esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 3 Anomaly-Based Intrusion Detection in Internet-Integrated CoAP WSN
### System Architecture  
We illustrate the architecture considered for the purpose of implementing and evaluating intrusion detection and prevention in Figure 1. This architecture considers the employment of a 6 LoWPAN border router (6 LBR) mediating communications between the WSN and Internet communications devices, the usage of CoAP sensing and actuating devices, and the clients of the resources supported by such devices, which may be either internal (in the same WSN domain) or external (in a different WSN domain or located in the Internet). Thus, CoAP clients associate with CoAP servers in order to request, actuate or observe particular resources available at the application-layer.  
As illustrated in Figure 1, we assume the presence of a device employed to support intrusion detection operations. This device owns the resources required for performing capturing of the communications in its range, thus acting as a network sniffer. Another important requirement is that this device also supports a high level language interpreter in order to support the machine-learning algorithms employed for anomaly detection. In our current implementation of this architecture, this role is assumed by the 6 LBR, and the fact that this device is responsible for filtering (mediating) the forwarding of communications between the Internet and WSN domains is also of help given that, after an intrusion has been detected, subsequent communications between the attacker and the attacked device may be denied. In the current implementation of the proposed architecture, CoAP server devices do not communicate with each other and CoAP client nodes request resources from servers at a rate controlled by a timer. As previously discussed, for the purpose of implementing and experimentally evaluating the considered architecture and the anomaly-detection mechanisms employed, we use the IoT-LAB [6] platform, configured to support a multi-hop topology. As a limitation of this platform, we consider requests to CoAP resources using GET requests only. Our implementation of the various devices in this architecture is performed in the Contiki operating system, in particular by building on top of the source code for the border-router (for the 6 LBR), the er-example-server (CoAP server) and the er-example-client (CoAP client) implementations.
# Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks
## 3 Problem Statement  
The goal of this article is to evaluate the trustworthy nodes and to detect the faulty nodes using the proposed trust evaluation scheme on the nodes. This improves the security of the IoT cloud. We assume the highest energy mobile node as an EDGE node called 'MEN', which is capable of collecting and fusing the data to the sensor cloud network. It also provides good computing power and a buffer facility for unused data. The set of sensors deployed in a \(50\times 50\) m\({}^{2}\) underlying cloud-based sensor network are \(sn=\{sn_{1},sn_{2},\ldots,sn_{n}\}\). The trusted nodes are identified by calculating the trust values considering various parameters. \(I^{dir}\) and \(I^{indir}\) are direct and indirect trust values used to evaluate the trustworthiness of the nodes in each cluster. Furthermore, the trusted nodes from every cluster are considered to route via trust paths for optimal data aggregation at the MEN.
## 2 Numerical Model
### Simulation Conditions  
The Skarstrom cycle [23] was employed in this study, where both beds undergo 4 steps. For clarification purposes, the schematics of the 4-step cycle is given in Figure 2. The sequence of the steps are as follows: pressurization with air (Pr), high-pressure feed to produce an oxygen enriched gas (Ad), countercurrent blowdown (Cb) and a low-pressure purge (Pu) by using the product gas from the Ad step. The gas flow directions of the four steps are shown by arrows in Figure 2. The duration time for these 4 steps are 7 s, 5 s, 3 s, and 5 s, respectively. In the Pr step, the mass flow rate is set to be 104 L min\({}^{-1}\) according to the particle compressor (ZW-700A). The low pressure at desorption step was set to be 1 atm. Table 3 lists the initial and boundary conditions used in the simulation. The usual no-slip boundary condition is applied at the \"wall\" and a zero normal gradient boundary condition is applied on the \"axis\" for all variables. The entrance and vent in Table 3 represent the position of the feed gas flow in and product flow out of the bed, respectively.  
The O\({}_{2}\) product recovery is given in Equation (13). The oxygen purity is mole fraction of oxygen.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sorbate** & \(k_{1}\) **(mol-kg\({}^{-1}\)-Pa\({}^{-1}\))** & \(k_{2}\) **(K)** & \(k_{3}\) **(Pa\({}^{-1}\))** & \(k_{4}\) **(K)** & **AH (kJ mol\({}^{-1}\))** \\ \hline O\({}_{2}\) & \(7.87\times 10^{-9}\) & 1541.211 & \(6.79\times 10^{-10}\) & 1968.24 & 12 \\ N\({}_{2}\) & \(9.86\times 10^{-9}\) & 2010.908 & \(1.67\times 10^{-9}\) & 2250 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adsorption isotherm parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **Pr Step** & **Ad Step** & **Cb Step** & **Pu Step** \\ \hline Duration (s) & 7 & 5 & 3 & 5 \\ \hline \hline \multicolumn{5}{c}{Boundary Conditions for bed \(1\)} \\ \hline Entrance & mass-flow-inlet & mass-flow-inlet & pressure-outlet & pressure-outlet \\ Vent & wall & pressure-outlet & wall & mass-flow-inlet \\ Adsorber wall & wall & wall & wall & wall \\ Adsorber axis & axis & axis & axis & axis \\ \hline \hline \end{tabular}
\end{table}
Table 3: The cyclic sequence time and boundary conditions for simulation.  
Figure 2: Schematic diagram of the Skarstrom cycle.  
The initial conditions for simulation and model parameters are listed in Tables 4 and 5.  
\[\begin{split}&\text{Product Recovery}=\frac{\text{Product flow rate }\times\text{Product O}_{2}\text{ purity}}{\text{Feed gas flow rate }\times\text{Feed gas O}_{2}\text{ purity}}\\ =&\frac{\text{(Amount of O}_{2}\text{ from Ad Steps)}-\text{(Amount of O}_{2}\text{ from Pu Step)}}{\text{Amount of O}_{2}\text{ feed in Steps Pr and Ad}}\end{split} \tag{13}\]
# The Circadian Effect Versus Mesopic Vision Effect in Road Lighting Applications
## 2 Materials and Methods
### Circadian Light and Circadian Stimulus  
CL\({}_{\rm A}\) mathematically describes the modeled spectral sensitivity of the human circadian system by [PERSON] et al. [28] and is defined as shown in Equation (3), where \(CL_{A}\) is the circadian light, 1548 is the constant setting for the normalization of \(CL_{A}\) such that 2856 K blackbody radiation at 1000 lx has a \(CL_{A}\) value of 1000, \(E_{\lambda}\) is the spectral power distribution, \(S_{\lambda}\) is the S-cone fundamental, \(mp_{\lambda}\) is the macular pigment transmittance, \(RadSat\) is the half-saturation constant for bleaching rods = 6.5 W/m\({}^{2}\), \(k\) = 0.2616, \(a_{b-y}\) = 0.7000, and \(a_{rod}\) = 3.3000. CL\({}_{\rm A}\) is measured in units of spectrally weighted flux per unit area. CS is a proposed metric based on the CL\({}_{\rm A}\) value with a maximum value of 0.7, as shown in Equation (4) [28], and is dimensionless. Due to a blue versus yellow (b-y) mechanism [28], the CL\({}_{\rm A}\) is expressed by a piecewise function depending on whether b-y, namely \(\int\frac{S_{\lambda}}{mp_{\lambda}}E_{\lambda}d\lambda-k\int\frac{V_{\lambda} }{mp_{\lambda}}E_{\lambda}d\lambda\), \(>\)0 or \(<\)0. If the yellow stimuli dominate, only the intrinsic sensitivity of the iPRGC contributes to the  
Figure 2: **(a)** The relative spectral sensitivity curve of the photopic vision V\({}_{\lambda}\), scotopic vision V’\(\lambda\), S-cone fundamental S\({}_{\lambda}\), and melanopsin sensitivity curve Mc\({}_{\rm A}\) (corrected for crystalline lens transmittance). **(b)** Spectral power distributions of high-pressure sodium (HPS), phosphor-converted (PC) light-emitting diode (LED) 1, and PC LED 2 with CCT 2238 K, 2497 K, and 6060 K, respectively.  
circadian response; otherwise, all photoreceptors contribute to the circadian response. The default calculations of \(CL_{A}\) and \(CS\) correspond to a one-hour duration of exposure.  
\[CL_{A}=\left\{\begin{array}{ll}1548\bigg{[}\int Mc_{\lambda}E_{\lambda}d \lambda+(a_{b-y}(\int\frac{S_{\lambda}}{mp_{\lambda}}E_{\lambda}d\lambda-k\int \frac{V_{\lambda}}{mp_{\lambda}}E_{\lambda}d\lambda)-a_{rad}(1-e^{\frac{-\int V \tau_{\lambda}E_{\lambda}d\lambda}{RadSat}}))\bigg{]}\\ &if\ \int\frac{S_{\lambda}}{mp_{\lambda}}E_{\lambda}d\lambda-k\int\frac{V_{ \lambda}}{mp_{\lambda}}E_{\lambda}d\lambda>0\\ 1548\int Mc_{\lambda}E_{\lambda}d\lambda&if\ \int\frac{S_{\lambda}}{mp_{\lambda}}E_{ \lambda}d\lambda-k\int\frac{V_{\lambda}}{mp_{\lambda}}E_{\lambda}d\lambda\leq 0 \end{array}\right. \tag{3}\]  
\[CS =0.7-0.7/[1+(\frac{CL_{A}}{355.7})^{1.1026}] \tag{4}\]
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 3 Prototype of 5G Integrated with TSN
### Setup of the 5G-TSN-Bridge
#### 3.2.2 Time Synchronization  
The integration of the 5G-URLLC system in the TSN based network is shown in Figure 7. The URLLC system acts as a 5G virtual bridge in the TSN setup with the device and the network side TSN translator functions at the two endpoints [15]. The system clock of the network side translator function and base station are synchronized to the 5G grand master clock. The network side translator function performs the ingress/egress timestamping using the TSN clock. Inside the 5G URLLC testbed, the base station uses periodic System Information Block 9 (SIB9) messages as part of the Radio Resource Control (RRC) signaling to carry the timing information for the UE [30]. The 5G UE extracts the timing information from the SIB9 message to be used by the device side translator function to perform ingress/egress timestamping using the TSN clock. The periodic SIB9 messages allow correcting any errors accumulated during the periodic interval. A finer SIB9 granularity allows lower time synchronization errors and vice versa. In this article, we report results based on an SIB9 periodicity of 5 s.  
Figure 5: Latency histogram of 100,000 samples on the URLLC testbed with UDP traffic with a payload of 32 bytes.
## 2 Experimental Setup and Procedures
### Cutting Scheme  
Before the main cutting, a series of cuttings is performed twice or thrice. These series of cuts are defined as pre-conditioning. Because the tunnel excavation machine excavates the damaged face in the field, pre-conditioning is performed to simulate conditions similar to the actual excavation site in the linear cutting test (Figure 5).  
Figure 4: Linear cutting machine (LCM): (**a**) overview of LCM, (**b**) stroke of servomotor in x- and y-direction, and (**c**) load cells and cutting tool.  
The main cutting tests were performed using penetration depths of 3, 6, and 9 mm; the spacings used for each penetration depth are listed in Table 2. Because the cutting speed does not affect the cutting performance [29], it was arbitrarily set to 12.5 mm/s. The cutting test consisted of five cuts. Rock fragments and tool forces were used only from the measurement area in Figure 5, and the results from the first cut were excluded from the analysis because they did not represent an interaction between the cuts.  
The specific energy (\(SE\)) represents the excavation efficiency of the tunnel excavation machine, which indicates the energy consumed to excavate the unit volume. The excavation efficiency is maximized when the specific energy is minimized, and this energy is obtained using Equation (1).  
\[SE= \frac{F_{c}\times l}{V_{c}} \tag{1}\]  
where \(F_{c}\) is the mean cutting force, \(l\) is the length of the cut, and \(V_{c}\) is the cutting volume. The cutting force (\(F_{c}\)) is the average of the second to fifth cuts measured in the measurement area, and the cutting volume (\(V_{c}\)) was obtained through 3D scanning.
# OTFS-Based Handover Triggering in UAV Networks
## 5 Proposed UAV-Ground Handover Triggering Based on DD Channel Estimation
### Proposed DD-Channel-Based Handover Triggering
This leads to a more stable metric for handover triggering, reducing the likelihood of false handover events caused by temporary channel degradations due to ICI. Similarly, filtering and smoothing RSS measurements can reduce ping-pong handovers by reducing short-term fluctuations. However, RSS-based handover decisions remain susceptible to large-scale fading, shadowing, and multipath interference, leading to suboptimal handover timing. The DD channel, by contrast, directly exploits Doppler characteristics, enabling predictive and mobility-aware handover triggering, particularly in high-speed aerial and ground communication scenarios.
## 1 Introduction
For instance, a company may provide its employees with Internet of Things-enabled wristwatches loaded with information on workplace safety [23; 24]. Using IoT technologies, managers may monitor malfunctioning systems and make informed estimates about how to deal with impending dangers and take preemptive action. When it comes to IoT solutions for the office, wearable technology is one of the most significant developments. [PERSON] et al. (2019) and [PERSON] et al. (2019) stated that examples of wearable electronics include smartphones and other \"smart\" mobile devices with built-in capability for synchronizing and organizing data [25; 26]. These innovative techniques provide companies with confidence in the safety of their staff. Monitoring susceptible infrastructure with the use of IoT technologies may help management anticipate threats and plan for appropriate countermeasures. It's possible that workers' usage of wearable technology might improve workplace health and safety [3; 27; 28]. These may enhance on-site monitoring of operational activities, streamline processes to boost efficiency and safety, and allow for prompt corrective action to be taken without requiring personnel to be relocated [29; 30]. IoT solutions may benefit organizations in a number of ways, including by increasing operational efficiency and providing a sense of security to employees. [PERSON] et al. (2020) and [PERSON] et al. (2020) found that most workplace fires are accidental, but many may be prevented if their causes are identified immediately [31; 32]. Possible improvements to fire protection sensors include the ability to pick up on rising temperatures, which might reveal critical moments for stopping the development of a fire. Software connected to the Internet of Things may help detect a sudden increase in temperature, a key clue to a fire's origin. Businesses may keep an eye on the temperature of the office with the use of fire safety sensors embedded into Internet of Things software or devices, allowing for rapid response in the event of a fire. This early detection might be highly useful for the speedy evacuation of persons and the swift implementation of the key preventive steps. Although the advantages of IoT implementation are proven by existing research, it is still uncertain whether we have a comprehensive view of the barriers that affect its implementation in the construction industry [17; 22]. The rationale for this study is based on the growing interest of Malaysia's construction sector on the IoT and the lack of evidence from existing literature specifically focusing on barriers to implementing the IoT. The first objective of this study is to identify the critical barriers of implementing the IoT in Malaysian construction industry. The second objective is to successfully utilize Structural Equation Modelling (SEM) approach to develop a model indicating all significant barriers.
# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
###### Abstract  
Lung cancer is one of the deadliest diseases worldwide. Computed Tomography (CT) images are a powerful tool for investigating the structure and texture of lung nodules. For a long time, trained radiologists have performed the grading and staging of cancer severity by relying on radiographic images. Recently, radiomics has been changing the traditional workflow for lung cancer staging by providing the technical and methodological means to analytically quantify lesions so that more accurate predictions could be performed while reducing the time required from each specialist to perform such tasks. In this work, we implemented a pipeline for identifying a radiomic signature composed of a reduced number of features to discriminate between adenocarcinoma and other cancer types. In addition, we also investigated the reproducibility of this radiomic study analysing the performances of the classification models on external validation data. In detail, we first considered two publicly available datasets, namely D1 and D2, composed of \(n=262\) and \(n=89\) samples, respectively. Ten significant features, according to univariate AUC evaluated on D1, were retained. Mann-Whitney \(U\) tests recognised three of these features to have a statistically different distribution, with a \(p\)-value \(<0.05\). Then, we collected \(n=51\) CT images from patients with lung nodules at the _Aziona Ospedalero--Universitaria \"Policlinico Rinuiti\"_ in Foggia. Resident radiologists manually annotated the lung lesions in images to allow the subsequent analysis of the malignancy regions. We designed a pipeline for feature extraction from the Volumes of Interest in order to generate a third dataset, i.e., D3. Several experiments have been performed showing that the selected radiomic signature not only allowed the discrimination of lung adenocarcinoma from other cancer types independently from the input dataset used for training the models, but also allowed reaching good classification performances also on external validation data; in fact, the radiomic signature computed on D1 and evaluated on the local cohort allowed reaching an AUC of \(0.70\) (\(p<0.001\)) for the task of predicting the histological subtype.  
radiomics; lung carcinoma; histological subtype; machine learning +
Footnote †: journal: _Sci. 2022, 12_, 5829. [[https://doi.org/10.3390/app12125829](https://doi.org/10.3390/app12125829)]([https://doi.org/10.3390/app12125829](https://doi.org/10.3390/app12125829))
## 5 Conclusions  
In this study, a ML and GA-based intelligent modeling framework was proposed for predicting air humidity and temperature at the end of the water trickling roadway. Twenty-one features were chosen as input variables. More than 200 pieces of data were collected from an independently developed modeling roadway to construct the dataset, in particular, the thermal water and flow rate were taken into comprehensive consideration first for training and validating of the LSTM, LSTM-GA, and PCA-LSTM-GA models. The thermal system enthalpy difference, MSE and \(R^{2}\) were chosen as the assessment indices. Several conclusions are summarized as follows.  
1. Thermal water trickling into the roadway can evidently change the enthalpy of the thermal system. The increase in upwelling water flow rate induces a linear rise of the enthalpy difference of humid air, but it barely affects the sensible heat of air. The increase in upwelling water temperature influences both the latent heat and sensible heat of air. As a result, the total enthalpy difference of the humid air rises nonlinearly.  
Figure 15: Application of PCA-LSTM-GA model.  
2. The PCA-LSTM-GA model is robust in predicting the air humidity and temperature at the end of the trickling roadway. LSTM is suitable for processing time series data. GA is efficient in hyperparameter tuning of the LSTM. PCA optimizes the hybrid model, raising its convergence speed and bringing about an increase in \(R^{2}\).
3. As demonstrated by the importance scores, the airflow temperature at the end of the water trickling roadway is mainly influenced by the surrounding rock temperature (IS 0.661) and inlet airflow temperature (IS 0.264). The airflow humidity at the end of the roadway with water trickling is mainly influenced by the rock temperature in water upwelling section (IS 0.577), inlet airflow temperature (IS 0.187), and upwelling water temperature and flow rate (total IS 0.136), and it has an evident time effect. The enlightenment given to us is that, for thermal control for this type of roadway, a composite heat insulation structure with jet grouting and support techniques for heat insulation should be arranged.  
In future work, the generalization of the proposed model can be promoted through employing a larger dataset with multiple variables. Moreover, we have developed relevant equipment and have an engineering practice method to apply the ML model.  
Data curation, Z.J.; writing--original draft preparation, J.W.; writing--review and editing, D.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 7 Conclusions  
The design of an energy-efficient routing algorithm has always been an important research issue for WSNs. In this paper, an adaptive clustering method based on an AP algorithm is presented, which can reduce the average data transmission distance of the network and provide load balanced routing effect. It firstly introduces the AP algorithm to calculate the initial cluster centers. Then a modified K-medoids algorithm is adopted to partition the whole network into clusters according to the previous initial cluster centers calculated by AP. Simulation results show that about 33.33%, 52.5% and 54.21% performance gain can be achieved in terms of energy consumption, and about 16.23%, 31.39%, 51.1% performance gain can be achieved in terms of network lifetime compared to the UCR-H, LEACH-AP and EDDUCA algorithms respectively.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON]-J.L. advised simulation settings; and [PERSON] wrote this paper.  
This work is supported by the National Natural Science Foundation of China (61772454, 61811530332, 61811540410). [PERSON] is the corresponding author.  
The authors declare no conflict of interest.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Value of p** & \(-\)4500 & \(-\)5000 & \(-\)5500 & \(-\)6000 & \(-\)6500 & \(-\)7000 & \(-\)7500 \\ \hline
**Converge time (s)** & 2.12 & 1.54 & 1.22 & 0.99 & 1.13 & 1.27 & 2.46 \\ \hline
**Cluster number** & 8 & 9 & 8 & 6 & 6 & 8 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different values of \(p\).