esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges
### Detection, False Alarm, and Miss-Detection Probability  
The detection probability is a metric used for correct detection by CRWS regarding the absence of PUs on the channel. The miss-detection probability is a metric for CRWS failing to detect the presenceof the primary signal on the channel, and the false-alarm probability is a metric for the CRWS failing to detect the absence of the primary signal.  
Sensing can be viewed as a binary hypothesis testing problem with hypotheses \(H_{0}\) and \(H_{I}\):  
\[\begin{cases}H_{1}:\text{Currently occupied by PU}\\ H_{0}:\text{Available for SUs}\end{cases} \tag{1}\]  
[PERSON] _et al._[52] defined the miss-detection probability (\(P_{m}\)) and false alarm probability (\(P_{\beta}\)) in CR networks as follows:  
\[P_{m}=P(H_{0}\,|\,H_{1}) \tag{2}\]  
\[P_{f}=P(H_{1}\,|\,H_{0}) \tag{3}\]  
In CR-WSNs, a false alarm and miss detection can violate the right of the incumbents on the channel, which is the violation of the main principle of CRNs. The right to access a network by the incumbents should be respected in any type of CRN. A false alarm can cause spectrum under-utilization and a missed detection might cause interference with the PUs. In addition, most application areas of CRWS discussed in this paper are very critical in terms of the delay and correctness of data. In CR-WSNs, a false alarm and miss-detection causes a long waiting delay, frequent channel switching and significant degradation in throughput. The issues of the false alarm and miss-detection probability for CR _ad hoc_ networks and IEEE 802.22 WRAN have been well studied. However, this area still needs to be explored for CR-WSNs. This area needs to be examined further to meet the research challenges of CR-WSNs.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 4 Categorization of Bridgeless Boost Converters
### Bridgeless Boost Converter, Interleaved Totem-Pole  
Totem-pole and GaN totem-pole topologies can be improved according to Figure 15(a) presents a BBC totem-pole with additional diodes (\(D_{3}\) and \(D_{4}\)) and inductors (\(L_{1}\), \(L_{2}\) and \(L_{3}\)) [133]. In this case, \(L\), \(L_{1}\), and \(L_{2}\) are coupling inductors and their function consists of reducing reverse recovery currents from diodes \(D_{1}\) and \(D_{2}\). Diodes \(D_{3}\) and \(D_{4}\) contribute to ZCS of \(Q_{1}\) and \(Q_{2}\). This converter is used in low voltage and power applications, and its efficiency is lower than the one of the totem-pole and GaN totem-pole (between 94% and 97%) topologies due to the additional components in the converter. In the same way, the GaN totem-pole can be modified by adding diodes \(D_{1}\) and \(D_{2}\) and inductors \(L_{2}\) and \(L_{3}\) according to Figure 15(b) [8; 23; 134]. Diodes and inductors comply with the same function as the converter in Figure 15(a) (reducing reverse recovery issues and contribute with ZCS); additionally, efficiency is also reduced requiring a robust control strategy and filtering to comply with \(THD_{i}\) limits given by the normative.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion
### Limitations in Real-World Deployability  
Despite these innovations, a notable gap remains between laboratory demonstrations and real-world applicability. A common challenge is generalizability: vision-AI models often overfit to controlled environments and fail to maintain their performance under real-world conditions, such as noise, lighting variability, occlusions, or sensor degradation. Additionally, multimodal fusion systems--though promising--introduce complexity in calibration, latency, and data synchronization across heterogeneous sensors (e.g., vision, tactile, force, and auditory).  
Furthermore, the reliance on data-intensive deep learning methods remains a bottleneck, especially in applications where annotated datasets are scarce or ethically sensitive (e.g., healthcare HRC). The absence of standardization in benchmarking and evaluation frameworks across domains exacerbates this issue, complicating cross-comparative validation.  
To overcome these barriers, future research should explore self-supervised learning from human-robot shared experiences, enabling cobots to continuously learn from their regular operation without a need for large datasets. Neuro-symbolic vision systems, which combine data-driven learning with logic-based reasoning, offer a path toward explainable yet robust visual understanding. Another promising direction is spatial-temporal scene imagination--robots capable of simulating future scenarios using predictive models grounded in visual data to proactively reconfigure tasks and avert risks.  
Furthermore, collaborative visual grounding--in which humans and cobots co-construct shared semantic maps--can enhance mutual understanding and task alignment. Finally, embedding affective computing into vision-AI pipelines could enrich collaboration through empathy-driven responses to human emotions and behaviors. This could make cobots contextually intelligent and socially attuned, in addition to their functional competency.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
## 3 Results and Discussion
### Volatile Compounds
The identification of 3-methyl-3-butene-1-ol was also associated with monofloral rosmary honey [75]. As far as aldehydes are concerned, it can be observed that the honey samples from Zone 1 present the highest concentration, 27.63%, followed by the ones from Zone 2 with a 22.34% concentration. The samples in Zone 3 have a relative aldehyde concentration of 13.43%. The aldehydes identified in the samples from all the three production areas are: 3-methyl-butanal, heptanal, 3-methyl-2-butanal, nonanal, furfural, decanal, benzaldehyde, lilac aldehyde, 5-methyl-2-furancarboxaldehyde, acetaldehyde and 3-phenylfencil-propenal. [PERSON] et al. [76] have established hexanal as a specific marker for the acacia honey produced in Poland, while methyl butanol and lilac aldehyde proved to be a specific marker for the Polish honeyhew honey. The following substances from the aldehyde group were also identified in the composition of these honeys: hexanal, nonanal, decanal, acetaldehyde and lilac aldehydes. Ketones, sulfur compounds, esters and aliphatic hydrocarbons were also identified in small amounts, but none of them could be considered markers for acacia honey. As other studies presented the idea that minor compounds also influence honey flavor, the table was organized in order to summarize the specific aromas of all the identified volatile compounds. Thus, it can be observed that the honey aroma is very complex and depends on the specific flora of each production zone, but also on the compounds secreted by the bees or on the transformations that occur during storage.
###### Abstract  
High data rates challenges and long traces from current state-of-the-art systems imply high attenuation. In the present article, we will present a detailed process of synthesis of equalizers, for choosing the correct one for a given application. The methods are based on scattering parameters applied on interconnections modeled as microstrip or stripline. Firstly, one may have an overview of types of equalizers, passive, active, and adaptive ones, and a detailed filter synthesis is applied in microwave systems having as start point the insertion loss of a given trace on a given substrate. Next, time domain analyses offer a better understanding of the performance of the interconnect, based on eye diagram inspection and the variation of waveforms with time. Finally, we will present results based on simulation of the equalizers network in a microstrip technology followed by discussions and conclusions. The study proposes to use equalizers in either the transmitter or receiver point, proposes a bridge equalizer with the cost of additional elements but improved constant input, output impedance, and also a new variant for single ended trace based on microwave resonator is proposed. Performance is demonstrated by results from simulations.  
equalization; signal integrity; high-speed; open stub; synthesis +
Footnote †: journal: Sensors
## 5 Experiment Results
### Comparison with Two Efficient Heuristic Algorithms  
Currently, the optimal solution for Solomon instances is constantly being refreshed by many researchers. Among them, [PERSON] et al. (2019) proposed the diversification and intensification technique (denoted as DI), while [PERSON] et al. (2019) proposed the two-stage hybrid algorithm (denoted as TSH). These two heuristic algorithms obtained the highest number of optimal solutions across all 56 Solomon instances. As discussed above, our test instances were expanded from Solomon instances. Therefore, to evaluate the performance of our algorithm, we selected the DI and TSH algorithms to solve R-VRPTWLST and compared the results with those obtained by the IIG. For each expanded instance, the compared algorithm was run independently 30 times, and the optimal fitness value is recorded in Table 7. In Table 7, the first column represents the expanded instances and the second column provides the best fitness value achieved among the compared algorithms for each instance. The next six columns provide the optimal fitness values and corresponding RPI values of the compared algorithms. It can be seen that (1) the proposed IIG obtained 39 optimal fitness values out of 56 instances, significantly outperforming the other two algorithms. (2) The average RPI value of the IIG algorithm is 0.01, which is 0.14 and 0.1 times those of the DI and TSH algorithms, respectively. This demonstrates that the proposed IIG algorithm is effective in solving the R-VRPTWLST. Figure 6 presents an ANOVA comparison of the three algorithms, showing a \(p\)-value of \(6.529\times 10^{-16}\), which is much lower than 0.05, indicating significant differences among the three algorithms.
## 5 Analysis and Discussion
### Security Aspects
We now consider another attack where a rogue NTR sends its public key to MRBS \(E[K_{NTR}(\text{P}_{\text{NTR}}+\text{E}[\text{P}_{\text{MRBS}}(\text{CH})])]\). This message can only be generated by a real NTR and not the other rouge NTR, as a rouge NTR message will be  
\[E[K_{Rowing_{NTR}}(P_{N\!R}+E[P_{MRBS}(CH)])\]  
This is not the intended party of communication; thus, the MRBS can ignore the message. It is seen that the main public key is obtained by the CA and not by the nodes; thus, changes to the M-I-T-M have failed, and the issue is solved. * Baby Step, Giant Step (BSGS) Method  
BSGS [35] method by shank is an impressive method for solving the DLP problem and focuses on collisions. It reduces complexity by \(\sqrt{N}\) times, that is, half the size. If we are using a 192-bit curve, after taking \(\sqrt{N}\), we have \(10^{16}\) points; thus, it will require \(10^{21}\) Zeta bytes to store the hash, but this is not a large figure compared to the recommendation of the proposed algorithm, which is on the order of \(10^{156}\) Octillion attempts to make it fairly impossible to crack the key. * Impersonation attack  
The proposed algorithm is robust against authentication attacks where the adversary may pose as a legitimate node. The problem of rouge relay is becoming more common due to the introduction of  
Figure 3: Key comparison of ECC concerning RSA and Deffie Hellman. non-transparent relays [36]. To avoid these attacks, our proposed algorithm uses a two-way approach; first, it encrypts the message using its private key, as shown in the equation below. \[[K_{NTR-p}(P_{NR}+E[P_{MRBS}(CH)])]\]  
The MRBS will check the Whitelist maintained by the CA and obtain public key \(P_{NTR}\), which will not open the message, and decryption will fail, as shown in the following equation. \[D[P_{NTR}E[K_{NTR-p}(P_{NR}+E[P_{MRBS}(CH)])]]=Fail\]  
Thus, the proposed algorithm is secure against impersonation attacks.
## 4 Discussion  
The results of this study demonstrate the potential of integrating Sentinel-2 satellite data with machine learning algorithms to predict olive tree fluorescence, a key indicator of plant physiological status. Measuring \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) is a feasible tool to detect drought stress in plants [83]. As demonstrated by [PERSON] et al. [84], the \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) value correlates with drought, providing valuable information on plant physiological changes under drought stress conditions [85]. Although the olive tree is a drought-tolerant crop, climate change and water scarcity could reduce this trait, affecting nutrient uptake, carbon assimilation, canopy dimension, oxidative pathways, phenology, and reproduction processes [86, 87]. Findings of [PERSON] et al. [1] show that old and young leaves respond to water stress by reducing water content, leaf density, and photosynthetic activities, highlighting how in this situation young leaves could keep the vegetative growth, while the older ones could sustain the younger leaves with a supplementary function. They also showed differences among olive cultivars, proposing 'Maurino' as drought-tolerant and 'Leccino' as a drought-sensitive cultivar. In addition, [PERSON] [88] demonstrated how high light intensity and drought stress negatively affect PSII efficiency, reducing \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) in the 'Coratina' and 'Biancolilla' cultivars.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 2 Material
### Field Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Damaged Stands**} & \multicolumn{3}{c}{**Non-Damaged Stands**} \\ \hline  & \multicolumn{3}{c}{**281 Stands**} & \multicolumn{3}{c}{**631 Stands**} \\ \hline Characteristics & Minimum & Average & Maximum & Minimum & Average & Maximum \\ \hline  & 0.05 & 1.89 & 33.96 & 0.03 & 0.91 & 7.24 \\  & 0.00 & 19.57 & 29.25 & 0.00 & 17.79 & 27.79 \\ Height, dm & 2.00 & 169.32 & 253.00 & 0.78 & 159.98 & 247.35 \\ Age, years & 1.00 & 56.20 & 96.61 & 0.63 & 49.27 & 98.92 \\ Basal area, m\({}^{2}\)/ha & 0.00 & 18.79 & 30.00 & 0.00 & 18.27 & 30.23 \\ Volume, m\({}^{3}\)/ha & 0.00 & 167.60 & 337.83 & 0.71 & 159.75 & 330.87 \\ Volume, pine & 0.00 & 52.92 & 145.50 & 0.71 & 41.13 & 182.43 \\ Volume, spruce & 0.00 & 84.31 & 304.67 & 0.00 & 68.43 & 284.36 \\ Volume, birth spp. & 0.00 & 21.89 & 110.80 & 0.00 & 34.87 & 107.63 \\ Volume, other br. l. & 0.00 & 8.48 & 103.00 & 0.00 & 15.32 & 67.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average stand-level areas and forest characteristics in training data, separately for damaged and non-damaged stands with volume of growing stock greater than 75 m\({}^{3}\)/ha. Figure 2: Damaged and non-damaged stands displayed on Sentinel-1 from 19 August 2017: VV polarization (**a**); and VH polarization (**b**) (EPSG:3067).
## 3 Results and Discussion
### Testing and Evaluation  
It is important to accurately identify the features in the dataset in order to employ machine learning techniques. Due to the small sample size, Grubbs' test was performed to determine the reliability of the measurements obtained in the study. This test is a statistical method used to detect outliers in a univariate dataset that follows a normal distribution, especially when the sample size is small [55]. In the Grubbs test, the G value is calculated as the absolute difference between the mean and the suspected outlier divided by the standard deviation, which is then compared to a critical value from the t-distribution to determine statistical significance, and if the G value exceeds the critical threshold value, the point is considered an outlier [55]. Accordingly, in this study, taking alpha = 0.05, the critical value for each input parameter was calculated as 2.938. The G value for surface hardness was 1.991, the G value for surface roughness was 2.251, the G value for residual stress was 2.182, and no outliner value was found for each parameter. For this reason, the data were used as data in machine learning algorithms. After the training and test datasets were created for this study, the dataset for each material underwent 5-fold cross-validation for all regression techniques. Consequently, the developed model can be applied to forecast the properties of various as-yet-uncreated steel material combinations, speeding up the hunt for workable materials with the optimal SP values. This study's conclusion was that ML models can be used to create high-performance materials in light of this. Moreover, the size and quality of the input dataset have a significant impact on how well ML methods can estimate data. Additionally, the range and diversity of input characteristics can fully describe the underlying mechanisms. This study looked at the algorithms' performance in terms of RMSE, R\({}^{2}\), and MAE values for Almen Intensity calculated using ML models (Table 8). Any ML algorithm may yield the best results in some folds of validation, but it may yield the worst results in other folds. Since the mean of the 5-fold cross-validation should be taken into account, after analyzing mean values, the best results for the DNN algorithm were found to be RMSE of 0.0731, R\({}^{2}\) of 0.9665, and MAE of 0.0613. The XGBR, GBR, ANN, ABR, RFR, DTR, and SVR algorithms, in that order, produced the best results.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Almen Intensity** & & **DNN** & **ANN** & **ABR** & **DTR** & **SVR** & **GBR** & **RFR** & **XGBR** \\ \hline \multirow{3}{*}{1-Fold Validation} & RMSE & 0.0670 & 0.0555 & 0.0618 & 0.0764 & 0.0992 & 0.0683 & 0.0536 & 0.0602 \\  & R\({}^{2}\) & 0.9719 & 0.9678 & 0.9601 & 0.9496 & 0.8946 & 0.  
The results show that, in order to achieve material with the highest Almen intensity, the ML algorithms did not yield results that were significantly different from those of the actual test data. This decreases potential experimental expenses and wasted experiment time when machine learning algorithms are used for prediction jobs.
## 4 Results and Discussion
### Vibrational Analysis
#### 4.4.4 Effect on Vibrational Parameters in Going from Monomer to Dimer  
In going from the C-I monomer to its D\({}_{1}\) dimer, noticeable changes were observed in frequencies, intensities and depolarization ratios for several normal modes. The computed frequencies for the \(\
u\)(O\({}_{12}\)-H\({}_{13}\)) and \(\tau\)(O\({}_{12}\)-H\({}_{13}\)) modes were strongly affected in going from the monomer C-I to its dimer D\({}_{1}\), due to the inter-molecular H-bonding. In going from the conformer C-I to dimer D\({}_{1}\), the modes \(\
u_{s}\)(NH\({}_{2}\)) and \(\tau\)(C-OH) of the COOH group were found to be enhanced by 20 and 16 cm\({}^{-1}\).  
In the dimeric form D\({}_{1}\), the modes of the two constituent molecules M\({}_{1}\) and M\({}_{2}\) did not show considerable changes in frequencies except in a few cases, namely \(\tau\)(C\({}_{3}\)-C\({}_{14}\)), \(\gamma\)(C\({}_{3}\)-C\({}_{14}\)), \(\beta\)(C\({}_{3}\)-C\({}_{14}\)) and \(\Phi\)(R)-4. However, the IR and Raman intensities changed considerably in many cases (first column of Table 5). Considerable changes in the IR and Raman intensities for different modes of the two molecules of D\({}_{1}\) arise due to the in-phase and out-of-phase coupling between the corresponding modes of the two molecules M\({}_{1}\) and M\({}_{2}\).
## 3 Fishing Gear Type Identification from Automatic Identification System Trajectory Data  
### Fishing Gear Type Identification  
Marine fish generally make periodic or seasonal movements for favored conditions like temperature, pH, dissolved oxygen, food like micro-planktons, and spawning. For example, when there is little tidal range, croakers stay near the bottom floor because planktons sink down; when there is a large tidal range, planktons float around and croakers swim around at middle-depth water. Hence different tidal ranges might require fishing ships to use different fishing gears. Different fishing gear types have different fishing patterns in movements and timings for net setting, waiting, and net hauling [11]. Figure 1 shows the trajectories of a fishing ship on a fishing trip, depicted from the AIS data. A fishing trip is composed of non-fishing activities and fishing activities. In Figure 1, (a) streaming and (b) waiting are non-fishing activities while (c) setting and (d) hauling are fishing activities.  
Figure 1: Trajectories of fishing and non-fishing activities. (**a**) Steaming (non-fishing), (**b**) waiting (non-fishing), (**c**) setting (fishing), and (**d**) hauling (fishing): the black tracks indicate a section with a speed of 6 knots or more and red tracks indicate ones with a speed of less than 6 knots.  
A fishing gear type is indicated by a fishing method requiring special gear. Offshore fishing methods include drift gill net, bottom trawl, longline, midwater longline, and purse seine, and so on. Each fishing gear type shows some unique patterns due to its setting and hauling styles. On the other hand, non-fishing activities such as steaming and waiting are included in all fishing gear types. Despite the existence of such unique patterns in fishing activities, the long sequences of mixed non-fishing and fishing activities for a fishing trip look very complicated and entangled. The complexity and irregularities of trajectories make it hard for fishery monitoring officers to determine the fishing gear types used on fishing ships from the ship trajectories.  
Fishing ships send out AIS messages and such messages are collected and monitored by the fishery authority or vessel traffic service stations. If the fishing gear types of ships are identified from their AIS messages, fishery authorities can control IUU fishing activities and overfishing [20]. Here we propose a method to identify the fishing gear types of ships from their AIS message data.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 3 Fractional Frequency Reuse
### ICIC Techniques  
In order to compare the techniques, two algorithms without fractional reuse of the frequency are also considered: Full Frequency Reuse (NoOp) and Hard Frequency Reuse (HFR). The former has a reuse factor of 1, the latter has a reuse factor of 3, as illustrated in Figure 1, and both transmit with the same power for the whole bandwidth.  
In scenarios with few users, the Full Frequency Reuse (NoOp) may present high data rates given the larger available bandwidth for each cell. However, higher interference is expected, and it may result in low SINR and high Packet Loss Ratio (PLR), especially for edge users. The HFR algorithm is more efficient in reducing interference, as all adjacent cells have disjoint bandwidths. However, each cell has only 1/3 of the available bandwidth, which can severely reduce throughput, depending on the offered load [21].
## 3 Operational Requirements
### Environmental Conditions
#### 3.3.4 Interaction with Other Elements  
The robot must operate safely in the presence of human workers, stopping or swerving to avoid accidents. Proximity sensors and collision avoidance algorithms are essential [53]. In large fields, there may be multiple robots operating simultaneously. A communication and coordination system is necessary to avoid collisions and optimize collective efficiency.