esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Results and Validation
#### 3.3.2 Production Results--Zone 4  
When tested on the most recent production batch, the KNN model for Zone 4 maintained superior performance over the baseline (Figures 19 and 20), achieving an MAE of 0.6758 h and an RMSE of 0.9330 h, compared to the baseline's 0.9612 h and 1.0722 h, respectively. The model also preserved a positive R\({}^{2}\) value (\(\approx\)0.24), confirming that it still explains a portion of the variance in the new data. However, these values represent a considerable drop from the performance observed during validation (R\({}^{2}\)\(\approx\) 0.72, MAE \(\approx\) 0.53 h), suggesting that generalization has weakened.  
This decline may stem from subtle forms of data drift, particularly the introduction of new operating scenarios or routes not previously represented. Unlike Zone 3, the degradation here is not severe, and the model continues to outperform the naive baseline.  
Figure 19: Actual vs. predicted \(\Delta\)time in the production scenario for Zone 4.  
Figure 20: Residuals vs. predicted \(\Delta\)time in the production scenario for Zone 4.  
Therefore, retraining is not strictly necessary at this point, and the decision to trigger a model update should be left to the fleet manager or system administrator, depending on operational priorities and risk tolerance.  
One notable pattern observed in the residual analysis is a consistent overestimation of durations in cases where the actual values are below 2.5 h. This localized prediction bias suggests that the model may not fully capture the behavior of shorter-duration events, potentially due to their underrepresentation in the training set or shifts in their underlying dynamics in the new data.  
Following the pipeline defined in the methodology, two possible courses of action are available:  
* Maintain the current model in production until the next validation cycle, considering that the current performance still surpasses the baseline and no critical degradation is observed.
* Initiate a retraining process, which may involve simply updating the model with the new data or conducting a more detailed revision to assess whether new outliers have emerged or significant behavioral changes in the fleet's operation justify further model adaptation.  
This decision should be made in alignment with operational priorities and the acceptable margin of prediction error in current maintenance planning or route management processes.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 6 Conclusions
### Practical Contributions  
* An automated experimental environment was built and executed capable of collecting, processing, and analyzing more than 440,000 URLs from five university laboratories, generating three large data sets useful for threat characterization in real academic environments.  
* BOOKIE allowed not only the display of cookies but also the prediction of the level of vulnerability to XSS attacks, offering personalized navigation recommendations based on the detected patterns.
* The application of K-Means and DBSCAN made it possible to effectively identify two main user segments based on their browsing behavior, improving the performance of the browser's recommendation system.
* The use of metrics such as Silhouette Score, Inertia, Calinski-Harabasz, and Davies-Bouldin empirically demonstrated the validity and precision of the applied models, with DBSCAN with eps = 10 offering the best cluster separation in the presence of noise.
Moreover, fibre-based NN architecture can be straightforwardly adapted to fibre-based optical communication networks and fibre-based optical-signal-processing methods, which present a mature and well-developed technology. For example, fibre-based optical Fourier transform incorporated into FESNA has enabled the first multi-channel neuromorphic signal processor [22]. Current developments in signal transmission and communications, such as 5G standards, focus on ultrawideband and flexible bandwidth signals [34, 35, 36]. Emerging 6G networks are envisioned to merge intelligent processing within the optical communication network [37]; it is, therefore, a requirement for future neuromorphic processing technology to support such flexible bandwidth signal architectures. Fibre mode-locking is a key to ultra-fast signal-processing as it enables processing over extremely short timescales. Moreover, it paves the way for controlling spectral properties, creating new opportunities in the development of spiking neural networks, which are of major practical importance. Fibre mode-locked laser represents a well-studied technology with ubiquitous dynamics [38; 39] and numerous techniques have been developed to augment and assess its capabilities [40; 41]. Furthermore, as biological neurons are known to exhibit complex frequency-varied responses to different types of stimulus (for example, the spike frequency adaptation effect [42; 43]), which play an essential role in neuronal processing and adaptation, future artificial optical neurons will need to encompass such effects in their design. In this work, we lay the foundation for such technology by proposing the first photonic system, which exhibits a spiking non-linear response in both power and spectral width governed by a power threshold. Here, we demonstrate the first design of a mode-locked fibre laser with non-linear operating regimes in both power and spectrum width. We show that the proposed laser system exhibits a non-linear power-dependent response with a threshold. We unveil the transition between two operating regimes where the signal experiences a change of attenuation coefficients in the power transfer function and the two-branch transfer function in spectrum width. These features are very important for developing a novel type of optical neuromorphic technology with variable bandwidth capability that can address optical communication system requirements and can unlock the ultra-high-speed neuromorphic processing of ultrashort pulses. The unique advantage of the proposed system is a non-linear spectral signal re-shaping dependent on signal power with a clear transition gap between the two stages. Since the transition is governed by a power threshold, the system can be used for spectrum coding as a novel type of neural coding. The presented fibre mode-locked laser system is simple and flexible and can be used in a wide range of applications, including in photonics and medicine, such as self-tuning lasers [44; 45] and smart sensors [46], as well as optical communications [47; 48] and in 5G and 6G networks [22; 49].
# A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
###### Abstract  
Recently, carbon nanotube field-effect transistors (CNTFETs) have attracted wide attention as promising candidates for components in the next generation of electronic devices. In particular, CNTFET-based RF devices and circuits show superior performance to those built with silicon FETs since they are able to obtain higher power-gain and cut-off frequency at lower power dissipation. The aim of this paper is to present a compact, design-oriented model of CNTFETs that is able to ease the development of a complete amplifier. As a case study, the detailed design of a high-gain CNTFET-based broadband inductorless LNA is presented.  
carbon nanotube; CNTFET; low-noise amplifier; LNA; compact model +
Article  
A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Algorithm Security Analysis  
In PFMLP, the key management center is only responsible for key generation and can not access any data. For the key management center, it does not even know what data the client has encrypted with the key, so it cannot collude with other parties to access the data illegally.  
The data received by the computing server is the ciphertext encrypted by the client, and all operations are homomorphic operation without a decryption process. It means that, on the computing server, all data are in the encrypted format, so, even if the server is compromised, the plaintext data cannot be obtained.  
The learning client obtains the key pair from the key management center, and then sends the encrypted gradient data to the computing server; after that, the computing server returns the result that is still in the encrypted format to the client after the calculation is completed. During the whole process, the client cannot access the data of other clients. The only data participated in the process is the data uploaded and the result returned, and they are all in the encrypted format, which can ensure data security.  
If an attacker wants to obtain data by attacking the computing server or a communication channel, he/she can only get the ciphertext. Since we can change the key pair during each iteration, even if the attacker is lucky enough to crack a few rounds of training results, he/she cannot obtain the final result. Even if the attacker is a participant, he/she cannot obtain data from other clients due to the client security analysis described above.
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 2 Related Works
### Problem Areas
#### 2.1.3 Research Goal (Research Aims and Objectives)  
In previous subsections, we have identified some of the limitations of current FRR solutions that can be addressed and therefore offer new solutions that overcome them. Our work focused on the development of a new FRR mechanism concerned with the identified issues. Then the main contribution of this work is the proposal of the so-called Bit Repair (B-REP) algorithm. The B-REP algorithm is designed to repair of all possible failures within the specific network (100% repair coverage) with low calculation complexity.  
The B-REP algorithm allows calculating an alternative path using standard link metrics or ignoring them, which means calculation without considering the metric's limitations. Accordingly, if a path exists, it will be found and used. At the same time with the B-REP mechanism, an administrator can define manually a custom alternative path.  
Several FRR mechanisms use various proprietary solutions to define an alternative route and to transit mechanisms related data. We opted for a standardized solution, namely multicast protocol-bit index explicit replication (BIER) [50]. This protocol uses a standardized BIER header (Figure 4) that contains a special field called bit-string (B-S, Figure 5). That allows us to use a header and its fields to define an alternative path as well as to transfer user data.  
Bit-string is an array of bits, in which each bit indicates exactly one specific router in the BIER domain [52; 53]. In other words, bit-string is an array data structure that efficiently stores router related information. The bits are arranged from the least significant bit (LSB) to the most significant bit (MSB). The use of bit-string in the FRR area will allow defining effectively an alternative backup path in the event of a link or node failure. The B-S value with the specified bits represents the routers through which the packet will be routed in the event of an error. It was the idea of using bit-strings that inspired us to develop the B-REP mechanism. A similar idea was used by greedy algorithms designed for fault-tolerant multicast delivery in the hypercube [54; 55; 56]. The authors suggested using a bit address to select the optimal hop-by-ho routing.  
Figure 4: BIER header [51].  
Figure 5: Bit-String.
## 5 Emerging Trends in FSO Communication  
FSO communication is evolving rapidly, integrating cutting-edge technologies to overcome conventional limitations and enhance performance. This section delves into the latest advancements shaping the future of FSO systems. Key trends include the incorporation of emerging technologies, which bolster system capabilities and adaptability. Enhanced FSO transceiver design aims to improve reliability and efficiency. The integration of quantum cryptography offers unprecedented security for data transmission. OAM-aided FSO systems introduce a novel method for multiplexing, significantly increasing data throughput. Additionally, the application of AI in FSO systems promises to optimize performance through intelligent decision-making and adaptive strategies. Together, these innovations are poised to revolutionize FSO communication, making it a more viable and robust solution for modern communication needs [2].
# Symmetry Criterion and Far-Field Control of Photonic-Crystal Surface-Emitting Lasers
## 3 Results and Discussion
### The Impact of Vertical Epi-Structure  
In addition to in-plane PhC structure or hole shape, the air filling factor, hole depth, vertical epi-structure, and even period number also have effects on the FFP, although they are not as direct as the hole shape. As shown in Figure 4, when the air filling factor was reduced from 20% to 13%, the FFP changed from the previous almost-circular single lobe in Figure 4a to the irregular shape with lower central intensity in Figure 4b. This trend is very similar to the structure with circular holes, although its FFP is not a ring as unique as the latter. This illustrates that the decrease in the air filling factor reduces the degree of asymmetry of the structure and then hence the modulation effect on the optical field. Therefore, the central intensity is still affected by the negative interference of the electric field to a certain extent. However, when the hole depth is increased from 160 nm to 320 nm, the central intensity begins to increase, and the spot is transformed towards a better single lobe as shown in Figure 4c, while the air filling factor remained unchanged at 13%.  
While all parameters in the PhC layer are the same as those of Figure 4b, including air filling factor, depth, etc., only the air claddings are replaced with an epi-structure as sketched in Figure 1a. The corresponding results are shown in Figure 4d-f. The only variable parameter among them is the period number of PhC, which is 31, 61, and 101, respectively. With the increase in period number, the far field eventually evolves from multi-lobes to an approximately single lobe, which is similar to the result shown in Figure 4b. The introduction of epi-structure lowers the optical confinement factor in the PhC layer and coupling coefficient, and the modulation effect of PhC on electromagnetic field becomes weaker. This is the reason why similar FFP appears at 101 periods, which is much larger than that in the PhC slab. This means that a larger period number in the epi-structure is required if a similar FFP in the PhC slab is wanted.  
Figure 4: The FFP and NFP of devices with right isosceles triangular air holes. The former three groups are PhC slab clad by air, with the same period numbers of 31, but with various air filling factors and hole depths of **(a)** 20%, 160 nm, **(b)** 13%, 160 nm, **(c)** 13%, 320 nm, respectively. The latter three groups are PCSELs clad by dielectric layers, with the same air filling factor of 13% and hole depth of 160 nm, but various period numbers of **(d)** 31, **(e)** 61, **(f)** 101, respectively.
# Pro-Pre and Postbiotic in Celiac Disease
## 3 Microbiota Is Altered in CD
### Changes of the Microbiota of Healthy Subjects at GFD  
The GFD in the healthy subjects influence the gut microbiota reducing the _Bifidobacterium_, _Clostridium lituseburens e Faecali bacterium prausnitzii_ and increasing the _Enterobacteriaceae_ and _Escherichia coli_[18]. One study showed that the bacterial profile remained relatively stable in healthy individuals on GFD but during the GFD period decreased _Veillonellaceae_, _Ruminococcusbromii_ and _Roseburiafaciens_. Whereas, _Victivallaceae_, _Costiridiaceae_, ML615J-28, _Slackia_ and _Coriobacteriaceae_ increased were described [19]. Moreover, using a low gluten diet, it is possible to observe an increase in unclassified species of _Costiridiales_ and _Lachospiraceae_. While, _E. hallii_ and _A. hadrus_, _Dorea_ and _T. blautia_, two species of the _Lachospiraceae_ and four species of _Bifidobacterium_ decreased [20]. The GFD influences the gut microbiome non-CD subjects increasing _E. Coli_, ML615J-28, _Slackia_, _Victivallaceae_, _Enterobacteriaceae_, _Costiridiaceae_, _Coriobacteriaceae_ and unclassified specie of _Costiridiales_ and _Lachospiraceae_, decreasing _C. litusbureuse_, _Lactobacillus_, _F. prasuntizii_, _Bifidobacterium_, _Dorea_, _B. werlaeae_, _Lachospiraceae_, _A. hadrus_, _E. hallii_, _Veillonellaceae_, _R. bromi_, _R. facies_.  
In conclusion, CD patients present a different microbiome respect to normal subjects in the acute phase of the disease, while eating gluten. GFD can modulate gut microbiota of CD patients respect to healthy subjects, and can induce some alterations in the microbiota in non-CD subjects at GFD. Most studies on gut microbiota in CD are descriptive, including different categories of patients on GFD, GCD, at risk both with, and without, symptoms. From these studies it is difficult to determine whether the onset of CD is due to the alteration of microbiota or whether this is secondary to the intestinal damage present in CD.  
In this context, pre-probiotics could have the characteristics to be useful to prevent or even ameliorate dysbiosis in CD. Post-biotics have not been tested in CD yet, but from the in vitro data they seem to be able to prevent some gliadin and gliadin peptides effects.
## 4 Experimental Results and Analysis
### Performance Analysis and Comparison
#### 4.1.2. Pest Classification Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Acc \%** & **F1** & **P** & **R** & **Y** & **T** \\ \hline
[12] & 61.93 & 59.2 & - & - & 2020 & Saliency + CNNs \\ \hline
[13] & 86.95 & - & - & - & 2023 & ResNeXt-50 \\ \hline
[14] & 55.43 & 54.18 & - & - & 2022 & Residual Network (DFF-ResNet) \\ \hline
[15] & 55.24 & 54.18 & - & - & 2019 & Residual network (FR-ResNet) \\ \hline
[16] & 52.32 & - & - & - & 2020 & ExquisiteNet \\ \hline
[17] & 71.48 & 64.06 & 65.85 & 63.22 & 2024 & Pre-trained CNN + genetic algorithm \\ \hline
[19] & 82.5 & - & - & - & 2023 & 3 pre-trained CNNs \\ \hline
[20] & 73.9 & 73.6 & - & - & 2023 & CNN + EFLM + AFFM \\ \hline
[21] & 73.29 & - & - & - & 2021 & CNN+ spatial attention \\ \hline
[22] & 74.69 & 74.36 & - & - & 2022 & Self-supervised transformer \\ \hline
[23] & 75 & - & - & - & 2023 & Attention aggregating transformer \\ \hline
[24] & 74.20 & - & - & - & 2023 & CNN + improved Vision Transformer \\ \hline
[25] & 78.15 & - & - & 69.24 & 2025 & Channel-Enhanced and Multi-Scale \\ \hline
[27] & - & 57.5 & 57.4 & - & 2023 & Improved yolov5 architecture \\ \hline
[28] & - & - & 85.55 & 84.25 & 2025 & Modified yolov7 \\ \hline
[30] & 65.6 & 60.3 & 60.9 & 59.7 & 2023 & CNN ResNet + attention Vision Transformer \\ \hline
[31] & 75.74 & - & - & - & 2024 & CNN + Transformers \\ \hline
[32] & 72.31 & - & - & - & 2023 & Fine-tuning \\  & & & & & 2023 & EfficientNets + SVM \\ \hline
[33] & 68.34 & 68.34 & 68.37 & 68.33 & 2024 & Visual regenerative fusion network \\ \hline
[34] & 60.7 & 59.6 & - & - & 2020 & Multiple-Instance LCNN \\ \hline
[35] & 81.7 & - & - & - & 2022 & InceptionV3 \\ \hline
**Our method** & 88.34 & 87.86 & 88.67 & 86.34 & - & - \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 10: Generated confusion matrix showing the outcome of the proposed approach. The diagonal-block distribution of the confusion matrix shows the challenge faced by the model in making correct subclass predictions in the case of samples generated from the same original class which are characterized by common visual features. In the enlarged view, all the categories falling in each sub-block along the diagonal actually belong to the pest class highlighted in red. The color gradation shows the magnitude of the classification errors as follows: the darker the color, the higher the number of misclassified instances.
## 2 Related Work  
### CNN AE Method for PSC Box Girder Bridges  
The following study is related to recognizing the corrosion of PSC box girder bridges. In [6], a deep learning approach for determining whether damage exists is presented in order to ensure the stability of the tendon, which is the most crucial component of the PSC bridge. They predict the severity of damage using simulated data from nine accelerometers and a convolutional auto-encoder (CAE). However, this approach has a restriction of only employing an indirect signal because the damage is assessed by putting an acceleration sensor on the bridge and monitoring the difference in shaking when a vehicle passes. For this reason, it displays substantial outcomes only when the damage is very serious.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Comparative Analysis of MAC Layer-Based UWAC Full-Duplex Protocols
#### 3.4.3 Energy Efficiency  
Pulse position coding reduces energy consumption in FD-CDMA-based protocols [46] by providing a special window of fixed length in which each of the data pings mustoccur. The BD-FD-MAC [36] protocol sends and receives ID-based control and data signals, which eliminates unwanted collisions and reduces the power consumption of UW sensor nodes. Transmission of ACK/NACK control packets in conventional MAC protocols takes a long time to propagate and consumes a high power. To solve this issue, IBFD-MAC [35] does not send any ACK packets to any active transmission and instead executes it correctly. If the transmission fails, the NACK packet is returned to the sender for retransmission by the IBFD-MAC. This approach saves lots of power. Unexpected collisions, hidden and exposed terminal issues increase power consumption between UW nodes in any MAC protocol, which FD-DA-CSMA [30] efficiently solves. For transmission, the system uses 10 W, and for reception, it uses 80 mW. FS-MACA [37] also uses two different channels for simultaneous transmission of control and data packets, as well as potential scheduling to prevent collisions, resulting in a more energy-efficient system. The FDCA-MAC [34] estimates average power consumption using the Sea Swarm network, which consumes considerably less power due to its collision avoidance algorithm. The TFH-MAC [69] is a highly efficient technique that continuously lowers power consumption as network load increases. The E-FD-MAC protocol [38], which is based on ID, effectively increases throughput and delays by using the FD communication channel to minimize power consumption. Although the FDCP-MAC protocol [39] is designed to reduce collision rates, collisions still occur as the network load increases, implying that it is possible to save a significant amount of energy on UW nodes.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 2 Materials and Methods  
### Overview of the Framework  
Figure 1 shows the overall structure proposed in this study. The framework includes four steps of photoacoustic image (PAI) generation, deep-learning image reconstruction, deep-learning image segmentation, and model validation and evaluation, which are comprehensively applied to photoacoustic image processing. Firstly, low-resolution photoacoustic images (LR-PAIs) were generated by k-Wave [32] simulation. Secondly, the generated photoacoustic images were further reconstructed using the improved EDSR-M network to obtain high-resolution photoacoustic images (HR-PAIs). Subsequently, a deep-learning model (DeepLabV3+ [30]) was used to segment the images before and after reconstruction to assist in verifying the reconstruction effect and assessing the accuracy of the reconstructed images. Finally, the combination of deep-learning models was evaluated and validated.