esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ensemble Learning Approach  
The ensemble learning method in sentiment analysis has been proven to be effective in improving the performance of predictions by combining the results of multiple models. The basic idea behind this approach is to leverage the strengths of different models for a more accurate prediction. An example of how ensemble learning can be applied in sentiment analysis is by using a voting-based approach. In this approach, multiple models, such as random forest, naive Bayes, and support vector machines (SVM), are trained on the same dataset. During the prediction phase, each model predicts the sentiment of the input text and the final prediction is made by taking the majority vote of all the models. In the study by [PERSON] and [PERSON] (2019) [52], the authors employed an ensemble of five machine learning models including naive Bayes, random forest, support vector machine, bagging, and boosting. The authors used a dataset of 34,661 customer reviews collected from Amazon, which was preprocessed using techniques such as stemming, case folding, stop-word removal, and N-grams. The results showed that the majority voting ensemble method using unigram achieved an accuracy of 89.4%. Similarly, [PERSON] et al. (2019) [53] evaluated the performance of logistic regression, support vector machine, k-nearest neighbors, and an ensemble of these models with majority voting for sentiment analysis. The dataset used consisted of 6328 positive and negative samples and the TF-IDF vectorizer was applied for feature extraction. The results of the 10-fold cross-validation experiments showed that the ensemble model achieved the highest accuracy of 98.99%. In 2021, [PERSON] and [PERSON] [54] evaluated four machine learning models, multinomial naive Bayes, k-nearest neighbors, logistic regression, and an ensemble of these models, for sentiment analysis. The dataset consisted of positive and negative movie reviews taken from IMDb, which underwent preprocessing procedures, including tokenization, stop-word removal, and word stemming. The TF-IDF vectorizer was utilized to extract features. The ensemble model achieved the highest accuracy of 89.40% on the IMDb dataset. [PERSON] et al. (2020) [55] conducted a study to assess the performance of five individual machine learning models and an ensemble of these models in sentiment analysis. The models included Bernoulli naive Bayes, multinomial naive Bayes, linear support vector classification, logistic regression, and Nu-support vector classification. The research used a dataset of movie reviews, consisting of 1000 positive and 1000 negative samples, sourced from the corpora community. The analysis utilized the top 3000 most frequent words as
## 5 Discussion  
X-ray transparent UHMW targets were used here to identify the penetration history of LSCs. X-ray transparent materials are by nature low in density, and this will affect penetration performance and may suggest questions about the present analysis. High-strain- rate dynamic conditions are typically non-linear, so the penetration performance for each jet group might be different against other types of target materials, and a different type of target material might produce slightly different results.  
The theoretical analysis addressed above is based on the location where the jet is created. In other words, all the data in Table 2 are based on the liner collapse point, and this is invisible in the X-ray experimentation. In addition to this, the jet created from the liner collapse point needs to fly around several inches (up to 6 inches, or 152 mm, into the target) before the flight pattern is captured by the X-ray film. This long-distance jet flight will create a large jet speed discrepancy between the theoretical calculation and the experimental measurement due to the severe jet speed reduction during flight. In order to handle this discrepancy, the comparison between the experimentation and the theoretical analysis was accomplished based on the following assumptions: (1) the LSC jet tip in the experimentation (or X-ray images) originates from the 2 \(\upmu\)s after detonation in Table 2; (2) the LSC jet slug in the experimentation (or X-ray images) originates after 10 \(\upmu\)s after detonation in Table 2. This assumption can be made because the data in Table 2 is designed to indicate the start and end point of the liner collapse.  
Based on these assumptions, the three X-ray images are connected and the corresponding theoretical data from the Table 2 are compared (Figure 9).  
In the present paper, the separation of Jet Group A and B was derived from the theoretical analysis based on the jet projection speed and the liner arc deformation rate. Both of these parameters are closely related to many different dynamic parameters during the jet formation and the extreme dynamic process of liner collapse. One of the interesting observations from the X-ray images is the distinct separation between Jet Groups A and B. This clear distinction indicates a significant and drastic change of the dynamic properties of the liner right at that collapse moment. If the clear distinction between Jet Groups A and B is related to the extreme dynamic properties of the liner materials, then the use of different liner materials would deliver an interesting result and should be investigated further.  
Figure 9: Connected X-ray images and corresponding jet properties at the liner collapse point.
## 3 Proposed Approach
### Algorithm for the Objective Function Calculation
If \(flag=1\), then return \((K_{rez}=1,P_{rez}=P,X_{rez}=X)\). // all the information was successfully transferred
5. For \(t=0\). \(6\). If \(flag=0\), then set \(K=K-dK\), // if failed to transmit then try to transmit less
6.  otherwise, \(K=K+dK\). // try to transmit more
7. Set \(dK=dK/2\). // reduce the varying step \(K\)
8. Call \(Calculate\_Traffic(K)\). 9. If \(flag=1\), then set \(K_{rez}=K,P_{rez}=P,X_{rez}=X\). 10. STOP and return \(K_{rez},P_{rez},X_{rez}\). ```  
**Algorithm 1**\(Calculate\_All\_Traffic\)  
```
1. Required: \(K\): amount of transmitted information. 2. Assign \(k=0\). // iteration number
3. Assign \(e>0\). // the stopping criterion
4. Assign \(p_{\max}\). // the second stopping criterion
5. Assign \(p_{i,j}^{(k)}=0\)\(\forall i,j\). // as probability of losses in the corresponding channel at the current iteration
6. Start an infinite loop: 1. Assign \(x_{i,j}^{(k)}=0\)\(\forall i,j\). // total intensity of the information flow through the corresponding channel at the kth iteration
7. For \(y=\overline{1},Y\): // for each target request * For \(h=\overline{1},\overline{L_{y}}\). // for each route in the target request * For \(t=\overline{1},\overline{l_{yh}-1}\). // for each node in the route * Add the part of information included in the corresponding channel at the current iteration: \[q_{yh}=\frac{\omega_{yh}}{\sum_{z=1}^{L_{y}}\omega_{yz}}Q_{y},\] (6) \[x_{m_{yh}[t],m_{yh}[t+1]}^{(k)}=\frac{Kq_{yh}}{\prod_{j=t}^{l_{yh}-1}\left(1-p _{m_{yh}[j],m_{yh}[j+1]}^{(k)}\right)}.\] (7)
8. Calculate \(p_{i,j}^{(k+1)}=p(x_{i,j}^{(k)},\mu_{i,j},N_{i,j})\)\(\forall i,j\). // probability of losses in the corresponding channel at the next iteration * If \(\forall i,j:|p_{ij}^{(k+1)}-p_{ij}^{(k)}|<e\), then STOP the algorithm and return the triple \(\left\{flag=1;x_{rez}=\{x_{ij}^{(k)}\};p_{rez}=\{p_{ij}^{(k+1)}\}\right\}\) as the result. * If \(\exists i,j:p_{ij}^{(k+1)}>p_{\max}\), then STOP and return the triple \(\left\{flag=0;x_{rez}=None,p_{rez}=None\right\}\). // the algorithm did not converge, there is no solution * If none of the criteria are satisfied, then assign \(k=k+1\) and move on to the next iteration of the loop. ```  
**Algorithm 2**\(Calculate\_Traffic\)
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Healthcare  
Health care refers to a system that involves the maintenance and enhancement of medical services. Therefore, a global struggle is happening to improve the quality of healthcare services [141; 142; 143]. Reference [144] highlights that GIS, taken together with non-spatial factors, is being used increasingly in healthcare. Discussing the role of GIS in healthcare, the examples of the use of GIS include those concerned with improving the quality of care [145], analyzing the spatial distribution of diseases [146; 147; 148], planning and assessing location-based services [149; 150], enhancing the accessibility of services [151; 152]. Section 1 of Reference [150] investigates the role of GIS in disease mapping and spatial analysis. They describe the basic elements of geographical analysis, cartographic operations relevant to public health, disease mapping from a statistical standpoint, and clustering methods. In addition, in Section 3, GIS applications in healthcare planning and policy are discussed. For the role of IoT, in Reference [153] two applications of IoT in medical systems namely Clinical Care and Remote Real-time Electrocardiogram (ECG) Monitoring are introduced. For example, in Clinical Care applications, Masimo Radical-7 can be used to monitor the physiological statuses of patients remotely and report it to a clinical staff [154]. IoT and healthcare studies also point out the implementation aspect. Reference [155] describes the implementation of an IoT-based In-hospital healthcare system using ZigBee mesh protocol to monitor the physiological parameters of patients. Reference [156] investigates technological solutions in order to facilitate clinical knowledge transfer and gathering using IoT. Sensors and actuators provided by IoT can be beneficial in evaluating the performance of medical equipment [157]. The condition of patients can also be monitored by tracking their medicine consumption through embedded sensors [154]. As discussed above, healthcare studies vary in topics and target area and the integration of IoT and GIS would provide valuable features. Reference [158] proposed a ubiquitous health system as remote monitoring of the patient system. Using smart-watches data like a heartbeat is measured and is analyzed by GIS according to the position of the patient. Finally, the information is sent to the patient-doctor via smartphones. As another scenario, a risk management system based on IoT, BIM, and GIS is proposed by [33] to monitor real-time risk factors and provide more effective responses. The processing layer of their system hosts GIS which provides the information related to hospitals located near the area they are monitoring with a set of computing and simulation tools.
## 3 Test Results and Analysis
### Mechanical Properties
#### 3.4.1 Degradation of Bearing Capacity
For RRAC members, the compressive bearing capacity of columns varies from \(-\)6% to 9%, the bending capacity of beams varies from \(-\)6 to 18%, and the shear capacity of beams varies from 6 to 12%. In general, the strength or bearing capacity of each specimen increases with the increase of the replacement percentage, that is to say, the strength of the RRAC members after subjection to high temperatures is not worse than that of the ordinary concrete member after subjection to high temperatures. Figure 14: Influence of variation parameters on bearing peak deformation. (**a**) Prism; (**b**) SRRAC column; (**c**) RRAC column; (**d**) SRRAC bending beam; (**e**) SRRAC shear beam; (**f**) RRAC bending beam; (**g**) RRAC shear beam; (**h**) Temperature; (**i**) Replacement percentage. Figure 15: Schematic diagram of energy equivalent method. Comparing RRAC members with SRRAC members, it can be seen that the change of the bearing capacity of RRAC columns with the increase of temperature is roughly equivalent to that of SRRAC members, but the bearing capacity degradation of RRAC beams is more serious than that of SRRAC members. The effect of the replacement percentage on the bearing capacity of RRAC members is basically the same as that of SRRAC members.
## 4 Conclusions  
The proposed two-step modification method showed good prospects for using ferric chloride as an activator to obtain carbon nanocomposites from natural raw materials that can be used to produce supercapacitor electrodes.  
X-ray diffraction studies showed the presence of Fe\({}_{3}\)O\({}_{4}\) nanoparticles in all synthesised carbon materials. Magnetic measurements made it possible to estimate the size of these nanoparticles from 29 nm to 50 nm, depending on the type of initial raw material and the method of composite synthesis.  
The specific electrical capacitances of the nanocomposites synthesised in two steps determined by two different methods were almost identical, indicating that Faraday processes have little effect on the formation of an electrical double layer on the carbon surface. This is also confirmed by measurements of cyclic voltammograms. An important advantage of supercapacitors made from synthesised nanocomposites over supercapacitors made from pure Fe\({}_{3}\)O\({}_{4}\) nanoparticles is their stability of electrical capacitance during long-term cycling.  
Modelling of impedance data made it possible to estimate the effect of meso- and micropores on the capacitance of the supercapacitor, as well as to calculate relaxation times during charging/discharging of these pores. It was found that the synthesised nanocomposites have a porous enough structure that the supercapacitors made from them can be used in devices and circuits that require rapid storage and release of electrical energy.  
Conceptualisation, E.S.; methodology, I.B. and E.S.; software, I.B.; validation, I.B. and E.S.; formal analysis, I.B. and E.S.; investigation, I.B. and E.S.; resources, E.S. and I.B.; data curation, I.B. and E.S.; writing--original draft preparation, I.B. and E.S.; writing--review and editing, I.B. and E.S.; visualisation, I.B.; supervision, E.S.; project administration, E.S.; funding acquisition, I.B. and E.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type of Nanocomposite** & **Relaxation Time for Mesopores,** & **Relaxation Time for Micropores,** \\  & \(\tau\) = C2\(\cdot\)R2** & \(\tau\) = C3\(\cdot\)R3** \\ \hline AC from beet pulp, 2-step synthesis & 0.15 & 1.01 \\ \hline AC from corn stalks, 2-step synthesis & 0.24 & 1.27 \\ \hline \end{tabular}
\end{table}
Table 4: Relaxation time of pore charge/discharge in AC materials.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 3 Network-on-Chip Architecture
### Data Packet Structure and Control Flow  
The design has been optimised for single-flit packets, and for higher scalability (up to 1024 PEs). Flits have a length of 43-bits. They are formed by a header 11-bits long and a payload of 32-bits. The header structure allows for routing the flits within the global 2D-mesh hierarchically and within the ringlets in a tile. We have incorporated the \(xy\)-DoR protocol in our design. Here, each flit is moved on the \(x\)-dimension first, and then along the \(y\)-dimension, in order to reach the destination. To support the \(xy\)-DoR, the flit header contains two fields, each 3-bits long. In such way, a regular mesh of up \(8\times 8\) routers can be created. Within a physical tile, the ringlets are numbered progressively from 0 (the top-left ringlet) to 3 (bottom-right). Thus, a 2-bits field is used to select the destination ringlet. Similarly, PEs within a ringlet are numbered progressively from 0 to 3. Another 2-bit field is used to select the final destination. Finally, a 1-bit field is used to control the virtual channel assignment. The source router does the assignment of the VC. Aiming at separating traffic generated by applications running on the chip and controlling traffic (i.e., configuration packets and statistic requests--reading links' counters), the latter is assigned to VC 0 by default. This design choice also contributes to simplifying routing and control logic. Figure 4 shows the structure of the single-flit packet used by our proposed NoC architecture.  
The adoption of single-flit packets allows us also to simplify the control flow mechanism. Routers' logic manages control signals used to enable packet transfer. This logic uses a backpressure mechanism, meaning every time the router has to send a packet, it raises a request signal towards the selected next hop. In case the selected router/ring station (next hop) has no free slots in the input queue to store the incoming packet, it resets the acknowledge signal. Resetting the acknowledge signal allows for temporary stopping transmission of packets at the source node. The complexity of such logic is reduced, although it ensures correctness and deadlock avoidance. Similarly, ring switches use a backpressure mechanism to control traffic within the bi-directional rings.
## 4 Results
### Model testing
On the other hand, UMAP's dimensionality reduction preserves the global structure of the sedimentary sequence (e.g., the independent clustering of limestone and the energy gradient distribution of coarse and fine sand), providing a high-fidelity feature space for the model. The experiments show that this combination achieves the highest Balanced Accuracy and F1-Score, 10.2% and 4.5% higher than the next best model (LightGBM), respectively, underscoring its engineering applicability for real-time recognition of heterogeneous thin reservoirs. Tables 5 and 6 show the performance of different models on various lithologies in this study. From the perspective of accuracy, the prediction results of each model for silt are slightly lower than the overall level, which may be due to the uneven distribution of data samples of this label. In addition, although the accuracy scores of CatBoost and Random Forest are very close, from the F1-score table, CatBoost performs significantly better than other models in multiple lithologies ( limestone, clay shale, and fine sand, etc.). the real lithology labels and the model's prediction results along the depth profile of the Hugin section in well F-14. Overall, the model's predicted lithology is highly consistent with the real labels. Particularly in the primary lithology sections (depth range 2700-2850 m), such as fine sand (F) and coarse sand (C), the prediction accuracy reached 89.2% and 86.5%, respectively, aligning with the continuity characteristics of high-energy river sand body deposits. In the limestone (LS) section (depth 2900-2950 m), the model successfully identified the independent clusters of limestone with an accuracy of 72.3%, with only a small portion misclassified as clay shale (8.1%). This misclassification could be attributed to local thin clay intercalations or logging response anomalies. Notably, in the silt (SI) and fine sandstone (FS) transition zone (depth 2850-2880 m), some cross misjudgment was observed (silt recall of 45.7%), reflecting the classification ambiguity caused by the gradual change in physical properties within the transition lithology. CatBoost's feature combination optimization significantly enhanced recognition ability, especially in these transition zones. Additionally, the prediction accuracy of clay shale (CS) in low-energy sedimentary segments (depth range 2950-3000 m) was 78.4%, demonstrating the model's strong discrimination ability for thin reservoir segments. In general, the UMAP + K-means feature enhancement strategy facilitated high-precision lithology identification for well F-14, with an overall Balanced Accuracy of 0.927 (Figure 17). This was achieved by fusing logging physical characteristics with drilling dynamic parameter response mutations, leveraging the unbiased gradient estimation mechanism of CatBoost.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### Wind Turbines  
A wind turbine's principal assemblies are tower; nacelle (containing the generator and supporting machinery, e.g., the transmission); and rotor, consisting of hub and rotor blades. Table 1 gives an overview of the average sizes of wind turbines installed in Germany in 2015 and 2018. The next generation of wind turbines will most likely feature rotor blade lengths of 60 m and beyond and a nacelle height of more than 150 m, resulting in a foot-to-tip height of more than 200 m.  
The tower is made of thick steel plates, while the nacelle and rotor hub are made of a steel frame cased with a thinner sheeting of either metal or fiber-reinforced plastic (FRP). Modern rotor blades are lightweight-design products, usually made of fiber-reinforced plastic (FRP) or carbon-reinforced plastic (CRP) [17], following the design rules for airfoils [18; 19]. They also contain trimming elements, such as chambers filled with lead, usually located at about 2/3 of the blade's total length. For lightning protection, the tips are made of metal, usually aluminum [20]. Thick conductors are installed inside the blade, which are connected to the metallic hub flange's lightning protection rings.  
Wind turbines come in several shapes and forms, specific to product and manufacturer. The tower is usually a truncated cone. Some manufacturers also use a tapered shape. While the rotor hub is almost always teardrop shaped, which is optimal regarding aerodynamics, the nacelle rarely \"completes\" the teardrop shape. Box shapes, with or without rounded corners, are much more usual, albeit a worse choice from an aerodynamics point of view.