esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Multi-Access Edge Computing (MAEC)
#### 2.1.1 Healthcare  
MAEC plays a critical role in the healthcare domain. For example, sensors can be attached to individuals affected by disasters. The signals and information can be transmitted to the nearby edge server for future analysis, aiding rescuers in assessing the overall situation [20]. In [21], the authors presented a three-level architecture for a smart health system. This system integrates a role model, layered-cloud architecture, and an MAEC paradigm that allows caregivers to collect the information regardless of their patient's geographical location.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Dataset Construction
#### 4.1.1 Data Feature Normalization  
AIS data have different scales of each feature variable, which need to be normalized by the maximum and minimum values to eliminate the bias of the restoration results due to the different scales of different feature variables. The specific calculation Equation (4) is as follows:  
\[Z_{i}^{\prime}=\frac{Z_{i}-Z_{\text{min}}}{Z_{\text{max}}-Z_{\text{min}}} \tag{4}\]  
where \(Z_{i}^{\prime}\) and \(Z_{i}\) represent the feature values before and after the normalization of certain data, and \(Z_{\text{max}}\) and \(Z_{\text{min}}\) represent the maximum and minimum values, respectively.  
Figure 4: Vessel trajectory restoration model framework.
## 5 Results and Performance Analysis
### Delay  
Figure 5a-d demonstrate the average end-to-end delay of E-OLSR, OLSR, AODV, DSR, and GRP under a varying number of nodes, speed, and altitude. From Figure 5b,d, it can be observed that, at the same data rate of 24 Mbps, the delay of all routing protocols decreases with the increasing number of UAVs.This is because of the increasing probability of packets  
Figure 4: Average throughput comparison of different routing protocols. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Performance Metrics** & **AODV** & **DSR** & **E-OLSR** & **GRP** & **OLSR** \\ \hline \multirow{3}{*}{Scenario 1} & Throughput (bits/s) & 703,987 & 14,138 & 807,018 & 214,578 & 682,993 \\  & Delay (s) & 0.04603 & 0.03709 & 0.01643 & 0.02034 & 0.00112 \\  & Data Drop Rate (bits/s) & 66.10 & 185.27 & 19.60 & 12.38 & 25.16 \\ \hline \multirow{3}{*}{Scenario 2} & Throughput (bits/s) & 618,075 & 10,409 & 791,997 & 178,115 & 682,993 \\  & Delay (s) & 0.00196 & 0.00358 & 0.00075 & 0.00245 & 0.00112 \\  & Data Drop Rate (bits/s) & 3021.12 & 6047.16 & 2993.20 & 5135.21 & 3385.23 \\ \hline \multirow{3}{*}{Scenario 3} & Throughput (bits/s) & 976,093 & 15,409 & 1,164,725 & 352,092 & 1,046,432 \\  & Delay (s) & 0.0129 & 0.0376 & 0.0022 & 0.0033 & 0.0024 \\  & Data Drop Rate (bits/s) & 366.77 & 1737.6 & 0.43 & 0.76 & 0.58 \\ \hline \multirow{3}{*}{Scenario 4} & Throughput (bits/s) & 1,107,482 & 136,878 & 1,141,475 & 347,206 & 1,030,007 \\  & Delay (s) & 0.00202 & 0.01074 & 0.00019 & 0.00773 & 0.00036 \\ \cline{1-1}  & Data Drop Rate (bits/s) & 4350.93 & 10,602.72 & 760.71 & 302.96 & 1570.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between different routing protocol.  
being routed rather waiting for transmission in the queue. From Figure 5c,d, it is shown that delay is lower at higher data rates due to faster packet transfer transmission. Among all protocols, DSR has the highest delay in all scenarios. In DSR, when a node sends a route request (RREQ), the destination replies with all the REEQs; as a result, the network beocs slower. Due to on-demand routing behavior, AODV also has higher delays. Compared to these five routing protocols, E-OLSR shows a comparatively low delay, which indicates better performance. Minimal holding time is responsible for this improved performance.
## 3 Results and Discussion
### Leaf Interception and Water Redistribution in the Soil
#### 3.6.2 Water Redistribution in the Soil  
The mean depth collected and water infiltrated in the interrow were similar but different from the mean Ta obtained with Equation (12), whereas Sa, WI and WR differed from one another. However, the sum of the different components involved in the interrow and row was approximately equal to the depth applied (Table 7). The higher water content in the crop line than in the interrow is explained by the fact that erectophile leaves (e.g., corn) have a leaf insertion angle with respect to the stalk smaller than 90\({}^{\circ}\), which causes water retention at this insertion point and a flow to the base of the stem of up to 50% of the total amount of irrigation applied [6]. An increase in water content in the crop line was also reported in a study that measured water in the soil with TDR sensors; the finding was attributed to the preferential flow through the stem [45]. Likewise, water content was found to be higher in the 0.20 m around the crop plant and in the first 0.10 m in depth of the soil profile compared with the water content in the interrow [46]. On the other hand, a preferential flow under the crop corn line was found [47]; the authors estimated that more than 40% of water received from an incident rainfall would be due to stemflow.  
The difference between the intercepted Id and the infiltrated water in the crop row (WR), corresponding to an Id of 40 mm (Table 7), might be due to some of the following: (a) water evaporated from the leaves. A work conducted in corn crop using spray sprinklers similar to those used in this trial reported losses of 1 mm [37]. Likewise, losses of up to 2.2 mm for days of high evapotranspiration were reported [8]; (b) water retained by crop residue. In this work, the maximum retention was 2 mm; (c) water redistributed below the soil depth evaluated. To estimate this, if we consider that the sum of (a) and (b) was about 4 mm, and that the difference between intercepted Id and WR was 9 mm, then the difference (5 mm) would have redistributed below 0.1 m.  
Considerable differences were detected between mean DUpa (considering depths and segments) and CUs. At R\({}_{1}\)AC, values were 53.4% and 97.5%, respectively, whereas at R\({}_{1}\)BC, they were 34.8% and 97%, respectively. Redistribution was very important and independent of the water application mode and may be explained by the soil texture and hydraulic conductivity associated with water content before irrigation events [48]. CUs above 90% were found in soil of volcanic origin and in soil of sandy loam texture, even when irrigation was performed with uniformity below 60% [49]. Likewise, a study relating the uniformity of the aerial part to that of the soil in a corn crop under stationary sprinkler irrigation found high water uniformity in the soil (approximately 95%) compared with the uniformity of irrigation application (between 50% and 90%) [50]. Our results agree with those findings, with DUpa and CUs for fully expanded corn crop of 40% and 90%, respectively.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 2 Materials and Methods
### Characteristics of Features Across Convolutional Layers  
Convolutional filters in deep learning models are designed to learn specific features from images. Each filter within a model layer captures different features. It is widely understood that the early convolutional layers, situated closer to the input, detect basic features like edges and textures. In contrast, the deeper layers identify more abstract features, such as parts of objects [14]. For example, in the context of analyzing CT images that capture organs, early convolutional layers might detect basic features such as edges and textures of tissues or organ boundaries. These layers are adept at identifying simple patterns and gradients in pixel intensities, which are fundamental components of an image. Moving deeper into the network, the convolutional layers start to recognize more complex and abstract features. These could include specific structures of organs or even patterns indicative of pathological changes. In advanced layers, the network might be capable of identifying and differentiating between complex organ shapes.  
Moreover, because of the nature of the convolutional operation, the initial layer closely mirrors the original input image, while the deeper layers progressively abstract the input to emphasize latent features. Therefore, as shown in Figure 1, visual analysis of the results from convolutional operations in intermediate layers can be challenging. As a result, **because the perturbations in adversarial samples consist primarily of simple noise**, it can be concluded that **they are readily detectable by specific filters in the first layer**, **facilitating straightforward visual analysis.**
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 3 Proposed Opportunistic Routing Protocol
### Cluster Restoration  
As nodes are most likely mobile in opportunistic networks, they reach the border of the cluster or become isolated. Hence, cluster restoration is required. The border node \(x\) starts cluster restoration by finding the best candidate from its noise neighbors to communicate with for restoring connectivity to cluster C, as illustrated in Figures 6 and 7. We present two scenarios of cluster restoration below.  
Scenario 1: once the node \(x\) becomes a border node of cluster \(C\), it must run an algorithm of cluster restoration or join other clusters that maintain the global \(MPC\), increasing the communication range, \(Rc\), of \(y\) as follows:  
\[MPC_{y,\ x}=MPC_{g}=\frac{R_{c}-d}{V_{x}+V_{y}} \tag{5}\]  
where \(V_{y}\) and \(V_{x}\) are the velocity of the border nodes \(x\) and \(y\) of two neighboring clusters, \(d\) is the distance between nodes \(x\) and \(y\), and \(Rc\) is the communication range of nodes in both clusters. Figure 6 demonstrates Scenario 1. In Scenario 1, we use Equation (5) to increase \(R_{c}\) to restore connectivity.  
Scenario 2: If \(x\) becomes isolated, the algorithm finds the minimum \(MPC\) between \(MPCx,y\) and \(MPCx,C\) and increases the \(Rc\) of this minimum to maintain \(MPC_{g}\) (Figure 7).  
Figure 5: Cluster-based message transmission.  
Figure 6: Scenario 1, where \(x\) is a border node of cluster C.  
In Scenario 2 (Figure 7), node \(x\) is considered as noise, \(x\
otin\) C, and is in the communication range of communication of both neighboring clusters. The cluster restoration approach tries to find the cluster that will establish connection with \(x\) first, i.e., find the minimum \(MPC\) between \(MPC_{x,y}\) and getMPC (\(x\), \(C\)). Then, this algorithm increases the \(R_{\rm c}\) of this minimum to the level of the global \(MPC_{\rm g}\). Thus, this guarantees a uniform period of connection in the network.  
The cluster restoration process to repair connectivity between two clusters is launched by the border nodes of one of the clusters. The cluster, which has the CH with the minimum weight (calculated using Equations (2)-(4)) will normally activate its border node to establish the connectivity with the other cluster (Figure 7).  
Algorithm 2 presents the pseudocode of cluster restoration based on Scenarios 1 and 2.  
```
Begin
1: Find \(x\) such as MPC \(y\), \(x\) = Max, where \(x\) is a noise
2: Relay \(x\) = FindBestCandidate(\(y\), \(x\)), where \(x\) noise & MPC\(y\),\(x\) is a max
3:if\(x\) is Member(C) then//Scenario 1
4:\(y\).increase\(Rc(\)getMPC(\(x\), \(y\)), \(MPC_{g})\)//increase \(Rc\) of \(y\) to maintain MPC \(x\),\(y\), same as \(MPC_{g}\)
5:else//Scenario 2
6:\(temp\) = min(getMPC(\(x\),\(y\)), getMPC(\(x\),\(C\)))
7:\(y\).increase\(Rc(temp\), \(MPC_{g})\)
8:endif
9:endif
10:endif
```  
**Algorithm 2** (Cluster Restoration)
# Synchronization, Control and Data Assimilation of the Lorenz System
## 3 Parameter Estimation  
We are now able to exploit the fact that the distance \(d\) goes to zero if \(p>p_{c}\) and \(D=0\), thus allowing the parameters of the master system \(\boldsymbol{r}\) to be determined by varying the parameters of the simulated replica \(\boldsymbol{R}\).  
However, because the convergence of \(d\) to zero is not monotonous with \(D\), we rely on a simulated annealing technique [20] that allows us to overcome the presence of local minima. We introduce a fictitious temperature \(\theta\), and assume that the distance \(d\) is analogous to an energy to be minimized.  
We assume that the the synchronization time \(\tau\) and the coupling direction \(C\) cannot be modified at will by experimenters, being fixed by the characteristics of the measuring instruments and of the actuators. We ascertain that the condition \(p>p_{c}\) holds, i.e., such that if the parameter distance is null, \(D=0\), synchronization occurs.  
The idea is the following: we simulate the coupled system for a time interval \(T\), measuring the state variable distance \(d\), after which one of the parameters \(\sigma,\rho,\beta\) is varied by a small amount. We repeat the simulation starting from the same conditions, except for the varied parameter, and compute \(d\) again. If \(d\) decreases, the variation is accepted. It is also accepted if \(d\) increases with a probability  
\[p_{\mathrm{acc}}=\exp\left(-\frac{\Delta d}{\theta}\right), \tag{23}\]  
otherwise it is discarded.  
The temperature \(\theta\) is slowly lowered (multiplying \(\theta\) by a factor \(1-\epsilon\)) every \(T\) time interval. As shown in Figure 5, in this way it is possible to exploit the synchronization procedure to obtain the values of the parameters in the master replica. In fact, for sufficiently low \(\theta\), the distance \(|d|\) between master and slave state variables (dashed line in figure) drops to zero. For similar (but not always the same) values of temperature, the distances \(|\Delta|\) between master and slave parameters also drop to zero (continuous color lines). Clearly, this procedure works only for deterministic dynamical systems with little or no noise on measurements, and with very low dimensionality.
## 3 Method
### Technical Route of the Method  
The working procedure for calculating the amount of diffuse gas in a formation proceeds as follows: initially, the formation pressure, \(P\), for different structural styles is computed using Formulas (10) and (11). Next, the amount of free gas content \(C_{l}^{0}\), at the bottom of the caprock is determined by using Formula (5). Subsequently, [PERSON]'s third law, expressed in Formula (3), is used to calculate the free gas content \(C_{l}^{L}\), at the top of the caprock. Finally, Formula (2) is utilized to determine the amount of diffuse gas, while taking into account the results of the previous calculations. The overall process is presented and illustrated in Figure 4.
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 3 Application Areas and Limitations of Inductive Coils
### Biomedical Imaging
#### 3.3.1 Head Injuries  
Head injuries, and in particular Intra-Cerebral Haemorrhage (ICH) and Cerebral Oedema (COE), correspond to excess of fluids in the intracranial region and require a rapid assessment to increase the chances of survival and to contain long-term consequences, potentially life-changing. The excess of fluid produces an anomaly in the conductivity of the brain tissue, which can in principle be detected via electromagnetic induction imaging techniques [4; 10]. However, this is very challenging, as in the case of ICH or COE, the encephalic regions occupied by fluids are only fractionally more conductive than the surrounding healthy tissue. On average, cerebrospinal fluid (CSF) and blood are 20 and 10 times, respectively, more conductive than the normal brain tissue (white matter) [11]. However, an EMI system with suitable performance would be able to identify the areas occupied by an excess of fluid, even with different levels of perfusion and hence different average conductivity [12]. Furthermore, if the EMI system has sufficient conductivity resolution, it will have the potential to distinguish the nature of the fluids involved, namely blood (ICH) or plasma/CSF (COE).  
EMI systems have inherently low-resolution, and in this respect they do not appear capable of competing with any MRI or CT scanners currently available. However, in case of head injuries,high resolution is not a requirement for the initial assessment of the injury as well as for monitoring the evolution of mild head traumas in the longer term. Additionally, EMI systems would have the major advantage of being easily deployable on the field. Head injuries need a rapid assessment, as the fluid build up leads to an increased intracranial pressure which may quickly lead to brain injuries. MRI and CT scan systems are bulky and expensive, so are not easily deployable. In contrast, low-cost and easy to operate EMI systems could be promptly available in all the settings (e.g., sport events, battlefield, ski resorts etc.) where head injuries occur more frequently. As the magnetic fields required by EMI for biomagnetism are very small and the lasers are very low power (typically \(\sim\)1 mW), the device is suitable for battery operation. Furthermore, EMI devices are low weight and very compact, thus easily transportable. EMI devices are also potentially suitable for prolonged or continuous monitoring, unlike MRI or CT scanners, which pose potential risks for the patient and unacceptable financial strain. In this respect, we notice here that the rf field used in EMI is very weak (of the order of 1 nT). For the frequency of interest--in the MHz range--there is no evidence that this may be harmful. However, systematic studies have yet to be performed.
## 4 Material Measurement Comparison
### Resonant Mesh Filters  
Resonant mesh filters offer good test materials because they have well-defined spectral features. Such filters typically consist of a thin wafer or film of THz-transparent polymer with deposited or embedded metal pattern of crosses on a square grid. The grid size (\(g\)), the size of the crosses (\(a\)), and widths of the lines (\(b\)) determine the central frequency of the filter and its bandwidth [35]. The filters used in this work were manufactured by Novalia Ltd by printing a 40 nm thick aluminium structure on a 23 um thick polyester (PET) substrate. Figure 6 shows the transmission of three of these filters, which were measured with all three instruments.  
Overall, the measured transmission spectra for each filter agree well among the devices. There is close agreement in the profile of the loss notch. The slight variations in the notch minima are due to the sensitivity of the filter transmission to their position in the THz beam. For example, a minimum of \(0.2359\pm 0.0031\) was measured in Filter 3 with TeraScan, compared to a \(0.1668\pm 0.0045\) with the T-Sweeper. This may be attributed to the fact that the dimensions of the mesh are similar to the wavelength. At higher frequencies, transmission measured by T-Sweeper is slightly above those measured by TeraFlash pro and TeraScan, which may be due to higher noise.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 1 Introduction
However, MICP requires specific conditions for microbial growth, which may limit its large-scale applicability. This study highlights the significant benefits of using sugar cane molasses as a sustainable and cost-effective stabilizer in soil stabilization, particularly in expansive clay soils such as those found in Tehsil Taru Jabba, Nowshera, Pakistan. The results demonstrate that the combination of molasses with cement significantly improved the geotechnical properties of the soil, including increased California Bearing Ratio (CBR), reduced plasticity index (PI), and decreased swelling potential. These improvements are crucial for enhancing the bearing capacity and stability of soils used in construction, particularly in regions where traditional chemical stabilizers are economically unfeasible. By effectively utilizing molasses as an agricultural byproduct, not only are the soil properties enhanced, but environmental and economic challenges are also addressed. The findings of this research underscore the potential for integrating agricultural waste products into civil engineering practices, offering a viable solution for sustainable and resilient infrastructure development in areas plagued by expansive soils.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Fabrication  
The tag (Figure 2) with the design parameters collected in Table 1 was fabricated by using a simpler and faster prototyping technique than the traditional photolithographic method, although with less accuracy. It consists of three main steps. In a first step, an adhesive and flexible copper sheet is pasted on a FR4 substrate and inserted into a laser milling machine (LPKF protolaser S). Then, the tag is shaped and cut on the copper sheet by means of the laser milling machine. In a second step, the tag is removed from the copper sheet pasted on the FR4 substrate by using a sticky tape. Then, it is pasted again on a flexible and adhesive Kapton polyimide substrate with a thickness of 70 \(\upmu\)m, which was characterized in Section 2. In the last step, the IC chip (NXP UCODE G2 XL) is mounted across the gap \(s\) between the two pads of the tag antenna (Figure 2). Once finished, the tag antenna is ready to be attached to a blood tube as it can be seen in Figure 6. Note that in our design, the inlay is directly attached to the Kapton polyimide substrate. The use of an intermediate absorbing layer between these two elements could be considered in future designs. This could help to introduce an additional isolation effect between the tag and the blood, and reduce absorption of  
Figure 5: Simulated generalized reflection coefficients of the optimized tag antenna in five different scenarios: (i) tag attached to a tube full of blood, (ii) tag attached to an empty tube, (iii) tag removed from the tube but keeping its initial curvature, (iv) tag removed from the tube and unfolded, and (v) tag unfolded and attached to a bag full of blood.  
electromagnetic energy. This approach will require additional research, for instance to determine the optimum thickness and materials for the absorbing layer. Also, depending on these factors, viability of the design for blood tubes will have to be assessed, especially with issues related to the attachment of the tag into blood tubes (available space, curvature).