esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Jamming Attack Models
### Reactive Jamming  
As in [4], reactive jamming is an attack strategy where the jammer will emit a packet with the objective of colliding with a legitimate packet. The jamming device keeps monitoring the wireless medium; once it detects a packet, it then generates a collision one immediately after. Note that the attack will be successful if at least one bit of the legitimate packet is altered, and there is no error correction scheme, only an error detection mechanism. This attack uses less energy than the random jamming strategy.  
In this paper, we use rates from 50 packets/s to 80 packets/s in order to observe the network behavior under the simplest attack and that is easy to detect by the neighboring nodes, under measurements of signal strength and collision rates. On the other hand, in the other extreme, one of the most difficult attacks to detect is reactive jamming. This is because the attacker node is observing the channel in order to listen to an activity, and it causes collisions.  
Table 1 presents the main characteristics and assumptions considered in this work on the implementation of two known types of jamming: random and reactive. With this table, we intend to justify the use of each of these types of jamming in order to compare the behavior of the network under each one. In addition, we will study these types of jamming by a fair comparison of two known routing protocols, AODV and DSR, and the proposed MPH, as explained earlier in [6] and evaluated under general performance metrics in [5].  
Other jamming attacks are more sophisticated. Intelligent jamming is aware of the MAC or network protocols, and they can interfere with specific packets in order to maximize the jamming gain. We shall not consider this type of jamming, since we would like to focus on general jamming strategies, which can be applied despite the underlying routing and MAC mechanisms.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Analyzed Types of Jamming**} \\ \hline
**Random** & **Reactive** \\ \hline Proactive & Reactive \\ Simple & More difficult to detect \\ Higher energy consumption & Lower energy consumption \\ Active mode (no idle periods) & Active mode (no idle periods) \\ Detection with signal strength mechanisms & More complex mechanisms for detection \\ Transmission packet rate = 50 and 80 packets/s & When a node detects activity, it transmits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative study of jamming: random and reactive.
## 3 Alternative Paths for Message Streams in Cluster-Tree Wireless Sensor Networks
### ARounD Protocol Mechanisms
#### 3.4.1 Synchronisation and Configuration of the Involved Nodes
After receiving the acknowledgements from the involved nodes (source, destination and RP nodes), the PAN coordinator will authorise the start of the new ARounD message stream using an _ARounD authorisation command_ frame (ARounD auth). Whenever the PAN coordinator does not receive all the required acknowledgements, it will retry to configure the missing nodes for a number of attempts defined at the application level. This parameter is configured based on timing requirements imposed by the supported application. If the configuration does not succeed after the number of defined attempts, the ARounD path will not be created. Note that involved nodes just start the ARounD communication after the authorisation of PAN Coordinator, avoiding the waste of energy. The _ARounD auth_ frame is sent to the involved nodes using indirect communication mechanisms. Figure 14 illustrates the format of this frame. The value \(0x0d\) is used for the _command frame identifier_ field, which is a value also not used by the standard. Similarly to the configuration procedure, the _Addressing field_ contains the _destination Cluster Identifier_ and _destination address_ fields, which corresponds to the node to be authorised. In turn, the _source Cluster Identifier_ and _source address_ fields correspond to the PAN coordinator.
## 3 Materials and Methods
### Selection of Suitable Situations  
Surveillance cameras can offer quality videos from traffic for subsequent analysis. It is important to note that there are many roads in the cities but also between them. It follows that it is not possible to install cameras for analytical purposes in all places. It is the most significant disadvantage of such cameras. Thus, it is important to search for an alternative that has to meet several requirements; this includes mobility in the environment, simple control, costs, and efforts associated with the operation. One such option are UAVs, which are adapted to such type of work; they are mobile and make meaningful records for analysis.  
Four specific situations that capture cars in traffic were considered and are shown in Figure 3. The view above the road level (Figure 3a) represents a front view, which can be recorded by a UAV. Such a view provides a relatively detailed frame of the car from the front side. Bird's eye view from the side (Figure 3b) represents a side view in which cars are too small to be detected with a high confidence score. However, such a view provides enough information about a particular car. Bird's eye view (Figure 3c) represents a view from above, in which cars are too small, and only rectangles can be recognized. The last picture (Figure 3d) poses an illustration of a video captured from a road level, which is not suitable for UAVs. However, it allows an effortless way to read a number plate. The distance from the camera is sufficient as well. Each situation has its pros and cons. The most significant features include a view from the front side or side view and a sufficient distance from the camera suitable for UAVs. It follows that the proposed experimental software service will conduct the best analysis for the views above the road level.  
The UAV should provide stable flight at a constant height in a vertical plane and stay in one designated spot when capturing the videos. Thus, the UAV should focus on a specific scene, while not being in the intentional movement, i.e., act as a static camera. The benefit, in this case, is that the UAV may be navigated to specific locations automatically and start the recording. However, there are possible challenges, mainly the severe weather conditions, e.g., wind disturbances or fog, which may introduce unintentional movements and following errors in the tracking within the region of interest (ROI). An approach to overcome this challenge was introduced by [PERSON] and [PERSON] [27] with very promising results.
# Unbiased Fuzzy Estimators in Fuzzy Hypothesis Testing
## 3 Fuzzy Estimation
### Estimation of the Mean of a Normal Variable with Known Variance  
If the random variable \(X\) follows normal distribution with known variance \(\sigma\), then the \((1-\beta)100\%\) confidence interval of the mean \(\mu\) of \(X\) derived from a random sample of observations of \(X\) of size \(n\) and sample mean \(\overline{x}\) is [1].  
\[\left[\overline{x}-z_{\frac{\theta}{2}}\frac{\sigma}{\sqrt{n}},\ \overline{x}+z_{\frac{ \theta}{2}}\frac{\sigma}{\sqrt{n}}\right] \tag{6}\]  
where \(z_{\widetilde{\theta}}=\Phi^{-1}\Big{(}1-\frac{\beta}{2}\Big{)}\) and \(\Phi^{-1}\), the inverse distribution function of the standard normal distribution. So according to (6), the \(\alpha\)-cuts of the possibility distribution of the fuzzy estimator \(\widetilde{\mu}\) of mean are as follows (see [29; 30]):  
\[\mu_{\widetilde{\mu}}[\alpha]=\left[\overline{x}-z_{\frac{\theta}{2}}\frac{ \sigma}{\sqrt{n}},\ \overline{x}+z_{\frac{\theta}{2}}\frac{\sigma}{\sqrt{n}}\right],\ \ \ 0\leq\alpha\leq 1 \tag{7}\]  
where  
\[z_{\alpha}=\Phi^{-1}(1-\alpha) \tag{8}\]  
Implementing (7), we get the fuzzy estimator \(\widetilde{\mu}\) of Figure 2.  
Figure 1: [PERSON]’s fuzzy estimator of the mean \(\mu\) of a random variable \(X\), which follows normal distribution with variance \(\sigma^{2}=2.3\), derived from a sample of \(n=100\) observations with sample mean \(\overline{x}=5\).
## 4 The Proposed Approach
### The Two-Stage Optimisation Model
#### 4.1.1 Stage 1: Heuristic for Feasible Solutions  
The first stage of the proposed two-stage optimisation model is based in a heuristic approach. This stage explore feasible container configurations by iteratively generating and assessing potential solutions. The first step after the data acquisition within the problem is the generation of a solution, creating an initial container arrangement for possible grouping. Each new configuration undergoes a validation process: First, it is checked for novelty to ensure that duplicate or previously tested solutions are not re-evaluated. Then, it is assessed for feasibility based on predefined constraints such as the container compatibility and load balance.  
If a solution meets these criteria, the containers are marked as candidates for PI-container grouping. When all possible configurations have been examined, the model formulates the PI-container groups.
## 2 Method
### Dependent Variables
#### 2.3.2 P300  
MW was assessed using P300 event-related potentials (ERPs) during a concurrent auditory oddball task. This approach was chosen because of the limitations of alternative physiological measures (e.g., eye-tracking, head movement) in capturing the effects on pedestrians' MW. These measures are primarily sensitive to visual load and susceptible to the 'looking but not seeing' phenomenon, where visual attention does not guarantee cognitive processing [40]. Additionally, such measures are influenced by factors such as stimulus duration, novelty, and complexity [41; 42], or brightness [35]. Furthermore, eye-tracking parameters vary in task-specific applicability, necessitating complementary measures to ensure robust and valid MW assessment [43; 44].  
In the oddball task, participants were required to count rare high-pitch tones (2000 Hz, 52.5 dB, 500 ms, \(p\) = 0.2) among frequent low-pitch tones (1000 Hz, 52.5 dB, 500 ms, \(p\) = 0.8), adhering to oddball paradigm standards [45]. A total of 60 high-pitch tones and 240 low-pitch tones were presented randomly, with 500 ms interstimulus intervals. Counting was opted instead of providing a physical response to the high-pitch tones due to the prior research finding that motor-evoked potentials could potentially interfere with the P300 effects [46].  
The P300 effect, elicited by high-pitch tones, manifests as the third positive deflection 250-450 ms post-stimulus, predominantly in centro-parietal regions [47]. In this study, it was quantified as peak amplitude within this window at the Cz electrode. Given that mental resources are limited, higher P300 amplitudes elicited by the oddball task reflect lower mental demands imposed by the primary task [46; 48].  
EEG data were recorded using the Neuroscan system and Curry 8.0 software with 64 Ag/AgCl electrodes (extended 10-20 system; Figure 4). Although only Cz data were analyzed, all electrodes were recorded for potential future use. Signals were digitized at 1000 Hz, and electrode impedance was maintained below 20 k\(\Omega\) using Easy Gel, adjusted as needed. Stimuli and event timing were controlled via E-Prime 2.0.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 4 Discussion
### Limitations and Future Developments  
Besides advantages discussed above, it is worth mentioning some current limitations of the proposed methodology. The current approach allows the detection of crops from a single agricultural year. If multi-year crop classification is required, the workflow can be applied for each year separately. Different measures can be used inside DTW to assess the similarity between two temporal sequences. We implemented the default Euclidean distance measure. Yet, there are other studies that proposed the utilization of Canberra distance instead [91], which can be seen as a weighted Manhattan distance, performing better than the classic Euclidean DTW distance in Landsat time series clustering. Therefore, further distance measures need to be tested carefully in the future. To further improve the computational speed, various temporal constraints, or bounds on warping window size proposed in the literature [28; 30; 92; 93] will be evaluated for various SITS classifications scenarios.  
Every crop modeling approach should have a well-defined domain of relevance, a mechanistic framework and an evaluation of the new model [94]. Besides achieving this, our proposed workflow is also meeting other criteria that guarantee a successful model, like simplicity, generality, robustness, accuracy, and fruitfulness [95].
## 2 Fundamentals of Frequency-Selective Surfaces
### Methods for Evaluating the Performance of Frequency-Selective Surfaces (FSSs)
These models represent the FSS as a combination of inductive and capacitive elements, which allows for a more intuitive understanding of its resonant behavior. By equating the FSS to a parallel or series LC circuit, researchers can quickly estimate resonant frequencies and bandwidths based on the physical dimensions of the FSS elements. While less accurate than full-wave simulations, equivalent circuit models offer a valuable tool for rapid design iterations and for providing insights into the tuning of FSS geometries for specific performance targets. These models are particularly useful during the initial design phase, where they can inform the choice of element shapes, periodicity, and substrate properties. Experimental Verification. Finally, after the FSS has been designed and its performance simulated, experimental verification is essential to confirm the real-world performance. This typically involves fabricating the FSS using methods such as photolithography or printed circuit board (PCB) technology and testing the fabricated structure under controlled laboratory conditions. Measurements are performed in anechoic chambers or free-space setups to ensure accurate characterization of the FSS's electromagnetic response. Key metrics such as insertion loss, return loss, and bandwidth are recorded to evaluate whether the FSS meets the intended design specifications. In some cases, environmental testing is also conducted to determine the impact of factors such as temperature, humidity, or mechanical stress on the FSS's long-term stability and performance. These evaluation methods provide a comprehensive approach for analyzing the performance of the FSS, ensuring that both theoretical designs and practical implementations meet the stringent requirements of modern electromagnetic applications. Combining numerical simulations with experimental validation is crucial to optimize FSS performance for specific use cases and to address challenges related to angular and polarization stability, fabrication tolerances, and material properties.
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 5 Numerical Illustrations
### Additional Discussions  
Table 2 compares the performance of the wholesale contract and revenue-sharing contracts with and without considering the LC effects. Under the wholesale contract, the expected profit of the supply chain is \(\pi_{\text{Nc}}=4600\) as discussed previously, with the retailer's expected profit at 2800 (60.9% of the total profit) and the manufacturer's expected profit at 1800 (39.1% of the total profit). We adjusted the contract parameters, \(w\) and \(\phi\), for the revenue-sharing contracts to achieve the same profit-sharing ratio, \(\alpha\), as the wholesale contract. Under the revenue-sharing contract without LC effects, the expected profit of the supply chain is \(\pi_{\text{Nc}}=4800\), with retailer's profit at 2922 and manufacturer's profit at 1878. It is observed that the revenue-sharing contract generates more profit compared to the wholesale contract. Under the revenue-sharing contract with LC effects, the expected profit of the supply chain is \(\pi_{\text{LC}}=5011\), with the retailer's profit at 3049 and the manufacturer's profit at 1962. It is evident that the coordinated contract results in even greater profit when LC effects are considered in the contract-design process. The proposed contract scheme not only maximizes the overall total expected profit but also ensures a win-win situation for both the manufacturer and retailer.  
Our investigation explores the integration of learning-curve (LC) effects within revenue-sharing contracts, revealing a mechanism conducive to supply-chain coordination across various bargaining-power dynamics between manufacturers and retailers. The adaptability of the proposed contract parameters (\(w\), \(\phi\)) to diverse profit distribution scenarios has been illustrated in Table 1. Our findings indicate a notable enhancement in supply-chain profitability with the inclusion of LC effects, surpassing the efficacy of traditional contracts reliant on static production costs. Through the new contract scheme, supply-chain profits can be distributed fairly, motivating all parties to leverage LC effects and enhance system efficiency. This coordination mechanism optimizes individual profits and culminates in mutual advantages, underscoring the strategic value of such adaptive contractual frameworks in supply-chain management.
# Lithium Metal: The Key to Green Transportation
## 4 Discussion
### Production Growth  
Given the last decade, which shows an increase in both production and manufacturing of devices that require LIB to function, their rise was fueled by next-generation electric car technology [4], and is expected to skyrocket in the coming years. The average of the annual growth in the last decade (which is equivalent to the mean of average growth of 10 consecutive years) of EV sales is 40% (exclude 2019), implies the increase of 30% in LIB, 12% in lithium production, 43% in charging stations, and -3% CO\({}_{2}\) emissions from road transportation.  
In 2016, there was a high demand for lithium, which encouraged company producers to increase their production. However, they ended up with overproduction in the next two years (2017 and 2018). Companies such as Pilbara Minerals and Altura Mining, for example, supplied glut production. Consequently, this overproduction directly dropped the price of lithium commodities between 2018 and 2020. For example, the LiOH price fell from $20.5/kg (January 2018) to $9/kg (December 2020), and the Li\({}_{2}\)CO\({}_{3}\) price fell from $19.25/kg (January 2018) to $6.75/kg (December 2020) [132]. Companies were forced to reduce production in 2019 and 2020 due to the price drop, resulting in the swing in lithium production (Figure 14). Unfortunately, COVID-19 found lithium production in its swinging state and made it worse. Moreover, the reduction in production affected the lithium market to a low extent due to the previous overproduction and COVID-19 restrictions in China that slow down manufacturing.  
Recently, the demand and sales of EV have skyrocketed. Its speed of increase must be proportional to the lithium supply. The waste of used LIB should be minimized by increasing the availability of technology for recycling. Lithium from both mining and recycling activities can satisfy electric vehicle and electronics demand in the long-term, and the enhancement of recycling may reduce the consumption share of lithium from mining, which should be saved for future uses. The annual increase of electric vehicle sales predicts a need for a huge amount of lithium in the future, so the plan of mobilizing more lithium resources should be put in place to avoid an early shortage of lithium. Based on the estimate, lithium consumption will reach 188,000 tons [44]. The demand for electric vehicles is increasing faster than the speed of lithium production, which means that the lithium necessary to produce LIB can exceed the lithium production in coming years. The demand may be further accelerated by the increasing geopolitical tension between oil producer and oil consumer countries.  
Even if the reported reserves seem to be enough for lithium demand, many lithium production activities need to be in place to respond fast to either short-term or long-term demand. Regardless of how far lithium exploration progresses, the effort to optimize lithium recovery and recycling should be stepped up to address long-term production.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 3 Materials and Methods
### Principle of the PAOC Technique  
The multipath signal has a longer propagation delay than the direct signal, implying that the correlation peak of the multipath signal lags behind that of the direct signal. Therefore, the forward offset correlator is less susceptible to the multipath than the prompt correlator. Figure 3 illustrates the effect of a multipath signal with a relative delay of 0.4 chips on different correlators. The quadrature-phase correlation value between the local signal and the direct signal is 0, so the quadrature-phase correlation value of the compound signal is equal to that of the multipath signal. As a result, in Figure 3b, the red and green lines overlap, and the purple and yellow lines overlap. Figure 3 clearly shows that the prompt (P) correlator is affected by the multipath signal, while the offset correlator (OC) with a forward offset of 0.8 chips is not affected by the multipath signal.  
In fact, the prompt correlator is affected by multipath signals with relative delays less than 1 chip. The offset correlator with a forward offset of \(\tau_{oc}\) chips is only affected  
Figure 3: Schematic diagram of the multipath effect on the offset correlator (OC) and the prompt (P) correlator: (**a**) in-phase correlation functions; (**b**) quadrature-phase correlation functions.  
by multipath signals with relative delays less than \(1-\tau_{oc}\) chips. Therefore, using the forward offset correlator instead of the prompt correlator for discrimination can effectively reduce the multipath sensitive area, thus mitigating carrier and subcarrier multipath errors. Furthermore, the multipath performance of the offset correlator improves with the increase of forward offset.  
However, as shown in Figure 3, the magnitude of the offset correlator output is smaller than that of the prompt correlator output. In the presence of noise, the SNR of the offset correlator output is poor. Moreover, the thermal noise performance of the offset correlator degrades with the increase of forward offset. As a consequence, using the offset correlator for discrimination requires a compromise between the thermal noise performance and multipath mitigation performance.  
It is observed that the prompt correlator output has a high SNR but is susceptible to the multipath. In contrast, the offset correlator output has a low SNR but is less susceptible to the multipath. The prompt correlator and the offset correlator are complementary in terms of both multipath performance and thermal noise performance. Based on this insight, this paper proposes a PAOC technique using the prompt correlator to assist the offset correlator for carrier and subcarrier discrimination. The PAOC technique combines the advantages of the prompt and offset correlators to substantially improve multipath performance with small noise performance loss.