esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Results Analysis
#### 4.5.3 Comparison with State-of-the-Art  
For fairness, each baseline is quoted on its native dataset exactly as reported in the originating paper; re-training all methods on a unified dataset is left to future replication studies. Table 3 offers a comparative summary of several Federated Learning (FL) approaches, spanning diverse datasets and highlighting each method's achieved accuracy as well as its computational or security implications. TrustBandit, for instance, demonstrates moderate accuracy (75.64% with 40% malicious clients) but suffers from elevated cost when data is highly heterogeneous. In contrast, CONTRA and Client Selection in FL obtain higher accuracies on standard benchmarks like MNIST or CIFAR-10 (up to 87.4% and 87%, respectively), yet they exhibit increased overhead and remain sensitive to adversarial behaviours. Approaches such as FSL and FedCS focus on performance improvements (e.g., 76.83% on CIFAR-FS and 79% on CIFAR-10) but have not explicitly addressed adversarial robustness or efficient scalability.  
Meanwhile, RCFL balances accuracy in the 75-82% range across MNIST and CIFAR-10 but lacks rigorous security threat evaluations. Although it improves accuracy to approximately 87% in CIFAR-10, it introduces an overhead of approximately 1.3\(\times\) while also raising concerns about the readiness for real-world deployment. By comparison, our proposed Fed-DTB achieves 86-88% accuracy under varying client configurations (10-100 clients) and demonstrates resilient performance in the face of malicious or low-performance clients. Despite a minor overhead increase, Fed-DTB uniquely integrates robust aggregation and adaptive weighting, thereby offering stronger protection against poisoning attacks. Fed-DTB is dataset-agnostic. Any researcher can retrain it on further benchmarks by using the same configuration template provided in our experiment files. Detailed instructions will be released once the paper is accepted.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **25 Clients** & **50 Clients** & **100 Clients** \\ \hline Final Accuracy & 0.8603 & 0.8724 & 0.8616 \\ Final Loss & 0.7602 & 0.6820 & 0.7183 \\ Final Precision & 0.9037 & 0.9070 & 0.9063 \\ Final Recall & 0.8603 & 0.8724 & 0.8616 \\ Final F1 & 0.8826 & 0.8782 & 0.8777 \\ Final ROC-AUC & 0.9899 & 0.9992 & 0.9975 \\ Total Excluded Clients & 16 & 31 & 56 \\ Average Training Time (secs) & 39.5981 & 66.1915 & 160.7224 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics for different client configurations.  
Figure 22: The average time distribution for configurations of 100 clients.
# Evaluation of Non-_Saccharomyces_ Yeast for Low-Alcohol Beer Production
## 3 Results and Discussion
### Microbial Analyses
#### 3.1.2 Phenolic off Flavor Production  
Ferulic and p-coumaric acids are the predominant phenolic acids in brewers' wort. They are extracted from plant materials (primarily malt) during the brewing process. These compounds can be decarboxylated by yeasts with adequate enzymatic abilities to form 4-vinylguaciol and 4-vinylphenol, respectively. These compounds bestow a beer with clove, spice, smoky, phenolic, medicinal, and spicy aromas [37]. In _S. cerevisiae_, the ability to decarboxylase these compounds is regulated by the PAD1 and FDC1 genes [38]. Although the used US-05 yeasts were supposed to be a negative control and not produce phenolic off-flavor, the obtained results show that they were POF (+) for ferulic acid but not for p-coumaric acid (Table 1). This might have been caused by the high temperature of cultivation (30 \({}^{\circ}\)C), which is much higher than the range suggested by the manufacturer (18-26 \({}^{\circ}\)C) [39]. Overall, the POF test using ferulic acid was found to be positive for WA1 and WO1 and inconclusive for KL1 and NI1 (Table 1). _W. anomalus_ was reported by other authors to produce these off-flavors. [PERSON] et al. [40] report a similar ability of _Z. meryae_, but the strains used in this study lacked the ability to produce these compounds.  
Regarding p-coumaric acid, very few strains showed the ability to degrade this compound. It was found only for WO1 and WA1. These strains were also found to degrade ferulic acid. No ability to reduce this compound was found for control strain T-58 (Table 1). Similarly to the results obtained by [PERSON] et al. [41], the ability to utilize p-coumaric acid was scarcer than for ferulic acid. Many wine-isolated yeast species typically have the ability to utilize these compounds [41; 42].
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO
### Demodulation of TEO  
The accurate capture of wave heads is one of the important factors in determining the accuracy of positioning. The waveform obtained by decomposition using the improved CEEMDAN algorithm does not fully identify the fault traveling wave head. At the same time, TEO is a nonlinear operator that can track the real-time changes of the measured signal waveform and plot the envelope of the locally adjacent sampling points of the modal component waveform with good resolution. This is a highly suitable processing method for detecting the wave head of traveling wave faults in transmission lines.  
The TEO demodulation process involves the instantaneous amplitude and instantaneous frequency of the signal.  
\[s(t)=a(t)cos[\phi(t)]=a(t)cos\bigg{[}\omega_{c}t+\omega_{m}\int_{0}^{t}q(\tau)d \tau+\theta\bigg{]} \tag{17}\]  
where \(a(t)\) is the time-varying amplitude; \(\phi(t)\) is the time-varying phase; \(\theta\) is the initial phase; \(q(\tau)\) is the normalized FM signal; \(\omega_{c}\) is the carrier frequency; \(\omega_{m}\) is the maximum frequency deviation.  
\[\omega(t)=\phi(t)=\frac{d\phi(t)}{dt}=\omega_{c}+\omega_{m}q(t) \tag{18}\]  
The energy operator of the single component signal is given in Equation (19).  
\[\psi\big{[}s^{\prime}(t)\big{]}=a^{2}(t)\omega^{4}(t) \tag{19}\]  
According to Equation (19), the instantaneous amplitude of signal \(s(t)\) can be obtained as follows:  
\[|a(t)|=\frac{\psi[s(t)]}{\sqrt{\psi[s^{\prime}(t)]}} \tag{20}\]  
The instantaneous frequency of the signal \(s(t)\) can be obtained from (20) as:  
\[\omega(t)=\frac{\psi[s(t)]}{\sqrt{\psi[s^{\prime}(t)]}} \tag{21}\]  
The TEO is used to process the single-component signal of the fault-traveling waveform after improved CEEMDAN decomposition, which can effectively detect the change of frequency component energy in the single-component signal. When applied in transmission line fault ranging, it can accurately calibrate the moment of sudden waveform change.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 2 System Model
### Asynchronous Random Access Protocol
Thus, in Figure 1, the first SIC step consists of decoding the second replica of User \(1\), since it is interference free. Then, the contribution of both replicas can beremoved from the received signal. Consequently, the first replica of User \(3\) becomes interference free. We decode User \(3\) and remove both replicas from the signal. We proceed in the same way for User \(2\). Since no interference is present any more, we assume that also User \(4\) can be successfully decoded._  
Note that replicas with the lowest level of interference are the ones with the highest chance to be correctly decoded. Interference-free replicas or replicas collided with a single interferer have the highest chance to be recovered in a SIC iteration. Considering the latter, collision of _two_ packets yields interference, which is either at the beginning or at the end of a packet. Consequently, an error-correcting code able to better protect the beginning or the end of a packet should come to the aid of SIC improving the random access's overall performance. In light of this, in Section 3, we focus on the design of error correcting codes able to better protect the beginning and end of a codeword.
## 1 Introduction and Motivation  
Companies operating in the manufacturing industry nowadays face significant challenges due to globalization, rapidly changing markets [1; 2; 3], digitalization [4], new regulations [5], and increased demand for product customization [6]. Industry 5.0 represents the manufacturing industry's most recent technological and social transformation. Starting with the first industrial revolution in the second half of the 18 th century, which led to a transition from an agricultural to an industrial society, the second and third industrial revolutions of automation and digitalization (Industry 4.0) followed. While Industry 4.0 represents a digital, data-driven, and interconnected industry, Industry 5.0 complements and extends these features with a need for a sustainable, human-centered, and resilient industry [7]. Innovation in manufacturing, particularly the adoption of new technologies and processes [8; 9; 10], was identified as a crucial driver for the success of this transformation processes, granting competitiveness [5].  
Nevertheless, the innovation process requires significant resources [10], encounters barriers to its effective completion [2], and involves changes that inherently present uncertain outcomes. The potential economic utility [11] alone does not provide the basis for a thorough analysis of the different alternative projects to be assessed. For this reason, a holistic perspective in innovation assessment is necessary [12; 13].  
Therefore, the scope of the research presented in this contribution is the solution to a design problem. It is derived from the above-mentioned need for an approach for the holistic assessment of innovation projects in manufacturing. A systematic and extensive literature review was conducted to identify existing approaches and the associated constraints. A holistic set of evaluation criteria was derived and synthesized. Those preparatory works allowed the structuring of a Multiple Attribute Decision-Making (MADM) problem to be solved by combining the Analytical Hierarchical Process (AHP) in a Fuzzy variant and the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS). First, the proposed assessment methodology was verified by both academic experts in the fields of innovation and manufacturing and industrial professionals. Then, the method was implemented, leading to its initial validation in a use case based on data regarding three innovation projects in an aerospace company.
## 4 Results
### Bland-Altman Plot  
Figure 7 shows the Bland-Altman plot for the ensemble model with multiple annotations compared to other models using the GDC metric; each row in the graph represents the implementation of a separate dataset. The impact of using multiple annotations varies across datasets. In the Knee dataset, for instance, the average performance with the MA model is 3% lower than using a single annotation, indicated by a negative mean difference (MD) of \(-\)3%. Conversely, for other datasets, the MA model leads to positive improvements. The Lung dataset sees a 1% increase, the HHS dataset a 5% increase, and the SJJ dataset a significant 12% increase in performance compared to using a single annotation. The Limits of Agreement (LoA) between the models provide another key insight. The datasets with high GDC scores show a very narrow LoA, indicating strong consensus among the models. Conversely, datasets with lower GDC scores, like SJJ and Knee, exhibit a much wider LoA, suggesting greater disagreement between the models' predictions. Moreover, the dataset dependence phenomenon is also true when comparing the MA model and regression model. The regression model achieves a \(-\)9% MD for the SJJ dataset, yet the MA model performs a higher score for the remaining datasets. In the case of the Knee dataset, the MA model scored +28% MD as the highest score, and +1% is the lowest positive MD for the Heart dataset for segmenting the lungs (HLS). Furthermore, when the results of the MA model are compared with different versions of MC dropout, MC with 40% and 60% dropout rates always achieved better (positive) MD; for MC with 20% and 80% dropout rates, there is a narrow MD (both positive/negative) between the models. In summary, the MA model shows weaker performance with the SJJ datasets compared to other datasets, achieving competitive but not optimal results. A similar pattern is observed with the LoA: narrower LoA values correlate with higher GDC scores in the datasets, while wider LoA values occur in scenarios where GDC scores are lower.  
Figure 6: Shows box plots for the GDC metric.
## 3 Results
### Characterization
#### 3.1.4 Nitrogen Adsorption Isotherms  
The physical characteristics (surface area, pore size, and pore volume) of SBA-15-PSSA are reflected by the data listed in Table 2. In Figure 6 are reported the N\({}_{2}\) adsorption-desorption isotherm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**TGA**} & \multirow{2}{*}{**DTG (\({}^{\circ}\)C)**} & \multirow{2}{*}{**DTA (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Assignment**} & \multirow{2}{*}{**Residue (\%)**} \\ \cline{2-2} \cline{6-7}  & **Temperature** & & & & & \\ (\({}^{\circ}\)C) & **(\%)** & & & & \\ \hline \multirow{3}{*}{** SBA-15-Br**} & 25–157 & 0.81 & 58 & - & Volatiles & 99.19 \\ \cline{2-7}  & 157–319 & 6.24 & 260 & (+) 249 & Initiator & 92.95 \\ \cline{2-7}  & 319–700 & 3.4 & 517 & (+) 494 & Amine & 89.55 \\ \hline \multirow{3}{*}{** SBA-15-PSSA**} & 25–100 & 2.87 & 66 & - & Volatiles & 97.12 \\ \cline{2-7}  & 100–212 & 3.33 & 154 & - & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & 93.80 \\ \cline{2-7}  & 212–365 & 2.53 & 363 & (+) 304 & & \\ \cline{2-7}  & 365–457 & 2.83 & 422 & (+) 442 & & 88.44 \\ \cline{2-7}  & 458–617 & 13.81 & 545 & (+) 556 & Polymer-based & 74.63 \\ \hline \end{tabular} (+): exothermic process; DTA: differential thermal analysis; DTG: derivative thermogravimetric analysis; SBA-15: pore-expanded mesoporous silica; SBA-15-PSSA: SBA-15-supported polystyrene sulfonic acids; TGA: thermogravimetric analysis.  
\end{table}
Table 1: Thermal properties of SBA-15-PSSA.  
Figure 5: Thermogravimetric analysis (TGA; black line, left \(y\)-axis), derivative-TGA (DTG; red line, the rightmost \(y\)-axis), and differential thermal analysis (DTA; blue line, right \(y\)-axis) curves of SBA-15-Br and SBA-15-PSSA. SBA-15: pore-expanded mesoporous silica; SBA-15-PSSA: SBA-15-supported polystyrene sulfonic acids.  
of SBA-15-PSSA and this catalyst's pore size distribution; the isotherm has the typical features of a type IV, with clear H1-type hysteresis loops, indicating the presence of cylindrical mesopores of fairly uniform sizes and shapes. The obtained values for surface area (726 m\({}^{2}\)/g) and average pore diameter (107.6 A) are consistent with literature reports, whereby researchers also claimed increased surface area of silica-supports when prepared at low initial temperature [3; 10]. However, the texture properties of functionalized silica (e.g., SBA-15-PSSA) are not much affected after modification. According to [PERSON] et al. [10], the surface area of functionalized silica is slightly reduced compared to SBA-15; however, the average pore size and distribution are not much influenced by functionalization, which is a promising observation from the standpoint of catalytic applications.
## 3 Experiments and Results
### Performance Evaluation Metrics of the Network Model
#### 3.2.2 Recall  
Recall represents the proportion of actual positive samples successfully identified by the model, and it is a key metric for measuring false negatives (\(FN\)). The formula for calculating recall is  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{7}\]
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 2 Background and Motivation
### Secret Sharing  
Secret sharing aims to provide both fault tolerance and confidentiality guarantees for the shared data via transforming the data input (called secret) into a set of coded outputs(called shares). A secret-sharing algorithm can be defined based on three configurable parameters, namely \(n\), \(k\), and \(r\) (where \(n>k>r\geq 0\)) [14], wherein a \((n,\ k,\ r)\) secret-sharing algorithm encodes a secret into \(n\) shares and the secret can be reconstructed from any \(k\) shares, while any \(r\) shares cannot be inferred (even partially) from any \(r\) shares.  
AONT-RS [15] is a secret-sharing algorithm that achieves security with reduced storage space. It combines [PERSON]'s all-or-nothing transform (AONT) [16] for confidentiality and Reed-Solomon (RS) codes [17] for fault tolerance. Specifically, AONT-RS first encrypts the secret into an AONT package with a random key. The package is then divided into \(k\) equal-size blocks which are encoded into \(n\) blocks (i.e., \(n\) shares) using systematic RS codes (meaning that \(k\) original blocks are kept in \(n\) blocks). In this way, the adversary cannot infer anything about the AONT package without obtaining the whole package.  
Many state-of-the-art encrypted deduplication schemes [2,4,5] are based on CD-Store [14], which deploys CAONT-RS, using a deterministic rather than a random key to enable deduplication atop AONT-RS. CAONT-RS generates the deterministic key based on the content of the input plaintext chunk, such that identical input plaintext chunks can generate the same ciphertext chunk; then, the same ciphertext chunks can be deduplicated and only one ciphertext chunk is stored, so as to save storage costs.
## 4 Results and Discussion
### Performance Analysis
For both subplots as shown the legend in subplot (**b**), the symbols \(\bigcirc\), \(\times\) and \(\star\) denotes the performances of single, day and night models, respectively. seen in the table's last row that, by excluding TNX input, a very similar proxy performance can still be gained. This means that the optimal O3 proxy can be deployed by utilising only two instruments: the NO/NO2/NOx analyser and a WS measurement device. The following results will then present only the three inputs based proxy that are NO, NOx and WS. Figure 9 presents a fraction of the time series results of real measurement of O3 concentration (red) and its proxy (blue) as well as its confidence interval (light green). Even though the prediction does not fit perfectly the real measurement data, some parts of predicted uncertainty capture the real data. Furthermore, it can be seen that the O3 proxy tracks very well the O3 concentration pattern, which is a good successful indicator from the developed proxy performance. The full time series data is not displayed because it is visually unclear to plot the whole time series of validation data set. To understand the overall performance of developed model, we produce a regression plot and an error histogram as shown in Figure 10. Regression plot, subplot (a), displays the scatter plot between the measured O3 (x-axis) and the estimated O3 (y-axis). The light blue represents 2\(\sigma\) uncertainty around the mean of O3 predictive distribution. It can be seen that the majority of O3 estimations lie within 2\(\sigma\) of uncertainty. Furthermore, the obtained R2 performance is at 0.83 on the scatter plot that is considerably higher than considering the simple dual model (day and night) as our previous work presented (R2 \(\approx\) 0.3) by [PERSON] et al. (2010). The error histogram, subplot (b), displays the difference between the measured and estimated O3 concentration. The evaluation through error histogram is also promising because it follows a Normal distribution where the peak is centred around zero with standard deviation is only \(\sim\)5 parts per billion (ppb). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proxy Inputs** & **MAE** & **RMSE** & **R2** \\ \hline NO2-NOx & 7.154 & 86.621 & 0.703 \\ NO2-NOx-WS & 5.594 & 50.409 & 0.83 \\ NO2-NOx-WS-TNX & 5.192 & 48.564 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The above table shows that the combination of the best four variables to understand the impact of proxy performance by reducing the required instruments for computing O3 proxy. Figure 9: The time series data of real O3 measurement (red), the O3 estimation (blue) produced by the BNN and its confidence interval (light green).
## 3 Case Study
### Discussion
#### 3.2.2 Analysis of Construction Timing  
The growth curve of Chengdu rail transit network  
Analyzing the construction process of Chengdu rail transit in the past 15 years (from 2005 to 2020), the reasonable growth curve of Chengdu rail transit was calculated by Equation (16). The growth curve of Chengdu rail transit is shown in Figure 7. It can be seen that the construction timing lags behind at the beginning, but it coincides with the reasonable growth curve in the twelfth year of construction.  
The actual construction time of several segments in the initial lagged slightly, so that the scale of the actual network could not reach the reasonable scale level. To a certain extent, this reduced the service capacity of the URT in the initial stages and resulted in additional resources being made available in later construction phases to improve the construction speed.  
The discussion of construction timing  
On the basis of the actual construction stage of the Chengdu rail transit, the lines were divided into different construction segments. The construction timing of each segment calculated by the double-level model is shown in Figure 8.  
Figure 7: Growth curves of Chengdu rail transit, actual, and projected, based on Equation (16).  
The red line in Figure 8 shows the calculated construction timing calculated by the model, and the blue line shows the actual construction timing. The calculated construction timing of several lines (Line 1 segment 3, Line 3 segment 2, Line 5, Line 7, and Line 10 segment 2) are consistent with the actual construction timing.  
At the same time, the actual construction timing of several lines lagged that of the model. The construction timing of Line 1 segment 1 lagged by 2.25 years; and that of Line 1 segment 2 lagged by 21 years. Meanwhile, Line 2, Line 4 segment 1, Line 3 segment 1, Line 4 segment 2, and Line 10 segment 1 lagged about 1 year. This is because, in practice, the construction time of Line 1 segment 1 was delayed, resulting in the delay of the construction time of subsequent lines. The poor geological conditions in Chengdu are also another reason for the delay in follow-up line construction.  
Fortunately, the construction speed of follow-up lines has gradually increased, marking up for the delays in the early stages. Chengdu rail transit will soon enter the key period of centralized construction. The network scale that needs to be completed in the initial stage has basically reached its goal. The construction timing is becoming more reasonable, which meets the needs of urban development.  
Overall, the effectiveness of the double model is verified by the case study, and thus, the model can provide quantitative theoretical support for the construction schedule planning of future URT networks. It should be noted that the CSM still has some limitations in terms of the selection of decision attributes, and more URT construction data, especially for small and medium-sized cities, should be included in the CTM.
## 4 Connectivity Architecture  
An autonomous vessel needs connectivity both inside the ship and outside to fulfill the requirements of different subsystems and services. While the in-ship network is mostly realized with cables, there are possibilities to use wireless connections, e.g., for different environmental sensors. Ship-to-outside connections are wireless.  
Figure 1: Key use case groups in 3 GPP maritime standardization.