esa-sceva/satcom-chunk-collection huggingface dataset:
# CNN Based Image Classification of Malicious UAVs
## 3 Results and Discussion  
### Overall Test Accuracy  
Table 3 illustrates the test accuracy of the image classification models in decreasing order for the initial training/test split discussed in Section 2.4. The test accuracy is equal to the proportion of correct predictions (i.e., predicted UAV type = actual UAV type) the trained model makes on the test image dataset.  
It is useful to compare the test accuracies represented in Table 3 with a baseline accuracy from a thought experiment. As previously discussed, of the three UAVs represented in the study, the DJI Mavic 2 Enterprise and DJI Mavic Air are visually similar, whereas the DJI Phantom 4 is quite different to the other two in appearance. If we assume a baseline trained model can always correctly predict a DJI Phantom 4 because of its difference in appearance, there will be 26 correct predictions from the test image dataset for this UAV alone. If we further assume that a baseline trained model cannot distinguish between the DJI Mavic 2 Enterprise and DJI Mavic Air, it will correctly predict each of these UAVs only 50% of the time on average, i.e., we will see 13 correct predictions out of 26 test images for the DJI Mavic 2 Enterprise, and 13 correct predictions out of 26 test images for the DJI Mavic Air. This results in a combined number of correct predictions of \(26+13+13=52\), and a baseline test accuracy of \(52/78=0.667\).  
The test accuracies of all four models represented in Table 3 are significantly higher than the baseline reference accuracy of 0.667 from the simple thought experiment. From this perspective, the results are very encouraging. The two best performing models are VGG-16 and Resnet-18. Of the other two lower performing models, AlexNet has an older architecture, dating back to 2012, and MobileNet v2 employs significantly fewer trainable parameters than the other models (see Table 1), which might explain these results.  
The test accuracies represented in Table 3 are contextual, i.e., specific to the current study and its design. It is very likely that the actual accuracy values will change significantly if the design of the study is amended. For example, introducing more UAV types will likely result in lower test accuracies since it is more challenging to distinguish between a larger number of object classes. Another example would be that increasing the distance between the observer and UAV when an image is captured will likely result in lower test accuracies since it is more challenging to accurately pick out a smaller object from an image than a larger one.
## 1 Introduction  
Advanced surgical techniques for combined anterior and posterior surgery to treat complex cervical conditions that are associated with deformity and neurologic deficiencies are increasing [1; 2]. To improve biomechanical outcomes, an intraoperative radiologic device such as O-arm could be used in a hybrid technique with posterior pedicle screws (PS) and lateral mass screws (LMS) [3]. In a clinical setting, asymmetrical cervical pedicle screw placement is not uncommon and depends on both the vertebral artery anomaly and the pedicle anatomy [4; 5]. Although there are a few biomechanical studies that describe posterior fixation methods with LMS and posterior PS [6; 7; 8; 9], the combination of both screw types and their effect on an allograft spacer in a surgically treated cervical segment is unknown.  
Here, we used finite element model (FEM) analyses to investigate the load sharing ratio between allograft spacers and different posterior fixation method combinations that are known to be closely associated with the fusion rate. We compared stability by analyzing the Peak von Mises stress of the allograft spacers and the yielding risk of peri-screw bone under hybrid motion control conditions, including flexion, extension, axial rotation, and lateral bending.
# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 5 Optimized Energy Model
The SSO progressive transition from exploration to exploitation efficiently achieves the trade-off between global search and local optimization. 3. Iterative adjustments: In the proposed system, SSO repeatedly optimizes job assignments and resource usage across IoT devices, fog nodes, and MEC servers. Each iteration assesses the overall energy consumption and delay of the current arrangement. The method adjusts the positions of salps (possible solutions) depending on the fitness function, which weights energy consumption and delay by \(\alpha\) and \(\beta\), respectively. This guarantees that the optimization procedure is consistent with the system energy efficiency and performance improvement objectives. 4. Constraint handling: SSO seamlessly incorporates the optimization problem restrictions into its framework. For example, if a candidate solution breaches latency, processing capacity, or energy constraints, it will be punished in the fitness evaluation. This adaptive technique directs the swarm to feasible and optimal configurations, while avoiding additional overhead for constraint-specific changes. 5. Scalability and adaptability: Scalability is one of SSO most significant advantages. As the number of IoT devices and network nodes grows, the algorithm can handle increased dimensionality without significantly increasing processing costs. This makes it ideal for dense deployments, where traditional optimization approaches may fail due to the exponential increase in the search space. 6. Robustness to dynamic environments: The SSO iterative nature and adaptive behavior make it robust to dynamic changes in the network environment, such as changing computing demands or fluctuating energy levels. This robustness is critical in real-world deployments, where resource availability and demand fluctuate, quickly.
## 2 Seawater Channel Modelling Elements
### Water Channel Impairments
The following equations account for such scenarios. This means that \(b(\lambda)\) can be expressed as follows:  
\[b(\lambda)=\alpha_{0}\,b_{es}(\lambda)+\alpha_{1}b_{rs}(\lambda)+\alpha_{2}b_ {rs}(\lambda)+\alpha_{3}b_{rs}(\lambda){=\sum\limits_{m=0}^{M}\alpha_{m}b_{m}(\lambda)} \tag{3}\]  
where \(b_{es}(\lambda)\) is the elastic scattering coefficient, \(\sum\limits_{m=0}^{M}\alpha_{m}=1\), \(\alpha_{m}\) is the event weight contribution, and \(M\) represents the number of scattering-modelled photon events. Using Equation (3) to substitute \(b(\lambda)\) in Equation (1), we obtain the following:  
\[c(\lambda)=a(\lambda)+\alpha_{0}\,b_{es}(\lambda)+\alpha_{1}b_{rs}(\lambda)+ \alpha_{2}b_{rs}(\lambda)+\alpha_{3}b_{rs}(\lambda) \tag{4}\]  
Equation (4) is a comprehensive equation that describes the attenuation coefficient \(c(\lambda)\). If we normalise \(c(\lambda)\), it can be considered a total probability of photon events that might occur during its propagation through the channel. \(c(\lambda)\) has a pivotal role in forming Beer-Lambert's Law (BLL), which can be expressed in terms of the transmitted optical irradiance at the source \(I_{0}\) at a radial distance \(r\):  
\[I(r)=I_{0}e^{-c(\lambda)r} \tag{5}\]  
Equation (5) is the minimum benchmark that any channel model should be compared with to assess the limits of the model's reliability. Hence, BLL describes the light attenuation over the travelled radial distance \(r\). Nevertheless, in reference to Figure 3A, BLL in Equation (5) does not include the operational parameters of the optical Tx, CIR (\(h_{c}(t)\)), and optical Rx and the receiver's FOV. However, Equation (5) is used as a benchmark for complex channel models [31].
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 3 Link Quality Based Routing Metrics for Tactical MANETs
### BER Metric  
Digital communications allow using BER measurements to estimate link quality. BER can be also used as objective metric to asses quality of service (QoS) for applications. Similarly to SINR, it is the average measure determined over longer time periods. Usually, there is a one-to-one mapping between SINR and BER, however, unlike SINR, measuring BER from pilot symbols circumvents the need for estimating the level of interference. However, BER as a link quality metric is rarely assumed in the literature [28]. One reason for not using the BER metric in practical networks is the lack of pilot symbols to make the BER estimates sufficiently reliable [22].  
We can conclude that to assess the performance of link between two nodes, both RSS and SINR measurements are useful. RSS is more readily measurable, however, SINR provides more accurate prediction of the link quality [2]. In the sequel, we combine benefits of RSS and SINR to design a new routing protocol for use in tactical MANETs.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 4 Design of the DBREMM
### Content of Blocks  
Upon reporting and verifying set of events \(E_{i}\), all nodes generate reputation updates. The RSU obtains event status list \(C_{event}\) and reputation update list \(C_{ reputation}\). Then, the RSU broadcasts \(C_{event}\) and \(C_{reputation}\) to all vehicles within its coverage range and sends a consensus request. Vehicle nodes with spare computing power respond to the consensus request and participate in the consensus.  
In the DBREMM, we use two blockchains collaboratively and they are called the event chain and the reputation chain, which are used for different purposes, respectively:  
1. _Event Chain_: Event chain stores the content of the event information as the form of status list \(C_{event}=\{W_{i},O_{ji},...O_{ki}\}\), including the first report node and report content, as well as all collaborative verification nodes, verification content, and trust conditions. \(W_{i}\) is the report information, including the status of first report node \(S_{vi}\) and event content \(E_{i}\). \(O_{ji}\) is the certification report, which was introduced in Section 4.4. \(C_{event}\) is recorded as a transaction in the block. The purpose of the event chain is to record all of the events that have happened and the information of all of the vehicles that have participated in the observation of those events. The superstructure of the event chain is the emergency response unit, which is responsible for the emergency response, broadcast, and post-event accountability;
2. _Reputation Chain_: Reputation chain stores updates of the reputation value, in the form of \(C_{reputation}\), including the trust value and social measures (if they occurred during the period of time when the events occurred). \(C_{reputation}\) is recorded in the block as a transaction in the form of \(\{W_{i},R_{t},R_{\tau-1},R_{social}\}\). The superstructure of the reputation chain is the TA, which is responsible for controlling the vehicle nodes according to their reputation values, for example, after a period of time, it gives realistic rewards to the nodes that maintain a high reputation value. Moreover, it discovers whether nodes are committing evil and adds them to the observation area. If the node is determined to be malicious, its key pair and digital certificate are immediately revoked to prevent it from continuing to participate in the network.
## 3 Worldwide Sites Investigated with the Synthetic Storm Technique  
To illustrate the general characteristics of \(P_{S,A}(D)\) and \(P_{S,B}(D)\) and their relationship, we report the results concerning the sites listed in Table 1, located in different climatic regions. The rain attenuation expected in GeoSurf radio--links is independent of the particular GeoSurf design (e.g., altitude and number of satellites), because all paths to/from a satellite of the constellation are always vertical (zenith paths) [1]. \(A(t)\) is calculated with the Synthetic Storm Technique [4].  
Besides being in different climatic regions, these particular sites are useful study-cases because: (a) On-site rain--rate time series \(R(t)\) (mm/h)--the input physical measurements to the SST [4]--with rain rate averaged in 1 min intervals, were continuously recorded for several years; (b) They are the sites of important NASA and ESA satellite ground stations (Fucino, Madrid, White Sands), or the site where long-term radio propagation experiments were performed in Italy (Fucino, Gera Lario, Spino d'Adda) or large cities (Prague, Norman, Tampa, Vancouver).  
Figure 2 shows the average annual probability distribution \(P(R)\) of exceeding \(R\) (mm/h, averaged in 1 min) measured at the indicated sites. The different climatic rain conditions of these sites are clearly evident by comparing the rain rate exceeded with the same probability.  
Figure 2: Annual probability distribution (%) \(P(R)\) of exceeding the value indicated in abscissa at the indicated sites. Spino d’Adda: continuous blue line; Gera Lario: continuous black line; Fucino: continuous red line; Madrid: continuous green line; Prague: continuous magenta line; Tampa: dashed red line; Norman: dashed magenta line; White Sands: dashed green line; Vancouver: dashed blue line.  
Figure 3 shows the average annual probability distribution \(P(A)\) of exceeding \(A\) (dB) at 80 GHz, circular polarization, calculated with Equation (29) of Reference [4]. The large differences found in Figure 2 are substantially reiterated.  
These probability distributions are, of course, the first information asked for by satellite communication designers because they indicate, for a given outage probability \(P_{out}=P(A)\), the power margin \(A\) necessary to maintain service continuity. For example, in the Spino d'Adda area (such as in Milan, 20 km away), if the rainfall outage probability tolerated by users of the GeoSurf satellite constellations is 0.01% of the time, then the minimum link power margin is 19 dB. In the Tampa area, it is 27 dB.  
The fade durations that add up to 0.01% of the time are, of course, made of many intervals whose impact on system design can be studied and assessed just according to \(P_{S,A}(D)\) and \(P_{S,B}(D)\) of the area; therefore, in the next section, we report these probabilities.
## 2 Materials and Methods
### RNS and Lipid Peroxidation in Blood Plasma Treated with Cold Plasma or PTS  
The nitrite (NO\({}_{2}\)\({}^{-}\))/nitrate (NO\({}_{3}\)\({}^{-}\)) colorimetric Griess assay [33] was used according to the manufacturer's protocol to measure the concentrations of RNS introduced by cold plasma and PTS treatment in isolated EDTA blood plasma at 2 h, 4 h, 8 h, and 24 h after treatment. The Griess reagents include 0.1% N-naphthyl-ethylenediamine dissolved in ionized water and 2% sulphanilamide in 5% HCl. This reagent reacts with nitrite in blood plasma and was incubated for 30 min at 37 \({}^{\circ}\)C to form a purple product. Then, the absorbance of each reaction mixture was measured at 540 nm using a BioTek microplate reader (BioTek, Winooski, Vermont, USA). Lipid peroxidation results from oxidative damage, and malondialdehydedehyde (MDA) is a valuable marker for oxidative stress, which was analyzed according to the lipid peroxidation assay instruction at 2 h, 4 h, 8 h, and 24 h after plasma exposure with cold plasma or PTS. Briefly, 20% trichloroacetic acid (TCA) was added to the blood plasma to precipitate proteins. Then, 0.6% thiobarbituric acid (TBA) was added to the mixture. MDA in the blood plasma reacts with TBA to produce an MDA-TBA compound. This compound was detected colorimetrically at 535 nm (BioTek, Winooski, Vermont, USA). MDA leveled up to 1 nmol/well.
## 2 VSI-Derived CSI SVM Modulator
### Modulation Index Relationship Between VSI and CSI  
The vectors for the CSI, as shown in Figure 2 above, are tabulated using the notation of upper switches and lower switches [7; 33]. For instance, I1 (100, 001) and I6 (100, 010) as shown later in Figure 5b below.  
The VSI modulation index is defined as the ratio of the maximum of the phase voltage and half of the DC link voltage:  
\[m_{VSI}=\frac{V_{p,\text{max}}}{V_{DC}/2} \tag{9}\]  
with a theoretical maximum of:  
\[m_{VSI,\text{max}}=\frac{2}{\sqrt{3}} \tag{10}\]  
whereas the maximum value of the CSI modulation index is:  
\[m_{CSI,\text{max}}=1 \tag{11}\]  
This limit in Equation (11) is easily understood because if the phase stays connected to the \(I_{DC}\) rail for the duration of the pulse period or if there is no freewheeling, the local mean value of the phase current can assume a maximum of the value of the current \(I_{DC}\) as shown in Equation (4) [32].  
From Equations (10) and (11), the relationship between the modulation indices of the CSI and the VSI can be determined as shown in Equation (12) below:  
\[m_{CSI}=\frac{\sqrt{3}}{2}m_{VSI} \tag{12}\]
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 5 Results and Analysis
### Cloud Layer Result Analysis  
#### 5.1.1 Execution Time  
Figure 5 displays the execution time for voting and stacking ensemble methods over all the models described in Table 2. The X-axis in Figure 5 refers to the duration in seconds to execute a model, while the Y-axis refers to the model number. Compared to the voting ensemble method, stacking takes a much higher execution time. According to our results, model number 8, with the voting technique, shows minimal execution time (9.96 s), with KNN, NB, and DT used as base classifiers.
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
###### Abstract  
The complete Stokes vector contains much more information than the radiance of light for the remote sensing of the ocean. Unlike the conventional radiance-only radiative transfer simulations, a full Mueller matrix-Stokes vector treatment provides a rigorous and correct approach for solving the transfer of radiation in a scattering medium, such as the atmosphere-ocean system. In fact, radiative transfer simulation without considering the polarization state always gives incorrect results and the extent of the errors induced depends on a particular application being considered. However, the rigorous approach that fully takes the polarization state into account requires the knowledge of the complete single-scattering properties of oceanic particles with various sizes, morphologies, and refractive indices. For most oceanic particles, the comparisons between simulations and observations have demonstrated that the \"equivalent-spherical\" approximation is inadequate. We will therefore briefly summarize the advantages and disadvantages of a number of light scattering methods for non-spherical particles. Furthermore, examples for canonical cases with specifically oriented particles and randomly oriented particles will be illustrated.  
ocean optics; light scattering; Mueller matrix; volume and surface integral methods Article
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 4 Simulation  
The performance of the proposed schemes was evaluated through MATLAB simulations. Since the proposed scheme addresses the shortcomings of the PI scheme and the DuRT scheme, we compared our scheme with the PI scheme [13] and the DuRT scheme [14] in the simulation. In [14], the DuRT scheme actually includes three localization methods, DuRT-M1, DuRT-M2, and DuRT-ALL, and the working conditions of the three methods are different. In our simulation, the conditions for using DuRT-M1 were satisfied, but the conditions for using DuRT-M2 and DuRT-ALL were not satisfied. Thus, in the simulation, we used DuRT-M1. For convenience, DuRT-M1 is denoted as DuRT.
## 4 Discussion  
### EO Studies in the EMMENA Region  
Over the past years, there has been an increasing trend in EO research in the EMMENA region (Figure 8). However, the number of EO studies in the EMMENA region represents only 3% of the EO studies carried out worldwide. Also, as we have shown in Table 2, the amount of EO research is highly uneven between countries within the EMMENA  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & **Ref.** & **Citations** & **Thematic Area * ** & **Focus** & **Country** \\ \hline
1 & [112] & 667 & DRR & LS & Regional \\
2 & [125] & 349 & DRR & FL & Iran \\
3 & [115] & 251 & DRR & LS & Iran \\
4 & [117] & 244 & DRR & LS & Iran \\
5 & [113] & 237 & DRR & LS & Algeria \\
6 & [114] & 218 & DRR & LS & Regional \\
7 & [124] & 199 & DRR & FL & Iran \\
8 & [116] & 174 & DRR & LS & Iran \\
9 & [118] & 165 & DRR & FL & Iran \\
10 & [68] & 159 & Water & GQ & Iran \\
11 & [65] & 159 & Water & GQ & Iran \\
12 & [123] & 153 & DRR & FL & Iran \\
13 & [107] & 131 & Land & SP & Iran \\
14 & [111] & 128 & Land & FB & Iran \\
15 & [55] & 125 & Water & GP & Iran \\
16 & [129] & 118 & DRR & FL & Iran \\
17 & [130] & 117 & DRR & FL & Iran \\
18 & [151] & 114 & Energy & OL & Morocco \\
19 & [119] & 114 & DRR & LS & Algeria \\
20 & [101] & 113 & Land & LU/LC & Oman \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reference (Ref.), number of citations up to 13 March 2023 (Citations), geographical focus (Country), thematic area and research focus (LS: Landslides, FL: Floods, GQ: Groundwater Quality, SP: Soil Properties, FB: Forest Biomass, OL: Optimized Location/Site for Energy Plant, LU/LC: Land Use/Land Cover) of the top 20 highly cited papers which include big Earth data methods and techniques.  
region. Iran is the most active EMMENA country in terms of EO publications, as shown both from the number of publications and the author affiliations. However, from our analysis we found that almost half of the studies were conducted by researchers not affiliated with the EMMENA region. We assume that, to some extent, the barrier for the expansion of EO research can be attributed to the lack of collaborations and knowledge transfer within EMMENA countries. Towards this direction, the recent establishment of a Centre of Excellence acting as a digital innovation hub for EO in the EMMENA region is a significant advancement in expanding EO research and in seeking future collaborations and knowledge transfer schemes among the EMMENA countries [211].  
The plethora of satellite data currently available are usually scattered among different platforms (Table 3), resulting in more time and work needed for data collection and preprocessing [18]. The development of a user-friendly central data hub (or data cubes) can resolve this issue. An example of such a central data hub is the GEO-CRADLE EU project which facilitates access to and sharing of geospatial data and information collected from satellites and ground-based networks in the North Africa, Middle East, and Balkans (NAMEBA) region.
## 2 Background Theories
### Empirical Mode Decomposition (EMD)  
EMD proposed by [11] is used to divide a signal without leaving the time domain. It can be equated to other analysis methods such as Fourier Transformation and Wavelet Decomposition. The EMD is beneficial for analyzing natural signals and it often applies to non-linear and non-stationary situations.  
The EMD distinguishes the complexity of the original signal into a series of Intrinsic Mode Functions (IMF) with amplitude and a residual difference. IMFs satisfy the following two conditions:  
1. The IMFs have only one extreme between zero crossings. In another word, the difference in number of maxima and minima is at most 1.
2. The mean of the wave of IMF is zero.  
The EMD decomposes the signal into IMFs through a sifting process. As shown in Figure 1, the sifting method can be explained using the following algorithm. Decompose a data set \(x(t)\) into IMFs \(x_{n}(t)\) and a residual \(r(t)\), as a result of which the signal can be described by (4)  
\[x(t)=\sum_{n}x_{n}(t)+r(t) \tag{4}\]