esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 5 Experiments  
This section contains experimental results to showcase the capabilities of the proposed UTM system. The objectives of these experiments are twofold: (i) we show the integration of the complete architecture, with all its functional modules interacting together to accomplish the specified UAS operations; and (ii) we demonstrate our system operating in real time in a realistic setup, to test its capabilities to solve different types of conflicts in an automated manner. For that, we have defined two use cases (Section 5.1) involving heterogeneous UAS and several types of conflicts, in order to validate all the modules in our UTM system. The tested use cases are realistic both in terms of the UAS operational parameters and the experimental setup (Section 5.2). Our experiments were carried out by means of _Hardware-In-The-Loop_ (HITL) simulations where the UAS operators and the UTM framework ran at different physical locations, with a real long-distance communication link in between. All of the results of the tests are described in Section 5.3.
## 3 Results
### Analysis of Temporal Variation in Saltmarsh NDVI Using PlanetScope  
The NDVI extracted from the 12 monthly PlanetScope images for the herbs, grasses, and sedges quadrats varied significantly between April 2020 and March 2021 based on the repeated measures ANOVA test with sphericity assumed (\(F_{11,\,429}\) = 104.57, \(p\) < 0.001). Similarly, the NDVI extracted for the _J. krawsii_ quadrats varied significantly across the study period, as indicated from a repeated measures ANOVA with a Greenhouse-Geisser correction (\(F_{2.70,\,13.51}\) = 18.23, \(p\) < 0.001). For all quadrats, there was a large NDVI peak in May (late autumn) as well as a smaller peak October-November (mid-late spring), followed by steady increases during summer (Figure 7). The NDVI of the herbs, grasses, and sedges quadrats showed comparatively greater temporal variation in NDVI from July to September than the _J. krawsii_ quadrats, where the NDVI remained relatively constant.  
Figure 6: Relationship between aboveground carbon content extracted from the resampled 3 m map derived from the ArborCam image and PlanetScope NDVI captured on 23 October 2020 for all 47 quadrat locations (r = 0.78, \(p\) < 0.001).  
Sensor and environmental factors were investigated to determine if there was any single, clear driver of the observed temporal variation of the PlanetScope NDVI. The tide level at the time of image capture had very little variation between monthly images, ranging from 0.6 m to 0.8 m, compared to the maximum tide height of 2 m (Table 3). The sun elevation at the time of image acquisition did vary seasonally, as expected, and was lower in the October 2020 image due to an earlier acquisition time, but it did not qualitatively appear to be directly related to the measured saltmarsh NDVI (Figure 8). Furthermore, the view angle was small ($5\({}^{\circ}\)) in all 12 images.  
Figure 8: Potential drivers of the NDVI variation measured in saltmarsh: sun elevation and view angle at the time each PlanetScope image was acquired.  
Figure 7: Temporal variation in mean NDVI extracted at each quadrat location from the 12 PlanetScope images (April 2020 to March 2021). Error bars show \(\pm 1\) standard error.  
The monthly rainfall showed a large variation between January 2020 and March 2021 (Figure 9a). There were large peaks in rainfall in February and from July to August 2020, with a smaller peak in May 2020. After September 2020, there were gradual increases in monthly rainfall until March 2021, similar to the annual rainfall trend for the Jervis Bay area. Changes in NDVI appeared to track changes in monthly rainfall, except for the large rainfall peak in July-August. There was no relationship between the monthly rainfall and NDVI when all months were included in the correlation analysis between these two variables (r = 0.04; \(p\) = 0.90; Figure 9b). However, when high rainfall data points for July and August were removed, there was a strong correlation between monthly rainfall and NDVI (r = 0.66; \(p\) < 0.05).
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 2 Native Bacterial Approach for Colorectal Cancer (CRC) Treatment
### Mucosal Immunomodulation  
The benefits of probiotics on the gut are inseparable from their intestinal immunomodulatory effects. Inflammatory cytokines were involved in colon carcinogenesis in 17 cases of human colon adenocarcinomas [33]. Dysbiosis of the intestine induces the secretion of proinflammatory cytokines and nitric oxide, which results in the occurrence of inflammation and CRC. Probiotics contribute to the reconstruction of the intestinal ecological balance and regulate the secretion of cytokines to restore intestinal homeostasis [22; 25]. For example, a symbiotic combination of the probiotic _Lactobacillus gasseri_ 505 and a prebiotic _Cudrania tri-cuspidata_ leaf extract exerted a cancer-protective effect by downregulating pro-inflammatory cytokines in an azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced CRC mouse model [34]. In addition, some studies show that probiotics improve antitumor immunity at a systemic level by enhancing and reducing the expression of T helper 17 (Th17) and regulatory T (Treg) cells, respectively, as well as inhibiting the expression of tumor C-X-C chemokine receptor type 4 (CXCR4) and major histocompatibility complex class I (MHC-1) [35]. _Lactobacillus plantarum_ from traditional fermented milk products was reported to show anti-CRC activities in a CT26 subcutaneous-tumor mouse model [36]. The oral administration of _L. plantarum_ suppressed tumor growth by increasing CD8\({}^{+}\) and natural killer (NK) cell infiltration into the tumor and promoting Th1-type CD4\({}^{+}\) T differentiation and the upregulation of interferon-\(\gamma\) (IFN-\(\gamma\)) levels [37]. Another study involved in checkpoint blockade immunotherapy revealed that the combinative administration of _L. acidophilus_ lysates and CTLA-4 mAb cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blocking antibodies inhibited colon carcinogenesis in AOM/DSS syngeneic BALB/c mice [37]. The  
Figure 1: Mechanism of wide-type probiotics for the prevention and treatment of CRC via modulating the colonization resistance of pathogenic bacteria, mucosal immunomododulation, and improving the intestinal barrier.  
treatment outcome was associated with increased numbers of CD8\({}^{+}\) T cells and effector memory T cells and decreased numbers of Treg and M2 macrophages [37].
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 5 Experimental Analysis
### Identification of Regimes
#### 5.1.3 Feature Importance Analysis  
To check the importance of the constructed features, a measure named the increased node purity [48] (illustrated as IncNodePurity in Figure 5) was calculated for each feature. The measure computes the total decrease in node impurity from splitting on the given feature, and then averages it over all of the component regression trees. The node impurity is quantified by the residual sum of the squares and is calculated only at the node at which that feature is used for that split. Based on the measure, the importance of each feature associated with each station was calculated and ranked. As similar results were obtained for all four stations, for illustration purposes, we only provide the results associated with Station 400081, as shown in Figure 5. From the figure, we can observe that the identified regimes at different times play an important role in forecasting. Accordingly, the regime at time \(t-1\) is more important than the regimes at time \(t-2\) and time \(t-3\). For different forecasting tasks, the most important traffic flow measure is distinct. For example, if the forecasted measure is the flow rate, the most important feature is flow rate at time \(t-1\). For the other two forecasting tasks, the most important feature corresponds to the forecasted traffic flow measure at time \(t-1\). Taken together, it can be seen that it is necessary to use the regimes as the representative features in traffic flow forecasting tasks.
## 4 Conclusions  
The significant outcome of this study involved the microzonation of the Malay Basin, focusing on soil characteristics to enhance the reliability of the existing offshore facilities in that region. The summary of the findings, with respect to these objectives, are as follows:  
* The primary objective was achieved. The soil characteristics based on the study area were analyzed for the Malay Basin, which consisted of soil types D and E. Soffer soil possesses the highest value of vibration predicted on the seabed (PSa).
* Objective two was achieved. The soil characteristics based on these data were analyzed. It was confirmed that most of the soil were soil types D and E, which were softer  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Type of**} & \multicolumn{4}{c}{**Site Coefficient**} \\ \hline \multicolumn{2}{c}{**Seismic Events**} & **DHRSa** & **Soil Type** & **Ca** & **Cv** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Coordination**  
soil types; these soil types usually give the basin effect, which is an amplification. The soil amplification factors were successfully developed and could be used in the development and maintenance of offshore facilities in the Malay Basin.
* Objective three was achieved. For the design practices and analysis, response spectra are important, and this study developed 'designed' horizontal response spectra for the 5% damping ratio because most of the shallow water structures, such as fixed-jacket platforms, fall in this damping ratio.  
In conclusion, the aim of this study, which was to develop a seismic microzonation map of the Malay Basin that could be used for the construction and maintenance of offshore facilities, was achieved. It is believed that the proposed peak surface acceleration, soil amplification factor, designed horizontal response spectral acceleration, and site coefficients reflect better the actual site conditions, while the effect of the shaking intensity of soil nonlinearity was taken into account. However, further investigations should be commenced, including enhancing the number of well-documented soil profiles, and a simulation of strong motion recordings at different soil site conditions.  
Conceptualization, M.M.; methodology, M.M.; software, M.M. and K.U.D.; validation, M.M., M.S.L. and A.A.; formal analysis, M.M. and K.U.D.; investigation, A.A. and N.H.A.H.; resources, M.M. and M.S.L.; data curation, M.M. and N.H.A.H.; writing--original draft preparation, M.M. and M.S.L.; writing--review and editing, M.M.; visualization, M.M. and N.H.A.H.; supervision, M.S.L., A.A., N.H.A.H. and K.U.D.; project administration, M.M. and M.S.L.; funding acquisition, M.S.L. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Yayasan Universitit Teknologi PETRONAS-Fundamental research grant (YUTP-FRG)-015 LC0-094 and 015 LC0-312 for funding this project.  
Not applicable.  
The authors declare no conflict of interest.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
###### Abstract
Moreover, because the CPS becomes large and the relationships of each CPS component become complex, the level of accuracy shown by a conventional CPS model and a real CPS decreases, which generates additional attack vectors [22; 23; 24]. From a control-theoretical viewpoint, large and complex systems can be represented as high-order differential equations [24], where a mathematical model with a high-order term is vulnerable to noise on the state variables [23]. Therefore, it is difficult to obtain an exact mathematical model of a complex physical system, and unconsidered mathematical terms of the inaccurate dynamic system model become vulnerabilities of the model-based attack detector, resulting in inaccurate detection. To overcome the limitations of legacy model-based cyber-physical security, data-driven anomaly detection methods (where abnormal data are acquired from numerous simulations and controlled experiments) are adopted in cyber-physical security [25; 26]. In particular, machine learning (ML), which depicts correlations between an input and output using massive amounts of data without modeling based on physical laws, is adopted in cyber-physical security in order to satisfy high-level safety and reliability concerns [22]. Furthermore, ML techniques enable a model to be generated for the massive and complex relationships of each component of the CPS, including various physical systems in the real world, heterogeneous network protocols, and the complicated application software in the cyber-world, where the generated model can enhance the security level of the CPS. In this paper, we provide a comprehensive survey of cyber-physical attacks and machine learning-based cyber-physical attack detection technologies. In particular, we focus on attacks that can damage physical systems and manipulate the physical processes. In addition, we mainly consider cyber-physical attack detection methods and attack handling methods with machine-learning techniques. There are a large number of surveys of physical model-based anomaly detection methods [27; 28] and network intrusion detection systems (IDSs) [29; 30; 31]. Thus, conventional deviation-based attack detection methods in control theory and IDS-based anomaly detection techniques are not covered. The rest of the paper is organized as follows. Section 2 introduces the hierarchical CPS structure and the roles in each layer. Section 3 provides the taxonomy of cyber-physicalattacks for each layer. Section 4 presents cyber-physical attack detection methods with machine learning techniques. Section 5 discusses the potential research directions for ML-based cyber-physical security in the context of real-time characteristics in the CPS, resiliency, and data generation methods for learning malicious behavior. Finally, we conclude this survey in Section 6.
## 5 Co-Simulation of CFD/CSD/RBD  
### Structural Model of HWB  
Because the aerodynamic model used in CSD/CFD/RBD co-simulation is completely consistent with the model used in CFD/RBD simulation, the aerodynamic model is not described here; only the finite element structure model of the aircraft is introduced. The FEM model of the aircraft is shown in Figure 11.  
Figure 10: Poles of short-period mode calculated by AVL and CFD method.  
Figure 9: The AoA response of aircraft at different Mach numbers based on CFD/RBD co-simulation.  
Firstly, the finite element modeling of the fuselage is introduced. The finite element model of the fuselage is shown in the figure, including structural elements such as fuselage skin, cabin lattice frame, wing ribs, wing beam and wing skin. The entire finite element model has nearly 3189 finite elements, which can represent more than 10,300 degrees of freedom. Three- or four-node curved plate elements are used to approximate the structure of the skin, beam, etc. Frames are represented by bending bar elements. The wing fin is represented by an axial rod element. As for the material properties of the element, the material is divided into spar material and skin material with reference to the literature [32], and the external skin and internal structure are, respectively, assigned two types of structural properties.  
Due to the relatively small size of the elevator, it is simplified to a rigid body model in the co-simulation. Moreover, the aircraft structure, except the elevator, is regarded as an elastic body. In order to avoid geometric interference between the rigid elevator and the elastic fuselage caused by fuselage deformation in the process of co-simulation, it is necessary to set some constraints on the structural model of the elastic fuselage: for the two walls of the fuselage near the control surface, a relatively rigid constraint should be applied on the corresponding finite element. Through this constraint, no relative movement of the two sides walls can be guaranteed, and a movement space with a fixed boundary can be provided for the motion of the elevator. The implementation method in this paper is to use RB2 constraints in the MPC function of MSC. Patran multi-point constraints to carry out relatively rigid constraints on the nodes on the fuselage wall near the elevator.  
Based on the above finite element model, the first four-order elastic modes of aircraft can be obtained through modal analysis, and the mode shapes and natural frequencies are shown in Figure 12.
## 4 CL-APCC Protocol
### Network Congestion Control Methods
#### 4.1.3 The calculation method for real rate of each node  
According to subsections 4.1.1 and 4.1.2, it is considered both from the node-level and system-level, which expresses the real rate of the node as below.  
The inputting rate of node in period \(t\).  
\[\lambda_{real,i}^{k}\left(t\right)=\alpha\lambda_{ang}^{k}\left(t\right)+\left( 1-\alpha\right)\lambda_{i}^{k}\left(t\right) \tag{19}\]  
The outputting rate of node in period \(t\).  
\[\mu_{real,i}^{k}\left(t\right)=\alpha\mu_{ang}^{k}\left(t\right)+\left(1-\alpha \right)\mu_{i}^{k}\left(t\right) \tag{20}\]  
In sum, the pre-control rate of the node-level is combined with that of the system-level to control congestion, which takes into account the relationship between one part and the whole of the network. In the practical application, CL-APCC protocol increases the value of weight \(\alpha\) when bandwidth and channel quality is in the higher proportion to effectively use the network resources in the whole. Whereas, CL-APCC protocol decreases the value of weight \(\alpha\) when the retransmission quantity of a certain node is lower and its child-node is less, which is aimed to make the node effectively use its own  
Figure 7: The pseudo-code for local network (grid) rate control method.  
resources (such as storage space and rate, etc.) to obtain the highest efficiency of the network in the whole.
## 6 Implications
### Problem Identification and Gap Addressing  
The first step in establishing an effective framework for IoT energy efficiency is identifying existing problems and addressing gaps in current systems. A thorough analysis of technical issues contributing to energy inefficiency is essential. Key issues include the following:  
* Inefficient Offloading and Scheduling Mechanisms: Current systems often lack effective task offloading and scheduling protocols, resulting in suboptimal energy usage.
* High Latency and Dynamic Topology Changes: The challenge of reducing latency and managing changing network topologies persists, impacting energy consumption and system responsiveness.  
* Inadequate Load Balancing and Node Deployment: Ineffective load distribution and suboptimal node placement can lead to increased energy consumption and reduced network performance.
* Resource Management: Effective resource coordination across IoT layers remains challenging, with current systems often struggling to balance energy, memory, and processing resources efficiently.
* Network Congestion and Clustering Inefficiencies: Managing congestion and optimising clustering structures are critical for improving IoT system efficiency. Issues such as inefficient cluster head selection reduce energy performance.  
To address these gaps, researchers and practitioners should conduct detailed reviews of the literature, identify technological limitations, and develop targeted strategies to reduce energy consumption. By systematically addressing these challenges, IoT networks can achieve improved energy efficiency without compromising QoS levels.
# Joint Deployment of Sensors and Chargers in Wireless
###### Abstract  
As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (Pols). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.  
wireless rechargeable sensor networks; sensor and charger deployment; aggregation effect; greedy search; particle swarm optimization +
Footnote †: journal: Accepted: 18 June 2024
## 5 Results
### Performance Criteria  
Classification models require certain parameters to evaluate their validity, and a classifier is only valid if it can predict correctly a label when information is provided. For a binary classification problem, such as Model 1, the prediction made will belong to one element in \(\{0,1\}\) where 0 indicates the negative class (\"No Fatality\"), and 1 the positive class (\"Fatality\"). The results can be presented in a confusion matrix, such as in Table 3.  
From this confusion matrix, several quality parameters can be determined, such as accuracy, precision, recall, and F-score:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{8}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{9}\]  
\[\text{Accuracy}=\frac{TP+TN}{TN+FN+FP+TP} \tag{10}\]  
\[F_{\beta}=(1+\beta^{2})\,\frac{\text{Precision}\cdot\text{Recall}}{(\beta^{2 }\cdot\text{Precision})+\text{Recall}} \tag{11}\]  
Figure 4: Schema of the database modeling.  
Positive real factor \(\beta\) translates into how many times recall is more important than precision. If \(\beta=1\), the harmonic mean of both criteria is returned, and the F-score is deemed balanced. However, if a model has class imbalances, measures such as accuracy, which is defined as the ratio between the number of correct predictions made and the total number of predictions made, can be misleading, and other measures might be more relevant [36]. This is the case in Model 1, since there is a ratio of 3:1 between the two classes; i.e., for every accident with at least one fatality there were three that had none.  
This type of performance analysis that can be displayed with a confusion matrix can be extended to a multi-class analysis. In that case, there will be a \(i\times i\) confusion matrix, with one column and one row for each individual class \(C_{i}\), and performance can be assessed using \(TP_{i}\), \(FP_{i}\), \(TN_{i}\), \(FN_{i}\), Precision\({}_{i}\), Recall; [36]. It is possible to estimate the overall performance of those multi-class models, by computing performances on average. They can be weighted, or macro averaged. The weighted form of average may not adequately reflect the quality of the criteria if there are severe class imbalances, because accurate predictions on a class overwhelmingly represented are to be expected, ad thus a higher performance score is misleading. To deal with class imbalances, criteria such as the Receiver Operating Characteristic (ROC) and Precision-Recall graphs can be used. The ROC curve is defined as a plot of the False Positive Rate (FPR) on the \(x\)-axis and True Positive Rate (TPR) on the \(y\)-axis.