esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In vegetation communities such as forest, cropland, savanna, shrubland, and grassland, the ability of NDVI time-series has been verified for the varied semi-arid and arid climates to obtain seasonal and inter-annual variability [15]. SAVI was also used for evaluating vegetation development in the semi-arid region of Mexico, which is closely related to NDVI [32; 33]. The NDVI and EVI derived from MODIS exhibited a highly linear correlation with gross primary product (GPP) compared to in situ flux quantities in the Sahel's semi-arid atmosphere [17; 34; 35]. Despite these applications, it was suggested that the detection findings of VIs in semi-arid and arid lands should be taken carefully because of the high uncertainty of VIs in thinly vegetative areas, complex vegetation composition, and landscape heterogeneity structure [36; 37]. Herein, the performance of vegetation indices SAVI, NDVI, and EVI are assessed for vegetation variability and dynamics in the semi-aridand arid environment of the province of Punjab, Pakistan. We derived NDVI, EVI, and SAVI and related them to their spatiotemporal variability throughout the research area, along with transects because phenological transitions in summer and winter are important factors for changeable crop development and plant growth in spring and autumn [25]. In the present study, two phenological metrics (i.e., for the start of the season and end of the season) were calculated from the MODIS-derived time-series vegetation indices and compared for each vegetation type. Furthermore, the appropriateness of vegetation indices for monitoring the complex vegetation cover in the province of Punjab was evaluated.
## 3 Design of the Direct Yaw Moment Control System
### Feed-Forward Control
The steady-state yaw rate gain compensation will not be fulfilled by the feed-forward part of DYC system, i.e., \(G_{e}=G_{r}^{\delta}(0)\). Then the resulting steering feed-forward compensation is \[G_{M}^{\delta}(s)\approx\frac{G_{r}^{\delta}(0)(\tau_{r}^{\delta}-\tau_{r}^{\delta 0 })}{G_{r}^{M}(0)}\frac{s}{\tau_{r}^{\delta}s+1}=K_{FF}\frac{s}{T_{FF}s+1}. \tag{24}\]  
Typical values for the controller gains and their dependency on the velocity are shown in Figure 5. From the control law indicated in Equation (23), the direct yaw moment control input command is proportional to the steering angular velocity cascading with the 1 st-order low-pass filter which effectively enhances the responsiveness of the vehicle yaw rate with respect to the driver steering angle input.
# Bio-Based Polymers with Potential for Biodegradability
## 3 Poly(lactic acid) (PLA) and Related Polymers
### Influence of UV Irradiation on Biodegradation  
In recent years, the agricultural industry has been using biodegradable polyesters, including PLA, to make films for mulching applications [137; 138]. These mulching films help retain moisture in the soil and modulate the surface temperature [139; 140]. It is well known that irradiation of polymers can lead to changes in the polymer structure via different mechanisms including chain scission (e.g., Norrish I or Norrish II) or crosslinking (by recombination of two free radicals) [141]. The impacts of solar radiation on the mechanical properties of PLA have been investigated because mulching films have extended exposure to sunlight. The impacts of irradiation on polymer biodegradability are also a concern, and as a result, there have been studies on polymers to examine the biodegradability after UV irradiation [141].  
Polymers that crosslink during irradiation tend to have reduced biodegradation because the higher molecular weight decreases the ability for uptake by microorganisms [142]. Mulching films must be sufficiently biodegradable that they can be tilled into the soil or collected with crop residue for composting without causing ecotoxicity to the soil [143; 144]. A recent study observed that PLA degraded by UV irradiation for extended periods of time reduces the rates of biodegradation by microorganisms suggesting the PLA was transformed into poorly assimilated solids [145]. However, a study by [PERSON] et al found irradiation of PLA leads to chain scissions rather than crosslinking [138]. Furthermore, this study found, at least for PLA biodegradation, that the specific surface area was a more important factor than the extent of photooxidation [138].
## 3 Experimental Results
### Results of ACR-TIRADS Classification System  
Random Forest, J48 DT, Ada Boost, and SVM models were used to assist the ACR-TIRADS system. The findings revealed that J48 DT yielded the top accuracy rate with 98.5587%. Table 5 shows the success rates for all classifiers. While Table 6 presents the confusion matrix generated for the J48 DT classifier, Table 7 illustrates the evaluation metrics for J48 DT.
## 5 Hybrid Localization Algorithms
### Centralized Localization Algorithms
#### 5.1.2 Silent Localization Using Magnetometers (SLUM)  
As discussed before, unknown nodes localization in UWSNs is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. However, sound scatters significantly in underwater environments, especially in shallow water [62]. Therefore, magnetometers are often more useful than acoustics. For instance, [PERSON] has explored magnetometers and the magnetic dipole of a vessel for target tracking [63,64]. [PERSON] _et al._[65] fused electromagnetic and acoustic data to track surface vessels using underwater sensor nodes. In [66], Silent Localization Using Magnetometers (SLUM) is proposed. To the best of our knowledge, this is the first time magnetic dipole tracking is used to localize unknown nodes.  
SLUM silently localize underwater unknown nodes equipped with triaxial magnetometers using a friendly vessel with known magnetic dipole. Unknown nodes are localized by listening to the messages of the dipole. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize unknown nodes. Each unknown node is further equipped with a pressure sensor and an accelerometer used for depth estimation and sensor orientation estimation, respectively. The trajectory of the vessel and the positions of unknown nodes are estimated simultaneously using an Extended Kalman Filter (EKF) [66,67]. In SLUM, sensor nodes are assumed to be connected by wire. As a consequence, common problems in UWSNs such as time synchronization, limited bandwidth, and limited energy resources are neglected. However, the additional hardware requirement is the most drawback of SLM, hence, SLM is a costly localization method.
## 2 Development and Validation of the Scaled-Down PCS Model  
This section begins by confirming the model's reliability through a combination of simulations and experimental tests on a scaled-down PCS. Following this, a full-scale model is developed to analyze and optimize the structural response under varying traffic load distributions.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Common WSN Routing Protocols for Data Gathering  
The common setup for a data collection network comprises a set of devices (e.g., sensors) and one or multiple sinks that collect the reports. In many such scenarios, the sensors are unevenly dispersed over the terrain and in some cases can be mobile, can have a different distance to the sink(s), and the data needs to traverse multiple hops before reaching the sink, in which commonly the sensors themselves serve as relays toward the sink. Consequently, the performance experienced by different sensors (e.g., energy consumption, latency, reliability) can be markedly diverse. Various solutions, including energy-aware routing, compressed sensing, efficient MAC novelties and architectural innovations, have been suggested to try to improve the overall performance and to balance its variability; many are scattered throughout this survey under different subjects (e.g., Sections 3, 4 and 6). In the sequel, we provide a brief review of routing protocols for WSN in general and data gathering in particular.
## 4 Experiments
### Experimental Results
#### 4.5.6 Training Efficiency  
We compared the computational cost of GSTGCN, DCRNN, STGCN, ST-Metanet, and Graph WaveNet on PeMSD7. For the sake of fairness, the training time is the time it takes each model to train one epoch, and the inference time is the time cost of each model to predict the traffic speed  
Figure 10: (**a**) Prediction performance of GSTGCN with a different number of residual blocks on PeMSD7. (**b**) MAE of each prediction step of GSTGCN and its four variants on PeMSD7.  
at 12 timestamps in the next hour on the validation data. Table 4 demonstrates the experiment results. We observed that, during the training phase, the fastest is GSTGCN, followed by STGCN and Graph WaveNet. GSTGCN runs eleven times faster than DCRNN and seven times faster than ST-MetaNet in training. Since DCRNN and ST-MetaNet use recurrent neural network to capture temporal dependencies, they need more time to train. For inference, GSTGCN is the most effective one, and the time cost of STGCN and DCRNN significantly increases because they need to iteratively predict the results of 12 steps, while GSTGCN and Graph WaveNet generate 12 predictions in one run. To further investigate the performance of the compared models, we plot the Mean RMSE of 12 steps on the PeMSD7 test set with increasing training epochs, as shown in Figure 9a. The figure suggests that our GSTGCN achieves easier convergence and faster training procedure.
## 2 Engineering Background and Calculation Model Establishment  
### Project Overview  
The main bridge of a certain project adopts a single tower and double-span cable-stayed bridge as shown in Figure 1. The central tower is an arc-shaped concrete tower, and the main beam is a steel UHPC composite steel box beam as shown in Figure 2. The cables are parallel steel cables with spatial double-cable surfaces and a fan-shaped layout. In order to improve the structural stiffness of the cable-stayed bridge and effectively reduce the deflection of the main beam and the horizontal displacement of the tower's top under a live load, an auxiliary pier is set at a distance of 41 m from the transition pier for both main spans, so that the span arrangement of the main bridge is \(41+168+168+41=418\) m.  
Figure 1: Bridge type layout, the number of each pier is shown below. (Unit: m).  
The main beam adopts a lightweight composite structure, consisting of a streamlined flat steel box beam and UHPC, ultra-high-performance concrete, panel. These two are connected by shear nails as shown in Figure 3, with a standard section beam height of 3 m (referring to the road's centerline). Cast iron block weights are installed at the auxiliary and transition piers. The cable tower is a double-column variable cross-section curved concrete tower with a total height of 125 m and a height of 106.628 m above the bridge deck. The columns and beams on the tower use hollow thin-walled sections; two rectangular section piers are installed on the bearing platform at the cable tower to support the main beam. The anchorage zone on the cable-stayed beam is located on the outer side of the sidewalk, with a spacing of 9 m along the bridge direction.  
The cross-section of the main bridge is shown in Figure 2. Other bridge design information is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Term** & **Detail** \\ \hline Design period & 100 years \\ \hline Vehicle load level & Highway—Class I; \\ \hline Bridge structure & Single tower double cable plane \\ \hline Structural system & Semi-floating system, with elastic limit devices and dampers installed vertically and elastic–plastic limit devices installed horizontally \\ \hline Type of stay cable & Parallel steel wire cable, 1670 MPa, PES7-109.PES7-121, PES7-139 \\  & PES7-151; spacing: 9 m (along the bridge), 2.5 m (vertical) \\ \hline Steel box girder & Q345 qD; Width 36.5 m, height 3 m \\ \hline Bridge deck pavement & 5 cm UHPC, \(\Phi\) 10 bi-directionally reinforced with a spacing of 37.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: The bridge design information.  
Figure 3: UHPC bridge deck structure diagram (Unit: mm).  
Figure 2: Cross-section of main beam (Unit: cm).
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
## 4 Experimental Results and Discussion
### Rotation Control by Chirped Spirals and Multibeam Superposition  
More sophisticated approaches for a control of the spectral Gouy rotation can be proposed on the basis of nonuniform phase shaping structures and by a coherent superposition [44]. In particular, an axially variable spectral rotation can be obtained by spiral gratings with radial gradients or spiral phase plates with appropriate phase profiles. By programming such structures in an SLM or steering the phase stroke of a radially curved spiral-phase MEMS mirror, the axial acceleration of rotation can even be tuned. Recently, it was shown that an angular frequency shift between two composite spiral phase zone plates can be used to adjust the wavefront rotation of an OAM beam [58].  
The interference of two or more vortices of slightly different rotational velocities of their spectral eyes should also enable one to temporally modulate wavepackets. The \"rotational beating\" is expected to generate spectral \"self-torque\" analogous to a spin torque [43]. For two concentric, co-rotating wavepackets with angular velocities \(\Omega_{1}\) (\(t\)) and \(\Omega_{2}\) (\(t\)), we can define a spectral self-torque  
\[\xi(t)\ =\ \frac{\partial[\Omega_{2}(t)-\Omega_{1}(t)]}{\partial t} \tag{8}\]  
where the spectral eyes rotate with an angular frequency that periodically oscillates with the propagation distance (and, correspondingly, in time). The degree of rotational pulse stationarity depends on the ratio between pulse duration and beat frequency. If the oscillation is slow, the intrapulse spectral map can, approximately, be considered to be quasi stationary. If the oscillation is fast enough, however, an intrapulse rotational chirp should be detectable at each distance and time.
# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 3 Fall Detection System  
Our methodology foresees developing a wearable system for detecting falls of older people, which takes advantage of low-power smart devices' capabilities and a neural network for movement detection recognition. In this work, we have followed an ML approach by using a neural network for fall detection, but we differ from related works in the system's design.  
While other related works exploit multiple sensors that collect movements and send data to a device that analyse them, in this work, we used a single device for activity monitoring and recognition through a neural network deployed on an Arduino nano 33 BLE Sense board. The board has a small size of 45 \(\times\) 18 mm, which makes it suitable for prototype wearables, and is equipped with several integrated sensors to measure environmental variables. In Figure 1, one can see the board's main components and input/output interfaces. This choice brings versatility and portability advantages, since the other related works' solutions are constrained to indoor environments that rely on non-portable infrastructures or require multiple sensors to be worn on the body.  
Another innovation of our work is that the monitoring board interacts with a smartphone to collect and manage events. The board can communicate with the smartphone through a Bluetooth Low Energy (BLE) module: when it detects a fall, it sends a notification to the smartphone. To avoid false alarms, a mobile application on the smartphone manages the notifications, asking to the user if there is an emergency. If no response is provided within 60 s, the smartphone forwards an alert by calling a healthcare professional and sending information about the location of the older person. Furthermore, the detected events are stored on the smartphone--one the one hand to give more accurate information to healthcare professionals and on the other hand to provide an efficient way to enhance the neural network's training, together with feedback provided in response to detected events.
## 2 Materials and Methods
### Degradation Experiments and Analytical Methods  
The SMX degradation experiments were conducted in 150 mL beakers at room temperature. The initial pH of the solution was adjusted by NaOH and H\({}_{2}\)SO\({}_{4}\). Typically, 0.1 g catalyst was mixed into 100 mL SMX solution (15 mg/L) for 30 min. Then, PMS was added to initiate the SMX degradation with concentration of 0.6 mM. Periodically, 1.5 mL of the solution was taken, filtered with a 0.22 \(\upmu\)m membrane, and quenched with Na\({}_{2}\)SO\({}_{3}\) solution (0.15 mL, 0.5 M). Finally, the sample was measured by high-performance liquid chromatograph (HPLC).  
Furthermore, the effect of catalyst dosage (0.025, 0.050, 0.100, and 0.200 g/L, PMS concentration (0.2, 0.4, 0.6, and 0.8 mM), initial pH (3, 5, 7, and 9), inorganic anions (10 and 100 mM chloride (Cl\({}^{-1}\)), nitrate (NO\({}_{3}\)\({}^{-}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)), and monohydric phosphate (HPO\({}_{4}\)\({}^{2-}\))) on SMX degradation in Bi@Fe/CN-3 system was explored in this work. L-histidine (100 mM), MeOH (100 mM), and TBA (100 mM) were employed as quenching agents in Bi@Fe/CN-3/PMS system.
## 4 Experiments
### Evaluation Metrics  
The evaluation methods employed in this study encompass commonly utilized semantic segmentation indices, namely the average intersection overlap ratio (\(mIoU\)), recall rate (\(Recall\)), pixel accuracy (\(PA\)), and \(F1-score\). The specific formulas for these metrics are presented as follows:  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FN+TP+FP} \tag{14}\]  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
\[PA=\frac{TP}{TP+FP} \tag{16}\]  
\[F1-score=\frac{2 TP}{2 TP+FP+FN} \tag{17}\]  
The mIoU in Formula (14) represents the average cross-ratio between the water body and background in the predicted results and ground truth values. The recall in Formula (15) denotes the ratio of pixels correctly classified as water bodies to the total number of actual water body pixels. The \(PA\) in Formula (16) signifies the proportion of true water bodies among all pixels classified as water bodies. Lastly, the F1-score in Formula (17) incorporates both accuracy and recall measures, serving as an averaged metric for evaluating the classification model performance. In the formula, \(TP\) represents the number of pixels correctly classified, \(FP\) represents the number of pixels wrongly classified, \(TN\) represents the number of pixels correctly classified as background, and \(FN\) represents the number of pixels wrongly classified as background. In addition, the size of the parameter is also introduced into the evaluation as a reference index. Assuming a red-green-blue three-channel original lake image is used as  
Figure 7: Multi-scale feature fusion structure.  
input to calculate the parameter quantity generated by the model, the parameter quantity of the model can reflect the efficacy of the designed efficient attention module, thereby reducing the overall network parameters while improving the accuracy.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
###### Abstract
The node's indirect or direct neighbours use this trust value to engage this node in network topology and route creation. Trust evolves over time with the changing trust metrics. This research work intends to focus specifically on the security of the RPL routing protocol in the IoT. A trust-based model is proposed to improve security in RPL-based IoT, against Rank and Blackhole attacks. The trust-based approach is applied for the detection and isolation of malicious nodes. Trust metrics are selected through critical analysis and investigation of their suitability to enhance security for RPL in a mobile IoT environment [20]. Moreover, the proposed model, named SMTrust, considers the mobility metrics for trust computation. SMTrust is evaluated considering static nodes, mobile sender nodes together with a mobile sink node. SMTrust routing algorithms are embedded into  
Figure 1: RPL-based Network in IoT. RPL, and the protocol is assessed in terms of network performance, including topology stability, throughput, packet loss rate, and power consumption. The performance of the proposed SMTrust model is evaluated via simulation using ContikiOS/COOJA simulator. The simulation results demonstrate that the performance of SMTrust is better as compared to standard RPL and existing trust-based methods, for instance, SecTrust [14], DCTM [16], and MRTS [15]. Following is the summary of contributions of this research work-at hand. * We intend to analyze and adapt trust metrics, including but not limited to, the node's behavior, characteristics, and mobility, in a bid to secure the RPL routing protocol. * We intend to improve the algorithms for trust computation and trustworthy parent selection for attack detection. * We intend to implement the preliminary SMTrust model, proposed in [21], by integrating it in the standard RPL routing protocol. * We intend to evaluate the proposed model via simulation, and parameters such as, topology stability, packet loss rate, throughput, and power consumption to determine its performance as compared to the existing methods. Though the number of IoT devices and applications is increasing exponentially, the security aspects are one of the factors hindering its widespread adoption. This research specifically will help in enhancing the IoT networks and routing security, and generally will facilitate the widescale adoption of the IoT. This research paper is structured as follows. Section 2 describes the motivation behind this research work and reviews the existing secure routing protocols. Section 3 explains the proposed SMTrust model along with the process flow, proposed parent selection algorithm, attack detection, and the computational complexity. Section 4 presents the experimental setup. Section 5 presents the simulation results and analyses the effectiveness as compared to the existing methods. Sections 6 and 7 provide the discussion, conclusion, including future work and directions, respectively.