80 LA, ConOptics, Danbury, CT, USA). The excitation wavelength was set to 800 nm with an average laser power of around 20 mW. The pulse width dispersion (\(\sim\)100 fs) at sample plane was ascertained by an autocorrelator (CARPE, APE GmbH, Berlin, Germany) and pulse compressor (Femto control kit, APE GmbH, Berlin, Germany). Two water immersed objective lenses (40\(\times\), NA 0.8 C Achroplan NIR--working distance 1.8 mm and 63\(\times\), NA 1.2 C Apochromat--working distance 0.28 mm Carl Zeiss, Oberkochen, Germany) were employed for all measurements. The fluorescent signals were collected by two GaAsP detectors (H7422P-40 MOD, Hamamatsu Photonics, Hamamatsu, Japan), interfaced to time correlated single photon counting modules (SPC 150, Becker & Hickl GmbH, Berlin, Germany). Two spectral detection channels were enabled by using a dichroic mirror (550 nm cut off, Semrock Inc, New York, NY, USA) combined with two filters 580/150 nm and 525/50 nm (Semrock Inc, New York, NY, USA) which enabled two channels 1 (red, 550-655 nm) and 2 (blue, 417-477 nm). A sample volume of \(\sim\)0.5 mL was placed in a cell dish with glass bottom (\(\mu\)-Dish 35 mm, Ibidi GmbH, Grafelfing, Germany) which was mounted on MPM stage. FCS measurements were performed by parking the laser beam at a specific location within the sample. Each measurement had the duration of 30 s and measurements were repeated 5 times to get statistical average. Autocorrelation traces were saved from each step and raw data extracted from SPCM64 software (version 9.82, Becker&Hickl GmbH, Berlin, Germany) and burst analyser (burst analyser 2, Becker&Hickl GmbH, Berlin, Germany). All FCS diagrams were plotted and analysed in MATLAB (MathWorks Inc., Natick, MA, USA) using a customized code (see Supplementary Material).