esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Proximate Composition of TPW, TPW-PEEL, and TPW-SEED
\begin{table}
\begin{tabular}{c c c c c} \hline
**g/100 g Dry Matter** & **TPW-70** & **TPW-LIO** & **TPW-PEEL** & **TPW-SEED** \\ \hline ash & 4.81 \(\pm\) 0.06 \({}^{\text{c}}\) & 4.16 \(\pm\) 0.04 \({}^{\text{b}}\) & 5.09 \(\pm\) 0.08 \({}^{\text{d}}\) & 3.08 \(\pm\) 0.11 \({}^{\text{a}}\) \\ \hline fat & 7.84 \(\pm\) 0.02 \({}^{\text{b}}\) & 9.21 \(\pm\) 0.37 \({}^{\text{c}}\) & 3.89 \(\pm\) 0.20 \({}^{\text{a}}\) & 25.87 \(\pm\) 0.18 \({}^{\text{d}}\) \\ \hline protein & 18.00 \(\pm\) 0.93 \({}^{\text{b}}\) & 17.46 \(\pm\) 0.02 \({}^{\text{b}}\) & 14.34 \(\pm\) 0.29 \({}^{\text{a}}\) & 27.12 \(\pm\) 0.11 \({}^{\text{c}}\) \\ \hline dietary fiber & & & & \\ \hline _total_ & 40.10 \(\pm\) 1.14 \({}^{\text{a}}\) & 41.16 \(\pm\) 1.37 \({}^{\text{a}}\) & 52.06 \(\pm\) 1.32 \({}^{\text{b}}\) & 41.30 \(\pm\) 1.89 \({}^{\text{a}}\) \\ \hline _soluble_ & 7.53 \(\pm\) 0.81 \({}^{\text{a}}\) & 7.57 \(\pm\) 0.88 \({}^{\text{a}}\) & 9.09 \(\pm\) 1.46 \({}^{\text{a}}\) & 5.40 \(\pm\) 2.20 \({}^{\text{a}}\) \\ \hline _insoluble_ & 32.57 \(\pm\) 0.44 \({}^{\text{a}}\) & 33.59 \(\pm\) 0.50 \({}^{\text{b}}\) & 42.97 \(\pm\) 0.17 \({}^{\text{d}}\) & 35.90 \(\pm\) 0.31 \({}^{\text{c}}\) \\ \hline
\begin{tabular}{c} available \\ carbohydrates \\ \end{tabular} & 29.25 \(\pm\) 2.04 \({}^{\text{c},d}\) & 28.01 \(\pm\) 1.66 \({}^{\text{b},c}\) & 24.62 \(\pm\) 1.67 \({}^{\text{b}}\) & 2.62 \(\pm\) 0.00 \({}^{\text{a}}\) \\ \hline energy (kcal/100 g) & 339.74 \({}^{\text{b}}\) & 347.09 \({}^{\text{c}}\) & 294.99 \({}^{\text{a}}\) & 434.44 \({}^{\text{d}}\) \\ \hline \end{tabular}  
* The data are presented as mean \(\pm\) SD. Statistical analysis was performed using one-way ANOVA followed by [PERSON]’s post hoc test. Letters next to value indicate statistically significant differences between groups for each element. TPW-70: tomato processing waste air dried at 70 \({}^{\text{c}}\)C; TPW-LIO: tomato processing waste dried by lyophilization; TPW-PEEL: peel fraction of tomato processing waste dried air dried at 70 \({}^{\text{c}}\)C; TPW-SEED: seed fraction of tomato processing waste air dried at 70 \({}^{\text{c}}\)C.  
\end{table} 
Table 2: Proximate composition and energy content of the TPW, TPW-PEEL, and TPW-SEED. LIO: 17% \(\pm\) 0.0%), which is expected given the analysis method used (Kjeldahl). These values are consistent with previous studies that report a range from 15.08 to 24.67% [50]. Dietary fiber content was also not statistically significantly different between samples dried using different methods. The content of insoluble fiber was 33% \(\pm\) 0.4% for TPW-70 and 34% \(\pm\) 0.5% for TPW-LIO, while the content of soluble fiber was 7.5% \(\pm\) 0.8% and 7.6% \(\pm\) 0.9%, respectively. These values are consistent with the literature for insoluble fiber, while the values for soluble fiber are somewhat higher [57]. Carbohydrate content was 23% \(\pm\) 2.0% for TPW-70 and 20% \(\pm\) 1.7% for TPW-LIO, with no statistically significant difference, aligning with the literature data [58].
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Segmentation Task in Histological Image Analysis
#### 5.5.2 Cell Segmentation  
Cell segmentation is essential for quantitatively analyzing cell populations and tumor characteristics in histopathology. Studies such as [PERSON] et al. [59] take an essential early step in using GANs for segmenting cells in histopathology images. Their approach is particularly noteworthy for being unsupervised; by leveraging GANs, they generate realistic-looking images that assist the model in learning cell-level features without relying on extensive manual annotations. This represents a significant contribution, as annotating histological data can be time-consuming and costly. However, while their work concentrates on learning visual representations and extracting cell-level features, the segmentation accuracy is limited since the model primarily aims to capture cellular structures rather than produce precise segmentation masks. Similarly, [PERSON] et al. [56] introduced a more advanced unsupervised framework that emphasizes stain style transfer using a multi-domain progressive GAN architecture. While their primary goal is stain normalization and harmonization across datasets, they also implicitly address segmentation challenges by enhancing the consistency in the visual appearance of histological images, which is crucial for robust cell segmentation.  
On the other hand, supervised approaches, such as the one by [PERSON] et al. [62], introduced SynCLay. This novel interactive synthesis framework enables users to create synthetic histology images from customized cellular layouts. Although their focus is on image generation rather than direct segmentation, the ability to generate realistic cellular structures proves highly valuable for training segmentation models. By simulating diverse and complex cellular arrangements, SynCLay generates augmented datasets that help segmentation models learn to identify and delineate cells in challenging situations, such as overlapping or morphologically varied nuclei.
## 4 Proposed Method
### Text Character Recognition
#### 4.2.2 Optical Character Recognition Model  
Figure 16 illustrates the overview of the OCR model developed to recognize the detected texts. Convolutional networks are used to extract features from the image first, then reshape and density are used to reduce the feature vector dimensions. Then, the bidirectional LSTM is used to process sequential data. Convolutional filters, max-pooling, and fully connected layers from a conventional CNN model with a Resnet backbone are used to build the convolutional feature extraction component. Dropouts are only utilized in full-connected layers, and max-pooling units are used after each convoluted and full-connected layer. These components are used to move a sub-window across the text image in order to extract a feature sequence from an input image. The final feature size is (64 \(\times\) 512), which is condensed into (64 \(\times\) 32), where each feature is a time step of input for the recurrent layers and consists of 512 vector elements. The horizontally divided image features are the sequential data fed into the LSTM.  
The recurrent connection uses historical contextual information by allowing information from previous inputs to stay in the internal states of the network. LSTM is a type of  
Figure 16: Optical character recognition model.  
Figure 15: Random grayscale images generated for the OCR model training.  
RNN that solves the vanishing gradient problem and is able to recognize long-term dependencies. The memory blocks that make up the LSTM layer are a collection of blocks that are connected recurrently. Three multiplicative gate units regulate the activation of a set of internal units called cells that make up each block. The gates allow for long-term information storage and access within the cells. Future and past contextual information is useful for many tasks, including text and voice recognition. However, traditional LSTM can only leverage previous contextual data in one way. Bi-directional LSTM (BLSTM) [56], which can learn long-range context dynamics in both input directions, can be used to get around this. The resultant product the output of a given image is transformed by the dense layer into an array representing the number of horizontal steps and character labels in the shape of 32 \(\times\) 1648. Here, 32 represents the time steps for the CTC layer, and 1648 represents the maximum number of labels.
## 3 Results and Discussion  
### Wind-Off Performance  
It is important to calibrate the wind-off performance of the linear AC-DBD plasma actuator at various actuation frequencies before using it in the wind tunnel tests. Figures 6 and 7 show the velocity contour and profiles in the x-direction (u) of the flow induced by the plasma actuator at various actuation frequencies during the wind-off condition. Just after the edge of the exposed electrode, the plasma-induced flow begins as a wall jet over the dielectric layer. However, after it leaves the edge of the trailer, it continues as a free jet which is subsequently called the plasma-induced jet.  
Figure 6: Jet velocity in the x-direction generated by the plasma actuator at various actuation frequencies. (**a**) 7 kHz, (**b**) 8 kHz, (**c**) 9 kHz, (**d**) 10 kHz, and (**e**) 11 kHz.  
It can be seen from Figure 6 that amongst all plasma actuation frequencies being studied the lowest and highest plasma induced jet velocity in the x-direction appeared when the actuation frequency was 7 (Figure 6a) and 9 kHz (Figure 6c), respectively. Maximum jet velocity occurred when the actuation frequency was 9 kHz at the normalised location x/H = 0.2 (Figure 7a) with its time-averaged magnitude equal to u = 1.98 ms\({}^{-1}\). In fact, the instantaneous plasma-induced jet velocity at this frequency can go up to 6 ms\({}^{-1}\). It should be noted that H is the height of the trailer model which is 125 mm. The jet velocity decreases progressively downstream, with the increase in distance from the plasma actuator, at all plasma actuation frequencies. Above 9 kHz, increasing the actuation frequency reduces the wall jet velocity in the x-direction, as shown in Figures 6c-e and 7.
# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
## 3 VPP Concept and Technology  
Smart grids are complex systems, difficult to manage, control, and operate, especially towards the edge, and when integrating renewable energy from distributed sources [2]. Demand response programs and prosumers' flexibility and energy trading in energy markets raised an optimization problem for modern smart grids, leading to defining of various virtual aggregation schemes, models, and algorithms [109]. Apart from TSOs and DSOs, new roles in the smart grid ecosystem have been defined, such as that of energy aggregators [101]. This is facilitated by the evolution of energy management systems through new architectures that involve dynamic interactions between the actors and the energy assets and the implementation of new energy services [110].  
Virtual power plants (VPPs) deal with the aggregation and management of distributed energy assets to meet specific optimization goals [3]. The creation and operation of VPPs involves the application of complex ICT techniques dealing with the modeling of the energy assets and energy flows, forecasting of local energy demand and generation, operation optimization to meet the defined goals using heuristics, and finally portfolio management and actuation for closing the loop [5; 6; 73] (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VPP Research Direction** & **No. Approaches** & **References** \\ \hline \multirow{4}{*}{VPP concepts and technology} & Digital twins of energy assets & 13 & [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21] \\ \cline{2-4}  & Energy forecasting & 14 & [1; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] \\ \cline{2-4}  & Optimization and coordination & 18 & [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] \\ \hline \multirow{4}{*}{VPP applications in smart grids} & Energy services delivery & 31 & [4; 5; 52; 35; 38; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94] \\ \cline{2-4}  & Energy automiles’ sustainability & 18 & [51; 52; 63; 64; 93; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Narrative review research directions studied.  
Figure 3: VPP concepts and technology usage.
# Residual Life Prediction of Gas-Engine Turbine Blades Based on Damage Surrogate-Assisted Modeling
###### Abstract  
Blade damage accounts for a substantial part of all failure events occurring at gas-turbine-engine power plants. Current operation and maintenance (O&M) practices typically use preventive maintenance approaches with fixed intervals, which involve high costs for repair and replacement activities, and substantial revenue losses. The recent development and evolution of condition-monitoring techniques and the fact that an increasing number of turbines in operation are equipped with online monitoring systems offer the decision maker a large amount of information on the blades' structural health. So, predictive maintenance becomes feasible. It has the potential to predict the blades' remaining life in order to support O&M decisions for avoiding major failure events. This paper presents a surrogate model and methodology for estimating the remaining life of a turbine blade. The model can be used within a predictive maintenance decision framework to optimize maintenance planning for the blades' lifetime.  
life; remaining useful life; condition-based maintenance; real-time prognostics; surrogate model +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Recently, due to the worldwide COVID-19 pandemic, the use of remote collaboration has increased [1, 2]. Various conventional video conferencing solutions [3, 4, 5] for remote collaboration have limitations in terms of realistically delivering the user's work [6, 7]. To overcome this, research to apply mixed reality (MR) to remote collaboration has recently been conducted [8, 9]. In remote MR collaboration, end-to-end latency possibly occurs because of various reasons, such as tracking, application, image generation, display, or network [10, 11, 12, 13]. It is difficult to completely remove the latency since most of the latency causes mentioned above are essential operations for MR. However, as latency affects the usability (e.g., interaction satisfaction) as well as the efficiency of remote MR collaborations, it is necessary to reduce it.  
Due to this need, various studies have been conducted to improve the latency. Conventional studies have focused on reducing latency between the moment of performing the interaction and delivering the performed interaction information. Action detection, such as contact between a human and an object, is required to find the performed interaction in a remote MR collaboration. In other words, in remote MR collaborations, it is difficult to determine whether the user had performed an interaction before such actions are detected. Defining the time to check whether the interaction is performed according to the user's intention in a remote MR collaboration as the interaction latency, conventional solutions have limitations because it is difficult to reduce latency.  
In this paper, we propose an interaction prediction method for reducing interaction latency in remote MR collaborations. The proposed method reduces interaction latency by predicting interactions between a human and a virtual object. Interaction prediction is performed using consecutive hand joint information as the input in human and virtual object interactions.  
This paper is composed as follows: in Section 2, we introduce the process of deriving frequently used gestures in remote MR collaboration based on conventional studies and selecting the gestures for this study. In Section 3, we propose a prediction method for selected gestures. In Section 4, we conduct an experiment with and without applying the proposed method and compare the experimental results to examine the effectiveness of the proposed method. Finally, in Section 5, we present conclusions and future works.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 4 Simulation Environment and Results
### Evaluation Metrics  
We use four metrics to explain our simulation results. The first two metrics concern the routing protocol. Also, the packet delivery ratio and end to end delay metrics are measured.  
* MPR ratio: This is the ratio of MPR's to the number of the nodes in the network.
* Routing overhead ratio: This is the total number of routing packets over the total number of received data packets.
* Packet delivery ratio (PDR): This is the ratio of the data packets successfully delivered to the destination compared to the packets sent from the source
* End-to-end delay: This refers to the time taken by a packet to be transmitted through any network from the source to the destination.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 4 Discussion of the Implementation
### Cryptographic Choices
Implementers can tune the frequency of these rotations based on the performance constraints and organizational risk thresholds, thereby tailoring security to meet specific compliance or throughput goals. Nevertheless, however, repeated temporary rotations of keys within highly distributed and heterogeneous networks have high operational overheads. Among these goals are secure distribution, on-time revocation, synchronization among geographically distributed nodes, and protection against additional threats in terms of key compromise due to misconfigurations, inadequate access controls, etc. Countermeasures against such threats when deploying SecureLLM into non-controlled networks include strong, automated, and hardened key-managing infrastructures. Effective countermeasures are centralized or distributed key-managing services with strict access controls and threshold cryptographic schemes to reduce the points of failure, as well as strict monitoring and anomaly detection practices for the timely detection of and responses to a compromised key. Moreover, Hardware Security Modules (HSMs) or Trusted Platform Modules (TPMs) may safely manage and store cryptographic keys, further lowering the risk of compromise. Strictly defined recovery and revocation practices, further backed by automation and strict recording, will be necessary to maintain operational security at scale, as well as to handle regulatory framework compliance. Organizations that foresee large-scale or long-term LLM deployments may also consider future-proofing their cryptography choices against quantum threats [47]. One potential route involves substituting classical ECC with post-quantum algorithms (e.g., CRYSTALS-Kyber or CRYSTALS-Dilithium for key exchange and signatures, respectively), although these algorithms typically have larger key sizes. In especially high-risk or long-lived data scenarios--where the operational lifetime of the model may outlast the current cryptographic standards--post-quantum readiness is integral to regulatory compliance and forward-looking security measures. In the context of SecureLLM, this shift could be managed via a modular cryptographic layer that decouples the encryption algorithms from the rest of the pipeline, allowing for \"plug-and-play\" updates as the cryptographic standards evolve. Finally, cryptographic logging techniques--often referred to as \"secure audit logging\"--may be integrated at this layer. Each cryptographic operation (e.g., key rotation, the encryption of gradients) can generate verifiable log entries stored on a tamper-evident ledger. Such logs are essential for compliance with regulatory frameworks like the EU AI Act, demonstrating precisely when and how data were protected [48]. While not all deployments require the same level of traceability, many regulated domains benefit from these logs, as they provide robust evidence of when key exchanges occur or when data undergo encryption, thereby aligning with both internal governance and external audit requirements.
## 5 Conclusions  
In this study, the proposed empirical rock-bolt pattern using RMR, Q, and RMi systems of rock-mass classification was discussed, and the rock-bolt performance was investigated. The rock-bolt performance during tunnel-support design was evaluated via pull-out test results, as per ASTM standards. The following conclusions were obtained from the study:  
* The empirical rock-mass classification system proposed 20 mm, fully grouted rock-bolt patterns during tunnel-support design, which is a function of the rock-mass quality and tunnel span. The performance of the rock bolts depends on the efficient load-transfer mechanism, which is determined through the rock-bolt pull-out test.
* Based on the rock-bolt pull-out test, the factors affecting their performance are divided into five groups, and each group is further subdivided, and the effect of pull-out test results is discussed.
* Certain factors, such as rock-mass quality, ground water flow, and stresses, cannot be controlled. As the rock-mass quality decreases, the length effect also decreases in the rock-bolt pull-out test due to the weak bond between the grout and drilled hole's periphery. In the case of groundwater, inflated steel-tube rock bolts have better performance than fully grouted rock bolts.
* Some parameters are critical for the safe and economic performance of the bolts, i.e., water-cement ratio, rock-bolt length, and time of support installation, and they should be considered in the design of the rock-bolt pattern during underground construction.
* The overall performance of rock bolts in terms of pull-out strength is good in rock mass that is good quality, irrespective of their installation location along the tunnel periphery.
* For the economical and efficient performance of fully grouted rock bolts, the factors investigated in this study should be considered, along with the empirical support patterns using rock-mass classification systems.  
[PERSON] supervised the research; [PERSON], H.K., and [PERSON] developed the proposed research concept; J.K. conducted the field test and data analysis; [PERSON] and J.-j.K. reviewed the final paper and made important suggestions and recommendations for the paper's revision.  
This work was supported by the National Research Foundation of Korea grant, funded by the Korean Government (NRF-2019R1A2C2003636). The authors ([PERSON], [PERSON], and [PERSON]) are very thankful to the Higher Education Commission (HEC) of Pakistan for the HRDI-UESTPs scholarship. The author [PERSON] is supported by Seoul Institute of Technology (SIT) (19-1-3, A study on the groundwater undrainage method and monitoring of excavation work and underground infrastructure to conserve groundwater).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech
### Analysis and Results  
Out of the 16 participants, transcriptions were available only for 13 participants as we could not record responses of 3 participants due to technical problems. LE ratings were not available for one other participant, also due to a technical problem with saving data. Flanker effect score was not available for a participant. For analysis purposes, these missing data were filled with the mean values of the responses of other participants. Sphericity and homogeneity checks were performed on the data with the JASP tool to ensure that assumptions of an ANOVA test are met.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 4 Discussion
The full expression blue-sky albedo product enables the quantification of landscape change on radiative forcing and the global energy balance as it represents the actual albedo given atmospheric conditions. The use of MODIS AOD, while reporting a slightly weaker correlation with tower data, than the AERONET AOD, was relatively stable in the blue-sky albedo production and provided reliable, global, daily, and coincident AOD information. The balance between spatial and temporal scale and geographic coverage was well achieved and the fixed value provided a reasonable estimation when no other data source was available. The availability of further development of higher resolution albedo products will be a valuable tool in the further examination of landscape trends in the higher latitudes.