esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### P2P-Assisted HTTP Video Streaming  
P2P-assisted HTTP streaming technologies integrate traditional client-server architectures with P2P collaborative mechanisms to effectively alleviate the load on central servers. Such systems typically employ hybrid architectures where servers handle initial content distribution and node coordination, while the P2P network manages horizontal content propagation and load balancing [28]. Building upon this, the SmoothCache system [31] represents an early and influential work in P2P-assisted HTTP streaming. Specifically designed for HTTP Live Streaming (HLS) protocol characteristics, SmoothCache developed a dynamic transmission scheduling algorithm based on segment urgency. Its core technical innovations include an application-layer congestion control mechanism that dynamically adjusts transmission priorities according to network conditions, an intelligent neighbor selection algorithm that identifies optimal upload nodes based on Round-Trip Time (RTT) and available bandwidth, and a proactive caching strategy that anticipates user demand and pre-caches popular content. Experimental results confirmed that SmoothCache significantly reduced server bandwidth consumption while maintaining a positive user playback experience in high-concurrency scenarios.  
Hybrid P2P-CDN architectures have further expanded upon this concept [32]. The ALIVE system, proposed by [PERSON] et al. [32], integrated Network Function Virtualization (NFV) and edge computing technologies to achieve dynamic Quality of Experience (QoE) optimization. Key innovations in ALIVE included a machine learning-based node matching algorithm capable of dynamically selecting optimal transmission paths based on network conditions and user preferences [33], a distributed transcoding mechanism that performed real-time transcoding at edge nodes to adapt to diverse device requirements, and multi-tenant resource scheduling through virtualization for efficient resource sharing.  
More recent research, exemplified by the RICHTER system [34], further introduced Self-Organizing Map (SOM) algorithms and online learning mechanisms to enable intelligent resource matching under large-scale concurrent requests. This system employed a collective optimization strategy, clustering user requests with similar characteristics to significantly enhance scheduling efficiency and resource utilization. Despite these advancements, existing P2P-assisted HTTP streaming systems still exhibit notable limitations. These include their continued reliance on central coordination nodes, which introduces single points of failure, inherent real-time limitations due to the HTTP-based transport protocol, and limited capabilities for optimizing latency in long-distance, cross-regional transmissions [35].
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 5 Results and Discussion
### CPU Utilization  
One of the AWS services we used in this implementation was Cloudwatch, which is for monitoring and managing services. Cloudwatch shows the CPU average utilization for AWS resources, the implemented IoT devices in this case. The objective was to validate the claims in [3, 9] that AIS solutions are lightweight in realistic settings. To this end, the CPU utilization was measured in each system for both the NPS algorithm and MNSA as shown in Figure 7. The NPS algorithm required less CPU utilization by up to 36.6% than the MNSA, hence, it is more suitable for lightweight IoT systems under all conditions depicted by all five systems. The results also showed that the CPU utilization dropped systematically when moving from system 1 to system 5 for both NPS algorithm and MNSA. We saw a decrease of 12.7% and 10.3% in CPU utilization for the NPS algorithm and MNSA, respectively. By increasing the volume size of the IoT device, we can decrease the CPU utilization in the device, which is one of the main objectives when implementing security methods in IoT devices.  
Figure 6: Malware detection performance results for the NPS algorithm and MNSA using AWS.
## 3 Results and Discussion
### Stability of the Nanofluids  
The stability of nanofluids is a significant feature that may decrease their applicability in engineering processes. Its characterization has been experimentally addressed in the literature through zeta potential analyses, UV-visible spectroscopy, electron microscopy, or sedimentation techniques, among others [38]. Our group has previously verified that light scattering measurements of average Z-size against time after preparation are an effective and consistent method for characterizing the stability of a dispersion of nanoparticles [24; 39].  
Figure 3 shows the time variation of the average Z-size for various 0.10 wt% W- and PG:W-based nanofluids. Figure 3a evidences quasi-constant values for both different-sized cluster aqueous nanofluids under shaken conditions during the 16 days of analysis. Nevertheless, the static aqueous samples show a size decrease with time, which is more pronounced for the sample that contains larger-sized clusters (192 nm). Figure 3b also shows a nearly constant value for both shaken PG:W-based nanofluids. However, the static samples show a less-marked decrease with time, which is slight for the nanofluids that include smaller-sized clusters (97 nm). Therefore, the stability analysis evidences the fact  
Figure 2: (**a**) XRD patterns of clusters A, B and C, (**b**) Raman spectra obtained with a 633 nm excitation wavelength, and (**c**) field-dependent magnetization of cluster A, registered at 300 K.  
that moderately stable nanofluids with readily recovered initial-dispersion conditions were achieved. The lowest-sized clusters remain better dispersed over time and the increasing amount of propylene glycol in the base fluid seems to favour the stability of the dispersions, as can also be observed visually.
## 3 Materials and Methods
### Model and Evaluation Process  
The proposed method combined generative adversarial networks (GAN) and SegNet, two deep learning models that show promising results in accurately segmenting pulmonary nodules in CT images. The GAN was used to generate additional training data for the SegNet model, while SegNet was used for the actual segmentation task. This approach improves segmentation accuracy and reduces the need for large amounts of manually annotated data. The proposed GAN-SegNet model outperforms other deep learning models such as VAE, Autoencoder, and U Net in terms of accuracy and AUC. However, the training process can be time-consuming without a high-end GPU. The GAN is a deep learning architecture composed of a generator network and a discriminator network, trained together in a minimax game to produce high-quality synthetic data. SegNet is an encoder-decoder architecture specifically designed for pixel-wise classification, capable of accurately segmenting complex structures in medical images. The proposed model \"GAN-Enhanced SegNet\" or \"G-Net\" for short, reflects the fact that GAN enhances SegNet's performance by generating additional high-quality training data. Overall, the GAN-SegNet model presents a promising approach for the automated detection and segmentation of pulmonary nodules in CT images, with potential applications in clinical settings.  
There is also a significant difference in segmentation while using GAN-based images. This is evident from the evaluation table in Table 2. We mainly used three parameters for evaluation, namely, dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. DSC is a measure of overlap between ground truth and segmented results. Sensitivity shows how much positive proportions are measured correctly. Positive results are represented as a proportion as PPV. The parameters are evaluated from confusion matrix as follows:  
\[\text{DSC}=2\text{TP}/(\text{FP}+2\text{TP}+\text{FN}) \tag{2}\]  
\[\text{PPV}=\text{TP}/(\text{TP}+\text{FP}) \tag{3}\]  
\[\text{Sensitivity}=\text{TP}/(\text{TP}+\text{FN}) \tag{4}\]  
Then, the receiver operating characteristic (ROC) curve of SegNet, during training the model with GAN and without GAN, was also evaluated. The area under the curve was calculated with the ROC curve.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training** & **Benign** & **Augmented** & **Malignant** & **Augmented** \\ \hline SegNet & 450 & 1280 & 430 & 1148 \\ \hline GAN & 225 & 640 & 215 & 574 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training the GAN—original and augmented images used for training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DSC** & **PPV** & **Sensitivity** \\ \hline SegNet + GAN + augmentation & 0.87 & 0.86 & 0.89 \\ \hline SegNet + GAN & 0.86 & 0.85 & 0.87 \\ \hline SegNet + augmentation & 0.80 & 0.79 & 0.82 \\ \hline SegNet & 0.78 & 0.77 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of SegNet + GAN.
## 2 Materials and Methods
### Data Analysis  
The motion data, EMG-signal, and ground reaction force were measured when synchronized. The data from the exoskeleton and the screwdriver were synchronized with the motion data through predefined events during data processing. To divide the continuous data into motion cycles, the start and end times of each motion cycle were determined manually and visually based on the motion data captured by Vicon. One motion cycle started when the participant had just reached the first object (the wooden component in task T1 or the screw in task T2) to be picked up and ended when the dominant upper arm returned to its neutral position (0\({}^{\circ}\) elevation) (also see Figure 3). For the analysis of the EMG-signal and push force, one cycle was then split into three main motion phases. The start and end of the phase arm lifting and lowering were detected by the local minimum and maximum of the curve of the shoulder elevation angle. The starting point of the screw phase (trigger pressed) was detected when the screwdriver battery-current threshold of 2 A was exceeded. If the screwdriver electrical current dropped below this threshold, it was detected as the end of the screw phase (trigger released).  
The collected EMG-signals were processed in the ProEMG (myon AG) software with a 20-200 Hz Butterworth bandpass filter 4 th order, rectification, and smoothing with a 300 ms root mean square. For the normalization of EMG-signal amplitudes, the highest signal amplitude from the three MVC recordings was used. The EMG-signals were then divided into 10 motion cycles and each cycle was time normalized to a 0-100 percentage of the motion. For each participant, an average EMG curve was calculated from the 10 cycles. The mean EMG corresponded to the mean amplitude of the average curve. The mean EMG of each motion phase was then calculated from the sliced EMG curve. Because of the small sample size, an intra-individual comparison of the mean EMG was performed instead of a statistical analysis.  
The calculation of the push force \(F_{pu}\) was based on the vertical component (\(F_{GRF,\,v}\)) of the measured 3-axis ground reaction force as follows: \(F_{pu}=F_{GRF,\,v}-G_{P}+G_{sc}\). Here, \(G_{sc}\) is the weight force of the screwdriver and \(G_{P}\) is the weight force of the participant including the exoskeleton, screwdriver, and measurement systems. This can be seen as a good approximation of the push force, especially during the screw phase. The mean value of each of the 10 cycles was calculated and displayed as exemplary for participant P3. All further analyses of the push force were performed during the screw phase only by calculating the mean value of the push force for each of the 10 cycles and plotting it as a boxplot. The postprocessing and preparation of figures was conducted in MATLAB R2020b.  
Due to a malfunction in the screwdriver dataslogger, the start and endpoints of the screw phase of participant P2 were not detected. Thus, the measurement data of the push force were not evaluated and P2 was excluded for the evaluation of the push force during the screw phase.
## 5 Improved Setup with a Fully Active Base Selection
For the other 50% of gates, [PERSON] is able to send her measurement to [PERSON] with a probability of one-half (in total, 25% of gates), namely, in those instances in which her base coincides with [PERSON]'s randomly selected base by a chance, while for the remaining 25% of gates, [PERSON] will receive nothing (his detectors will remain silent), resulting in a four-fold drop in the effective key rate with respect to no eavesdropper. Even though this higher loss introduced by [PERSON], equivalent to 6 dB, is, in principle, easier to for [PERSON] and [PERSON] to detect, the situation is still unsatisfactory because (a) [PERSON] may be able to compensate for the  
Figure 4: Receiver for a BB84 protocol with an exclusive active receiver; the receiving basis is determined by the quantum random number generator QRNG, which flips the mirror (M) between two possible angles, each of which reflects _all_ of the light into one and only one measurement basis. loss (as explained before), and (b) [PERSON] can obtain complete knowledge of the key without being detected. In the case of a **pulsed blinding light**, very strong laser pulses keep the detectors thermally blinded. In the original DBA described in Refs. [4; 7], which was performed on the setup shown in Figure 2, it was noted that such strong pulses will cause simultaneous detections in all affected detectors. For this reason, [PERSON] keeps the blinding pulses outside of gates so they do not generate extra in-gate coincidences and sends them frequently enough to keep all the detectors blinded at all times. However, when this attack is applied to the setup in Figure 4, [PERSON] is not sure which base is blinded because the bases are selected at random with a probability of one-half; therefore, she needs to increase the blinding rate by at least twice. Even so, because of the binomial statistics of the base selection, a base might occasionally escape blinding and generate an extra in-gate coincidence when hit by a fake state. While the details of this behavior need to be investigated further, it is safe to assume that in the best scenario for [PERSON], she will be able to achieve blinding and retrieve the full key, albeit at the expense of a 6 dB loss caused merely by the random base choice. The unavoidable price paid for the pulsed blinding causes out-of-gate coincidences between the two detectors in the active (selected) base.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 8 Performance Evaluation
### Experiment on Feature Selection
#### 8.2.3 Performance of Feature Selection  
We evaluated the feature selection method on the fault detection model performance, with results listed in Table 7. Although the CRFS maintains fewer selected features, it offers similar accuracy to the model that utilizes all features. The CRFS outperforms the accuracy of CS with its larger number of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Server** & **Component** & **Features** & **Detection Time (ms)** & **Packet Size (KB)** \\ \hline \multirow{6}{*}{Edge} & \multirow{2}{*}{Cooler} & CRFS (5) & 7.31 & 95.9 \\  & & All (17) & 26.74 & 343.78 \\ \cline{2-5}  & & CRFS (7) & 9.53 & 194.16 \\  & & All (17) & 27.63 & 343.78 \\ \cline{2-5}  & \multirow{2}{*}{Pump} & CRFS (7) & 11.85 & 240.56 \\  & & All (17) & 28.21 & 343.78 \\ \cline{2-5}  & \multirow{2}{*}{Accumulator} & CRFS (6) & 8.99 & 189.32 \\  & & All (17) & 27.45 & 343.78 \\ \hline \multirow{6}{*}{Cloud} & \multirow{2}{*}{Cooler} & CRFS (5) & 152.56 & 95.9 \\  & & All (17) & 261.52 & 343.78 \\ \cline{2-5}  & \multirow{2}{*}{Valve} & CRFS (7) & 168.75 & 194.16 \\  & & All (17) & 288.35 & 343.78 \\ \cline{2-5}  & \multirow{2}{*}{Pump} & CRFS (7) & 180.14 & 240.56 \\  & & All (17) & 293.11 & 343.78 \\ \cline{2-5}  & \multirow{2}{*}{Accumulator} & CRFS (6) & 157.13 & 189.32 \\  & & All (17) & 272.52 & 343.78 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of server types.  
Figure 7: Detection times by the servers.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 1 Introduction
Motivated by the fact that melody originates from the predominant pitched source having comparatively higher chroma energy than others, and its salience usually decreases with time over note duration, a chroma-level note tracking and pitch mapping-based melody extraction method is proposed in this paper. Specifically, the chromagram is first constructed by HPCP. Then, chroma-level notes are tracked by dynamic programming (DP), which takes into account of both salience and smoothness. Next, pitch matching is carried out at two levels: coarse pitch mapping according to note-level salience, followed by fine tuning with the modified Euclidean algorithm. Finally, voicing detection based on the salience of the fine-tuned pitches of each note is done to determine the presence of melody. Experimental results show that the proposed method achieves good performance at different signal-to-accompaniment ratios, and can accurately track the detailed dynamic changing over each note for ordinary pitch modulations. Moreover, there is no pitch shifting between different octaves within one note. The proposed method differs from our previous work in the following two aspects. (1) Their processing procedures are different. In the previous work, short-time Fourier transform is computed first, then multiple pitches are estimated based on modified Euclidean algorithm, and melodic contours are created and selected in the end. In contrast, chroma-level notes are first estimated by HPCP and dynamic programming, then pitch mapping, fine tuning, and voicing detection are implemented to get the final melodic pitch sequence in this work. (2) The modified Euclidean algorithm is employed for different purposes. It is used for multi-pitch estimation in the previous work, while it is for pitch fine tuning in this one. This method is also different from [PERSON] and [PERSON]'s in that it outputs the melodic pitches instead of pitch classes. The proposed method first adopts HPCP feature to measure the salience of melody at the chroma level. Then it obtains the octave information back by the pitch mapping. The main contributions of this paper include: (1) a new architecture for melody extraction (i.e., chroma-level note tracking followed by accurate pitch mapping) is proposed, which is different from the general architecture with multi-pitch estimation plus pitch trajectory tracking or selection; (2) pitch mapping is accomplished by two steps, coarse mapping and fine tuning, to follow the dynamic changing of melody; (3) the modified Euclidean algorithm is introduced to fine tune the coarse pitches.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 6 Experimental Assessment
### Complexity/Cost Analysis of the Proposed MB(oSDM) S-Bvt
Specifically, this increase is mapped with the cost term of using BPFs (\(2\times\frac{CBPF}{B}\)), which are static filters using very cheap technology of the order of few hundred euros. The seemingly extra cost of deploying the MB(oSDM) S-BVT is justified by the substantial improvements it brings to the optical network's capabilities. In particular, the increased available spectral resources and the dynamic capacity scalability by enabling multiple slices in different bands beyond C-band. It is also worth to point out that due to the higher-cost of technology beyond C-band it will exist a trade-off between performance/capacity and cost. This trade-off introduces a strategic decision point that network planners and operators will carefully evaluate from a holistic network perspective before taking specific actions.
## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Proposed Methodology  
The suggested method includes 2 algorithms: one for network routing and the other for selecting the CH. As JILFO and CH select the ideal route between the BS and CH, ICOA is utilized to determine appropriate sensors. Via the JILFO-generated channel, following that, the CHs send the data obtained to the BS. The overall process of the proposed methodology is shown in Figure 2.
## 3 Results and Discussion
### Live/Dead Experiment (Control, 30 s, and 30 s with CAT)  
The transferred CAP generates RONS, including hydroxyl, atomic oxygen radicals, and nitrogen species, as shown in Figure 3. RONS-induced death of cells is a strategy for cancer treatment [32]. At low levels, RONS play crucial roles during redox homeostasis  
Figure 4: (**a**) The image of transferred cold atmospheric plasma, (**b,c**) thermal images captured by the IR camera.  
Figure 5: (**a**) Cell culture and collection of L929 and B16F10 cells. (**b**) Cell viability of L929 and B16F10 cells before and after plasma treatment. (**c**) B16F10 cell morphology was imaged by optical microscopy at different magnifications before and after transferred CAP treatment. (* \(p\) < 0.05).  
and in biological signals. However, high levels of RONS are able to break the balance, causing irreversible oxidative damage to lipids, carbohydrates, proteins, and DNA. RONS can be catalytically decomposed into water and oxygen by CAT, which defends cells from RONS oxidative damage [32]. CAT is a biological enzyme that plays key roles in cellular antioxidant defense mechanisms precipitated by the accumulation of RONS. To further examine the CAT and CAP relationship on B16F10 cancer cells, CAT was added to melanoma cancer (B16F10) cells before transfer-CAP treatment in the following cell viability test. In the live/dead study, 1 mg CAT/50 mL was pre-added to the cell culture medium for the co-culture of B16F10 cancer cells, and then cells were treated with transferred CAP. As shown in Figure 6a, the fluorescent-green represents live cells and red represents dead cells, which differ in the two treated groups. In the control group (B16F10 cells plus fluorescent dyes), 30 s of CAP treatment, leads to cell death. In the group after co-culture with CAT, it can clearly be observed that CAT might preserve cells alive even after treatment with transferred CAP. In addition, the MTT assay was used for further quantification (Figure 6b). The MTT data suggest that CAT adds potential protection to the transferred CAP resulting in a higher cell viability, compared to cells treated by the transferred CAP without CAT (Figure 6b). Furthermore, Image J software was used to quantitatively calculate green live cell fluorescent signals (Figure 6c). As shown in Figure 6b,c, the transferred CAP-treated group given CAT had higher cell viability than that of the transferred CAP-treated group without CAT. The addition of biological CAT is recognized to be a mechanism of cellular defense against RONS. Its function helps to balance the amount of cell RONS, whereas an imbalance between RONS and RONS-scavenging enzymes can lead to an event called oxidative stress [33].