esa-sceva/satcom-chunk-collection huggingface dataset:
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol
### RARZ Algorithm (Example Scenario Taken from Simulation)  
1. In Figure 6, Node S (Red Node) in ring 4 broadcasts the packet. Nodes in the lower rings 3 and 2 will receive the packet and start their timers as per Equation (1).
2. The timer of the node (blue) located in ring 2 expired first due to its energy level being the highest and its location (ringID 2) as compared to the nodes in ring 3.
3. Upon receiving the packet, which is sent by a node (blue) in ring 2, nodes (green) in ring 3 will cancel their timers. The nodes (yellow) in ring 1 will start their timers because they are located in the lower ring. The time of the node having a higher energy level will expire first and then it relays the received packet to BS. Hence the packet reaches BS in three hops instead of four.  
Figure 6: Routing illustration of RARZ.
# Detection of Tampering by Image Resizing Using Local Tchebichef Moments
## 3 Proposed Method  
A passive detection method is presented for image resizing forgery detection in this paper. Figure 3 shows the implementation process diagram of our presented algorithm. Similar to the process of most  
Figure 1: Resized image obtained by different retargeting methods.  
Figure 2: Residual LTM (local Tchebichef moments) diagram obtained by different re-targeting methods: (**a–d**) correspond to the residual LTM diagram of Figure 1a–d, respectively.  
existing forensics methods, our proposed method consists of two parts, i.e., a training part and a testing part. In the training process, tampered images and their corresponding original images are adopted as data sets. First, all the training images are preprocessed. Second, the LTM histogram features are extracted from preprocessed images. Finally, a training model is obtained by using ensemble learning based on extracted features. In the testing process, the LTM histogram features are extracted according to the same steps in the training part. Finally, the extracted features are used by the trained ensemble classifier to distinguish which resizing method a tested image is re-targeted.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 2 System Structure and Working Principle
### System Structure  
As can be seen from Figure 3, the electrode GND (GND is the ground electrode) located on the forehead produces the common reference ground voltage for the system to reduce interference [12]. The two electrodes EOGL and EOGR are located midway between the left and right eye and the left and right temple, respectively, to obtain eye movement information. The chart below shows the system block diagram for EOG measurement.  
Figure 1: The change of EOG in right eye movement.  
Figure 3: Schematic diagram of the EOG measurement.  
Figure 2: The change of EOG in left eye movement.  
As shown in Figure 4, the system is composed of the signal acquisition module which consists of three electrodes, signal amplifying circuit, off-chip EMI filter, multiplexer, low noise amplifying module, high precision sampling circuit ADS1191 (ADS1191 is a chip with Low-Power, 2-Channel, 16-Bit Analog Front-End for biopotential measurements, produced by Texas Instruments), main control module CC2640 as the MCU for this system (CC2640 is a low power Bluetooth wireless microcontroller, produced by Texas Instruments), peripheral button, and led indicator lamp. The electrodes and the Electro Magnetic Interference (EMI) filter circuit form the front end of the EOG signal acquisition, which extracts the EOG signal and sends it to the multiplexer (MUX, integrated in ADS1191) module after it has been filtered by the EMI filter. The signal is then converted from a weak current signal into a voltage signal by an amplifier circuit, which is sampled by a high-precision sampling module at a frequency of 256 Hz and sent to the microcontroller (MCU) master control module. Additionally, the peripheral devices, such as buttons and LED indicators are used for external interaction with the system.
## 3 Related Description of IEEE Std. 802.15.6
### Two-Hop Extension  
An example smart health care monitoring system that includes a WBAN with a two-hop extended star network topology is shown in Figure 2. As can be seen, vital information obtained by WBAN nodes is displayed on a monitoring unit through a WBAN hub.  
In IEEE Std. 802.15.6, a node and a hub can utilize the two-hop extension to exchange frames through another node, except in the medical implant communication service band. In Figure 2, the terminal, intermediate nodes, and the hub function as relayed nodes, relaying nodes, and the target hub of a relayed node, respectively. Here, a relayed node or the target hub can initiate a two-hop extension at times determined by the initiator. Note that a relaying node can exchange its frames with the hub directly.  
A relayed node shall not send its frames to a relaying node in contended allocations provided by the target hub [17]. Thus, a scheduled access phase can only be utilized in the case of a two-hop extension. Therefore, the managed access phase as defined in IEEE Std. 802.15.6 is only used in this study.
## 4 SDR Testbed for Experiments  
For the experiments, we use an RTLSDR 2832 device for spectrum analysis connected to a PC by the USB port, and for the MCRN, an NI USRP-2922 is used with GNURadio [31] with a basic CR protocol implemented. This device works in a range of 400 MHz to 4.4 GHz. To emulate the PUE, we use another USRP which transmits a malicious signal in the selected frequency. We use the uplink (UL) signal to measure the energy. The CBS detects the PU/PUE signal and, according to these values, it assigns a frequency and sends the SU signal. The PUE detects PU signals and sends malicious PUE signals to confuse the CBS detection system which recognizes it as a PU, as can be seen in Figure 4.  
In the learning process, we use the primary network of an operator to measure the signals in GMSK and OFDM in a real phone call. We estimate the energy signal for each case. This information is the input for the SVM system with the SNR signal.  
For experiments, the PUE is configured to transmit a signal in the absence of a PU signal to avoid interfering with the operator's network. It has previous knowledge of the PU frequencies, signals, and characteristics to detect it, but the PUE can interfere with this network in a hostile configuration, subsequently affecting PUs and SUs. A python program is implemented to receive the energy data from RTLSDR and the SNR and calculate the entropy. With these data, the program extract the behavior in the learning process and calculates the support vectors and the hyperplane.  
In the detection process, the program receives these data, and with the previous knowledge of signals, the SVM decides if there is a malicious PUE present in the system using scikit [32]. This can be seen in the flowchart in Figure 5.  
Figure 4: Software-defined radio (SDR) testbed for experiments.  
Figure 5: SVM system flowchart.  
The device performance parameters of center frequency, bandwidth, instantaneous dynamic range (IDR), noise floor, noise figure and sensitivity under the evaluated experiment can be seen in Table 1 according to [33].
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.7 De-Synchronization Attack  
Generally, after the session key is established, \(U_{i},\ GWN,\ \text{and}\ MS_{j}\) have no need to update any parameters; thus, the de-synchronization attack is impossible. In our proposed protocol, \(U_{i}\) needs to update the parameters for the next authentication. Then, \(U_{i}\) checks whether \(B_{12}^{*}\) is equal to \(B_{12}\). Luckily, the checking operation can detect this attack in a timely manner. That is, the occasion in which \(B_{12}^{*}\
eq B_{12}\) holds implies that this attack interferes with the normal update of parameters, and the user only asks \(GWN\) to run the update operation of \(B_{12}\) again.
# Differential Expression of MicroRNAs in Dark-Cutting Meat from Beef Carcasses
## 2 Materials and Methods
### Bioinformatatic and Statistical Analysis  
Raw sequencing datafiles were transferred from the Illumina BaseSpace cloud-based storage system to the TAMU TIGSS High Performance Computing Cluster (HPCC) for further processing. The overall sequence quality of each sample was assessed via the FastQC program. Low-quality sequence data (Phred score \(<\) 20) [23] and the adapter sequences were removed with the Trimmomatic v. 0.32 genomic trimming program [24; 25]. FASTA files from miRbase containing known _Bos taurus_ sequence data for known precursor hairpins and mature miRNAs (miRNA hairpins = 1064 and mature miRNAs = 1030), the hairpin.bta.fa, and mature.bta.fa files (miRBase v.22.1) were uploaded to the TIGSS HPCC for further analyses [26]. Filtered sequence reads that passed initial quality control were mapped to precursor hairpins and mature miRNA sequences, then quantified via the mirDeep2 software package [27]. This process generated a miRNA expression profile for all mature miRNAs expressed in each sample. Sequence read counts were organized into an analysis matrix for comparison of NON (\(n=53\)) and DFD (\(n=25\)) samples.  
Data were filtered to remove any miRNAs that possessed \(\leq\)10 count reads across all samples. Once filtered, the count matrix was normalized via DESeq2 software (v. 1.34.0) with the median of ratios method that scales the raw count data to account for sequencing depth, followed by differential expression analysis by using a generalized linear model [28; 29; 30]. With this approach, read counts were modelled to a negative binomial distribution and a Wald test was utilized for hypothesis testing to identify potentially differentially expressed transcripts. To correct for multiple testing, \(p\)-values generated by the Wald test were adjusted at a false discovery rate (FDR) of 5% according to the method of Benjamini-Hochberg [31; 32]. An adjusted \(p\leq\) 0.05 accompanied by a fold difference \(\leq\)0.8 or \(\geq\)1.2 was set as the cut-off for differentially expressed miRNAs. This cut-off reflects an expression difference between groups of at least 20% and a maximum of 5% probability of false discovery.
## 5 Building Resilience in Software-Defined IIoT-Edge Networks
### Autonomous Networks: Self-X Network Management
For example, [PERSON] et al. [170] developed a _\"multi-agent deep deterministic policy gradient (MA-DDPG)\"_ framework that facilitates transient load (traffic burst, elephant flow, and mice flow) detection and prevention in SDN-IoT networks. The framework uses two RL agents, where one agent ensures efficient multipath routing optimization while the other agent ensures malicious DDoS traffic detection and prevention. [PERSON] et al. [171] developed _\"AgNOS,\"_ an agent-based framework for the autonomous control of software-defined networks to address the arduous task of handling the total distribution of control between autonomous systems. The framework uses agents to mitigate DDoS attacks on several autonomous systems. Further, [PERSON] et al. [172] designed _\"NetworkAI\"_, an intelligent network architecture for self-learning control strategies in software-defined networks. [PERSON] et al. [173] developed _\"EARS\"_, an intelligent, experiential network architecture utilizing deep reinforcement learning (DRL) for autonomous routing in software-defined networks. The DRL strategy integrates a closed-loop control system with network monitoring technologies to manage network environments plagued by link congestion and inefficient bandwidth distribution across flows. Lastly, [PERSON] et al. [174] proposed _\"Reinforcement Learning and Software Defined Networking for  
Figure 10: A self-managing software-defined IIoT–Edge network tailored for next-generation offshore wind farms (see Figure 6) by incorporating the European Telecommunication Standards Institute (ETSI) zero-touch network and service management framework [156]. Intelligent Routing (RSIR)\"_, a novel approach to SDN routing that uses a knowledge plane to monitor the network, gather link-state data, and identify optimal paths for intelligent routing amidst traffic fluctuations. While still a subject of ongoing research, the frameworks proposed by [170; 171; 172; 173; 174] incur significant computational costs during training, complicating the creation of an optimal AI/ML decision-maker module. Additionally, security is a major concern in developing and deploying autonomous systems. Managing the extensive data and network parameters knowledge base also presents challenges, particularly due to the rapid rate at which the network monitoring module samples data. Leveraging the power of AI and ML, these frameworks can autonomously manage and optimize network traffic and proactively address the performance, security, reliability, and scalability challenges before they impact network operations. Integrating such technologies into SDNs transforms traditional networking infrastructures into intelligent, self-optimizing systems that significantly enhance operational efficiency and reliability. This paradigm shift toward intelligent networks is crucial for handling the increasing complexity and scale of modern network demands, ultimately paving the way for more resilient networking architectures.
## 1 Introduction
But the expansion joints can lead to misalignment between the station and the tunnel. In the studies mentioned above, except for Refs. [16; 17], one end of the tunnel connects to the shaft/station while the other end is considered infinitely long. These experimental conclusions do not apply to the case of the culvert-frame combined underground structure in this investigation, where two cut-and-cover frames connect to the undercutting structure at both ends. The vertical shafts in Ref. [17] connect to the bottom of the laminar container, resulting in different dynamic characteristics from the CCU underground structure discussed in this study. Additionally, the tunnels in these studies are modeled as continuous tubes, with deformations revealed by strain data, except for the shield tunnels in Refs. [20; 23]. The deformations of jacked culverts mainly occur at the joints, which differ from those of continuous tubes. Limited research has focused on the seismic responses of CCU structures to longitudinal input motions. [PERSON] et al. [24] conducted shaking table tests on a shaft-tunnel junction under longitudinal excitations. It is found that accelerations, strains, and joint extensions in the section of the tunnel near the shaft are amplified. The distributions of joint extensions differ between longitudinal and transverse excitations. However, the shaft-tunnel junction studied by [PERSON]. [24] consists of only one stiff shaft connected to shield tunnels, making the results inapplicable to other CCU structures with different configurations, such as metro stations with two cut-and-cover frames connected to both ends of the jacked culverts. This paper investigated the differing responses of a CCU structure to longitudinal and transverse excitations through shaking table tests, whose facilities are located in the Multi-functional Earthquake Lab, Jiading campus of Tongji University. The CCU structure comprised twin jacked culverts connected by two box frames at both ends. A white noise case evaluated the dynamic characteristics of the model soil ground. Data on accelerations and joint displacements were recorded during a series of synthetic earthquake motion cases. Excitations were applied in both longitudinal and transverse directions to investigate the influence of excitation direction on seismic responses.
## 6 LoRaWAN and NB-IoT Implementations
### Design of a LoRaWAN Testbed  
The LoRaWAN implementation is based on a star network topology, separated into its front-end and back-end, as shown in Figure 3. The Things Network (TTN) is an open LoRaWAN platform that runs server infrastructure as a service. The Pysense node joined the Fipy gateway in the Over the Air Activation (OTAA) mode added to TTN through the TTN application programming interface (API) with limited time on-air of 10 messages in the downlink and 30 s duration per device per day. A hypertext transfer protocol (HTTP) web application interface and database is integrated with the LoRaWAN application server to display TTN data. LoRaWAN private infrastructure may deliver close to 846 s/day time on-air as specified in the LoRa Alliance specifications. This reflects the potential challenges of applying LoRaWAN protocol to different applications of different performance requirements sharing the same network resources. Private LoRaWAN platforms will be needed if the requirements of latency, reliability, and transmission patterns of different IoT applications in developing countries are to be satisfied, though as an alternative to licensed spectrum IoT solutions such as NB-IoT and LTE-M.
# Time Series Analysis by Fuzzy Logic Methods
## 2 Materials and Methods
### DMA-Morphological Analysis: Formalization  
By utilizing DMA methods, it becomes feasible to construct various forms of elementary measures (1), and therefore, also take into account (2) morphological ones.  
Moving forward, we need three types of localization at the node \(t\):  
\[\begin{array}{ll}\text{two-sided:}&U(t,\Delta)=\left\{\bar{t}\in T:|\bar{t}- t|\leq\Delta\right\}\\ \text{left:}&U^{l}(t,\Delta)=\left\{\bar{t}\in T:t-\bar{t}\leq\Delta\right\} \\ \text{right:}&U^{r}(t,\Delta)=\left\{\bar{t}\in T:\bar{t}-t\leq\Delta\right\} \end{array}, \tag{5}\]  
where \(\Delta\ll|T|\) is the local view parameter.  
Let us present a construction \(Q\), expressing, in an elementary context \(*\), the deviation of the record \(x\) from its value \(x(t)\).  
\[\begin{array}{l}x^{ul}(t)\leftrightarrow Q_{x}^{ul}(t)=\frac{\Sigma\left(x(t^{+})-x(t) \right)\cdot\left(t^{+}\in U^{l}(t,\Delta)\right)\wedge\left(x(t^{+})>x(t) \right)}{\left|U^{l}(t,\Delta)\right|}\\ x^{dl}(t)\leftrightarrow Q_{x}^{dl}(t)=\frac{\Sigma\left(x(t)-x(t^{-})\right) \cdot\left(t^{-}\in U^{l}(t,\Delta)\right)\wedge\left(x(t^{-})<x(t)\right)}{ \left|U^{l}(t,\Delta)\right|}\\ x^{ur}(t)\leftrightarrow Q_{x}^{ur}(t)=\frac{\Sigma\left(x(t^{+})-x(t) \right)\cdot\left(t^{+}\in U^{r}(t,\Delta)\right)\wedge\left(x(t^{+})>x(t) \right)}{\left|U^{l}(t,\Delta)\right|}\end{array}. \tag{6}\]  
The series \(x^{*}(t),*\in\{ul,dl,ur,dr\}\), quantifies the \(*\)-deviation of the record \(x\) from its value \(x(t)\) at node \(t\). This is only the first half in the definition of the elementary measure \(\mu_{x}^{*}(t)\) at node \(t\). The second half is the answer to the following question: \"To what extent can \(*\)-deviation \(x^{*}(t)\) be considered large?\". The answer is obtained by comparing \(x^{*}(t)\) with the values \(x^{*}(t^{-})\) at the remaining nodes \(t^{-}\in T\), that is, with the pattern \(x^{*}(T)\). In DMA, this is called the maximum measure of the series \(x^{*}(t)\) and is denoted as \(\operatorname{mes}\operatorname{max}x^{*}(t)\):  
\[\mu_{x}^{*}(t)=\operatorname{mes}\operatorname{max}x^{*}(t). \tag{7}\]There are several of its designs. Here is one of them (\(\sigma\)-construction):  
\[\mu_{x}^{*}(t)=\frac{\sigma_{-}^{*}(x,t)-\sigma_{+}^{*}(x,t)}{\sigma_{-}^{*}(x,t) +\sigma_{+}^{*}(x,t)}, \tag{8}\]  
where  
\[\begin{array}{c}\sigma_{-}^{*}(x,t)=\sum(x^{*}(t)-x^{*}(t^{-})):t\in T\wedge \left((x^{*}(t)>x^{*}(t^{-})\right)\\ \sigma_{+}^{*}(x,t)=\sum(x^{*}(t^{+})-x^{*}(t)):t\in T\wedge\left((x^{*}(t)<x^ {*}(t^{+})\right)\end{array}.\]  
The next part of the work is devoted to the implementation of the scenario (5)-(8) on separate examples for the situations described in Section 2.1.3.
## 3 Aim, Materials, and Methods
### Integrated Intelligent Perception Mode of \"Sky Air and Ground\"
#### 4.1.2 \"Airborne\" UAV Perception System  
The \"Airborne\" man-machine perception system is an intelligent technical system that integrates advanced sensors and high-speed data processing technology. In the context of disaster relief, this system receives a vast amount of information from the \"Sky\" satellite communication system, including images, sounds, temperature, humidity, and pressure. It then transmits real-time situational updates regarding building damage status and road conditions, as well as the number and locations of trapped individuals in the disaster area. This invaluable information provides crucial support to rescue personnel by offering timely, accurate, and comprehensive intelligence assistance to the ground command system.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 2 Materials and Methods  
### Capacitively-Coupled ECG and Bio-Impedance System and Positioning in the Car Seat  
The capacitive sensor system that was evaluated is shown in Figure 1 and is described in detail in [27]. For this evaluation, the system was implemented on a seat-covering pillow for easy transfer between vehicles, but it can also be integrated directly into a car seat in case of a permanent installation. It consists of an array of ccECG sensing plates, implemented in this case using 15 rigid sensing plates in the back side, and 2 conductive textile sensing plates at the legs. This array configuration permits to pick up ECG signals for different driver body sizes, shapes and seating positions. Also 4 ccBioZ plates (2 for current injection and 2 for voltage sensing) are included in the back side, together with one unused plate in the center. In the bottom side a larger conductive textile plate is used for capacitive Driven Right Leg (DRL) feedback to reduce common mode interference.
## 3 Simulation Results and Sensitivity Analysis
### Influences of Traffic Loads on the Von-Mises Stress of the Pipe  
From the above analysis, the region and the compaction degree of the backfill are considered to jointly determine the Von-Mises stress of the pipe. Vehicles transmit traffic loads to pipes through the pavement structure and backfill, so the mechanical response of pipes to the traffic load is constrained by the backfill state. In the experiment described in this section, the Von-Mises stress reaction of the pipe was studied by changing the magnitude and speed of the traffic load.  
The value of the stress caused by the traffic load was proportional to the load magnitude (Figure 11), and the stress fluctuated from smooth to acute with the increasing speed of the traffic load; however, the maximum and minimum stress values were almost unchanged (Figure 12). Although the compaction degree of the backfill had a limited influence on the stress caused by the traffic load, a lower compaction degree was associated with a greater fluctuation in stress. The compaction degree of L1 or L2 mainly affected the stress at the invert, and L3 had the greatest effect on the stress at the crown and springline. In general, the higher the Von-Mises stress caused by earth pressure, the greater the fluctuation in the stress caused by traffic loads. However, when L1 was loose, the invert Von-Mises stress was lowered, but the fluctuation in the stress caused by the traffic load was more substantial (Figures 11a and 12a). In general, if the backfill does not meet the required compaction degree, then the pipe region in contact with it is more sensitive to the traffic load.  
Figure 11: The Von-Mises stress under different load magnitudes: (**a**) change in L1 compaction degree; (**b**) change in L2 compaction degree; (**c**) change in L3 compaction degree.