esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle
### LG Mode with Orthogonal Properties  
The light field of the general vortex in polar coordinates is expressed as:  
\[E(r,\theta,z)=E_{0}(r,\theta,z_{0})\exp(-ikz)\exp(il\theta), \tag{1}\]  
where \(k\) is the wave vector. \(l\) is the azimuth value, \(E_{0}(r,\theta,z_{0})\) is the complex amplitude of incident light. \(z\) is the transmission distance.  
After transmission for a certain distance, polar coordinates are converted to Cartesian coordinates and diffraction integrals are used, the amplitude of vortex beam is expressed as:  
\[E(x,y,z)=-i(\frac{k}{2 Az})^{l+1}(\frac{y-ix}{\sigma})E_{0}\exp(ikz)\exp[ikz(x ^{2}+y^{2})/2\sigma^{2}Az]. \tag{2}\]  
where \(A\) is light field amplitude. \(\sigma\) is the spot size.  
It is well known that the most commonly used OAM beam is the LG beam, due to its simplicity of generation. The azimuth index is constant \(l=0\) and the radial indexes are \(p=2\), 4, two \(LG_{lp}\) modes are adopted in this study which can be described as [22]  
\[\begin{array}{ll}u_{{}_{lp}}(\rho,\phi,z)&=\frac{B_{lp}}{w(z)}\frac{\sqrt{2 }\rho}{w(z)}|^{l}|\exp(-\frac{\rho^{2}}{w^{2}(z)})L_{p}^{|l|}(\frac{2\rho^{2}} {w^{2}(z)})\exp(ik_{0}\frac{\rho^{2}}{2R(z)})\\ &\qquad\qquad\times\exp[-i(2p+|l|+1)\zeta(z)]\exp(il\phi),\end{array} \tag{3}\]  
where \(u_{{}_{lp}}(\rho,\phi,z)\) represents light field intensity of the \(LG_{lp}\) in a circularly symmetric form. \(B_{lp}\) is the normalization constant which can be expressed as \(B_{lp}=\sqrt{2p!/\left[\pi(p+|l|)!\right]}\). \(L_{p}^{l}(\cdot)\) is the generalized Laguerre polynomial. \(w(z)=w_{0}\sqrt{1+\left[\lambda z/\left(\tau w_{0}^{2}\right)\right]^{2}}\) is the beam spot of \(LG_{l=0,p=0}\) which \(w_{0}\) is the radius of the beam waist. \(k_{0}\) represents the wave vector of a light beam in a vacuum. \(R(z)\) equals to \(z-iz_{R}\) and \(z_{R}\) is the Rayleigh range. \((2p+|l|+1)\zeta(z)\) is the Gouy phase shift with \(\zeta(z)=\tan^{-1}z/z_{R}\).  
Moreover, the orthogonality of LG modes in the transmission is reflected if they have either a different \(p\) value or a different \(l\) value, which is expressed in cylindrical coordinates as [23]:  
\[\int_{0}^{\infty}\int_{0}^{2\pi}u(r,\phi,l_{1},p_{1},w_{0})u^{*}(r,\phi,l_{2}, p_{2},w_{0})d\phi dr=\left\{\begin{array}{ll}0,l_{1}\
eq l_{2}\text{ or }p_{1}\
eq p_{2}\\ 1,l_{1}=l_{2}\text{ and }p_{1}=p_{2}\end{array}\right., \tag{4}\]  
where \(l_{1}\) and \(l_{2}\) denote the topological charge. \(p_{1}\) and \(p_{2}\) are radial nodes. According to Equation (4), LG modes are mutually orthogonal only when \(l_{1}\
eq l_{2}\) or \(p_{1}\
eq p_{2}\), which offers a new degree for future communication systems.  
Figure 1: The schematic of the OAM-DM-WDM-OFDM-PON system transmitter.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Analyte** & **Linearity** & **LOD (ng** & **Recovery** & **Matrix Effect** & **Nitrogen** & **Desorption** & **Refs** \\  & & **(ng g\({}^{-1}\))** & **g\({}^{-1}\))** & **(\%)** & **(\%)** & **Reeonstitution** & & **Nice** \\ \hline MSPE-GC-\(\mu\)BCD & 3 insecticides & 1–20 & 0.23–0.3 & 82–113 & not reported & yes & 1 & [36] \\ MSPE-GC-\(\mu\)BCD & 3 insecticides & 0.1–5 & 0.07–0.13 & 72.8–109.6 & not reported & yes & 1 & [37] \\ MMIP-HPLC-UV & 1 acphate & 10–5000 & 0.1 & 89.2–93.4 & not reported & yes & 3 & [38] \\ MSPE-HPLC-UV & 6 rgytrendids & 5–500 & 0.63–12 & 76–99.5 & not reported & yes & 1 & [39] \\ MSPE-HPLC-CD & 6 carbamates & 5–200 & 0.58–2.06 & 90.34–101.98 & not reported & yes & 3 & [40] \\  & 5 OPPs, 2 & & & & & & & \\  & OCPs, 2 & & & & & & & \\ MSPE-GC-MS/MS & carbamates, 2 & 0.1–100 & 0.006–0.03 & 79.2–109.4 & no & no & 1 & This work \\  & pyrethroids, & & & & & & & \\  & and 2 triazoles & & & & & & & \\ \hline \hline \multicolumn{7}{l}{\(\mu\)BCD: micro electron capture detector; MMIP: magnetic molecularly imprinted polymers; HPLC: high-performance liquid chromatography; UV: ultraviolet detector; DAD: diode array detector.} \\ \end{tabular}
\end{table} 
Table 3: Comparison of the current study with other MSPE techniques used for vegetable sample preparation.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 5 Validation, Evaluation and Obtained Results
### Best Effort  
Firstly, it is important to understand the natural characteristics of the default (best effort) implementation. Hence, it is pertinent to test its functionalities without taking advantage of the slice-based QoS manager. The main purpose is to find what are the implemented solution limitations, showing potential drawbacks that some processing environments and network characteristics may provoke.  
Hence, one interesting result would be to find what is the maximum achievable bandwidth that the default version of our implementation can handle. The test was performed for two scenarios, using the TCP and UDP transport layer protocols. Only one Iperf3 client was used in each case, which gradually increased the connection requested bandwidth. The test result is depicted in Figure 13.  
As can be seen by the above chart, despite one noticeable peak of 4.6 Gbit/s, TCP stabilised roughly at 1 Gbit/s, while UDP found a balance at approximately 3 Gbit/s. The observed chart reports that TCP has a stabilised best effort bitrate smaller than UDP. The proposed solution addresses two scenarios where several end devices are allowed to exist and that may serve a wide range of applications. Hence, it showed how both protocols behave when traffic provided by different devices is simultaneously flowing through the solution.
# Pharmaceuticals in Coastal Waters: An UHPLC-TOF-MS Multi-Residue Approach
## 3 Results and Discussion
### Application to Field Samples  
The driving force for optimization of the present method was, as previously stated, the monitoring project BIOPHARMA. With 31 sampling sites and over 60 compounds to monitor, a reliable multi-residue method was fundamental. Therefore, once it was fully optimized and validated, it was applied to the analyses of samples collected in triplicate at the Tejo estuary. Results reflected the presence of pharmaceuticals throughout the system, with their detection in all collected samples (n = 93). All 8 therapeutic groups were present with a total of 32 analytes confirmed. The most represented groups were antibiotics and \(\beta\)-blockers, with sulfathiazole and bisoprolol detected at all sites [18]. Regarding the recently updated EU Watch List [15], 4 out of the 5 pharmaceuticals included in the method were consistently detected in the Tejo Estuary [18]. Venlafaxine presented a detection frequency of 87.3%, followed by trimethoprim (32.3%), ciprofloxacin (29%) and sulfamethoxazole (3.2%).  
For full details of the results of the environmental analyses, see the works of [PERSON] and colleagues [18] and [PERSON] and co-authors [19].
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 5 Discussion
### Lessons Learned--Impact on LoRaWAN Security  
#### 5.1.1 Availability  
As clearly visible from Table 2 and from Table 3, DoS attacks, in the form of various flavors of jamming and wormhole attacks, have drawn considerable attention. Due to LoRa-chirp-spectrum modulation, a typical LoRaWAN packet airtime spans between milliseconds and seconds. As it turns out, the resulting relatively slow data rate allows for implementations of reactive LoRaWAN jammers with commodity low-cost hardware such as microcontrollers equipped with standard LoRa modems [12]. Consequently, this might enable further threat scenarios for LoRaWAN networks supporting over-the-air updates, as a malicious firmware update could turn a legitimate LoRaWAN device into a jammer. Secondly, the availability of the components to assemble and setup such a jammer does not necessarily require expert level knowledge, which widens the feasibility of LoRaWAN jamming.  
In terms of analysing the risk of jamming-based DoS attacks, reactive jamming might result in a higher impact for certain applications. For instance, reactive jamming might pose a serious issue towards applications relying on event-based communication such as flood or gas-leak sensors, since the oblivious jammer can only be first detected after the collision between a jamming frame and a legitimate packet. Hence, such applications would benefit from a so called \"heart beat\" messages to improve detectability of jamming. Furthermore, as pointed out by [11; 13], the blocking of the wireless access is not the only form of DoS in LoRaWAN. Firstly, a LoRaWAN modem can be forced into reception, which increases the current consumption and thus, reduces the device lifetime. Secondly due to the vulnerabilities connected to the adaptive data rate algorithm, the wormhole attacks can force the LoRaWAN device to higher DR parameters, which ultimately causes a large impact on the device battery life.  
Parallel to the studies on the attacks themselves, robustness evaluations, as well as novel detection and mitigation strategies, have been demonstrated. For LoRaWAN applications where a packet loss due to a certain degree can be tolerated, the LoRa physical layer is able to support good message throughput even in the presence of a jammer [70]. In such use cases, an additional jamming-detection listener node could be integrated as a part of the intrusion detection system, which delivers information on intentional interference. Lastly, for security-critical applications, the cryptographic frequency hopping medium access techniques offer a viable solution to improve message throughput in the presence of multiple jammers.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear  
The information data of indirect online monitoring includes cutting force, vibration, acoustic emission, temperature, current, power, workpiece surface roughness, and multi-information fusion.
## 3 System Model
### Simulations Results
#### 4.3.5 Average Temperature Rise Consumption with Different Node Density  
Figure 9 represents the comparison of temperature-rise of ATEAR, Enhanced-ATEAR, MRRP, SHR, and TARA protocols within networks having a different number of nodes, i.e., 5-13 at simulation time 300 s in Figure 9a and 1000 s Figure 9b respectively. It can be observed from both graphs that even by changing the size of the network performance of ATEAR and Enhanced-ATEAR was better than MRRP, SHR, and TARA in terms of average temperature rise. At 300 s simulation time in Figure 9a, initially, the temperature of the network rose in case of MRRP, SHR, and TARA is 37.8 \({}^{\circ}\)C, 38.3 \({}^{\circ}\)C, and 37.5 \({}^{\circ}\)C even at 300 s and as the number of nodes increased, the temperature reached 40.336 \({}^{\circ}\)C in the cases of MRRP, SHR, and TARA. Contrary to this, it can be seen that both ATEAR and Enhanced-ATEAR protocols showed consistent temperature rise both at 300 s and 1000 s with changing node density. On the other hand, MRRP, SHR, and TARA, the temperature rise was increased up to 48.6 \({}^{\circ}\)C at 1000 s and changing nodes, which is impracticable for a human body. Thus by changing network size, the average temperature of proposed protocols performed better.
## 2 Methods
### Practical OTA-Based Gyrator
#### 2.2.2 Compensation of Parasitic Conductance  
When a parasitic conductance of a gyrator node is to be decreased as described in the previous section, a simple compensation circuit with negative input conductance, shown in Figure 6, can be used. This circuit is connected with its COMP terminal to the node where the parasitic conductance is to be compensated.  
The conductance of the COMP terminal relative to ground equals to  
\[G_{\text{COMP}}=-g_{\text{mC}}+G_{\text{P}} \tag{12}\]  
and can be set to an appropriate negative value by the electronic setting of \(g_{\text{mC}}\). Here \(G_{\text{P}}\) is the total parasitic conductance of OTA\({}_{\text{C}}\) input and output terminals. If the implementation of OTA\({}_{\text{C}}\) is identical with the OTAs of the gyrator, all the parasitic conductances are equal (\(G_{\text{P}}=G_{\text{P1}}=G_{\text{P2}}\)). From (8), it is apparent that when the admittance \(G_{\text{P1}}\) is compensated in this way to almost zero value, the cut-off frequency \(\omega_{\text{GPI}}\) can be theoretically shifted to very low values, and the maximum magnitude of the gyrator input admittance can be increased significantly. However, the demands on the accuracy of the compensation circuit increase, and it is important to prevent reaching a negative conductance of the node. The conductance \(G_{\text{P2}}\) is worth to be compensated when it affects the gyrator input admittance at high frequencies between \(\omega_{\text{GP2}}\) and \(\omega_{\text{CP2}}\), i.e., when the solid line (and not the dashed line) in Figure 5 holds. Remember that when \(\alpha\) tends to unity, the conductance \(G_{\text{P2}}\) is an important damping factor of the above-mentioned LC resonance, and thus, it cannot be excessively reduced.  
It should also be noted that when connecting the circuit in Figure 6 to a gyrator node, the parasitic capacitance of this node increases by the own parasitic capacitance of the compensation circuit, which is equal to \(C_{\text{P1}}\) and \(C_{\text{P2}}\), assuming that all OTAs are the same. Thus, it is necessary to take this property into account in the relations containing the parasitic capacitance of the node being compensated. Note that the circuit in Figure 6 cannot be used for compensating the gyrator parasitic capacitance.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Experimental Results
#### 8.5.3 Comparative Analysis  
To contextualize our results, we compare them with the theoretical expectations and baseline performance of similar systems (Table 4).  
* Throughput: Our achieved throughput of 33.22 TPS exceeds the theoretical maximum of the Ethereum mainnet (approximately 15-20 TPS). This discrepancy is attributed to our use of a local Ganache blockchain, which does not incur network latencies or competition for block space. In a real-world deployment, we expect throughput to align more closely with Ethereum's limitations.
* Latency: The observed latency of 26 ms is significantly lower than Ethereum's average block time of 13 s. Again, this reflects the ideal conditions of our local environment. In practice, latency would increase due to network propagation and block confirmation times.
* Gas Consumption: Compared to a basic ERC-721 token minting operation (which typically consumes around 100,000 gas), our registerItem function's consumption of 126,276 gas represents a modest 26% increase. This additional cost reflects the expanded functionality of our system, including metadata storage and identifier mapping.
* Batch Operations: The efficiency of our batch registration (20.4 ms per item in a batch of 5) demonstrates a 21.5% improvement over individual registrations (26 ms per item). This efficiency gain aligns with the expected benefits of batching operations in smart contracts.
* Function Execution Time: The 14 ms execution time for getItemDetails is commendable, considering the function retrieves multiple data points (identifier, metadata, and owner address). This performance is comparable to read operations in optimized database systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Our System** & **Ethereum Mainnet** & **Centralized Database** \\ \hline Throughput (TPS) & 33.22 & 15–20 & 1000+ \\ \hline Latency (ms) & 26 & 13,000 (avg) & \textless{}10 \\ \hline Write Cost & Variable & Variable & Negligible \\ \hline Decentralization & High & High & None \\ \hline Immutability & High & High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with Baseline Systems.  
Figure 7: Gas Consumption for Different Operations.  
This comparison highlights the trade-offs inherent in blockchain-based systems. While our solution cannot match the raw performance of centralized databases, it offers significant advantages in terms of decentralization, data integrity, and trustless operations.  
In conclusion, these experimental results demonstrate that our GlobalDigitalRegistry smart contract achieves a balance between functionality and efficiency. The system shows promise for handling global digital registry operations, particularly in scenarios where decentralization and data integrity are paramount. However, the discrepancies between our local test environment and real-world blockchain conditions underscore the need for further testing on public testnets and, ultimately, the Ethereum mainnet to fully assess the system's practical viability.
## 4 Case Study
### Case Description
#### 4.1.2 Express Cargo Flow Data  
Twenty express cargo flows are set up, with each flow consisting of 400 items. The departure times, origin-destination (OD) stations, and types of express cargo are randomly assigned. Specific data are detailed in Table 9.
# The PIV Measurements of Time-Averaged Parameters of the Synthetic Jet for Different Orifice Shapes
## 2 Methods
### Validation of PIV Measurement  
The validation consists of the comparison of the centerline synthetic jet time-averaged velocity with the PIV and hot-wire measurement methods. The eight-channel ATU-08 hot-wire anemometer was used for velocity measurements (see [26]). The probe was calibrated in the range of 0.1-50 m/s, with an accuracy of \(\pm\)0.05 m/s in the velocity range of 0.1-2.6 m/s and \(\pm\)0.14 m/s for a velocity greater than 2.6 m/s.  
Based on Figure 4, it should be considered that the PIV measurements were carried out correctly. The convergence between PIV and hot-wire measurements was complete.  
Figure 4: The comparison of the synthetic jet centerline time-averaged velocity obtained with PIV and hot-wire at \(Re=5000\), \(St=0.5\), and \(L_{0}=2\). Circular orifice.
# A Microfluidic Approach for Biosensing DNA within Forensics
## 4 Biocompatibility of Materials for Forensic Microfluidic Devices
### Glass  
Due to its transparency in the visible range, which gives opportunities for optical detection, glass is widely used a material for (forensic) DNA analysis on-chip. PCR and CE can be easily integrated on-chip due to the beneficial electro-osmotic-flow/electrical characteristics of glass [17; 80; 81].  
For glass surfaces it is reported that silanization of the surface is required to minimise surface adsorption of Taq polymerase. [PERSON] et al. investigated the use of dynamic coating (polyethylene glycol (PEG) 8000, polyvinylpyrrolidone (PVP) and hydroxyethylcellulose (HEC)) versus passive (or static) coating (epoxy (poly)dimethylacrylamide (EPDMA)) of the reaction chamber of a PCR glass chip. Covalent silanization results in 50-120% of product relative to the amount obtained with conventional PCR in polypropylene tubes. The use of HEC ends in total inhibition of the reaction, whereas the use of PEG 8000, PVP and EPDMA results in 13%, 78% and 72% of relative product amount,respectively [48]. Other coatings that can be used to passivate the glass surface are SigmaCote [82] or dichlorodimethylsilane [83].
## 3 Localization Algorithms in WSNs
### Range Free Localization Algorithm
#### 3.1.2 Analytical Geometry Based  
Most popular alternatives suitable for range free localization algorithms are based on analytical algorithms [7; 9; 29; 33] which evaluate theoretically the average hop distance of the network using the statistical characteristics of the network deployment. The obtained average hop distance is locally computable at each sensor node and likewise other range free method, it has to be broadcasted to other sensor nodes.  
To cope with the problem of anisotropy in a network, pattern driven localization scheme [29] is proposed. For anisotropic environment, this paper devised two methods to calculate the estimated distance between anchors and sensors based on whether the anchor is slightly detoured or strongly detoured from normal sensor nodes. For slightly detoured anchors, it utilizes the information from the nearest anchors (namely reference station) and this reference station must be within three or four hops away from normal sensor nodes. Which means that, the anchors distribution density must be very high. It devised one method to discard the strongly detoured anchors. However, no indication of how many anchors fall in the strongly detoured category because it may be impossible to accurately determine which anchors are slightly detoured and which are moderately or strongly detoured. The author in [7; 9] deals with this problem by calculating the angle of the detoured path between anchor and sensor nodes. Another analytical algorithm [33] argues that average hop distance and number of hops between anchor and sensor nodes are not sufficient to calculate accurate position of the sensor nodes. It also depends on number of forwarding nodes (which forward any data between two nodes). By utilizing this information along with other information, the author in [33] showed that further accuracy can be achieved.
### Contribution  
The main contributions of the paper are as follows:  
1. Advanced preprocessing of the dataset, including data cleaning, encoding, and application of Principal Component Analysis (PCA) [12]. Although PCA is a well-known method, we have specifically optimized it for efficient tabular data integration into our deep learning model, focusing on the unique demands of skin lesion classification.
2. Implementation of medical-specific image augmentation techniques, such as precisely controlled adjustments to brightness and color. While data augmentation is a common practice, our approach uses a low ratio of alterations to maintain the diagnostic integrity of medical images, thus enhancing the robustness of the model without compromising the diagnostic relevance of the images.
3. Development of a custom multi-input CNN model, complete with detailed specification of hyperparameters, which integrates both medical imaging and patient metadata to improve diagnostic accuracy.  
(4) Comprehensive analysis of the efficiency of different model configurations, providing insights into which non-default parameters significantly enhance classification performance, thereby informing future optimizations and applications.  
The remaining part of paper is organized as follows. Section 2 reviews related works, providing a comprehensive background and situating our study within the existing scholarly landscape. In Section 3, Methods and Materials, we detail our data preprocessing techniques and the materials employed in our study. Section 4 discusses the implementation, where we delve into the technical specifics of our custom multi-input CNN model, including hyperparameters and the computational resources used. The paper concludes with Section 5, where we summarize our findings, discuss the implications of our research, and suggest potential avenues for future work.