esa-sceva/satcom-chunk-collection huggingface dataset:
# Inferring TLB Configuration with Performance Tools
## 3 Results  
### Data Presentation  
In the analysis of the Intel Xeon CPU, we focused on creating a gradient plot and a heatmap for TLB-specific events.  
The gradient plot shows the rate of change of the specific'set' and event with respect to 'ways'. The graphs include a legend with the respective color for each'set'. This plot provides another perspective for the analysis of HPCs, enabling a quick visual interpretation of which line is the first to increase and how strong the rate of change is. This visually represents the various 'ways' and'sets'. In combination with Tables 1-3, this study infers the TLB configuration. Although the data from the tables is useful, it does not represent the final configuration. This paper demonstrates how, in combination with the gradient plot, the correct configurations are determined. Finally, it is assumed that the size of the TLB is known. This is a reasonable assumption since the size can be determined, as it was, by iterating through increasing combinations of 'ways'-'sets' combinations. To confirm the results of the gradient plot analysis, this study includes heatmaps.  
In our heatmap layout, the'sets' are plotted along the x-axis, while the 'ways' are plotted on the y-axis. This arrangement allows for an intuitive understanding of how changes in 'ways' and'sets' correlate with the output of these events. Additionally, the heatmap includes a vertical legend on the right side. This legend uses a color gradient to denote the intensity of the event's output. The colors range from dark blue, indicating a zero value, to yellow, which signifies the highest recorded value. This color coding provides a quick and effective means to gauge the relative magnitudes of the events across different combinations of 'ways' and'sets'.  
For the Intel Core i9 CPU, we applied the same methodologies as described previously, including the use of identical tools for data collection and analysis. This approach ensured that the only variable in our comparative study was the CPU model itself. By maintaining a consistent methodology, we aimed to isolate the CPU as the primary factor influencing any observed differences in the heatmap data.  
For the IBM Power9, we attempted to apply the same methodologies. We utilized C programs and shell scripts for data collection and analysis. The main difference lies in executing these programs directly from the command line, rather than using VSCode, for data collection and analysis. A significant distinction was also found in the availability and interpretability of HPC events. While the HPC events for the Intel CPUs are well-known and yield interpretable values, provided the rest of the experiment is set up correctly, this is not necessarily the case for the Power9 HPCs.
# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method
### Algorithm Design
#### 4.2.3 Optimal Task Cache  
In solving the task cache problem, we can treat the task offload variable as a constant. Meanwhile, the Formula (7) can be transformed into a function with \(\rho\) as the independent variable. The formula as shown in the follows,  
\[\begin{split}& f(\rho_{s})=\Big{(}1-\varphi^{0}\Big{)}(1-\rho_{s})T _{s,q}^{h}\\ & s.t.\\ & C1:T_{s,q}^{lm}\leq D_{s},\forall s\in V,q\in Q,\\ & C2:\rho_{s}\in\{0,1\},\ \forall s\in V.\end{split} \tag{10}\]  
As shown in (10), C1 denotes that the task needs to be completed within the deadline, and C2 indicates the value range of the task cache state.  
The Formula (10) can be transformed into a linear programming problem about \(\varphi\). This problem can be solved using the branch and bound method to solve the optimal value [41; 42; 43]. The specific process of the algorithm is shown as follows.  
As shown in Algorithm 3, the satellite's task set is converted into a tree, which represents all possible combinations of tasks that need to be cached locally (line 5). The abounding function prunes some non-optimal branches in the tree structure. Each branch uses the depth-first rule DFS to calculate its cost. When all branches are searched, the algorithm returns the optimal solution [41].  
```
Input: Given a directed graph \(G=(V,E,T)\), Given time range \(\tau\), Given the atomic task request queue \(v_{q}\) on each satellite. Output: An array of offloading tasks \(C(V)\)  set \(timeRange=\tau\)  set \(G^{{}^{\prime}}=G(V,E)\) repeat repeat  set \(tree(s)=\mathit{tranToTree}(O(V))\) until\(V\) repeat \(C(V)=search(tree)\) until\(tree\) until\(timeRange\) return\(C(V)\)
```  
**Algorithm 3** Satellite mission buffer calculation method  
As for the influence of the cache size on the offloading decision, the cache of tasks is realized by using the storage and communication capabilities of high orbit satellites. High orbiting satellite resources are precious, so they can not occupy too much space for caching. The cache size depends on the frequency of similar tasks, and tasks with high frequency can be cached. We cache the task calculation results that occur many times to the satellite. The larger the cache space, the fewer tasks to offload, and the greater the total revenue of the satellite network.
# K-EPIC: Entity-PerceIved Context Representation in Korean Relation Extraction
## 4 Experiments
### Results and Analysis
#### 4.3.1 Comparison on the K-EPIC Method  
The performance of the proposed K-EPIC on BERT-Ko-RE [10] and KLUE-RE dataset [11] are depicted in Tables 4 and 5, respectively. First, the proposed K-EPIC\({}_{V}\) method demonstrates a significant increase of 35.97%p for weighted-F1, on average, in comparison to non-EPIC. The difference illustrates that the entity position tokens enhance the RE performance. We also demonstrate that K-EPIC\({}_{S}\) achieves the best performances in mBERT, KLUE-BERT, KoBERT, and KorBERT, among the other methods in all metrics. In the case of HanBERT, K-EPIC\({}_{S}\) obtains the highest micro-F1, and K-EPIC\({}_{SE}\) presents the highest weighted-F1, with 73.72 % and 78.62 %, respectively. KoELECTRA, which has a different model architecture, achieves the highest scores in K-EPIC\({}_{V}\).  
Table 5 demonstrates the results on the KLUE-RE dataset. Similar to previous experimental results, the performance on our K-EPIC\({}_{V}\) increases by 32.44%p for weighted-F1, on average, including all language models, in comparison to nonK-EPIC. Similarly, HanBERT, KLUE-BERT, and KoBERT show the best performance when they utilized K-EPIC\({}_{S}\). The rest of the language models perform better when they exploit K-EPIC\({}_{SE}\).  
From our comparative analysis, we find that the inclusion of the start position token has a positive impact on the final performance. We assume that the closeness to theentity is related to the result. Since Korean postposition is usually located next to the root word, the start position token has more proximity of the entity in the input of the sentence [29]. As a result, K-EPIC\({}_{S}\) mostly achieves the best performance among five Korean pre-trained language models, whereas the performance utilizing K-EPIC\({}_{SE}\) may have been obstructed by the end position token obtaining dissimilar representation to the entity. These results show that our K-EPIC\({}_{S}\) method makes efficient predictions with the entity position information.
## 3 System Description and Main Components
### Communication Channels  
Figure 6 depicts the interaction between the VR application and the Home Assistant. Utilizing the RESTful API, the login module posts user credentials for authentication and receives an authorization token in return, which must be included in the HTTP header of subsequent requests. The initial state for all devices is acquired through a GET message, comprising GPS coordinates for accurate localization within the virtual environment and the current state values of the IoT devices. Device actuations, such as controlling light dimming, are executed by sending PUT messages to the RESTful API with the new value. Upon receiving a correct response, the device is updated to reflect the real status. Following initialization, synchronization is achieved by receiving state updates through subscription to the MQTT service of the Home Assistant. Lastly, visualization of charts and dashboards for retrieving historical data of the devices is facilitated through an embedded browser communicating with the Grafana dashboard of the Home Assistant.  
Figure 5: User interfaces for navigation and interaction in the VR-based Digital Twin: (**a**) selecting a scenario to visit. Possible choices are the internal laboratory, the outdoor streetlight zone, and the parking lots. (**b**) Interaction with 3D virtual objects by pointing the controller ray. Interactive objects are highlighted in yellow when hovering. (**c**) Selecting a teleport cylinder to move to a different position (about 4 m away from the current position).  
Figure 6: Communication channels between the IoT platform (Home Assistant) and the VR Digital Twin. Using a RESTful API, the Digital Twin can collect data and send control commands.
## 3 Results  
### MODIS-PSN Model Validation  
In this study, four vegetation ecotypes were selected from the Qinghai-Tibet Plateau: evergreen confierous forests, wetlands, alpine grasslands, and shrublands. Comparison with MODIS GPP data reveals that the scatter plots and fitted curves of the simulatedversus observed GPP data demonstrate that the model effectively simulates the GPP trend. The results showed that the coefficients of determination between the simulated GPP and the GPP obtained from the flux site for the MODIS-PSN model evergreen coniferous forest, alpine grassland, wetland, and shrubland were 0.91, 0.80, 0.89, and 0.94, respectively; the model and station R\({}^{2}\) data are similar to the results of a study by [PERSON] et al. [40] and are higher than the coefficients of determination between MODIS GPP products and flux sites, which were 0.65, 0.69, 0.86, and 0.77, respectively. The Root Mean Square Error (RMSE) values of simulated GPP compared to flux sites were 16.01 gC/m\({}^{2}\)/8 day, 10.84 gC/m\({}^{2}\)/8 day, 6.77 gC/m\({}^{2}\)/8 day, and 6.99 gC/m\({}^{2}\)/8 day, respectively, while the Root Mean Square Error values for MODIS GPP versus flux sites were 21.06 gC/m\({}^{2}\)/8 day, 13.59 gC/m\({}^{2}\)/8 day, 7.55 gC/m\({}^{2}\)/8 day, and 13.92 gC/m\({}^{2}\)/8 day, respectively. The simulated data are all lower than the MODIS GPP product, indicating that the accuracy of the simulated GPP product has been greatly improved. The results of alpine grassland validation are shown in Figures 2 and 3.
## 2 UAV-Assisted Communication Network with Multi-Mobility Multi-Trajectory Cases  
UAV communications, as an important component of the space-air-ground-sea integrated communication network, can play an important role in various emergency communication scenarios. As shown in Figure 1, the flexible deployment of a UAV in the air can make it a mobile BS, and can provide wireless communication for ground BSs, end users, ships at sea, and satellites. UAV-assisted communications can provide an effective guarantee for maritime communications and ground wireless communications, especially in military and urban transportation emergency communications; the wireless channel between the ground user and the BS is complex and full of interference, which can lead to poor communication quality. In military operations, the ground BS is easily destroyed, and the signal cover range of the BS is limited. UAVs, as aerial BSs for auxiliary communications, can provide effective communication support for ground users, and can also move flexibly, to eliminate communication blind zones caused by lack of ground BSs, and by complex mountain environments. With regard to urban transportation emergency communications, because fixed ground BSs cannot meet sufficient and sudden communication needs, the simple deployment and abundant number of UAVs can realize the emergency communication needs of a large number of users on the ground.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Comparison of Different Versions of EfficientNet  
We designed this experiment to choose the EfficientNet version for our proposed framework that classifies chronic wounds more accurately. For this purpose, we fine-tuned the EfficientNets (B0-B7) to classify injuries. We trained the different EfficientNets on the augmented training set of both datasets (AZH and Medetec) for 3-class classification, and then we evaluated the trained model on the testing set. The results of the experiment are reported in Table 3.  
EfficientNet B5-B7 are larger models with more depth and several parameters. These models are computationally complex and lead to the model overfitting problem because of the smaller size of the wound datasets. From the results in Table 3, it can be interpreted that the EfficientNet-B4 classifies the wound types with the highest accuracy. So, based on these results, we utilized EfficientNet-B4 in our proposed framework, SEEN-B4.
## 3 Linear Optical Methods for Pulse Characterization
### Dispersion Measurement of Optical Elements
Cooled \(CCD\) can reach such signal-to-noise levels, where _SSRI_ is capable to provide 0.02 fs\({}^{2}\) and 0.7 fs\({}^{3}\) precisions in \(GDD\) and \(TOD\), respectively. Without cooling, 0.1 fs\({}^{2}\) and 2 fs\({}^{3}\) can be considered typical values. The effect of bandwidth, fringe visibility and density, spherical phase fronts (based on Gaussian beam approximation), optical path fluctuations and intensity irregularities were also examined separately in Reference [57]. When the determination of specific phase derivatives are the aim of an experiment, the precision is affected by the measurement accuracy of the geometrical length of the object. Depending on the length of the object, e.g., interferometric length measurement methods (for under mm scale), micrometer screw gauge (for mm scale), vernier caliper (for mm and cm scale) or laser rangefinder (for cm and m scale) are to be used. Hence, the geometrical length can be determined with an accuracy higher than 0.1 percent, which is at least one order of magnitude better than the accuracy of the phase derivatives. A modified version of crossed-beam interferometry-based techniques combined with _SPIDER_ and _FROG_ methods resulted in the so-called spatially encoded arrangement for temporal analysis by dispersing a pair of light \(E\)-fields, _SEA-TADPOLE_[51]. This method reveals interference patterns almost identical with that of the _SSRI_ methods, although the experimental implementations of the two methods are different. The implementation of spatial encoding significantly improved the accuracy of the former _SPIDER_ and _FROG_ methods [58; 59; 60; 61; 62].
## 2 Design, Fabrication and Characterization
### Characterization  
Two sub-miniature-A (SMA) connectors were connected to the ends of the strip conductor to obtain a solid connection with the silver paste, as shown in Figure 1b. The microstrip was stretched by a program-controlled moving platform. A vector network analyzer (Keysight E5071C) with typical 50 \(\Omega\) port resistance was used to record the RF characteristics of the microstrip under a strain from 1 GHz to 18 GHz. Short-open-load-thru (SOLT) calibrations were conducted to reduce the error before measuring the RF characteristics [21]. The DC resistance was measured by a digital source meter (2400, Keithley Instruments, Cleveland, United States). The morphology and microstructures of microcracks were characterized by a scanning electron microscope (SEM, ZEISS GeminiSEM 500).
## 2 System Model
### The Second Scenario (the Benchmark)
\]  
\(\theta_{3n}^{(D_{2})}\) is the adjustable phase applied by the \(n\)th reflecting element of \(S-I_{3}-D_{2}\). In addition, \(h_{I_{3}D_{2},n}\) is the channel gains with \(h_{I_{3}D_{2},n}=d_{I_{3}D_{2},n}^{-\alpha/2}\bar{h}_{I_{3}D_{2},n}e^{-i\theta_ {3n}^{(D_{2})}}\)[35], where \(d_{I_{3}D_{2}}\) is the distance for the \(I_{3}-D_{2}\) link, and \(\delta_{3n}^{(D_{2})}\) is the phase of the channel gains. \(\overline{h}_{I_{3}D_{2},n}\) following a Rayleigh distribution is the amplitude of the channel gains. Moreover, we assume that the IRS has full knowledge of the channel phases of \(h_{I_{3}D_{2},n}\). Similar to (9), the resulting SNDR at the legitimate user \(D_{2}\) to decode \(x_{1}\) can be formulated as  
\[\gamma_{x_{1},D_{2}}^{(2)}=\frac{\gamma\chi_{1}\Big{|}B_{1}^{(1)}+B_{2}^{(1)}+ B_{3}^{(1)}\Big{|}^{2}}{\gamma\chi_{2}\Big{|}B_{1}^{(1)}+B_{2}^{(1)}+B_{3}^{(1)} \Big{|}^{2}+\Big{(}\mathrm{Y}_{S}^{2}+\mathrm{Y}_{D_{2}}^{2}\Big{)}\gamma\Big{|} B_{1}^{(1)}+B_{2}^{(1)}+B_{3}^{(1)}\Big{|}^{2}+1}, \tag{17}\]  
where \(B_{3}^{(1)}=d_{S_{3}}^{-\alpha/2}d_{I_{3}D_{2}}^{-\alpha/2}\eta\sum\limits_{n =1}^{N_{3}}\bar{h}_{S_{1}S_{n}}\bar{h}_{I_{3}D_{2},n}\). To obtain the maximum value of \(\gamma_{x_{1},D_{2}}^{(2)}\), we need to eliminate the channel phases. Similar to the method in [35], by setting the phases \(\theta_{3n}^{(D_{2})}=\phi_{3n}+\delta_{3n}^{(D_{2})}\) for \(3n=1,\ldots,N_{3}\). After SIC, the resulting SNDR at the user \(D_{2}\) to decode \(x_{2}\) can be formulated as  
\[\gamma_{x_{2},D_{2}}^{(2)}=\frac{\gamma\chi_{2}\Big{|}B_{1}^{(1)}+B_{2}^{(1)}+ B_{3}^{(1)}\Big{|}^{2}}{\Big{(}\mathrm{Y}_{S}^{2}+\mathrm{Y}_{D_{2}}^{2}\Big{)} \gamma\Big{|}B_{1}^{(1)}+B_{2}^{(1)}+B_{3}^{(1)}\Big{|}^{2}+1}. \tag{18}\]
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 5 Immersive Audio-Visual Experiments
### Experimental Procedure  
Overall, an entire experimental session was divided into three sub-sessions: Display Session, Training Session, and Main Session. Figure 3 presents an illustration of the sub-sessions used during all three perceptual experiments, which are described next. The same procedure was used in all three experiments.  
* _Display Session_ In this session, participants observed a set of original video clips and their distorted versions (processed video sequences--PVS). The session had the objective of familiarizing the participants with the quality range of the sequences in the experimental session. In this session, an original source stimuli sequence and a corresponding PVS were presented to the participant. This procedure was repeated for each HRC of all experiments. Once the display session was over, the experimenter asked the participant if he/she have perceived the differences between the sequences.
* _Training Session_ In the training session, the participant performed a demonstration run of the main session. For this session, the objective is to train subjects on how to insert their responses using the quality assessment interface. After each test stimuli was presented, participants were asked to rate the overall audio-visual quality using a five-point Absolute Category Rating (ACR) scale, ranging from 1 to 5. The quality scale was labelled (in Portuguese) as \"Excellent\", \"Good\", \"Fair\", \"Poor\", and \"Bad\" (Figure 4). A second question is related to the personal opinion of the participant regarding the video clip content. A second five-point ACR scale is presented to the participant with the following labels: \"Intriguing\", \"Interesting\", \"Neutral\", \"Uninteresting\",  
Figure 3: Sub-sessions of the perceptual experiments.  
and \"Boring\" (Figure 4). The labels for the second scale were inspired by the speech experiment reported in [16].
* _Main Session_ In this session, the actual experimental task was executed. A number of video clips from the entire stimuli pool were presented to the participants in a randomised fashion. No repeated content was allowed; that is, no two videos had the same (source) content. In each session, participants assessed five PVSs corresponding to each HRC, with each PVS being rated by approximately five participants. To avoid fatigue, a break was introduced in the middle of the experiment. Overall, the entire experimental session lasted, on average, 50 min.
# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 1 Introduction
This paper aims to propose an IoT botnet detection model based on DBO-Catboost. In this model, the DBO algorithm is improved by incorporating a centroid opposition-based learning strategy to generate the initial population. The improved DBO algorithm produces initial solutions that are closer to the algorithm's optimal solution. The improved DBO algorithm is then used to optimize the hyperparameters of Catboost, and applied to the IoT botnet detection. The experiments utilize real IoT datasets, Botnet and Bot-IoT, and make comparisons with existing detection models. The experimental results demonstrate that the IoT botnet detection model based on IDBO-Catboost outperforms other state-of-the-art classifiers in terms of accuracy and F1 score. It also exhibits higher detection efficiency and possesses a certain degree of generalization capability.
## 5 Discussion
Each one has its own constraints and is more suitable for specific conditions concerning economic costs, hydraulic/sedimentologic characteristics, equipment availability, field staff capacity, desired spatial-temporal resolution and hydrological regime. Figure 17: Temporal variation of a longitudinal bed profile from Delft3D simulations using Van Rijn formulations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Advantages** & \multicolumn{2}{c}{**Disadvantages**} \\ \hline  & & & \(\bullet\) & Limited spatial resolution. \\  & & & \(\bullet\) & Limited temporal resolution. \\  & & & \(\bullet\) & Intrusive method, causing bed disturbance. \\  & & & \(\bullet\) & High uncertainties related to the sampler \\  & \(\bullet\) & Physical sampling allowing bed & & (hydraulic) efficiency and the sampling \\ Helley-Smith Sampler & \(\bullet\) & material analysis. & & efficiency. \\  & \(\bullet\) & Cheaper instrument. & \(\bullet\) & Time-consuming and dangerous during floods. \\  & & & \(\bullet\) & The representativeness of the sample is affected by the presence of bedforms. \\  & & & \(\bullet\) & Originally developed for environments carrying coarse sediments. \\ \hline  & & & \(\bullet\) & The measured average moving-bed velocity is influenced by the ADCP frequency and the acoustic pulse length. \\  & \(\bullet\) & Fisher spatial and temporal resolutions compared to physical sampling. & \(\bullet\) & The measured average moving-bed velocity is influenced by bedload particle grain size. \\ ADCP-BT & \(\bullet\) & Nonintrusive method. & \(\bullet\) & Uncertainties related to the determination of the parameters: thickness of the bedload active layer and sediment concentration in the bedload layer. \\  & \(\bullet\) & Results are useful to correct water discharges estimates based on the bottom-tracking feature. & \(\bullet\) & The representativeness of the sample is affected by the presence of bedforms. \\  & & & \(\bullet\) & Specialized (and expensive) equipment and estimated staff are needed. \\  & & & \(\bullet\) & There must be bedforms (especially dunes). \\  & & & \(\bullet\) & A systemic bias related to dune migration not captured in different time-sequenced 3D bathymmetric data must be corrected. \\  & & & \(\bullet\) & Necessity to determine the geometric bedforms characteristics and celerity to correct the scour volumes and bedload estimates. \\ Modified ISSDOTv2 & \(\bullet\) & The method is applied over longitudinal bathymmetric profiles. \\  & \(\bullet\) & Bedload estimates tend to be more accurate, since they consider a wide area and the spatial variability of bedload transport. \\  & & & \\ \end{tabular}
\end{table} 
Table 4: Advantages and disadvantages of methods applied to estimate bedload sediment transport in sand-bed rivers with bedforms.