esa-sceva/satcom-chunk-collection huggingface dataset:
# IPv6 Convergence for IoT Cyber-Physical Systems
## 1 Introduction
#### Cyber-Physical Systems  
Cyber-physical systems (CPSs) are the seamless integration of compute, software (in the form of computational algorithms), and physical components such as embedded sensors, processors and actuators that are designed to sense and interact with the physical world. A CPS may be as simple as an individual device, or can consist of multiple cyber-physical devices that form a system, or can be a system of systems (SoS). Cyber-physical systems of systems (CPSoS) are generally large-scale systems that provide services that go beyond the services of any of their component CPSs. They also operate and are continuously evolving over long periods of time [1]. The actions of CPSs and constituent components must be both dependable and interoperable.  
In Section 2, we provide a brief overview of wireless technologies commonly associated with IoT networks, with a particular focus on low-power wide-area networking. We then describe the motivation for adopting a converged IP approach, and follow up with our proposed model in Section 3. In Section 4, we detail our experimental approach used to explore an implementation of the model. We present initial findings in Section 5. Finally, we conclude in Section 6.  
Figure 1: Convergence towards a common IPv6-based data and control plane connecting Internet of Things (IoT) endpoints and fieldbus devices with decentralised applications and services.  
Figure 2: Complex multitopology heterogeneous WiFi, low power wide area (LPWA), mesh, and cellular networks with a common data and control plane supporting multiple different applications and services.
# Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments
## 3 Methodology
### Sentiment Labelling  
Every year, the number of digital exchanges increases significantly, and content analysis gives a mechanism to comprehend the views, ideas, and reactions that lay beneath the online language. It is especially valuable in the era of social media, that may give you a sense of how people feel about certain topics. Data collected from sentiment studies on social networking sites has a wide range of applications, ranging from minor applications like improving brand building activities to bigger public concerns like guiding government ideology and forecasting economic growth [5; 39]. As people are expressing their ideas and emotions more freely nowadays, sentiment analysis is quickly coming up as an indispensable tool for monitoring and understanding opinions in all forms of data, including social media.  
Sentiment labeling can be considered as a subdivision of natural language processing that involves determining the underlying emotion in a textual content [40; 41]. This involves measuring the sentiment polarity of an entity. Sentiment polarity refers to the inclination of the text, whether the emotion is more of negative or positive. Polarity score is a float value of the range [\(-1.0\), \(1.0\)].  
After converting the textual data into vector representations, the polarity of each text is measured based on lexical approach. NLTK is a python library that provides the user with simple access to large sources of lexical knowledge. Textblob is an NLP module present in NLTK that helps users to facilitates the investigation and manipulation of large amounts of textual data. Textblob returns sentiment polarity of a sentence based on positive and negative words used [42].  
The polarity and subjectivity of a statement are returned by TextBlob: \(-1\) denotes a negative emotion, whereas \(1\) indicates a pleasant emotion. This polarity is reversed when using negative terms. TextBlob is used as a labeling system to perform fine-grained analysis. For instance, emoticons, exclamation points, emojis, and like (button) are all examples. Between [0, 1] is where subjectivity is found. Quantifying the quantity of subjective information in a piece of writing is called subjectiveness. The more subjective the material, the more likely it is to be filled with personal opinions instead of facts. Intensity  
Figure 3: Vectorization.  
is an additional TextBlob property. TextBlob uses 'intensity' to determine how subjective a piece of content is. The severity of a term affects whether it alters the following word or phrase. Adverbs are often employed in English as modifiers (e.g., \"excess amount\") [43; 44].
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Application of the Prediction Model
#### 3.3.3 Training the Learning System That Predicts the Channel Attenuation  
In this stage, we trained the learning system. We used the LSTM model, an artificial neural network designed to recognize patterns in data sequences, such as numerical time  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Index** & **Time** & **Azimuth** & **Elevation** & **Range** & **EIRP** & **Xmtr** & **Xmtr EIRP** & **Atmos** & **Rain Loss** \\  & **(deg)** & **(deg)** & **(km)** & **(dEW)** & **Gain (dB)** & **Intensity** & **Loss (dB)** & **(dB)** \\ \hline
1 & 18:20 & 79 & 5 & 2092 & \(-528.5\) & \(-558.5\) & \(-539.5\) & 0.3663 & 0.0024 \\
2 & 18:21 & 82 & 5.048 & 2088 & \(-528.3\) & \(-558.3\) & \(-539.3\) & 0.3633 & 0.0024 \\
3 & 18:22 & 86 & 5.124 & 2081 & \(-528\) & \(-558\) & \(-539\) & 0.3587 & 0.0023 \\ - & - & - & - & - & - & - & - & - \\
589 & 28:07:00 & 426 & 5.136 & 2081 & \(-527.7\) & \(-557\) & \(-539\) & 0.3582 & 0.0023 \\
590 & 28:08:00 & 430 & 5.06 & 2087 & \(-528\) & \(-558\) & \(-539.3\) & 0.3629 & 0.0024 \\
591 & 28:09:00 & 433 & 5 & 2092 & \(-528.3\) & \(-558\) & \(-539.5\) & 0.3666 & 0.0024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of the records used for the training stage. The first and last three records. Reprinted with permission from ref. [1]. Copyright 2021 IEEE.  
series data emanating from neural networks as they have a temporal dimension. We used Matlab libraries to execute the LSTM. Below is the Matlab code:  
_clcl_  
_clear all_  
_close all_  
_session1_DataTrain=xlsread('DataTrain_10 GHz_.csv',1,'A1:G590')';_  
_normalizedData=normalize(session1_DataTrain);_  
_session2=xlsread('session2_10 GHz_csv',1,'A1:G58')';_  
_normalized_Session2=normalize(session2);_  
_session3=xlsread('session3_10 GHz_csv',1,'A1:G506')';_  
_normalized_Session3=normalize(session3);_  
_output=xlsread('DataTrain_10 GHz_csv',1,'H1:I590')';_  
_% import normalized data+sessions+output_  
_numFeatures = 7;_  
_numHiddenLUnits1 = 100;_  
_numClasses = 2;_  
_%define the architecture of the neural network_  
_layers = [sequenclInputLayer(numFeatures),lstLayer(numHiddenUnits1),fullyConnectedLayer_  
_(numClasses),regressionLayer];_  
_%define the LSTM network_  
_options = trainingOptions('adam',..._  
_'MaxEpochs', 250,..._  
_'GradientThreshold', 1,..._  
_'InitialLearnRate', 0.005,..._  
_'LearnRateSchedule','piecewise',..._  
_'LearnRateDropPeriod', 125,..._  
_'LearnRateDropFactor', 0.2,..._  
_'Verbose', 0,..._  
_'Plots',training-progress');_  
_'odefine the optimizer and learning rate_  
_net=trainNetwork(normalizedData,output,layers,options);_  
_'osave the model_  
_outputPrediction_For_Session2=predict(net,normalized_Session2)';_  
_outputPrediction_For_Session3=predict(net,normalized_Session3)';_
###### Abstract  
Little work has been reported on the magnitude and impact of interference with the performance of Internet of Things (IoT) applications operated by Long-Range Wide-Area Network (LoRaWAN) in the unlicensed 868 MHz Industrial, Scientific, and Medical (ISM) band. The propagation performance and signal activity measurement of such technologies can give many insights to effectively build long-range wireless communications in a Non-Line of Sight (NLOS) environment. In this paper, the performance of a live multi-gateway in indoor office site in Glasgow city was analysed in 26 days of traffic measurement. The indoor network performances were compared to similar performance measurements from outdoor LoRaWAN test traffic generated across Glasgow Central Business District (CBD) and elsewhere on the same LoRaWAN. The results revealed 99.95% packet transfer success on the first attempt in the indoor site compared to 95.7% at the external site. The analysis shows that interference is attributed to nearly 50 X greater LoRaWAN outdoor packet loss than indoor. The interference measurement results showed a 13.2-97.3% and 4.8-54% probability of interfering signals, respectively, in the mandatory Long-Range (LoRa) uplink and downlink channels, capable of limiting LoRa coverage in some areas.  
LoRaWAN; interference; received signal strength; live multi-gateway; non-line-of-sight; internet of things +
Footnote †: journal: _computers_
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 3 Materials and Methods
### Similarity Measurement Module  
Concerning the features extracted by the deep feature extraction network, the shallower-level features have accurate localization information and the deeper-level features have rich semantic information. We use the multilayer feature fusion method to bring out the feature extraction ability of the deep feature extraction network, which in turn improves the tracking ability of the ILSN [9].  
To achieve multilayer feature fusion, the features output by layer3, layer5, and layer7 network layers in the optimized feature extraction network are fused. Due to the modification of the network structure, the resolution of the three layers of features is consistent while the number of channels is different. To facilitate the import of the similarity measurement module, the number of feature channels in the three layers was adjusted from 44, 134 and 448 to 256 using a convolutional layer with a convolutional kernel. The features were then normalized to facilitate subsequent feature fusion.  
The adjusted template features and search area features were imported into the similarity measure module, respectively, and the response maps \(S_{i}\) and \(B_{i}\) were calculated for the corresponding classification branches and regression branches. Due to the modification of the network structure, the resolution and number of channels of \(S_{i}\) and \(B_{i}\) calculated by the three layers of features are consistent. The overall response maps \(S_{all}\) and \(B_{all}\) were derived directly using linear weighting.  
\[\begin{split} S_{all}&=\sum\limits_{i}^{n}\alpha_{i} S_{i}\\ B_{all}&=\sum\limits_{i}^{n}\beta_{i}B_{i}\end{split} \tag{6}\]  
Finally, the response graph scores of the overall classification branch and the regression branch were used to generate the current frame prediction frame.
## 1 Introduction
To overcome these challenges, the congruential summation-triggered communication scheme (CSTCS) and quantization are introduced, with the aim of reducing communication frequency while retaining as much information as possible available to the system. The data packet loss process is established using a set of Bernoulli random variables. Under the premise of strong correlation in the received data, an identification algorithm is designed using estimation compensation and statistical methods, and its convergence is proved in conjunction with the law of large numbers for martingales. In cases where the mean-square convergence rate is difficult to express theoretically, the relationship between the convergence rate and the proportion of data used for packet loss estimation is represented using numerical fitting methods. Furthermore, for FIR systems under the proposed communication scheme and data packet loss, the communication rate is given and the trade-off between convergence property and network resources is discussed. The main contributions of this paper are summarized below:  
* Differently from most of the existing studies, this paper simultaneously considers the combined effects of quantization, data packet loss, and event-triggered mechanisms. * When the probability of packet loss is unknown, CSTCS is redesigned to obtain a new identification algorithm. * Numerical fitting is used to obtain an approximate expression for the mean-square convergence rate that is difficult to obtain directly, which in turn allows the trade-off between convergence property and network resources to be resolved. The remainder of this paper is organized as follows. Section 2 describes the system model, introduces CSTCS, and describes the communication uncertainty mechanism. Section 3 designs the identification algorithm in situations where the packet loss probability is known and proves its convergence. Section 4 redesigns the identification algorithm for the scenario where the probability of packet loss is unknown and analyzes its convergence performance. Section 5 discusses the communication rate in the context of unknown packet loss probability and the trade-off between this and convergence performance. Section 6 validates the theoretical results through numerical simulations. Finally, Section 7 concludes the paper and suggests directions for future work.
###### Abstract
[16] investigated the permeability and soil-water characteristic curves of soil after wetting-drying cycles. [PERSON] et al. [17] investigated the effect of wetting-drying cycles on the shear behavior of unsaturated compacted soils and concluded that the degree of wetting-drying and the normal stresses suffered have an important effect on the shear strength. In addition, [PERSON] et al. [18] used numerical modeling to study the variation in slope infiltration with the rainfall intensity and analyzed it to illustrate the effect of infiltration on slope stability. [PERSON] et al. [19] evaluated the slope stability under wetting-drying cycles based on the limit equilibrium method and finite element analysis. [PERSON] et al. [20] used numerical analysis to simulate the slope stability and slope flow under wetting-drying cycles, and it was concluded that the softening of the soil caused by the process of wetting-drying cycles was the cause of slope damage [21]. [PERSON] et al. [22] analyzed the seepage and stability of slopes with different gradients after wetting-drying cycles using the analysis software Geo-Studio 2018. The above studies have confirmed the weakening effect of wetting-drying cycles on the microstructure and physical and mechanical properties of slope soil, and the study of the slope stability of open-pit mines under wetting-drying cycles is of great significance for the safety of actual production. However, the existing research results only consider the effect of wetting-drying cycles on the strength attenuation of the rock body, and not the effect of wetting-drying cycles on the infiltration characteristics of the soil. There are few reports on the combined effects of the transient stability of slopes on the soil strength and infiltration characteristics under the influence of wetting-drying cycles. Existing studies are not fully applicable to the wetting-drying cycle conditions suffered by the open-pit mine slopes in the Cam Pha area, Vietnam, so the modeling and simulation of the wetting-drying cycle conditions in the site during the dry and rainy seasons using the FLAC3D6.0 software would provide an important reference for the actual slope conditions in the area. In this paper, the stability of open-pit mine slopes under the influence of the rainy season in the Cao Son open-pit mine, in the Cam Pha area of Vietnam, is studied. A flow-solid coupling secondary development process and a numerical simulation scheme of wetting-drying cycles are established to study the effects of different rainfall cycles on the seepage flow field within the slope. We study the changes in the flow field, mechanical field, and slope stability coefficient in the slope during different periods of rainfall; reveal the mechanism of slope destabilization under different rainfall cycles; and analyze the slope stability and deformation by combining the change rule of slope displacement, the plastic damage area and form, and the maximum shear strain increment under different periods of wetting-drying cycles, to ensure that the open-pit coal mines in the rainy season in the Cam Pha area, Quang Ninh Province, display field stability.
# Probabilistic Seismic Safety Assessment of Railway Embarnments
## 6 Numerical Analysis  
Incremental dynamic analyses are performed on three categories of railway embankments. The geometric scheme of the considered embankments is illustrated in Figure 3 and their geometrical specifications are presented in Table 3. The selected embankments are rested on considerably stiff ground; therefore, the interaction of soil and embankment is not considered in this study.  
The numerical model is implemented using PLAXIS finite element code in 2D (plane strain) [44]. For the FEM domain, 15-noded triangular elements covering the embankment's trapezoidal shape are used. By considering the first two modal periods, the mass and stiffness proportional Rayleigh damping coefficients were determined. The ballast, capping and embankment layers are modeled using material model, which provides responsible soil behavior modeling for shear stress exceeding the internal friction resistance between the material particles. The mechanical properties of several layers of embankments are tabulated in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Parameters for Embankment 1 (EI)**} & \multicolumn{3}{c}{**Parameters for Embankment 2 (E2)**} & \multicolumn{3}{c}{**Parameters for Embankment 3 (E3)**} \\ \cline{2-9}  & **Ballast** & \
\begin{tabular}{c} **Capping** \\ **Layer** \\ \end{tabular} & **Embankment** & **Ballast** &
\begin{tabular}{c} **Embankment** \\ **Layer** \\ \end{tabular} & **Embankment** \\ \hline Module of elasticity, E (RPa) & 389,000 & 120,000 & 250,000 & 389,000 & 250,000 & 389,000 & 140,000 & 67,000 \\ Density, \(p\) (kg/m\({}^{3}\)) & 2200 & 1935 & 1800 & 2200 & 1800 & 1590 & 1937 & 1733 \\ Poisson coefficient, \(v\) & 0.3 & 0.3 & 0.3 & 0.3 & 0.3 & 0.3 & 0.35 & 0.35 \\ Friction angle, \(\phi\) (\({}^{\circ}\)) & 35 & 20 & 34 & 35 & 34 & 45 & 45 & 40 \\ Cohesion, c (RPa) & 5 & 5 & 32.5 & 5 & 32 & 5 & 0 & 0 \\ Permeability (m/s) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) & \(10^{-4}\) \\ Dilatancy angle, \(\psi\) (\({}^{\circ}\)) & 5 & 0 & 4 & 5 & 4 & 15 & 15 & 10 \\ R & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 \\ \hline Saturation, \(\gamma_{\text{SAT}}\) (kN/m\({}^{3}\)) & 21.575 & 18.976 & 17.652 & 21.575 & 17.652 & 15.6 & 19 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material modeling parameters for embankment’s layers [45].  
Figure 3: A geometric scheme of the considered railway embankments.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs  
The design of IRSs is a novel and challenging issue under the signal processing point of view. However, some recent works can be found in the literature that consider different setups and different metrics to design the IRS phase-shift matrix. We group the analyzed works according to the configuration of the communication setups, i.e., the number of transmit/receive antennas, and we also distinguish between single and multiuser scenarios. Within each group, the works address different communication scenarios with different characteristics, such as narrowband or wideband transmissions, LOS or NLOS links, perfect or imperfect CSI, and they also consider different metrics to optimize the overall system performance.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 4 Use Cases for Homomorphic Encryption for Cloud Computing in Manufacturing
### Summary of Use Case Analysis  
In summary, the use of homomorphic encryption in combination with cloud computing is suitable for different use cases from the manufacturing environment. However, the use case analysis showed that homomorphic encryption would not improve every possible use case. When using the morphological box to develop the use cases, it is noticeable that not all possible combinations of characteristic values were used. First, regarding cloud benefits, the cloud is not used for data storage in the use cases since pure data storage makes the use of in-use encryption methods obsolete. The type of cloud used is limited to the public and the community cloud in the use cases. Second, regarding cloud type, the use of a private cloud is not applicable, as a private cloud is usually used by a single company, which configures it and can specify the security of the infrastructure in advance. Access to the cloud can be restricted as required, and the cloud can also be hosted locally if there are special security requirements.  
There is no data exchange or shared data use with other companies, as is possible with the other cloud types. For this reason, the use of homomorphic encryption is not advantageous compared to classic encryption, since the in-use data utilization is not exploited. Third, regarding homomorphic encryption category, either FHE or SWHE methods are used, but not PHE methods. In principle, the use of a PHE method is possible; however, the application possibilities are limited, since only additions or only multiplications are possible. In the use cases considered, models of varying complexity are applied, but they all use both additions and multiplications. In addition, cloud computing is typically used to move the computation of complex models to a cloud. FHE and SWHE methods are better suited for such models.
# Survey of Blockchain-Based Applications for IoT
## 4 Identity and Access Control
### Authentication
One approach proposed to address this limitation is the concept of Shadow TPM (SdwTPM) for smart embedded devices (SEDs) in healthcare, where TSEDs (with hardware TPMs) serve as trust anchors for N-TSEDs [28]. Physical Unclonable Functions (PUFs) present another option for hardware-based authentication. Unlike digital certificates, which require secure storage for private keys, PUFs generate a unique hardware fingerprint derived from the physical characteristics of a device's silicon. Since this fingerprint is intrinsic to the device and cannot be cloned or extracted, PUFs provide a lightweight, hardware-embedded authentication method without the need for secure key storage. They provide hardware-level security by generating unique, unclonable identities that exploit tiny manufacturing variations in the CMOS that occur naturally during fabrication. These variations are unique to each device, making it impossible to clone or predict their responses. A PUF works by generating a unique response based on its internal physical properties when it receives an input signal (challenge) from a device. This response acts as a hardware fingerprint, which is then verified against a pre-stored reference in a secure database or blockchain to authenticate the device and ensure its legitimacy. PUFs serve as a lightweight authentication alternative for resource-constrained IoT devices, addressing the limitations of traditional cryptographic methods, but it has its challenges also [29]. PUFs are vulnerable to machine learning-based modeling attacks, where an adversary collects challenge-response pairs (CRPs) and uses ML algorithms to create a software model that accurately predicts responses to new challenges. Optimal PUF designs are needed to produce the maximum number of CRPs necessary for modeling the PUF [29].