esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Reliable Transmission Design Mechanism  
#### 2.1.1 Basic LL-ReTx Mechanism  
Our unit is integrated into each node, and the unit structure of each node is identical. However, one acts as the receiver and the other as the sender.  
The mechanism can be summarized as follows: the sender temporarily stores the data packets in a cache while assigning to each packet a sequence number that increments by 1. When packet loss occurs, the receiver identifies the discrepancy between the sequence number of the received packet and its own expected sequence number, as outlined in Algorithm 1. In response to this detection, the receiver goes through the packet processing flow on the switch, as shown in Figure 1, and then sends a negative acknowledgment (NACK) to the sender, indicating which packet was lost.  
```
1:Input: pkt
2:Variables:Expected_Num
3:ifpkt.pkt.num == Expected_Numthen
4:Expected_Num \(\leftarrow\) Expected_Num + 1
5:forward()
6:elseifpkt.pkt.num!= Expected_Numthen
7:Expected_Num \(\leftarrow\) max(pkt.pkt_Num) + 1
8:Call \"Processing Flow of Reliable Transmission\"
9:send NACK back to Sender()
10:endif
```  
**Algorithm 1** Packet processing algorithm in the receiver  
Upon receiving the NACK, the sender retrieves the corresponding cached packet and retransmits it to the receiver. This approach, which utilizes the receiver to send NACKs, is specifically designed to conserve bandwidth and represents a significant departure fromthe previous LL-ReTx mechanism [16; 17]. By implementing this scheme, we enhance the efficiency of packet retransmission while minimizing unnecessary network traffic.
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
## 4 Experiment
### Evaluation
#### 4.3.2 Evaluation of Aligned Contrastive Loss  
Few-shot detection results for Aligned Contrastive Loss are shown in first and second row of each setting in Table 3. The model with Aligned Contrastive Loss has accuracy improved on AP50, AP75, APs, APm and API. The lowest APs accuracy of baseline is only 0.69% due to the small number of data, and this phenomenon proves that the model doesn't fully exploit data. When Aligned Contrastive Loss is added, APs accuracy increase by 14.76% (5 shot), 15.32% (10 shot), and 15.22% (30 shot).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Method** & **AP** & **AP50** & **AP75** & **APs** & **APm** & **API** \\ \hline \multirow{3}{*}{5 way-5 shot} & Baseline & 10.46 & 18.93 & 11.12 & 0.96 & 5.60 & 13.99 \\  & Baseline+Aligned \({}^{1}\) & 11.47 & 20.62 & 11.34 & 15.72 & 6.80 & 15.13 \\  & Baseline+Fusion \({}^{2}\) & 12.47 & 21.87 & 13.32 & 12.74 & 5.69 & 16.08 \\ \hline \multirow{3}{*}{5 way-10 shot} & Baseline & 11.94 & 21.50 & 12.53 & 5.94 & 7.22 & 15.94 \\  & Baseline+Aligned & 12.62 & 23.60 & 12.58 & 21.26 & 7.36 & 16.56 \\  & Baseline+Fusion & 14.46 & 25.59 & 15.50 & 13.30 & 7.99 & 18.78 \\ \hline \multirow{3}{*}{5 way-30 shot} & Baseline & 16.02 & 27.89 & 17.22 & 0.69 & 7.90 & 20.53 \\  & Baseline+Aligned & 17.87 & 32.07 & 18.77 & 15.91 & 9.87 & 22.20 \\ \cline{1-1}  & Baseline+Fusion & 18.98 & 32.31 & 20.65 & 11.33 & 9.88 & 24.81 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Aligned: Aligned Contrastive Loss. \({}^{2}\) Fusion: Multi-Scale Fusion.  
\end{table}
Table 3: Detection result for Aligned Contrastive Loss and Multi-Scale Fusion.  
Figure 8: The detection result of single-branch and two-branch object detection models.  
The AP accuracy for each category is shown in first and second row of each setting in Table 4. The feature and size between coppers and fatigue are very similar, which puts a burden on model learning, as shown in Figure 2. With Aligned Contrastive Loss, AP accuracy for these two defects is improved, indicating that our model learns more distinguishable features.
## 3 MDM-FSO System Simulation Setup Model
The values of these system parameters are shown in Table 2.  
It is worth emphasizing that when a bias voltage is applied to a LiNbO\({}_{3}\) device, it causes a change in polarization that leads to different refractive indices for various optical modes in the waveguide. This variation can result in mode coupling [26; 27], where energy is transferred between optical paths, affecting the link's performance and efficiency. The extent of mode coupling depends on the magnitude and orientation of the applied electric field. If the bias voltage aligns with the crystal's principal optical axis, it can enhance certain modes while suppressing others, allowing for tailored spectral properties. However, misalignment can cause unwanted scattering and loss, compromising signal integrity. Moreover, the nonlinear nature of the electro-optic effect in LiNbO\({}_{3}\) can introduce complexity into the analysis of mode coupling dynamics, especially at higher electric fields [28]. This nonlinearity may result in phenomena such as SPM, where the effective refractive index changes with the intensity of the light, further intensifying mode interactions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value Used** & **Interpretations** \\ \hline n1 & 1.446 & Core index \\ \hline \(\lambda\) & 1550 nm & Wavelength \\ \hline \(\xi\) & 2.94 μm & Mode field radius \\ \hline L & 13.0 km & Fiber length \\ \hline \(\alpha\) & 0–1.4 dB & Fiber loss \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three Mode Fiber Simulation Parameters. In summary, the interplay of electro-optic effects and mode coupling in LiNbO\({}_{3}\) under varying bias voltages presents challenges and opportunities for optimizing optical devices, with ongoing research aimed at improving system performance. Figure 6a shows the post-fiber spectra at 40 Gbps after transmitting the combined mode signal over a 13 km link, with equally spaced channels. Modal dispersion and four-wave mixing were effectively mitigated in the 120-GHz-spaced MDM transmission, ensuring minimal degradation of system performance. The optical signal was then transmitted through the free-space optical (FSO) channel for further propagation. Figure 6b illustrates the variation of mode power with wavelength; the received power at 1550 nm for the LP\({}_{01}\), LP\({}_{11}\), and LP\({}_{02}\) was evaluated to be \(-17.94\) dBm, \(-19.985\) dBm, and \(-20.62\) dBm, respectively. This introduced a power penalty of 1.01 dB between LP\({}_{01}\) and LP\({}_{11}\) channels and 1.67 dB between LP\({}_{01}\) and LP\({}_{02}\) modes.
## 2 Materials and Methods  
### Study Area  
The field trials were conducted for three years (2016-2018) at the Werer Agricultural Research Center (WARC), Amibara district of the Afar region of Ethiopia. WARC is located at an altitude of 740 m above mean sea level between 9\({}^{\circ}\)12\({}^{\prime}\)8\({}^{\prime\prime}\) N latitude and 40\({}^{\circ}\)15\({}^{\prime}\)21\({}^{\prime\prime}\) E longitude (Figure 1). The study area is mainly characterized as semi-arid with mean annual temperatures ranging between 19 \({}^{\circ}\)C and 34 \({}^{\circ}\)C. The mean annual Class A pan evaporation is 2800 mm, which is five times higher than the mean annual rainfall (570 mm) [17]. The study area consists of an extensive alluvial plain, which was historically formed by the deposits of the Awash River. The area has flat topography with a slope gradient ranging between 1-2%. The predominant soil types are weak-structured Vertisols and Fluvisols. The Vertisols are silty clay to clay whereas Fluvisols are sandy loam to silty loam in texture [18]. The constituents of Fluvisols are muscovite/illite clay minerals while Vertisols are dominated by montmorillonite clay minerals.
## 3 Results and Discussion  
### Characterizations of Tyrosine/Chitosan/rGO Biocomposites  
#### 3.1.1 Morphology Analysis  
SEM was used to inspect the morphologies of four types of sensor electrodes: SPCE, rGO/SPCE, chitosan/rGO/SPCE, and tyrosinase/chitosan/rGO/SPCE (Figure 1). SEM images indicated that rugged surfaces were formed after coating rGOs onto SPCE (Figure 1A,B). Furthermore, the surface of the chitosan and tyrosinase/chitosan modified rGO/SPCE electrodes was smoother and more homogeneous compared to that of the rGO-coated electrodes (Figure 1C,D).  
Vibrational spectroscopy was used to verify the immobilization of tyrosinase on chitosan modified electrodes. Figure 2A shows attenuated total reflection fourier-transform infrared spectroscopy (ATR-FTIR) spectra of the chitosan-modified electrode with and without tyrosinase. Chitosan/rGO/SPCE and tyrosinase/chitosan/rGO/SPCE show peaks in the characteristic C=O stretching band of the amide groups at 1643 and 1537 cm\({}^{-1}\) in the N-H bending of the secondary amide, respectively; these may be due to the hydrogen bonding between chitosan and some residual oxygenated groups in rGO, and electrostatic interaction between positive chitosan charged molecules and anionic species on the rGO surface. In addition, the characteristic absorption curve of tyrosinase/chitosan/rGO/SPCE was close to that of tyrosinase (Figure S1), indicating the existence of tyrosinase on chitosan/rGO.  
The hydrophilicity of the tyrosinase/chitosan/rGO/SPCE biocomposite electrodes was characterized by measuring the contact angles of a water droplet (Figure S2). Treating rGO with chitosan resulted in a more hydrophilic surface, and the water contact angle changed markedly from 133.80\({}^{\circ}\) to 58.55\({}^{\circ}\). However, the contact angle increased to 74.92\({}^{\circ}\) after tyrosinase treatment, suggesting successful entrapment of proteins on electrodes.
## 4 Results and Discussions
### Effect of Different Injection Pressure on CO\({}_{2}\)-Ecbm  
#### 4.2.1 Variation of Reservoir Pressure  
In the process of CO\({}_{2}\)-ECBM, the gas pressure continues to increase with time, and with the increase of injection pressure, the gas pressure in the reservoir changes more obviously (Figure 13). Especially in the early stage of gas injection, increasing the injection pressure can make the gas pressure in the reservoir increase rapidly in a short time, making CH\({}_{4}\) gas easier to produce. The pressure differential and concentration differential are the driving force of the seepage and diffusion of CO\({}_{2}\) and CH\({}_{4}\) in the coal seam. When CO  
Figure 12: Distribution curve of reservoir permeability along the model diagonal (**a**) \(CBM\) (**b**) \(CO_{2}\)-\(ECBM\).  
is injected, the reservoir pressure is greatly increased, and the pressure differential is also increased, which promotes the gas to flow and diffuse to the production well faster. This is one of the reasons why CO\({}_{2}\) injection can increase the production of coalbed methane.
# Compact Design and Impact Ionization: Utilizing Small-Sized Thyristors in a 4-Stage Marx Generator
## 3 Results  
The measurements obtained using a capacitive probe highlight the necessity for high-speed measurement accuracy in our system. As depicted in Figure 13, the output signal demonstrates the incremental addition of each individual Marx stage voltage, accompanied by an increase in its slew rate with each successive step (first stage \(U_{1}\), second \(U_{2}\), and so on). This enhancement is primarily attributed to the cascading impact of the [PERSON] generator stages, which are assembled using thyristors that switch in impact ionization mode.  
The first stage of the [PERSON] generator achieves a maximum amplitude of 4.4 kV with a 10% to 90% slew rate of 3.2 kV/ns when triggered at a 1.5 kV thyristor biasing voltage. Each subsequent stage sharpens the falling edge of the curve and reaches a higher absolute maximum, culminating in a maximum amplitude of 11.2 kV and a 10% to 90% slew rate of 13.3 kV/ns at the fourth stage. Detailed measurements for each stage are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Stage Test Point**} & \multicolumn{2}{c}{**Maximum**} & \multirow{2}{*}{**Fall Time (ns)**} & \multirow{2}{*}{**Slew Rate (kV/ns)**} \\  & **Amplitude (kV)** & & \\ \hline TP1 & 4.4 & 1.1 & 3.2 \\ \hline TP2 & 5.6 & 0.7 & 6.6 \\ \hline TP3 & 7.3 & 0.7 & 8.2 \\ \hline TP4 & 11.2 & 0.7 & 13.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of maximum amplitude, fall time, and 10% to 90% slew rate (\(dU/dt\)).  
Figure 13: Comparison of the output voltage at each of the four Marx generator stages (TP1 to 4, Figure 4), with each thyristor biased at 1.5 kV. Measurements taken with the capacitive probe on a 50 \(\Omega\) load. The signals are triggered at the same value to clearly demonstrate the sharpening of the falling edge.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 2 The Biology of _Ulva_ sp.
### Phyto hormones
[PERSON] (2012) [34] noted a higher rate of _Kappaphycus alvarezii_ (Rhodophyta) callus formation in PES media compared to CW media when supplemented with IAA. However, the optimal induction of callus in this seaweed occurred on solid PES medium containing 0.1-1 mg L\({}^{-1}\) of 1-naphtheneacetic acid (NAA) or 6-Benzylaminopurine (BAP) [34]. Furthermore, NAA, phenylacetic acid (PAA), or a mixture of NAA, PAA, and BAP produce an optimal callus growth of _K. alvarezii_[29]. To induce seaweed callus, according to [PERSON] et al. [35] and [PERSON] [36], \(\alpha\)-lipoic acid (ALA), a growth promoter and retardant, can also be utilized. [PERSON] et al. [37] obtained the development of callus in all segments of _Saccharina japonica_ (Phaeophyceae), at a concentration of 100 mg L\({}^{-1}\) of ALA; nonetheless, at excessive concentrations, the growth of callus-like cells was suppressed. The two scientists referred to above also reported that supplementation with UNI, a triazole-type inhibitor, led to maximum callus induction among PGRs of the genus _Sargassum_ (Phaeophyceae). [PERSON] et al. (2016) [35] further demonstrated that this inhibitor could hinder cellulose production, leading to the formation of calluses. Apart from the aforementioned growth regulators, evidence suggests that picloram (PIC), indole-3-butyric acid (IBA), 2-isopentenyladenine (2 iP), zeatin (ZEA), gibberellic acid (GA), and N-(2-chloro-4-pyridyl)-N'-phenylurea (CPPU) are also effective in regulating growth in macroalgae callus cultures [29; 35]. Another study on the _Ulva_ genome ended up providing more information about the hormonal role in development, with the existence of genes responsible for the biosynthesis of abscisic acid (ABA), ethylene, salicylic acid, and auxin (IAA). While culture studies have indicated the presence of Gibberellic acid (GA3) production in axenic _Ulva_ sp., no precursor of GA3, such as ent-kaurene (CPP synthase, ent-kaurene synthase), has been identified in the _Ulva_ sp. genome. Additionally, the _Ulva_ genome lacks homologs of angiosperm hormone sensing and signaling genes, suggesting the existence of a distinct and independent hormonal signaling mechanism in _Ulva_ sp., which may bear similarities to those observed in microalgae, such as diatoms (Bacillariophyceae) [4].
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases
#### 3.3.5 Phase 4--Verification of the Signature and Application of the Instructions  
The last phase of the protocol begins when entity B uses a hash function with the values of the identifier of entity D\((ID_{D})\) and the message \(M1\), such that \(H(ID_{D},M1)\to h_{M1}\). The  
Figure 8: Phase 3: Decryption of the information received from the sensors, signature generation of entity A from the MI data, hash value generation of the previous signature, the decrypted data, and the identifier of B.  
Figure 7: Phase 2: Transmission of sensed data in plaintext from entity C to entity B. Transmission of encrypted sensed data from entity B to entity A.  
output of this function is sent together with the signature and the message \(MI\) to entity D. Subsequently, entity D must verify the hash value it receives with the one it calculates, to verify the integrity of the information. Once this has been checked, \(s\), \(P_{kA}\), and \(MI\) are taken as inputs to give as output a Boolean value determined by the verification function \(V_{k}(s,MI,P_{kA})\to T/F\) that defines if the signature is authentic and therefore safe to be applied by the actuators on the patient's body. Figure 9 shows the phase diagram, where all the functions performed by each of the entities can be seen.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Operational Considerations
#### 5.2.4 Job Safety Assessments (JSAs)  
JSAs are a formalised way of showing that steps have been taken to reduce risk levels to people, property, and other aircraft to as low as reasonably practicable [21]. The great thing about them is their flexibility, whereby hazards and associated risks pertinent to a specific operating area can be taken into account and managed, in combination with other operational procedures, to manage more general risks. For example, many airports have their own requirements for flying in their airspace, which are additional to any local aviation regulations [84]. Just over 20% of unmanned aircraft users typically conduct a job safety assessment prior to flight [21]. Being familiar with the operating area, aware of risks, and planning flights accordingly is critical for effective risk mitigation [85], and JSAs provide evidence that this has taken place.
## 2 Materials and Methods
### Feature Extraction
#### 2.4.3 Fuzzy Features  
The extraction of the fuzzy features was based on the fuzzified version of the enhanced images. The thresholding technique was useless for this kind of feature. The functions have a fuzzy matrix as an input and a value as an output. The features that were extracted during this method are the area, the perimeter and the compactness [44].  
For the fuzzy area, the function is:  
\[fuzzy\ Area(\mu)=\sum_{m=1}^{M}\sum_{n=1}^{N}\mu_{m,n} \tag{8}\]  
where the matrix has dimensions \(M\times N\) and \(\mu_{m,n}\) is membership participation of each pixel of the image.  
For the fuzzy perimeter:  
\[fuzzy\ Perimeter(\mu)=\sum_{m=1}^{M}\sum_{n=1}^{N-1}\|\mu_{m,n}-\mu_{m,n+1}\|+ \sum_{m=1}^{M-1}\sum_{n=1}^{N}\|\mu_{m,n}-\mu_{m+1,n}\| \tag{9}\]  
For the compactness of the image, the function was proposed by [PERSON]:  
\[compactness(\mu)=\frac{fuzzy\ Area(\mu)}{[fuzzy\ Perimeter(\mu)]^{2}} \tag{10}\]  
Compactness obtains its maximum value when the interest area is a circle [45].
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 2 Background
Phase alignment can be achieved, for example, by a cophase and sum circuit that can be realized, for instance, as a rake receiver that has the ability to demodulate each incoming signal independently. By correlating the incoming signals to delayed replica versions, a rake receiver can detect and reduce the relative phase offsets originating from the multipath propagation [19]. Figure 1 illustrates a generic rake receiver with two input channels [19]. The incoming signal of each antenna is processed in two channels to correlate the I- and Q-codes depicted as \(c_{i}\) in Figure 1, where \(i\) resembles the I- and Q-parts of the incoming signals. After correlation, the signals are sampled at frequency \(1/T\), depicted by the switch in Figure 1. Then, each signal is amplified by the corresponding gain factor \(h_{ij}\), where \(j\) resembles the antenna path. Finally, the output is achieved by summing all processed signals [19]. Resulting from its rather complex structure, a rake receiver usually has energy requirements in the mW area. There exist mainly four diversity systems that can be combined with diversity techniques. [PERSON] [17] categorized them into scanning diversity, selection diversity, maximal-ratio combining, and equal-gain diversity. The latter two methods combine several independent sinusoidal signals to increase the signal strength. Their phases need to be aligned to avoid destructive interference before summation, in order to achieve this. For reasons of simplicity, we quickly review the concepts of selection diversity and equal-gain diversity in the following two paragraphs. More details about the methods as well as the other mentioned diversity techniques can be found in literature--for example, in [14; 17].
## 4 Conclusions  
This work presents a voting ensemble approach based on pre-trained deep learning models for PQD classification using time-frequency images. In this process, CWT is used to convert 1-D PQD signals into 2-D time-frequency images. Four DCNNs including ResNet-50, VGG-16, AlexNet and SqueezeNet are employed to classify sixteen PQD classes for four datasets, i.e., without noise, 20 dB noise, 30 dB noise, and random noise. In the case of the individual model, ResNet-50 produced the best results for accuracy (99.75%), precision (98.04%), recall (98%), and F1-score (98%). To further enhance the performance of the classification model, a voting ensemble approach is presented. The proposed approach outperformed the individual deep learning models for accuracy (99.98%), precision (99.97%), recall (99.80%), and F1-score (99.85%) with the noiseless dataset. The results confirm the suitability of the proposed approach using image classification applications for PQDs in the context of smart cities, where reliable energy quality is crucial for the operation of various connected devices and infrastructure. In the future, it is intended to explore new deep learning models with real-world datasets to demonstrate their effectiveness in a real-time smart city environment, contributing to enhanced energy management and resilience.
## 5 State-of-the-Art
### Debugging  
Traditional offline debugging techniques such as logs and dumps are unsuitable for IoT due to their overhead on devices and their inability to capture contextual information to identify the source of bugs. In [64], the authors discussed the extension of the WARDuino IoT platform with an online debugging tool called WOOD, designed to overcome the challenges of debugging IoT applications. Online debugging seems more fitting for IoT, as it allows remote debugging; however, it faces issues such as the probe effect, non-reproducibility, and high latency. The same work explores an out-of-place debugging technique that brings the state of a running application to the developer's machine, ensuring low latency remote debugging. It discusses features of WOOD such as capturing, moving and reconstructing debugging sessions, and updating live code. Similar work on \"out-of-things\" debugging is demonstrated in [65] to recognize the debugging as a major challenge for IoT developers due to unique properties such as non-deterministic data and hardware restrictions. Out-of-things debugging is always-on and works by transferring the state of a failing application to the developer's machine. The developer can then locally debug the application, apply necessary operations, and commit bug fixes to the device. This approach provides a flexible interface for accessing remote resources and mitigates debugging interference by eliminating network delays during debugging operations. The above work implemented the debugger on a Wasm Virtual Machine and demonstrated its suitability for IoT systems based on various metrics. A systematic study was conducted in [66] to understand and detect bugs in Wasm runtimes, a platform often used for various software applications including mobile and desktop apps. Cnosidering that bugs in Wasm runtimes can lead to application crashes, the researchers collected and studied 311 real-world bugs from GitHub posts and categorized them into 31 distinct bug types, providing common fix strategies for each. They developed a pattern-based bug detection framework to automatically identify bugs in Wasm runtimes and successfully discovered 53 previously unreported bugs in five popular Wasm runtimes. Of these, fourteen were confirmed and six were fixed by the runtime developers.