esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Simulation Analysis
#### 4.2.5 Comparison of the Proposed Mechanism with the Other Two Cases
As indicated by the table, under the condition that the total demand of the user side remains unchanged, Case III opens the bilateral market and allocates the carbon emission cost on the power generation side and Case II transfers the carbon responsibility from the generation side to the user side on this basis, both of which reduce the system carbon dioxide emission, making the carbon emission cost of Case II and Case III significantly lower than that of Case I. In Case II, the new energy generator has the largest income because the users have the highest enthusiasm to buy new energy. It is conducive to the new energy generator to sell a large amount of electricity when the outputs of wind and solar power are high to maximize profits. In addition, the income of thermal power generators in Case II and III has declined. Under the background of considering carbon cost on the generation side of Case III, the thermal power generator increases the quotation to ensure profitability but excessive quotation will lose the bilateral trading power. Moreover, bilateral transactions between users and new energy generators narrow the space for thermal power generators to sell electricity so the revenue drops more; although the electricity sold by thermal power generators in Case II is smaller than that in Case III, the final income is slightly greater than that in Case III because the thermal power generator does not need to consider the cost of carbon responsibility. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Wind Power** & \multicolumn{2}{c}{**Photovoltaic**} & \multicolumn{2}{c}{**Thermal Power**} & \multicolumn{1}{c}{**User Electricity**} & \multicolumn{1}{c}{**Carbon Emission**} \\  & **Generator** & **Generator** & **Revenue/S** & **Cost/S** & **Cost/S** \\ \hline Case I & 164,000 & 35,505 & 1,525,800 & 3,229,636 & 561,860 \\ Case II & 180,820 & 38,323 & 934,951.9 & 2,544,523 & 488,594 \\ Case III & 164,840 & 27,470 & 880,939 & 2,446,900 & 504,369 \\ \hline \end{tabular}
\end{table} 
Table 4: Economic comparison under different cases. Figure 12: Bilateral power purchase of users under Case II and III. From the perspective of users' electricity cost, Case II decreased by 21.2% compared with Case I. From one perspective, in the pool market, the market clearing price is settled by the highest quotation of the bid-winning generator while the price of the bilateral transaction can both truly reflect the market supply, demand relationship, and the real price of power products and reduce the cost of electricity for users. From another perspective, bilateral trading provides a place for users to consume new energy and users can freely choose the type of power supply in the bilateral market, thereby reducing the cost of carbon responsibility. Case III has the lowest electricity consumption cost for users because Case III does not take into account the cost of carbon emissions on the user side which leads to a reduction in the cost of electricity to the user.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.4 Mobile Agent Framework (MAF)  
This framework [59] is built on top of Mate which runs under TinyOS. It provides a _breadcrumb_ model for communication, where agents may leave per-node data behind them for future agents to access (though an agreed index). An agent's per-agent state is propagated along with the agent code. The agent propagation is under the control of the agents, and supported by a number of _FwdAgent_ functions in the framework. These support reliable, unicast, and broadcast forwarding requests. Agents may terminate their own execution on a node by exiting, or may continue indefinite execution in a loop structure with sleep statements.  
As for Mate, the flexibility provided by the support for mobile agents might prove to be useful enabling technology for autonomous updates, in addition to the ability to propagate state as well.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA A-To-I Modification in GBM  
A-to-I RNA editing is abundant in the human transcriptome and plays essential roles in RNA processing, such as post-transcriptionally altering codons, introducing or removing splice sites, and affecting the base pairing of the RNA molecule with itself or with other RNAs [139]. All 3 editing mediating enzymes, ADARI (ADAR), ADAR2 (ADARB1), and ADAR3 (ADARB2), is downregulated in brain tumors and ADARB2 is also negatively correlated with GBM grades. ADAR1 and ADAR2 overexpression in GBM cell line inhibit cell proliferation, suggesting that reduced A-to-I editing contributes to the pathogenesis of this disease [88]. In addition, abnormal expression of an ADAR2 alternative splicing variant also downregulates A-to-I RNA editing in glioma [89], because ADAR2 can prevent GBM tumor growth via modulating an important cell cycle pathway involving S-phase kinase associated protein 2 (Skp2), p21 and p27 proteins by introducing an A-to-I editing in the pre-mRNA of a phosphatase cell division cycle 14B (CDC14B) [90]. Additionally, A-to-I editing in miR-376a-3p is attenuated in GBM, thus promoting invasiveness of this disease [140]. These results show that A-to-I editing is essential for GBM progression (Figure 2, I part), and RMPs of A-to-I modification may be potential targets for GBM treatment.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 3 Fundamental Frequency Responses of Two Model Grounds  
The test results of FF model and ST model under white-noise cases are given in the Table 6. It is clear that, though the fundamental frequency of ST ground is a little higher than that of FF ground, the value is insignificant. In respect to the interaction between the soil and tunnel, the presence of the tunnel will not seriously affect the deformations of ground. It must be a result of the lower flexibility of the segmental lining, ratio 929 [19], as shown in Table 3.  
Comparing the test results of WN-1 with WN-2 of each model, as can be seen from Table 6, (1) the fundamental frequency of both grounds decreases after sinusoidal inputs; and (2) the maximum relative difference of the fundamental frequencies is 0.81% for the FF ground and 1.20% for the ST model. These results show that the fundamental frequency of the model ground has not significantly changed after seven sinusoidal excitations cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Test ID**} & **Input** & **Fundamental** & **Relative Difference** \\  & & **Motions** & **Frequency (Hz)** & **(\%)** \\ \hline \multirow{2}{*}{FF} & WN-1 & \multirow{2}{*}{White noise} & 4.99 & \multirow{2}{*}{0.81} \\ \cline{2-2} \cline{5-5}  & WN-2 & & 4.95 & \\ \hline \multirow{2}{*}{ST} & WN-1 & \multirow{2}{*}{White noise} & 5.00 & \multirow{2}{*}{1.20} \\ \cline{2-2} \cline{5-5}  & WN-2 & & 4.94 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fundamental frequency of grounds.  
Figure 11: Time history of input motion, with frequency of 2 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Test ID**} & **Input Motions** & **Frequency (Hz)** \\ \hline WN-1 & White noise & 0–50 \\ \hline Sin-2 & Sinusoidal Wave & 2 \\ \hline Sin-3 & Sinusoidal Wave & 3 \\ \hline Sin-6 & Sinusoidal Wave & 6 \\ \hline Sin-9 & Sinusoidal Wave & 9 \\ \hline Sin-11 & Sinusoidal Wave & 11 \\ \hline Sin-14 & Sinusoidal Wave & 14 \\ \hline Sin-17 & Sinusoidal Wave & 17 \\ \hline WN-2 & White noise & 0–50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Testing cases.  
It implies that the behavior of the soil remains nearly constant under a given intensity of 0.1 g inputs.
# Sustainability as a Challenge and Driver for Novel Ecosystem 6G Business Scenarios
## 3 Results
### Communities Scenario 1D
#### Business Theme #2
Application of future immersive digital realities can be found across industries facilitating novel ways of learning, understanding, and memorizing subjects in many sciences such as chemistry, physics, biology, medicine, and astronomy. Telco Broker Scenario 2B  
In this incumbent-driven value creation scenario, value configuration is open and ecosystem. Traditional mobile network operators have focused on retaining the customer relationship and transformed to capture value from data and services via the service platform ecosystem. Technology vendors manage and orchestrate the network technology ecosystem and offer an efficient infrastructure as a service. End-to-end automatized service-driven network management and dynamic service provisioning are used for efficient, flexible, and scalable extraction of value from connectivity. For the spot markets of virtualized network resources with certain performance attributes, automated service management is the key enabler across the network, with full key performance indicator (KPI) granularity. In particular, virtualized communication, computing, storage, and analytics resources at the intelligent edge of the architecturally disaggregated 6G system have become a new source of value creation enabling real-time hyperlocal access to applications and services such as augmented and mixed reality. In distinct locations, sub-network and private local network deployments can provide premium connectivity based on service-level agreement. Premium service requires security guarantees and proofs of the verifiability and trustworthiness of the network. The decoupling of technology platforms in the telco broker scenario has lowered the barriers for entry and allows multiple stakeholders to participate in the ecosystem. Moreover, fine-grained modular network architecture and open vertical and horizontal interfaces combined with the service platform marketplace facilitate the long tail of mass customization, localization, and closer user interaction. This will open business to a variety of smaller payers, leading to innovation, and possibly to commoditization.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Multi-Robots Path Planning
#### 4.4.2 Results Analysis  
The results for the three approaches are shown in Table 4. It can be seen from the data in the table that multiple robots S\({}_{1}\) and S\({}_{2}\), based on the quantized artificial bee colony algorithm, increase the path length and shorten the running time by using priority-based path planning compared with those in S\({}_{1}\) and S\({}_{2}\) in QABC. After the introduction of the circular collision detection operator, the safety and directivity of the second layer's local path planning in the two-layer programming method are enhanced, and the ability to search the optimal solution using QABC is improved. Meanwhile, the application saves time, reduces work cost, and effectively improves the work efficiency of multi-robots. The optimization efficiency of QABC is improved by approximately 13.31% and 17.87% compared to the other two algorithms, respectively.
# Generating Job Recommendations Based on User Personality and Gallup Tests
## 4 Experimental Evaluation
### Pre-Processing  
Before analysis, input data undergoes rigorous preprocessing:  
* **Job Descriptions**:
* Raw text is cleaned (stopword removal, lemmatization) and structured into (title, sector, industry) tuples.
* GPT-4 processes these tuples using the prompt in Figure 2 to produce a ranked list of the 34 CliftonStrengths traits per job.
* Trait weights are min-max scaled to [1, 100] to prevent magnitude dominance in Euclidean distance calculations.
* **Gallup Test Results**:
* Global trait weights are monitored via a 34 \(\times\) 34 matrix tracking cumulative weights across jobs.
* Traits exceeding \(\pm 2\sigma\) from the mean undergo log-normalization (Equation (2)) to mitigate skew that could disproportionately affect Euclidean distances.
* Corrected weights are clipped to [5, 95] to preserve ordinal relationships while limiting extreme values.  
We also have a baseline method that follows a different approach: First, it collects user test responses to the Strengths Assessment Test and sends them to GPT-4, which generates a concise personality summary. This summary is then converted into a numerical embedding using SentenceTransformers. Each profession's description is also converted to vector embeddings. We then calculate the cosine similarity between the user's embedding and the embedding of each profession to measure how closely they match. The professions are ranked by similarity, allowing the system to suggest careers based on a deep semantic understanding. By comparing our proposed method with this baseline, we aim to assess the benefits of incorporating personality traits into job recommendations using different distance metrics.  
The preprocessing pipeline was designed to ensure equitable performance across all distance metrics. For instance, min-max scaling and outlier clipping mitigate Euclidean distance's sensitivity to magnitude variations, while L2 normalization of baseline embeddings guarantees cosine similarity and focuses solely on angular similarity. These steps enable meaningful comparisons between our trait-based method and the semantic-search baseline.
# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 2 Development of Optical Convolution Neural Network
### Optical CNN Based on Optical Diffraction
In 2023, using the two-dimensional Dammann grating as the key element for generating multiple displaced images, massive parallelism convolution acceleration was experimentally demonstrated with a computing accuracy of 8 bit [71]. A spatial light modulator (SLM) was used for data input, and the distance between the data input plane and Dammann grating corresponded to the kernel stride. The MNIST database was classified with an accuracy of 97.3%. Figure 3d shows the lensless architecture of the optical CNN [72]. In this scheme, the amplitude mask was placed close to the sensor. Different pixels of the input image were transmitted through different portions of the amplitude mask, underwent different attenuations, and finally focused on the camera sensor. The image sensor performed a weighted sum of the beams, where the weights were determined by the amplitude mask. The accuracy was up to 97.21% on the MNIST database. The on-chip Fourier transform scheme was also investigated to realize the optical CNN [73; 74; 75; 76]. The Fourier transform property of integrated star couplers was simulated to realize the optical CNN in 2020 [74]. In 2022, the optical integrated diffractive neural network (IDNN) chip in Figure 3e was fabricated on the silicon-on-insulator (SOI) platform [73]. Two on-chip cascaded diffractive cells were used to conduct the operation of the Fourier transform and inverse Fourier transform, where 10 Mach-Zehnder interferometers (MZIs) were used to load the input data and 10 MZIs were used for kernel adjustment. A convolution operation was performed on the proposed integrated diffractive neural network chip, and the recognition of 1D sequences and 2D images was carried out experimentally. In addition to optical CNN, direct classifications of Iris flower, MNIST dataset, and Fashion-MNIST dataset were conducted using the integrated diffractive neural network chip with experimental accuracies of 98.0%, 91.4%, and 80.4%, respectively. In addition to the above realization, other interesting schemes have also been reported, such as in situ training [77], position robustness [78], incoherent light-based [79], and so on [80; 81], which contributed to the further development of optical CNNs. Figure 2: The typical structure of a “\(4F\) system” to realize an optical CNN.
## 3 Materials and Methods  
### Dataset Acquisition and Processing  
Two image datasets of tomatoes of the \"Plum\" variety at different ripeness stages were collected in greenhouses. Both datasets were made publicly available at the open-access digital repository Zenodo:  
* AgRobTomato Dataset [61];
* RpiTomato Dataset [62].  
AgRobTomato Dataset images were collected on two different days (6 and 8 August 2020) at a greenhouse in Barroselas, Viana do Castelo, Portugal. To increase the representativeness of the data, the mobile robot AgRob v16 (Figure 1a), controlled by a human operator, was guided through the greenhouse inter-rows and captured RGB images of the tomato plants using a ZED camera (see [[https://www.stereolabs.com/zed/](https://www.stereolabs.com/zed/)]([https://www.stereolabs.com/zed/](https://www.stereolabs.com/zed/)), accessed on 15 August 2021), recording them as a video in a single ROSBag file. The video was converted into images by sampling a frame every 3 s to reduce the correlation between images, ensuring an overlapping ratio of about 60%. The images collected on the two days were merged, resulting in a dataset of 449 images with a resolution of 1280 \(\times\) 720 px each.  
From a different greenhouse located in Amorosa, Viana do Castelo, Portugal, on 15 June 2021, a total of 60 tomatoes with different ripeness stages were selected and RGB images of each fruit were captured from different perspectives. The images were taken with a Raspberry Pi Computer Model B (see [[https://www.raspberrypi.com/products/raspberry-pi-4-model-b/](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/)]([https://www.raspberrypi.com/products/raspberry-pi-4-model-b/](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/)), accessed on 15 August 2021) with 4 GB RAM, connected to a Raspberry Pi High Quality Camera (see [[https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera/](https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera/)]([https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera/](https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera/)), accessed on 15 August 2021) (12.3 MP and 7.9 mm diagonal image size) with a 6 mm (wide angle) CS-mount lens with 3 MP (Figure 1b). A total of 258 images were obtained, which made the RpiTomato Dataset.  
The general focus of the ML field is to predict an outcome using the available data. The prediction task can be called a \"detection problem\" when the outcome represents a single class. If the outcome represents different classes, it means a \"classification problem\" [63]. This study aims to detect and classify tomatoes using two one-stage object detection frameworks (SSD and YOLO). When it comes to classification, to differentiate the fruits  
Figure 1: AgRob v16 (a) and the Raspberry Pi High Quality camera (b) used for image collection.  
according to their ripeness stage, four classes were defined based on the USDA colour chart for fresh tomatoes [64], as presented in Figure 2.
# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 3 Case Study
### Criticality Analysis (CA)
As shown in Figure 6, no maintenance variables have been inserted in the model, not because the maintenance activities have not been considered, but because they have been incorporated within the failure rate functions of the various failure modes. For the FM9 failure mode, a misalignment of the guide of an IF THEN ELSE cycle, with a cycle time of two quarters, has been set. This means that, thanks to the adjustment operations carried out every two months, the conditions of the guide, seen from the point of view of the alignment, are returned to the 0 state, as shown in Figure 7.  
On the other hand, for the failure mode FM10 shown in Figure 8, a cycle time of 2 years was set. This is because the guide is changed every two years, therefore its wear conditions are reset. Figure 6: AS-IS model. \[\text{f(t)}=\lambda*exp^{-\lambda st}\]0 AS-IS FM9 Guide misalignment. For the failure modes FM19 (transfer chain breakage) (Figure 9) and FM1 (tunnel chain breakage) (Figure 10), on the other hand, cycle times have been set as 3 and 4 years, respectively, because the two chains are changed with such intervals as required by the preventive maintenance plan drawn up by the manufacturer. \[\text{f(t)}=\lambda*exp^{-\lambda st}\]1 AS-IS FM10 Guide usury. \[\text{f(t)}=\lambda*exp^{-\lambda st}\]2 AS-IS FM1 Tunnel chain breakdown. \[\text{f(t)}=\lambda*exp^{-\lambda st}\]3 AS-IS FM19 Transfer chain breakdown. Once the trends of the various failure rates have been identified, all that remains is to identify that of the machine. To do this, the total failure rate is obtained from the probability that all events can occur. The events can be considered as s-independent. The complete system status is presented in \[\text{f(t)}=\lambda*exp^{-\lambda st}\]4  
From the graph (Figure 11), it can be seen how the maintenance actions greatly influence the system's failure rate. Looking at all the graphs, you can also guess that the failure machine rate, the failure rate of the entire machine, is greatly influenced by the failure rate of the FM9 guide misalignment. This is because it is the most frequent failure that occurs. The simulation was made regarding the AS-IS status of the system, thus considering the maintenance policies adopted in the company. In the following, some corrective actions and their possible results are presented.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 3 Materials and Methods
### Packet Structure
After each symbol, the timing and phase error DPLLs are updated. The DPLLs each consist of an error detector and a loop filter. The exact phase error of the \(k\)-th symbol \(\delta_{k}\) is  
\[\delta_{k}=\angle(a_{k})-\angle(a_{k}), \tag{9}\]  
where \(\angle\) denotes the phase angle of a complex number. To avoid computing the angle explicitly--which is computationally expensive--we approximate the phase error by  
\[\delta_{k}\approx\Im\{a_{k}\cdot a_{k}^{*}\}, \tag{10}\]  
where \(\hat{a}_{k}^{*}\) is the complex conjugate of the symbol decision. With the PSK constellation points on the unit circle and assuming small errors, the approximation is valid as  
\[\Im\{a_{k}\cdot a_{k}^{*}\}=|a_{k}|\sin(\angle(a_{k})-\angle(\hat{a}_{k})) \approx\angle(a_{k})-\angle(\hat{a}_{k}). \tag{11}\]  
The loop filter for the phase DPLL is an integrator with a filter gain \(g_{\text{P}}\). After every symbol, the estimate of the total phase error \(\hat{\phi}_{k}\) is updated as  
\[\hat{\phi}_{k}=\hat{\phi}_{k-1}+g_{\text{P}}\cdot\delta_{k} \tag{12}\]  
Analogously, the error for the sample timing error DPLL is computed using a derivative timing error detector. The timing error for the \(k\)-th symbol, \(e_{k}^{\text{t}}\), is approximated as  
Figure 7:0  
where \(\hat{y}_{Nk}\) is the slope of the signal of the \(k\)-th sample, and \(N\) is the number of samples per symbol. We provide a detailed explanation of the derivative timing error detector in Appendix B. The loop filter is again an integrator with filter gain \(g_{\text{t}}\), such that the update of the interpolator input \(\beta_{k}\) is computed as  
Figure 7:1