esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 2 Literature Review  
Introduced in 2017 by [PERSON] et al. [10], transformers were considered a breakthrough in deep learning with their sole reliance on attention mechanisms and significant improvement in training time. They were mainly utilized for Natural Language Processing (NLP) tasks, before [PERSON] et al. [5] introduced ViTs in 2021. An image is processed as a sequence of patches from a transformer encoder, similar to NLP, which significantly outperforms state-of-the-art models in both accuracy and resources. [PERSON] et al. [11] studied the performance of transformers on mobile devices by comparing lightweight CNNs (including MobileNetV2 [12] and EfficientNetB0 [13]) and various late and large transformers. Despite concluding that late transformers tend to perform very similarly to lightweight CNNs in terms of overall memory footprint, inference latency, and energy consumption, the authors concluded that models with a hybrid CNN-transformer architecture are very promising for on-edge deployment, which is explored in this study. This section explores the use of ViTs in classifying gastrointestinal diseases from the Kvasir-Capsule dataset, along with the possibility of deploying ViT classifiers on edge devices for medical diagnostics.
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 3 Formulation of the Embedding Extraction with Short Utterances  
Current state-of-the-art speaker verification relies on the pipeline embedding-backend. Utterances are first converted into compact representations, the embeddings, which feed the decision backend to obtain the score. Among all available representations, two of the most popular ones are i-vectors and xvectors. Both have been widely tested in speaker verification obtaining great results. First we will try to understand how we store the speaker information in these embeddings and then study its drawbacks for short utterances.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Machine Vision Methods  
#### 2.4.1 Image Processing  
The estimation of fruit yield for an orchard using a machine vision system has three components: (i) the detection and count of fruit in images; (ii) relating image fruit count to fruit load of an entire tree and (iii) the estimation of all trees, or a statistically relevant sample of trees in the orchard. Most reports to date report on the first stage, detection and count in images, rather than report on accuracy at a whole tree or orchard level.  
Great advances have been made in the detection algorithms, in terms of improved speed, reduced computing requirements, with detection accuracies consistently over 95% [35]. The methodology of deep learning for on-tree fruit detection was reviewed in 2019 [35] and is therefore not considered in depth in this review, but rather, attention is given to the implementation of the technology in the orchard. In brief, initial attempts at fruit detection in tree canopy images used image segmentation techniques based on color, texture, shape and other fruit 'features' [36]. Within the last decade, the advent of 'deep learning' using Convolutional Neural Networks (CNNs) has revolutionized machine vision applications in general. For tree crops, the technology has been applied to the estimation of flower and fruit load [37; 38; 39; 40; 41; 42] with accuracies well over 90% reported for the detection of flowers or fruit visible in images of tree canopies.  
The deep learning architectures employed in fruit detection have evolved within the past five years, e.g., from Faster Regional Convolutional Neural Networks (FRCNNs) to lighter architectures such as You Look Only Once (YOLO). Current novelty lies in benchmarking new architectures against the current'state of the art' in terms of performance in object detection and in speed and computing resources required. In general, deeper architectures require more training and greater computing power for implementation but provide improved accuracy compared to shallower architectures. However, given fruit detection is a relatively simple single class task and as the fruit load estimation task for large orchards requires the processing of large data volumes, the trend points towards the use of lighter weight architectures.  
However, not all fruit can be seen from outside the canopy, leading to 'occlusion error' on machine vision estimates. This error forms the main limitation to the adoption of machine vision technology for crop load estimation. Effort in machine vision research should be directed to techniques for whole tree load estimation rather than into model architecture redesign for small gains in object detection performance. Likely topics include camera orientations and the use of features related to the proportion of occluded fruit. The documentation of variability in occlusion error between seasons for a given orchard and to canopy architectures that reduce occlusion error is also required. Such canopy architectures will also suit robotic harvesting.
## 2 Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis  
We collected smartphone GNSS data in a real environment and analysed their quality.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in HetNets
#### 4.2.3 AI-Driven Coordination in HetNets  
The increasing complexity of HetNet deployments in B5G, especially involving dense and overlapping tiers of macro-cell, micro-cell, and SC, poses new coordination and interference challenges. To address these, 3 GPP has introduced architectural support for AI-driven coordination through the integration of RICs, particularly within open RAN frameworks. Near-RT RICs are designed to operate at the control plane of the RAN and facilitate dynamic decision-making for RA, BF management, and IMI [181; 182].  
In the context of HetNets, near-RT RICs can support dynamic handover optimization by learning UM patterns and traffic distribution across cells. They can also perform cross-tier interference suppression, coordinating between macro-cell and SC by adjusting power levels, assigning beam directions, or reconfiguring scheduling patterns. Furthermore, traffic-aware radio RA is enabled by analyzing load conditions and user QoS requirements, ensuring balanced utilization of available spectrum across tiers [183]. These functions are realized through extensible applications (EAPP) deployed on RIC, which leverage RT telemetry, AI models, and standardized interfaces to implement feedback-driven optimization strategies [184].  
The adoption of AI-enhanced RICs for HetNet coordination allows for more scalable and adaptive IM compared to traditional static or semi-static solutions. However, challenges remain regarding EAPP interoperability, AI model generalization across deployment scenarios, and maintaining low-latency responses under constrained edge computing environments [185].
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 3 Synthesis and Characterization of TWNTs
### Brief Summary: Synthesis of TWNTs  
In summary, while DWNTs can be readily produced via the arc-discharge method, the CCVD method and the pea pod-derived method, the production of TWNTs is definitively more challenging. To the best of the author's knowledge, there is no arc-discharge method recipe that provides a reasonable outcome in terms of TWNT selectivity relative to other carbon materials produced during the growth process. The CCVD methods are good for producing DWNTs, but present a very narrow window of parameter tunability that leads to a highly selective TWNT production relative to other carbon materials. The attempts to use the CCVD method to grow TWNTs usually result in a mixing of DWNTs and TWNTs, which is not trivial to be separated due to similar physicochemical properties presented by both the DWNTs and the TWNTs. The most prominent method to grow TWNTs seems to be the pea pod-derived method, which produces TWNTs from large diameter DWNTs that have either C\({}_{60}\) or C\({}_{70}\) molecules encapsulated to form the innermost tube. This method produces high quality TWNTs with good selectivity relative to other carbon nano-products produced during the growth process.
## 4 Engineering Example
### Generalization Performance Verification  
In order to further investigate the performance of the method proposed in this paper on other similar projects, a prestressed steel-cylinder concrete pipe with the same monitoring system in a coastal area is tested, which has similar service environment and material properties as the original experimental subject, and multiple corrosion sensors are deployed in key sections. The model structure and modeling process are preserved unchanged during the testing process, and strong correlation features are selected as inputs to verify the generalization ability of the framework. Meanwhile, for comparative analysis, the same hyperparameters are used to construct LSTM, GRU, and non-adaptive MTGNN models separately. The performance comparison of similar engineering models is shown in Table 5. In order to visually compare the differences, the data of each index of the above models are plotted and displayed as bar charts and line graphs, and the comparison results are shown in Figure 12. From Figure 12, it can be seen that the adaptive MTGNN model also shows good performance in similar engineering, and its MAE, RMSE, and MAPE are better than the other models, which confirms the effectiveness of the spatio-temporal correlation feature extraction mechanism in cross-engineering scenarios.  
The prediction framework proposed in this paper is based on multivariate feature fusion and dynamic graph structure learning, which improves the robustness of the model to noise and distributional bias and can be generalized to similar engineering applications. Follow-up studies can further explore how to optimize the model structure and parameters to further improve its adaptability under different engineering conditions.  
Figure 11: Representation of the prediction accuracy of different models by radar charts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & **MAE (\(\times\)10\({}^{-3}\))** & **RMSE (\(\times\)10\({}^{-3}\))** & **MAPE (\%)** \\ \hline LSTM & 3.538 & 4.484 & 9.093 \\ GRU & 3.916 & 5.003 & 10.144 \\ Non-adaptive MTGNN & 3.419 & 4.245 & 8.216 \\ Adaptive MTGNN & 3.081 & 3.780 & 7.157 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of similar engineering model performances.
## 3 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Percentage** \\ \hline Less than Primary education & 0.30\% \\ \hline Primary education & 1.89\% \\ \hline Lower secondary education & 15.22\% \\ \hline Upper secondary education & 37.97\% \\ \hline Post-secondary non-tertiary education & 8.18\% \\ \hline Short-cycle tertiary education & 8.92\% \\ \hline Bachelor or equivalent & 14.75\% \\ \hline Master or equivalent & 10.53\% \\ \hline Doctoral or equivalent & 1.65\% \\ \hline Don’t know/No Answer/Refuses & 0.60\% \\ \hline Source: [47]. \\ \end{tabular}
\end{table} 
Table 3: Characteristic of respondents—level of education. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Citeria**} & \multicolumn{8}{c}{**Category**} \\ \cline{2-9}  & **Descriptive** & **1: Very** & **2: Rather** & **3: Rather** & **4: Very** & **99: Don’t Know/No** \\  & **Statistics** & **Unsatisfied** & **Unsatisfied** & **Satisfied** & **Satisfied** & **Answer/Refuses** \\ \hline \multirow{6}{*}{C1} & Min & 0.54\% & 4.73\% & 14.51\% & 4.21\% & 0.22\% \\ \cline{2-9}  & Max & 46.64\% & 35.32\% & 53.44\% & 75.98\% & 21.35\% \\ \cline{2-9}  & Mean & 9.81\% & 17.02\% & 39.65\% & 28.07\% & 5.46\% \\ \cline{2-9}  & Std & 9.03\% & 6.54\% & 8.32\% & 13.34\% & 3.80\% \\ \hline \multirow{6}{*}{C2} & Min & 1.69\% & 4.88\% & 22.00\% & 4.28\% & 0.13\% \\ \cline{2-9}  & Max & 46.85\% & 35.73\% & 52.59\% & 65.98\% & 8.06\% \\ \cline{1-1} \cline{2-9}  & Mean & 12.92\% & 19.17\% & 39.77\% & 26.38\% & 1.77\% \\ \cline{1-1} \cline{2-9}  & Std & 9.53\% & 8.51\% & 6.86\% & 13.39\% & 1.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive statistics of responses by category, broken down by criteria for the entire study. An analysis of the data presented in Table 5 allows for the formulation of several interesting observations and conclusions. The distribution of responses by category shows significant variation across different types of questions. Considering Category 1, the lowest average percentage of very unsatisfied respondents (6.68%) was in relation to cultural facilities such as concert halls, theatres, museums, and libraries, while the highest average percentage of very unsatisfied respondents (14.91%) was related to cleanliness. In Category 2, the lowest average percentage of rather unsatisfied respondents (10.73%) was with
## 3 Proposed Research Methodology
### Word Embedding Techniques
#### 3.3.2 fastText Subword  
fastText subword [24] benefits the training process by sharing common roots in words. It contains 2 million word vectors (600 billion tokens) having 300 dimensions, and uses subword information for training. fastText subword consists of millions of vectors that are trained on common roots or tokens. It provides support by breaking words into subwords or by joining subwords into a single word. For example, individually represented, \"for\" and \"give\" can be represented as \"forgive\" in order to learn dictionary-level representation. Character level embedding helps with representing misspelled and slang words.
## 4 Results and Discussion
### 16 m Far-Field Distance  
The error ratios of force coefficients are presented in Figure 13 and the related mean error ratios under different ranges of AOA are contained in Table 15. The optimal results are (0.05%, 0.00%), (0.85%, 1.35%), (3.13%, 1.20%), (5.43%, 1.14%), (6.37%, 1.70%) and (7.76%, 1.46%), in order, and the corresponding simulation configurations are SA+400 formula, SST+NACA4, SST+NACA4, SST+132 formula, SA+132 formula, and SA+132 formula. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), although the \(C_{d}\) of SA+132 formula is the minimum, the \(C_{l}\) is relatively large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, SST+NACA4 is preferred. At the range of (0\({}^{\circ}\), 4\({}^{\circ}\)), similarly, the result of SST+NACA4 is the best. At the range of (0\({}^{\circ}\), 10\({}^{\circ}\)), SA+400 formula obtains the minimum \(C_{l}\) while the \(C_{d}\) of SA+132 formula is the best. Because the \(C_{d}\) is a magnitude greater than the \(C_{l}\), the optimal result is achieved by SA+132 formula.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 1 Introduction
However, sharing the key incurs additional encryption and encoding overheads from secret sharing, which will impair the performance of the entire system. Therefore, it is still challenging to efficiently apply secret sharing to decentralized server-aided encryption. In this paper, we propose a new decentralized server-aided encrypted deduplication storage system, called ECDedup, which couples secret sharing with decentralized server-aided encryption to achieve the encryption and encoding of both keys and data chunks in a single process, thereby enabling efficient decentralized server-aided encrypted deduplication. The main contributions of this work include:  
* We are the first to leverage secret sharing to realize decentralized server-aided encryption in the encrypted deduplication storage systems, where we first use the coding matrix instead of the key to couple the encryption process with the encoding process,so as to achieve the secret sharing of both keys and data chunks in one encoding process (Section 2). * We propose ECDedup, which realizes efficient decentralized encrypted deduplication via sharing the coding matrix to multiple key servers, and improves the matrix generation performance via two optimization methods (Section 3). * We implement the ECDedup prototype and we also deploy a blockchain integration to further strengthen the security of our prototype (Section 4). Our experiments show that ECDedup improves the client throughput by up to 51.9% atop existing encrypted deduplication methods (Section 5).
## 3 Results  
### Stress Wave Propagation Velocity Analysis  
From the one-dimensional wave theory, the theoretical wave velocity in the probe rod can be calculated from Equation (1) [30, 31, 32, 33, 34, 35, 36]:  
\[C=\sqrt{E/\rho} \tag{1}\]  
where \(C\) is the wave velocity (m/s), \(E\) is the static elastic modulus of the probe rod (10\({}^{9}\) Pa, the average elastic modulus of the probe rod in the test is 195 \(\times\) 10\({}^{9}\) Pa), and \(\rho\) is the probe rod density (value is 7850 kg/m\({}^{3}\)). The theoretical wave velocity in the probe rod is 4987.5 m/s.  
Wave velocity between test points in the probe rod is equal to the spacing between test points divided by the time interval between axial stress peaks at the test points. The measured wave velocities between the test points are presented in Table 2. Comparing the measured data in Table 2, it can be seen that the measured wave velocity of the probe rod is 4547.7\(\sim\)4752.6 m/s and the measured wave velocity is less than the theoretical wave velocity, which is about 91.2-95.3% of the theoretical value. The theoretical wave velocity is derived from an ideal one-dimensional elastic isotropic rod. Considering that the probe rods are connected in the form of joints, joint looseness, sediment infiltration, and other factors will affect the measured wave velocity, so the measured wave velocity deviates from the theoretical wave velocity and it can be considered that the test results are consistent with the one-dimensional wave theory.
## Materials and Methods
### Characterization  
The absorption spectra of the samples were measured using a Thermo Genesys 10S UV-visible spectrometer (Thermo-Fisher Scientific, Waltham, MA, USA), operated at a resolution of 1 nm. Standard quartz cells with an optical path length of 10 mm were used for sample characterization. Scanning electron microscopy (SEM) and the corresponding elemental analysis were conducted using a JEOL JSM-7800F field-emission scanning electron microscope (JEOL, Tokyo, Japan) equipped with an X-ray energy-dispersive spectrometry (EDS) instrument. Transmission electron microscopy (TEM) was performed using a JEOL JEM-2100 transmission electron microscope (JEOL, Tokyo, Japan). High-resolution TEM (HRTEM) and the corresponding elemental analysis were performed using a Thermo Fisher Titan transmission electron microscope. The 1,1-diphenyl-2-picrylhydrazyl (DPPPH) free radical scavenging assay was conducted according to procedures [33]; specifically, different concentrations (1-8 mg/mL) of AuNPs in an equal volume of 1.2 mL were added to 1 mM DPPH in a 50 \(\mu\)L methanol solution. After 30 min of reaction at room temperature, changes in the absorption peak of DPPH at 517 nm were measured for further calculating the clearance rate. The absorption value was incorporated into the following formula to calculate the clearance rate of DPPH free radicals: percentage of inhibition of DPPH radical scavenging activity \(=A_{o}-(A_{f}-A_{i})/A_{o}\), where A\({}_{0}\) is the absorbance of pure water (blank), A\({}_{f}\) is the absorbance of a solution with DPPH, and A\({}_{i}\) is the absorbance of the solution without DPPH. Moreover, the antioxidant properties of the prepared AuNPs were studied through EPR spectroscopy (Bruker EMSplus spectrometer, Billerica, MA, USA).
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 2 Significance of Energy Efficiency in IoT Use Cases
### Simulation Oriented Characterization
Saturation time values allow one to evaluate the ability of a routing algorithm to effectively adjust to another topology due to the death of nodes. In a 100 node density case, the saturation time of AODV is 33 min, which is less than the saturation time of FFSC (39 min), while ER-RPL and REL lay between AODV and FFSC. The network lifetime of FFSC is 69 min, which is 9, 15 and 24 min more than REL, ER-RPL and AODV, respectively. Therefore, in terms of network lifetime and saturation time, FFSC is the most suitable protocol, followed by REL, then ER-RPL, and AODV is the least suitable. The reliability of the existing routing protocols AODV, ER-RPL, REL and FFSC has been measured using _packet delivery ratio_ (PDR) which is described as the ratio of the number of successfully transmitted packets to the total number of transmitted packets. Figure 6c illustrates the PDR in the simulated scenario with 100 nodes for all packet transmissions, while establishing an interval from 75% to 100%. As can be seen in Figure 6c, FFSC, which delivers 97% packets successfully outperforms AODV (85%), ER-RPL (91%) and REL (95%), respectively and AODV delivers the least packets successfully.