esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cryptanalysis of [PERSON] et al.'s Protocol
### Session Key Disclosure Attack  
A malicious attacker \(\mathcal{A}\) can perform the side channel attack on TPD [1; 2; 3] and OBU. Accordingly, \(\mathcal{A}\) can obtain values stored in OBU and TPD, and also obtain transmitted messages through insecure channels. Thus, \(\mathcal{A}\) can compute the session key using the obtained values.  
**Step 1:**: \(\mathcal{A}\) can obtain \(P\) in \(OBU_{i}\) and \(X_{i},P_{pri_{i}}\) in \(TPD_{i}\) using side channel attack. And \(\mathcal{A}\) also can obtain the value \(RID_{i}\) through transmitted message. Subsequently, \(\mathcal{A}\) can compute \(s_{i}=P\oplus RID_{i}\oplus P_{pri_{i}}\).
**Step 2:**: \(\mathcal{A}\) can obtain \(ID_{i+2}\) and \(T_{1}\) from transmitted messages and \(\mathcal{A}\) obtains the value \(ID_{RSU_{j}}\), which is public value. Therefore, \(\mathcal{A}\) can compute \(SK_{RID_{i}}=s_{i}\oplus h(ID_{i+2}||T_{1})\oplus ID_{RSU_{j}}\).
**Step 3:**: finally, \(\mathcal{A}\) obtains the previous session key \(SK_{RID_{i}}\) and can trick other OBUs or RSUs.  
Figure 4: Message signature phase of [PERSON] et al.’s protocol.
## 4 Discussion
### Influence of Tidal Level  
Because the wetlands in the tidal flats over the YRD are greatly affected by tides, the effect of tidal height on synergetic classification accuracy should be considered when using multi-source images to classify coastal wetlands.  
Combined with the tidal data of Dongying Port, the tidal heights are 103 cm and 106 cm of SAR and hyperspectral, respectively, and the tidal difference between SAR and hyperspectral image acquisition time is approximately 3 cm. The OHS image in 2020 corresponds to saltwater, but the GF-3 image in 2019 corresponds to tidal flat. According to the Advanced Land Observing Satellite World 3D-30m (AW3D30) v3.2, the average slope of the estuary is estimated at 2.14%. A tidal difference of 3 cm corresponds to a horizontal  
Figure 14: Daily mean temperature and precipitation in the YRD in 2019 and 2020 derived from the fifth generation ECMWF reanalysis (ERA5) in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS).  
change of nearly 70 m along the coastline. As the spatial resolution of the classification is 10 m, 70 m corresponds to 7 pixels. Therefore, the difference in tide level could introduce uncertainty in the classification results.
# A Statistical Approach to Discovering Process Regime Shifts and Their Determinants
## 3 Methodology
### Algorithm and Statistical Tests  
We now present the method to identify regime shifts in the SoI's process output (i.e., the dependent attribute of interest) and to find its causal relationship with potential determinants. The high-level algorithm is first presented (Figure 3) and discussed, and details of statistical modeling and testing are discussed in the latter part of this section.  
As shown in Figure 3, the procedure requires an _initialization_ phase, in which the first step is to model the system and its properties (denoted as \(Y\) and \(p(y)\), respectively) via the SSPM framework. The modeling approach is already discussed in Section 3.1 above. Based on \(p(y)\), a dependent property \(p^{\prime}\left(y\right)\in p(y)\) of interest is identified, whose time series event log data are considered for process mining. Furthermore, all determinants \(d\in D\) are identified, which may be related properties and/or couplings (internal or environmental). Once the model is formed, the evaluation or the mining criterion is set for \(p^{\prime}\). This criterion is based on the analyst's preference and on the attribute of interest. Accordingly, the criterion is translated to a minimally described model \(B\), i.e., with no determinants initially considered. This minimal model allows the discovery of all regime shifts, irrespective of their causes. Thus, in the first _post initialization_ step, model \(B\) is fitted and tested for all regime shifts using the time series data for \(p^{\prime}\) with a statistical procedure. This procedure involves determining and dating all present regime shifts, the details of which are discussed later in this section. If no regime change is detected, the procedure is terminated; otherwise, the procedure moves to the phase for _modeling and testing the impact of determinants_. In this phase, a potential (or even exhaustive) subset is extracted from \(D\) (i.e., \(d^{\prime}\subseteq D\)). This list is then ranked (\(D^{\prime}\): the ranked list) based on the earliest chronological change in respective  
Figure 2: A General System Representation.  
determinants, i.e., a determinant is placed as the first item in the set, which sees the first change in its state on the timeline, etc. Notably, the ranking and testing in this sequential and incremental manner allow simulating the actual timeline of events that may have caused various regime shifts. In this step, when an independent determinant variable, based on its rank, is added to the base model, the updated model (\(B^{{}^{\prime}}\)) is then fitted and tested again for regime shifts. During testing, if a regime shift found in the previous phase remains undetected, it is inferred that the determinant has (at least partially) caused the formation of this shift. Finally, the fitted model is used to _compare the impact of determinants having a causal relationship with \(p^{{}^{\prime}}\)_.
## 1 Introduction
The logistics task generates after the machine processing is finished. However, this kind of production-logistics system is time-consuming, which may cause high costs. 3. How can key manufacturing resources be integrated with cloud service platforms to provide the foundations for production-logistics collaboration? Despite the achievements in Industry 4.0 and CPS models, it is not clear how to implement production-logistics collaboration in an FMS. An integrated framework is needed to describe the relationships among different components of the FMS and the cloud service platform. In order to address these challenges, this paper investigates the mechanism of production-logistics collaboration in an FMS. A self-adaptive collaboration method based on a timed colored Petri net (TCPN) is proposed for production-logistics systems. The proposed method combines the schedule of token sequences in the TCPN model with real-time status of key production and logistics equipment such as machines and AGVs. In the simulation experiment, colored Petri nets (CPN) Tools is used to validate the performance and applicability of the proposed method. Three key performance indicators (KPI), including waiting time, makespan, and electricity consumption are considered. Several contributions are significant to the literature. In the first place, intelligent modeling of key equipment is implemented by equipping the equipment with embedded devices or sensors and establishing a knowledge base. The behaviors of key equipment are associated with the TCPN model. Thus, key production and logistics equipment, such as machines and AGVs, are made'smart' to actively publish logistic tasks and actively request logistics tasks. Secondly, an integrated framework based on a cloud service platform is introduced to provide the basis for self-adaptive collaboration of production-logistics systems. The capacity and real-time status of key equipment are packaged as cloud services to be published on the cloud services platform. Thus, the production-logistics collaboration is switched to the matching of tasks with services. Thirdly, simulation studies are carried out to compare the proposed method with the event-driven method. The results present the outperformance of the proposed method in reductions of waiting time, makespan, and electricity consumption. The rest of this paper is organized as follows. Section 2 introduces an integrated framework based on cloud service platform for production-logistics systems. Section 3 performs a detailed description of a TCPN-based self-adaptive collaboration method for IoT-enabled production-logistics systems. Section 4 presents an industrial case and a simulation experiment to evaluate the fulfillment and feasibility of the proposed method. Section 5 draws conclusions and describes future work activities.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
###### Abstract  
Our long journey on the road of telecommunications is continuously evolving. We have experienced several technological changes, modernizations, optimizations, and various mergers in the past decades. Virtualization and 'cloudification' of legacy telecommunication equipment has made communication networks not only more flexible, but also opened new doors. Brand new types of services have become available thanks to the ongoing fusion of the two domains of telecommunications and IT (Information Technology). This overview paper first discusses the evolution of services with an enhanced focus on mobile networks. Then, the possibilities offered by IT are shown. Finally, some examples are given of how Communication Service Providers and end users can benefit from these recent changes.  
communication services; cloud; API; CAMEL; INAP; IT; TAS; VoLTE; IMS data channel 1
## 2 Materials and Methods
### Total RNA Isolation, Library Preparation, and RNA Sequencing  
For the transcriptome analyses of HaCaT cells in response to the treatments (GP-P, GP-C, and GP-CR), the cells were seeded at a density of 1 \(\times\) 10\({}^{6}\) cells per well in a six-wellplate and incubated for 24 h. They were then treated with 0.1% of either of the three extracts (GP-P, GP-C, and GP-CR) or distilled water (control) for 24 h. Three biological replicates were harvested for each condition. Total RNA was isolated from the cells using TRIzol reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer's protocol, and the quality of the extracted total RNA was measured using Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). RNA samples were quantified using a Thermo Scientific Multiskan GO microplate spectrophotometer (Fisher Scientific Ltd., Vantaa, Finland), and purity was assessed using the ratio of absorbances at 260 nm and 280 nm (A ratio between 1.8 and 2.0 was considered acceptable). Total RNA with an RNA integrity number (RIN) value \(\geq\) 7 was used for library preparation. RNA sequencing libraries were prepared using the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Twelve libraries with their respective indices were paired-end sequenced using the HiSeq X system (Illumina).
## 4 CNQF Prototype and Testbed Implementation  
Based on the proposed CNQF architecture described in the previous section, a working prototype has been implemented in Java. Presently, distributed entities in the RMS and MMS subsystems which provide capability for adaptive policy-based QoS management based on CNQF architecture have been built using the _NetBeans IDE 6.9 Java SDK_. Furthermore, a testbed has been implemented using Linux machines to evaluate the CNQF prototype. Brief descriptions are outlined in this section.
# Schumann Resonances and the Human Body: Questions About Interactions, Problems and Prospects
## 3 Results
### Earth's Magnetic Field and Schumann Resonance Frequencies: Origin and Essence
The study results revealed significant long-term associations between the Earth's local magnetic field and acute myocardial infarction. SRs from SBeta (15-32 Hz) and SGamma (32-65 Hz) have been found to play an important role in establishing long-term relationships. 4.3 Results of Studying the Interaction of Schumann Resonances with the Urinary System of the Human Body  
All organs in the human body are functionally united and influence each other's functioning. Therefore, the human body is a complex, interconnected, multi-hierarchicalfunctional system. This confirms the fact that pathologies of the kidneys and cardiovascular systems often occur simultaneously and have a combined progression. For example, the occurrence of acute heart failure is associated with acute kidney damage, and chronic kidney disease is present in more than 90% of patients with heart failure [120]. After reliable correlations were established regarding the influence of the Earth's electromagnetic field on the occurrence of myocardial infarction [119], a hypothesis arose about the possibility of the influence of SRs on the exacerbation of chronic kidney diseases. In 2023, a study was carried out on the correlation between episodes of kidney disease and SRs [121]. The study had several limitations and was exploratory. Its results are preliminary and justify the need for further research in this direction. The authors concluded that changes in SRs are correlated with episodes of kidney disease. A preliminary conclusion was made that the influence of the SRs on the pathogenetic mechanisms of kidney diseases is exerted in the opposite direction compared to that on cardiovascular diseases. But it is important to establish the correlation between SRs and kidney function. This may be another new and unexplored pathogenetic mechanism of NCDs and cardiorenal syndrome. In 2024, a theoretical study searched for new pathogenetic mechanisms of cardiorenal syndrome associated with the influence of the Earth's magnetic field and identified possible mechanisms [122]. The authors consider this direction promising for continued research because if SRs are synchronized with the activity of the nervous system and the cardiovascular system, then the kidneys, as organs associated with blood circulation, should also respond to changes in SRs.
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 3 Material and Methods
### Fog Systems-Based Security Maintenance
They have limited constraints and not enough memory, transmissions, and processing resources. They are not able to communicate with the sink node directly;
* _Data sense:_ All the deployed sensors, actuators, and IoT devices collaborate in collecting the data. In addition, the data are transferred to the sink nodes through integrating fog systems. The fog nodes are explored for reducing communication delays;
* _Relay node:_ The data are obtained from the deployed sensors and forwarded towards the relay node. The roles for relay nodes are not predefined, they are chosen using quality-aware parameters;
* _Decision making:_ In this state, a distributed machine learning technique is applied using network statistics to offer the optimal results for attaining a reliable communication system;
* _Data security:_ In this end, security actions are performed in this state. It identifies faulty nodes and false messages to detect the comprised data using private values. It increases the reliability of remote users that are connected to cloud systems.
###### Abstract  
To deal with the problem of reliability degradation of radar emitter identification (REID) based on the traditional five parameters in a complex electromagnetic environment, a new feature extraction method based on the autocorrelation function of coherent signals, which makes full use of the coherent characteristic of modern radar emitters, is proposed in this paper. The main idea of this paper is utilizing the instantaneous autocorrelation function to obtain the correlation results of coherent and noncoherent signals. To this end, a new feature parameter, named the ratio of the secondary peak value to the main peak value (SMR), is defined to describe the difference of correlation results between coherent and noncoherent signals. Through simulation analysis, the feasibility of using SMR as the coherent feature for REID is verified. In order to evaluate the effectiveness of the coherent feature, an analytical hierarchy process (AHP) was introduced to compare the comprehensive performance of the coherent feature and the existing parameters, and then convolution neural network (CNN) and support vector machine (SVM) were selected as the classifier to check the recognition capability of the proposed feature. Simulation results show that the proposed feature can not only be used as a new feature for REID but can also be used as a supplement to existing feature parameters to improve the accuracy of REID as it is more insensitive to the signal-to-noise ratio (SNR) and signal modulation type changes.  
radar emitter identification; coherent feature; instantaneous autocorrelation function; SMR; analytical hierarchy process (AHP) 2020; 10.3390/app10155256
## 1 Introduction
Therefore, plasma is generated on the surface of the skin without external gas injection, and the plasma generation area is much larger than that of plasma jets [15]. The term \"floating electrode\" implies that a metal object is separated from the ground. These techniques are used in skin care because of their ability to prevent cavities and burn damage. Additionally, by applying FE-DBD plasma, a complete skin sterilization effect can be achieved without blood clotting and damaging surrounding tissues, and its ability to sterilize microorganisms has recently been proven [16]. Therefore, FE-DBD plasma is currently used for HuNoV GII.4 treatment in salted claims [12] and the control of highly resistant _Escherichia coli_, methicillin-resistant _Staphylococcus aureus_ (MRSA), and _Bacillus stratosphericus_[17; 18]. However, there have been no studies confirming or evaluating the effectiveness of FE-DBD plasma treatments on _S. aureus_ and _Salmonella_ Typhimurium in fried fish paste. Hence, in this study, the antibacterial efficacy of FE-DBD plasma therapy against _S. aureus_ and _S._ Typhimurium on fried fish paste was verified. Additionally, the physico-chemical characteristics of plasma-treated fried fish paste were evaluated in this study.
# Dynamic Analysis on the Parametric Resonance of the Tower-Multicable-Beam Coupled System
## 2 The Dynamic Model of the MCS
### The Vibration Equations of the Tower and the Cable
For the reduced Cii\(\epsilon\) cable dynamic model of the i\(\epsilon\) subsystem, it should satisfy the following boundary and continuation conditions:  
\[v_{ci}(L_{ci},t)=-V_{bi}(t)\cdot cos\theta_{i} \tag{10}\]  
\[u_{ci}(L_{ci},t)=-V_{bi}(t)\cdot sin\theta_{i} \tag{11}\]  
\[w_{ci}(x_{ci})=\frac{m_{ci}\mathcal{B}\cdot L_{ci}{}^{2}\cdot cos\theta_{i}}{2H_{ci }}(\frac{x_{ci}}{L_{ci}}-\frac{{x_{ci}}^{2}}{L_{ci}{}^{2}}) \tag{4}\]0  
\[w_{ci}(x_{ci})=\frac{m_{ci}\mathcal{B}\cdot L_{ci}{}^{2}\cdot cos\theta_{i}}{2H_{ci }}(\frac{x_{ci}}{L_{ci}}-\frac{{x_{ci}}^{2}}{L_{ci}{}^{2}}) \tag{4}\]1  
Following the assumptions given in the previous part, the in-plane lateral vibration displacement of an ideal cable without considering the bending stiffness can be processed by the method of separating variables [8]:  
\[w_{ci}(x_{ci})=\frac{m_{ci}\mathcal{B}\cdot L_{ci}{}^{2}\cdot cos\theta_{i}}{2H_{ci }}(\frac{x_{ci}}{L_{ci}}-\frac{{x_{ci}}^{2}}{L_{ci}{}^{2}}) \tag{4}\]2  
where \(\varphi_{cin}(x_{ci})\) represents the mode shape of the nth-order and is selected as: \(\varphi_{cin}(x_{ci})=sin\frac{a\pi x_{ci}}{L_{ci}}\). According to [PERSON]'s experiment results [26], the fundamental mode occupies the main position in the free vibration of the taut string. Thus, the following research only studies the first order of the mode shape (\(n=1\), \(\varphi_{ci}(t)=\varphi_{ci1}(t)\)). Inserting \(h_{ci}\) and \(v_{ci}(x_{ci},t)\) into Equation (7), by some simplifications, the PDEs of the Cii\(\epsilon\) cable can be obtained as follows:  
\[ \begin{array}{ll}\Bigl{\{}H_{ci}+\frac{E_{ci}^{*}A_{ci}}{L_{ci}}&[-V_{bi}(t) \cdot cos\theta_{i}-V_{t}(t)\cdot sin\theta_{i}\\ &+\int_{0}^{L_{ci}}\frac{\partial w_{ci}(x_{ci})}{\partial x_{ci}}\frac{ \partial w_{ci}(x_{ci})}{\partial x_{ci}}dx_{ci}\\ &+\int_{0}^{L_{ci}}\frac{\partial w_{ci}(x_{ci})}{\partial x_{ci}}\Bigr{)}^{2} dx_{ci}\Bigr{\}}\cdot\frac{\partial^{2}\varphi_{ci}(x_{ci})}{\partial x_{ci}}+\frac{E_{ci}^{*}A_{ci}}{L_{ci}} \\ &\cdot[-V_{bi}(t)\cdot cos\theta_{i}-V_{t}(t)\cdot sin\theta_{i}\\ &+\int_{0}^{L_{ci}}\frac{\partial w_{ci}(x_{ci})}{\partial x_{ci}}\frac{ \partial w_{ci}(x_{ci})}{\partial x_{ci}}dx_{ci}\\ &+\int_{0}^{L_{ci}}\frac{1}{2}\cdot\biggl{(}\frac{\partial w_{ci}(x_{ci})}{ \partial x_{ci}}\biggr{)}^{2}dx_{ci}\biggr{\}}\cdot\frac{\partial^{2}w_{ci}(x_{ ci})}{\partial x_{ci}{}^{2}}\\ &=m_{ci}\cdot\frac{\partial w_{ci}(x_{ci})}{\partial t^{2}}+c_{ci}\cdot\frac{ \partial w_{ci}(x_{ci})}{\partial t}\frac{\partial}{\partial t}\end{array}  \tag{15}\]  
where \(E_{ci}^{*}\) represents the elastic modulus of the Cii\(\epsilon\) cable revised by the Ernst Method [27]; \(A_{ci}\) represents the cross-sectional area of the Cii\(\epsilon\) cable.
## 2 Traditional Dark Channel Prior
### Estimation of Atmospheric Light  
Atmospheric light \(\mathbf{A}\) comprises pixels of the observed image for which \(t(\mathbf{x})=0\) in Equation (1); there is no direct light and the distance is infinity in Equation (2). In the outdoor image, this generally represents the intensity of the sky region. To estimate atmospheric light \(\mathbf{A}\), the highest luminance value is considered in the haze image \(\mathbf{I}\)[3]. If an image contains a white object, the atmospheric light \(\mathbf{A}\) is misestimated, and optimum atmospheric light \(\mathbf{A}\) is estimated using dark-channel value [5]. Initially [PERSON] et al. [5] determined the top 0.1% brightest pixels in the dark-channel image, and chose the highest intensity pixels from those same pixels in haze image \(\mathbf{I}\). Although this approach is useful because it can estimate atmospheric light \(\mathbf{A}\) by ignoring small white object, the size is limited below the patch size.
## 4 Validation of Proposed Method
### Database and Conditions  
The 12 utterances in the ATR database (B set) [16] were used to evaluate the frame synchronization process and blind detection process. The original speech signal had a 16-kHz sampling frequency, 16 bit quantization, 8.5 s duration, and one channel (mono).  
The LP order \(P\) is 12 because the sampling frequency is 16 kHz. The frame length of the proposed method is 20 ms, the same as the frame length of the LP analysis. The bit rates (bps) in these validations were 4, 8, and 16 bps. Twelve random bit strings were used for each bit rate. The embedding-strength level for embedding the security code was set to \(-20\sim 0\) dB in 5-dB increments.  
Figure 4: Proposed frame-synchronization process when the number of reference frames is one. When the number of reference frames is two or more, this process obtained the frame synchronization position by arithmetic averaging of the cross-correlation results.
## 8 Internet of Things (IoT)  
The IoT supports connecting devices, services, and systems using machine-to-machine (M2M) communications [82]. It consists of four layers: physical, gateway or network, transport, and application layers, as presented in Figure 8[82]. It is a platform where any device can share and collect data without manual intervention over a network.  
Physical and data link layer Physical medium: The physical layer defines the physical medium used for IoT data transmission, which is mostly a wireless connection, e.g., Wi-Fi, Zigbee, and cellular. The choice of the physical medium depends on several factors, including range, power consumption, data rate, and application requirements [82].  
The data link layer is responsible for framing IoT data into packets or frames for transmission. These frames often include headers and trailers for addressing, error checking, and synchronization [82].  
Gateway and network layer Data are transferred between IoT nodes and the cloud through hardware known as gateways. It provides connectivity for forwarding packets over LAN and WAN Internet protocol (IP) layers. It mitigates the volume of data via preprocessing and filtering. Also, it provides a high-performance and reliable infrastructure for IoT devices [83].