esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Integration of CBAM, Transformer, and JPU Modules  
In our proposed retinal vessel segmentation framework, the collaboration between the CBAM, Transformer, and JPU modules plays a pivotal role in enhancing feature representation, contextual understanding, and segmentation accuracy. This subsection delves into the interactions between these modules and their collective impact on model performance.  
The CBAM first processes the input feature maps by applying both channel and spatial-attention mechanisms. By recalibrating feature maps, CBAM emphasizes important features while suppressing irrelevant ones. The output from CBAM serves as enriched input to the Transformer module, which further processes these features by capturing long-range dependencies and contextual relationships. This synergy allows the Transformer to leverage the discriminative features enhanced by CBAM, resulting in a more robust feature representation that is essential for accurate segmentation.  
Mathematically, the integration can be represented as follows:  
\[\text{Transformed Features}=\text{Transformer}(\text{CBAM Output}) \tag{10}\]  
Here, the output of the CBAM module is fed into the Transformer module, enhancing the contextual understanding of the input data.  
Following the Transformer module, the Joint Pyramid Upsampling (JPU) module fuses multi-scale features to refine the segmentation output. The JPU leverages the feature representations processed by the Transformer, allowing it to effectively capture fine details while integrating contextual information across different scales. The fusion process in JPU utilizes the outputs from various convolutional operations to create a comprehensive feature representation:  
\[\text{JPU Output}=\text{Conv2D}(\text{Concatenate}(\text{Transformed Features},\text{conv1}\times 1,\text{ conv2}\times 2,\text{conv3}\times 3)) \tag{11}\]  
In this equation, the features output from the Transformer are concatenated with the results from the JPU's convolutional operations, enabling the model to effectively leverageboth high-level semantic information and low-level spatial details. This integration is crucial for accurately segmenting vessels of varying sizes and intensities.
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 3 Joint Optimization Algorithm for the Relay Transmit Power and PS Ratio
### Case 1  
When \(P_{RF}^{i}(t_{i})<P_{th}^{1}\) (i.e., \(s_{i}=0\)), we have \(P_{H}^{i}=0\) and \(P_{i}=0\), leading to a communication outage between \(R_{i}\) and \(D\). As a result, the EE is zero.
# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 4 Identifying Metaphors  
Depending on the approach used in metaphor identification, systems and models for machine metaphor identification can be divided into 3 main groups (The division is inspired by the division given by [PERSON] [14], with the addition of neural network approaches and vector representations of words. In her paper, [PERSON] divides the methods of identifying linguistic metaphors into (1) Approaches using hand-coded knowledge and lexical resources, (2) Statistical learning, (3) Metaphor identification as a Classification Problem):  
1. Hand coded knowledge and use of lexical resources
2. Statistical and machine learning methods
3. Neural networks and word embeddings  
Early approaches to metaphor identification were based on hand coded knowledge and dictionary creation, which is a challenging, time-consuming task, and it is questionable whether it is even possible to annotate and list all metaphors in a language, bearing in mind that novel metaphors appear every day and that language is a living organism that is constantly changing. With statistical and machine learning methods, the problem of metaphor identification is approached as a problem of supervised [22; 23; 28; 29] or unsupervised learning [22; 30; 31], while some authors combine both approaches [32]. These approaches use traditional statistical methods, and some of the models also use static word embedding (e.g., Ref. [23]).  
Approaches with neural networks also use, in addition to neural networks, some of the word embeddings, being static [33; 34; 35; 36; 37; 38; 39; 40] or contextual word embeddings [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51] (Brief overview of word embeddings is presented in the section _Neural networks and word embeddings_). It is these models that have given the best results in identifying metaphors in recent years.  
Below is a more detailed overview of the above systems and models. In the Appendix A, a table with an overview of metaphor identification research, organized by the year in which each of the research was published, is presented.
## 5 User Configuration  
Figure 8 presents examples of how dry eye patients, either children or adults, may use the tears microinjector. It may be placed inside a waterproof portable case attached to the arm (like the ones for cell phones or ipads) with Velcro tape, the shirt pocket, or any other comfortable option chosen by the user. It is convenient (but not mandatory) to attach the silicon capillary coming from the micro injector to the arm of comfortable or already prescribed glasses. One end of the capillary is connected to the syringe reservoir in the microinjector and the other end must be attached to the patient's eye commissure, held in place with medical tape. An Android-based cell phone is required to configure the parameters of the device and to control all processes. To start the microinjection of tears, the user must follow the next procedure:  
1. Turn on the device by pressing on the start button.
2. To connect his/her Android-based cell phone via Wi-Fi with the ESP8266 EX microcontroller web server. The name and password of the network must be previously assigned when programming the microcontroller. Easy to remember names are suggested, like \"Dry Eye\" for the network's name.
3. Once connected, a web browser opens at the IP address of the server, and the user-friendly interface shown in Figure 9 is displayed. This interface is controlled by the Algorithm A1, it has enter and touch buttons to perform every possible action offered by the microinjector and to introduce input values.  
* _Intervals_: waiting time between consecutive injections, \(t_{inter}\) (number in s).
* _No. Steps_: number of steps to be rotated by the engine, \(n\).
* SAVE : Saves input parameters.  
Figure 7: Continuous pulsed flow of the microinjector. \(t_{inter}\) indicates the time interval (in s) between consecutive injections, \(t^{\prime}\) is the active injection time where the microprocessor pushes the plunger.
# Wear Behavior of Uncoated and Coated Tools in Milling Operations of AMPCO (Cu-Be) Alloy
## 3 Results and Discussion
### Wear Analysis  
Each of the tested tools were subjected to SEM analyses, to evaluate the wear. The flank wear of the tools (VB) was also assessed, and the values obtained are presented later in this section.  
The influence of the feed rate and cutting length in the tools was studied, offering a comparison between the coated and uncoated tools. In Figures 8-10, the wear of the tools tested at 750 mm/min and 1500 mm/min feed rate, and 18 m, 36 m, and 48 m cutting lengths can be observed.  
By observing Figures 8-10, it is possible to conclude that increasing the feed rate to 1500 mm/min induces a significant increase in tool wear, for both coated and uncoated tools. However, there is a difference in wear behavior for both these tool types: for the coated tools tested at a feed rate of 750 mm/min, a premature wear of the coating can be found out at a cutting length of 18 m. With the increase in cutting length, the amount of wear sustained by the tools also increased significantly. This increase in wear was observed in uncoated tools; however, in a lesser degree, it was particularly found for higher cutting length values.
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 5 Conclusions  
Rejection of ions in single salt solutions were found to be pH-dependent. Owing to their large size, all-metal cations (Mg, Cu, and Mn) were highly rejected (97% and above) within the tested pH range. Below the membrane IEP, cation rejection increased slightly as a result of stronger electrostatic repulsion between the cations (co-ions) and the positively-charged membrane (e.g., the rejection increased from 97% at pH 5 to 99% at pH 2).  
The speciation of divalent sulfate to monovalent bisulfite at pH 3 and below accounted for the sharp fall in overall sulfate rejection. Notably, sulfate rejection fell from 99% at pH 4 to 84% at pH 2. The effect of sulfate speciation on rejection was more pronounced under conditions of a weaker membrane charge shielding at lower ionic strength.  
In general, the rejection of ions in mixed salt solutions were lower compared to the rejection of single salt solutions mainly due to the effect of membrane charge shielding at increased ionic strength. For a sodium silicate solution, silicate rejection fell from 52% to 38% when magnesium sulfate was added to the feed solution. Donnan effects were more prominent at higher salt concentrations. Rejection of monovalent co-ion (chloride) decreased with increasing concentration of an accompanying divalent co-ion (sulfate).  
For the model AMD solution, a minimal rejection was observed at pH 4. The shift in IEP from 4.5 to 4.0 was most probably due to divalent anion adsorption at the relatively higher ionic strength of the solution. Copper showed an interesting trend of increasing rejection with increasing pH most probably attributed to the formation of a negatively charged complex formation, while other metal cations displayed rejection values in the opposite direction.  
Conceptualization, K.L.Z. and S.V.; methodology, K.L.Z. and S.V.; validation, Y.W.S., K.L.Z. and S.V.; formal analysis, Y.W.S.; investigation, Y.W.S; resources, K.L.Z.; data curation, Y.W.S.; writing--original draft preparation, Y.W.S; writing--review and editing, S.V and K.L.Z.; visualization, Y.W.S.; supervision, K.L.Z. and S.V.; project administration, K.L.Z.; funding acquisition, K.L.Z. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank LANXESS-LAB Ionenaustiauscher GmbH (Bitterfeld, Germany) for financial support. [PERSON] acknowledges his scholarship grant provided by the European Commission--Education, Audoviusal, and Culture Executive Agency (EACEA), under the program: Erasmus Mundus Master in Membrane Engineering-EM3E (FPA No 2011e0168, Ed. I, [http://em3e-4 sw.eu](http://em3e-4 sw.eu)).  
The authors declare no conflict of interest.
## 5 Simulation Results and Analysis
### Validation Effectiveness of the DRFTM  
#### 5.2.1 Discussion of Tree Number Parameters  
The initial performance of a single decision tree in a random forest model tends to be relatively poor due to the risk of overfitting. The introduction of attribute perturbation in random forests can further increase model variance, resulting in decreased performance. Additionally, using numerous decision trees in random forests can make the algorithm slow, which requires finding a balance between efficiency and detection capability in trust evaluation models. A commonly used metric for evaluating classification models is area under the curve (AUC). AUC reflects the area under the ROC curve and measures the classification performance, specifically the ability to detect malicious nodes in a trust evaluation model. It provides a numerical value that helps determine the better classifier.  
In Figure 4, we observe the interplay between the number of decision trees, AUC, and runtime. As illustrated in Figure 4a, there is a rapid increase in AUC as the number of decision trees rises. Beyond a certain threshold, the AUC stabilizes at a relatively high value, showing only marginal improvement with further increases in the number of trees. Figure 4b demonstrates a linear correlation between runtime and the number of decision trees. While, theoretically, a higher number of decision trees may lead to more accurate trust evaluation, our experiments indicate that an optimal detection accuracy is achieved at around 25 decision trees. Given the consideration of runtime impact, we set the number of decision trees to 25 in this study.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 3 The NOMA System-Level Simulator
### Structure of the NOMA System-Level Simulator  
The researchers in [10], presented the structure of their simulator in three major blocks; The transmitter, channel model, and the receiver. The channel model connects the transmitter and receiver blocks and is used to transmit downlink data, while signaling and uplink feedback are considered to be error-free. Similarly, our system-level structure of NOMA simulator consists of three major building blocks; interference structure, downlink channels and measurement models. The link measurement model measures the link quality and performs link adaptation and resource allocation for each user in a group, where resource allocation is accomplished by considering the difference in channel gains between the users paired together. Based on the measurements from the link measurement model, the link performance model predicts the BER of the link, based on the received signal-to-interference noise ratio (SINR) and the selected modulation and coding at the transmitter, as presented in [20]. Figure 3 illustrates the interaction between the link measurement model and link performance model in the form of a block diagram.  
The implementation of the presented simulator follows the structure shown in Figure 4. Transmission sites were created to generate the network topology. Each transmission site comprises of one BS, which is responsible for assigning resources, precoding matrices, power allocation, and a suitable modulation and coding scheme to each user connected to the BS. The resources, modulation and coding schemes are assigned to each user based on the channel gain of each user in a group. For simplicity, we considered two users in each group, that is a pair of users with one user having a high channel gain and the other user having a low channel gain. For performing SIC, the users with a stronger channel gain follow the same interference cancellation techniques as explained in the earlier sections. The scenarios considered in our system-level simulator are explained in the following section.  
Figure 3: Block diagram of NOMA system-level Simulator.
## 3 Results and Discussion
### PLS-Discriminant Analysis (DA)  
Following the PCA analysis, PLS-DA classification was used to classify samples according to the feed source used to rear the BSFL instars (fifth instar larvae and sixth instar pre-pupae). The PLS-DA models were built separately for the two instars as PCA revealed that the physiological age of the larvae might play a role in the classification of larvae samples based on their feed source. The coefficient of determination (R\({}^{2}\)) and standard error in cross-validation (SECV) obtained for the prediction of the feed source used to rear the fifth and sixth instar were 0.91 (SECV: 0.14) and 0.89 (SECV: 0.17), respectively. To identify the main regions of the NIR spectra that contribute to each of the models, the PLS loadings were interpreted. Figure 6 shows the optimal PLS loadings used for each of the PLS-DA models developed. It was observed that bands at 10,336 cm\({}^{-1}\), 7264 cm\({}^{-1}\), 5738 cm\({}^{-1}\), 5200 cm\({}^{-1}\), and 4900 cm\({}^{-1}\) contributed to explaining the PLS model obtained. The loadings used by the PLS model based on the fifth instar were higher than those observed for the sixth instar in the same wavenumbers, 10,192 cm\({}^{-1}\) (O-H), 7296 cm\({}^{-1}\) (O-H), 5840 cm\({}^{-1}\) (C-H and C-H\({}_{2}\)), 5264 cm\({}^{-1}\) (C=O), and 4976 cm\({}^{-1}\) (N-H). In addition, the loadings were  
Figure 5: Loadings derived from the principal component analysis from the black soldier fly samples analysed using near infrared reflectance spectroscopy.  
opposite around 8704 cm\({}^{-1}\), 8320 cm\({}^{-1}\), and 4200 cm\({}^{-1}\) associated with C-H aromatic groups. Overall, these results indicated that the main source of variation between different feed sources and the two different instars are associated with differences in lipids (e.g., fatty acid profiles), protein content, and the presence of aromatic compounds.
## 2 Materials and Methods
The second loading condition was the torque during the rotation. Thus, indications of the maximum torque according to each manufacturer were used (Table 1). Figure 3 represents both forces in the instrument at the same time. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Instrument \#25** & **Manufacturer** & **Conicity (\%)** & **Cross Section** & **NiTi** & **Torque (N-cm)** \\ \hline \multirow{2}{*}{Hyflex CM} & Coltene/Whaledent, & & & & \\  & Altsätten, Switzerland & 4 & Quadrangular & Heat-treated & 2.5 [10] \\ \hline F360 & Komet, Lemgo, Germany & 4 & Quadrangular & Conventional & 1.8 [10] \\ \hline \multirow{2}{*}{One Curve} & Micro Mega, Besancon, & & & & \\  & France & 6 & & & \\  & & & & & \\ \hline F6 skytaper & Komet, Lemgo, Germany & 6 & & & \\ \hline \multirow{2}{*}{Protaper Next X2} & Dentsply Maillefer, & & & & \\  & Baillagues, Switzerland & 6 & Rectangular & Heat-treated & 5.2 [11] \\ \hline \end{tabular}
\end{table} 
Table 1: Technical features (manufacturer, conicity and NiTi alloy) of analyzed instruments and maximum torque recommended by manufacturers (N.cm). \begin{table}
\begin{tabular}{c c c c c} \hline
**Instrument \#25** & **Volume of Instrument (mm\({}^{3}\))** & **Reaction Force (N)** & **Stress Peak (MPa)** & \\ \hline Hyflex CM & 6.30 & 0.69 & 35.73 & 44.30 \\ \hline F360 & 6.07 & 0.89 & 220.05 & 253.41 \\ \hline One Curve & 9.20 & 1.91 & 77.04 & 155.50 \\ \hline F6 Skytaper & 8.28 & 0.39 & 265.22 & 298.01 \\ \hline Protaper Next X2 & 8.07 & 0.64 & 117.78 & 151.02 \\ \hline \end{tabular}
\end{table} 
Table 2: Volume of instruments, reaction force (N) and stress peak of NiTi instruments according to the dentine elastic modulus (18 GPa and 42 GPa). Figure 3: 3D model simulating the in vitro curved canal used for laboratorial studies. Two different loading conditions were considered at the same time: the reaction force (bending) during the instrument insertion (small red arrow) and the torque (large red arrow). Individual canals were modeled according to the instruments conicity and shape, in order to standardize the interaction with the canal walls. Figure 2: Reaction force in bending condition (flexibility) calculated for each instrument during the insertion into the canal. (**a**) Instruments with 4% conicity. (**b**) Instruments with 6% conicity. The cutting-edge motion was simulated in one direction. Therefore, during the loading phase, the instrument rotated, and its curved region pushed the dentine walls in reaction to the curved shape of the canal. For this time point, quasi-static analyses were performed with a non-failure condition since a linear elastic behavior was assumed for all components. The results were reported in terms of strain and von Mises stress for the instruments and von Mises stress for the endodontic canals. For the endodontic instrument, a patch longitudinally to the instrument length was used to analyze and to plot the stress distribution. The stress peaks are summarized in Table 2.
###### Abstract  
In the context of Internet of Things and 5G communication, ensuring the accuracy and reliability of data transmission is critical, especially as the future 6G communication system places a greater emphasis on highly reliable transmission. However, the existing error correction schemes for 5G are found to be insufficient in their error correction capability for the 6G environment. To address this issue, this paper proposes an enhanced error correction scheme, which is an improvement over existing methods. This scheme is based on a combination of cyclic redundancy check (CRC) codes and Polar Codes, optimized through the use of a Successive Cancellation Decoding algorithm. The objective is to enhance the error detection and correction capabilities during data transmission. By leveraging the synergistic application of CRC and Polar Codes, the proposed scheme introduces enhanced sequence interleaving techniques and error pattern analysis, which significantly improve the decoding algorithm's performance and accuracy. Experimental results demonstrate that the proposed scheme, in comparison to the traditional Successive Cancellation Decoding algorithm, effectively reduces the block error rate (BLER), thereby enhancing the high reliability of data transmission.  
6G; error correction; successive cancellation decoding; CRC; polar codes +
Footnote †: journal: Applied sciences  
Article
# Towards Client Selection in Satellite Federated Learning
## 2 Motivation  
In this section, we demonstrate how SFL performance can be improved when the LEO satellite plays the role of the PS. One of the benefits of LEO satellites is their short orbital period. For example, the period of a satellite in a circular orbit with an altitude of 500 km is approximately one hour. However, the restricted communication range between the GSs and satellites inherently limits the access time. The communication window between an LEO satellite and a GS is typically only a few minutes long.  
We simulated a LEO satellite constellation with a total of 120 satellites. We separately counted the number and duration of accesses for the two cases: an LEO satellite as the PS and a GS as the PS. We constructed Walker delta constellations at three different orbital heights of 400 km, 500 km, and 600 km, whose inclinations are 40\({}^{\circ}\), 45\({}^{\circ}\), and 50\({}^{\circ}\), respectively. At each height, five orbits were placed, each with eight satellites.  
In the satellite-to-ground scenario (a GS as the PS), we placed a GS at a location in Shanghai, China, and counted the number and duration of all 120 satellites' visits to the GS within 24 h. In the satellite-to-satellite scenario (an LEO satellite as the PS), we monitored visit numbers and durations from other satellites to the PS.  
As Figure 1 shows, although the PS satellite cannot access all other satellites, some of them can establish a very long communication window with the PS, which is difficult for GSs to achieve. The statistical results show that the average access duration when the satellite is the PS is 6571.9 s, while it is only 555.3 s for the GS. Similarly, Figure 2 shows the number of times the satellite accesses when the GS and the satellite, respectively, are the PSs. When a GS is the PS, each satellite has a relatively equal chance of access, but the access frequency is much lower than when the satellite is the PS. Specifically, when the PS is a satellite, the average number of accesses is 30, while for the GS it is only 8. Furthermore, Figure 3 illustrates the numbers, durations, and temporal relationships of accesses between the clients and the PS. When a GS is the PS, the access in different satellites has no significant differences. However, when a satellite is the PS, some satellites can establish a stable and continuous connection, while others can access the server intermittently, and still others cannot access the server during the simulation period.  
In summary, in FL, we hope that clients have longer connection windows and more frequent access to the PS, but the accessibility performance in the satellite-ground scenario is far less than in the satellite-satellite scenario. Moreover, the revisit time of some satellites is too long in both the satellite-ground and satellite-satellite scenarios, which produces performance loss in both synchronous SFL and asynchronous SFL. Therefore, it is necessary to filtrate clients to improve the overall training performance.  
Figure 1: Comparison of the duration of satellites-to-ground and satellites-to-satellites visits.