esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Analysis
### Student Learning Analysis Lising Regression
#### 5.4.2 Boruta Feature Selection  
The Boruta feature selection method yielded a set of selected features, namely \"code_module\", \"studied_credits\", \"total_click\", \"late_rate\", and \"fail_rate\", all of which were assigned a ranking of 1. The Boruta feature selection technique was employed to identify the optimal features for each machine learning model, namely LR, SVR, RFR, and GB. The features that were chosen for analysis included \"code_module\", \"studied_credits\", \"total_click\", \"late_rate\", and \"fail_rate\". Each model's performance was assessed using two evaluation metrics: MAE and RMSE. Figure 5 illustrates the results of the four machine learning models when utilizing the Boruta feature selection technique.  
The findings indicate that the Gradient Boosted model exhibits higher accuracy than the other models, as evidenced by its lower MAE and RMSE values of 12.93 and 18.28, respectively. The RFR model exhibits satisfactory performance, as indicated by its MAE value of 13.52 and RMSE value of 18.46. The SVR and LR models exhibit higher values of MAE and RMSE than the remaining models, suggesting a lower level of predictive accuracy.  
The findings suggest that the utilization of Lasso and Boruta feature selection methods in conjunction with GB and RFR models can proficiently predict the \"weighted_score\" dependent variable. This can aid educators in recognizing students who are at risk and equip them with the essential assistance to excel in their academic endeavors. The Lasso regression model is employed with the RFR and GB techniques, resulting in an MAE of 18.92 and 19.1 and an RMSE of 24.29 and 23.95, respectively. Conversely, utilizing the Boruta feature selection method in conjunction with GB and RFR results in Mean Absolute Errors of 12.93 and 13.52 and RMSEs of 18.28 and 18.46, respectively.  
In our study, both MAE and RMSE values are quite similar for various models, which might make it challenging to distinguish the \"best\" model based solely on these metrics. Since both MAE and RMSE are measures of error, smaller values are generally better. However, the choice between them can also depend on the particular distribution of errors in your data and what you consider to be more important: error magnitude or error variance. Given that the Gradient Boosted model with the Boruta feature selection technique provides the lowest MAE and RMSE in regression tasks, and the XGBoost model yields the highest accuracy in classification tasks, these models can be considered the best performing based on these metrics.  
Figure 5: ML models’ outcome using Boruta feature selection.
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 5 Conclusions and Discussion  
In this paper, the Hammerstein structure is used to describe the rate-dependent hysteresis characteristics of linear reluctance actuators. From the result of identification, we can draw a conclusion that Hammerstein's structure has an excellent effect in modeling the rate-dependent hysteresis. Meanwhile, a new control strategy that combines inverse model compensation and the discrete sliding mode control based on the disturbance observer was proposed, which can effectively suppress the hysteresis effect. To evaluate the effectiveness of the proposed control strategy, two existing control strategies, which are widely used at present, are also implemented. The simulation results show that it is superior to the PI controller combined with inverse compensator and even DSMC combined with inverse compensator, and confirm the effectiveness of the novel control method in alleviating hysteresis. Next, applying the algorithm in this article to the reluctance motor and achieving high-precision flux tracking control of the reluctance motor will be the research focus of our future work.  
Conceptualization, Y.L. and Q.M.; methodology, Q.M. and Y.D.; software, Q.M.; validation, Y.L. and Y.D.; formal analysis, Q.M.; investigation, Y.D.; resources, Y.L.; writing--original draft preparation, Q.M.; writing--review and editing, Y.L. and Y.D.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (52075132 and 52105546) and the Opening Foundation of State Key Lab of Digital Manufacturing Equipment & Technology, China DMETKF2020024.  
**Institutional Review Board Statement:** Not applicable.  
Figure 19: Tracking the performance of three controllers.
## 3 FMCW LiDAR Research Progress
### Advances in FM Linearity Research
This advancement reduces the impact of external temperature and driving signal fluctuations on the laser, facilitating more straightforward  
Figure 13: (**a**) Multi-frame target images with sampling interval of 1 s (10 frame/s). (**b**) Multi-frame target images with sampling interval of 0.25 s (40 frame/s). ©Elsevier. Copyright 2020 Optics Communications [51]. adjustments. This innovative approach serves as a vital point and source of inspiration for further progress in FMCW LiDAR technology. In 2024, a team from Pusan National University in South Korea [55] introduced a laser source for a frequency-wavelength sweep laser (FWSL) and demonstrated a solid-state FMCW LiDAR radar system integrating an FWSL with an acoustic optical deflector (AOD). Demonstrating a coherence length of 1.2 km, a bandwidth of 160 nm, and rapid scanning at 100 kHz at a wavelength of 1535 nm. The system captures high-resolution 4D images of small targets in real-world environments at a real-time frame rate of 200 \(\times\) 45 pixels per second. This advancement not only enhances LiDAR performance but also opens new possibilities for high-precision remote-sensing. Although frequency modulation linearity has made significant progress in improving the measurement accuracy and system performance of FMCW LiDAR, achieving fully linear frequency modulation is still a challenge. Current technological progress can already meet certain industrial application needs. However, in wider application scenarios, such as autonomous driving and long-distance space exploration, these technologies still need to be further optimized. Future research will need to focus on developing more efficient modulation techniques and light sources, as well as improving the system's stability against environmental factors. With the continuous development of new light sources and modulation strategies, the optimization of frequency modulation linearity will not only promote the application of radar technology in traditional fields but also open up more possibilities for the application of radar technology in emerging markets.
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 5 Design
### Example Dynamic-RPL Scenarios
Figure 9 demonstrates how the algorithm operates if the network becomes highly congested with unstable topology. It starts with S1 initiating a global-instance and establishing a global-DODAG after having its local-DODAG converged successfully. Then, N1-N4 globally attaches to S1 upon the reception of its global-DIO messages. These are the only global-nodes eligible to join the global-DODAG at this stage. Based on the advertised LSOF information, the algorithm at N1 discovers the performance degradation of the local-DODAG. Accordingly, N1 performs the _Split_ operation and initiates a separate local-DODAG. N5-N7 then become global-nodes eligible for processing global advertisements. Then, they globally attach to N1 and join the global-DODAG. However, such a _Split_ movement is not enough to rectify the adverse situation in LD1. Therefore, N4 disjoins the local-DODAG and initiates a separate one after being decided using the algorithm. As a result, N12-N15 globally attach to N4 and join the global-DODAG. Figure 8: An example scenario of a single-DODAG Dynamic-RPL network (before running the dynamic multi-DODAG algorithm). After that, the overall network performance of LD1 becomes better and no more _Split_ movements are taken. However, the newly initiated local-DODAG using N4 still experiences stability and congestion issues. This causes a decision to be made using N12 to perform the _Split_ operation and initiate a separate local-DODAG. As a result, the network converges into a more stable state with better overall performance. Although it is expanded into multiple local-DODAGs, the network becomes more reliable while still having a manageable topology.
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Very Small Aperture Terminal  
With the widespread use of VSAT in the modern maritime industry, some aspects of the VSAT network, such as the transparent transmission and the openness, need to be improved to counter security threats, especially unauthorised access and interception attacks [41]. In 2014, IOActive [42] tested several VSATs from different vendors and concluded that because they used plain text transmission with no authentication, encryption, security, or verification of personal information, all the tested devices were vulnerable at the implementation levels. As a result of the weak protection, attackers can send false signals or malicious code to the device to disable it or compromise the system, preventing the vessel from safely navigating. As reported in [41; 42], AIS aggregators typically provide ship location data. The real risk is that VSAT network interfaces can be found on the Internet using tools such as the Shodan Ship Tracker. This can reveal valuable and sensitive information such as brand names, product codes, and other data that could be used in cyberattacks. Standard information is typically available on vendors' websites, and many terminals continue to use the same factory settings, including the username and server password. An attacker can alter the GPS coordinates and settings, as well as download malicious software if he/she finds an open VSAT interface, and this enables further network hacking and provides access to critical management systems [43].
## 2 Direct \(\alpha\)-Halogenation of Aryl Ketones
### Metal Halides (MX)
#### 2.1.7 Miscellaneous Halogenation Agents
Aryl ketones underwent exclusive \(\alpha\)-bromination, highlighting the excellent regioselectivity of this route. Figure 13: Scope of the SiCI\({}_{4}\)- and UHP-mediated bromination. An alternative versatile \(\alpha\)-halogenation method was reported by [PERSON] and [PERSON] in 2011 [128]. An effective halogenation agent was obtained upon the coupling of bromo- or chlorotrimethylsilane with KNO\({}_{3}\). The procedure yielded eight \(\alpha\)-chloroketones and five \(\alpha\)-bromoketones (Scheme 47). Dihalogenation was a burdensome issue in this Si-mediated reaction. The \(\alpha\),\(\alpha\)-dihalogenated products were obtained for each substrate, complicating purification. [PERSON] demonstrated that in the \(\alpha\)-chlorination of aromatic ketones, 1,3-dichloro-5,5-dimethyl-hydantoin (DCDMH) acts as an inexpensive chlorination agent (Figure 14) [129]. A silica gel-supported route and a \(p\)TsOH-catalysed method were therefore developed. The former converted the aromatic ketones with a significantly higher efficiency than the latter. Together with the shorter reaction times (1 h vs. 8 h, respectively), the SiO\({}_{2}\)-supported route seemed to be superior. However, this route was not as selective as the \(p\)TsOH promoted version. The chlorination of nitro-substituted aromatic ketones yielded a mixture of the \(\alpha\)-chlorinated ketone and the ketal. Furthermore, for methoxy-substituted substrates, ring chlorination was exclusively observed. Alumina-supported \(\alpha\)-bromination and \(\alpha\)-iodination protocols have been developed [130]. [PERSON] described a solvent-free hexamethylenetetramine-bromine (HMTAB)-mediated halogenation. Eleven aromatic \(\alpha\)-bromoketones were obtained in good yields (70-80%) upon grinding HMTAB (1.6 equiv), basic alumina and the substrate in a mortar. The homogenous solid mixture was subsequently irradiated in a MW for 5-10 min. Electron-withdrawing and -donating groups were well tolerated, as well as acid-sensitive ones (e.g., OH, NH\({}_{x}\)). The main advantages of this procedure are its short reaction times and the solvent-free conditions. CAN demonstrated excellent catalytic activity in the \(\alpha\)-chlorination of (a)cylic ketones [131; 132]. This route employed the rather unconventional acetyl chloride as the chlorine source, yielding one \(\alpha\)-chloroarylketone (i.e., 2-chloro-1-phenylethanone) in 87%. The authors claimed that \(\alpha\)-chlorination was exclusively observed. In 2019, [PERSON] and co-workers designed a bis(1,3-dimethyl-2 imidazolone)hydrotribromide (DITB)-mediated halogenation [133]. The \(\alpha\)-bromination of a set of ketones was established upon the addition of DITB (1.1 equiv.). Seven \(\alpha\)-bromoketones could be isolated among which five aromatic examples could be isolated in moderate to good yields (Scheme 48). Methoxy-substituted aryl ketones were obtained in significantly lower yields, which was ascribed to dibromination of that substrate.
## 3 Analog Fronthaul Based on the Development of a Real-Time A-IFoF Transceiver
The execution time for the accomplishment of the transmitter-side processing functions for each stream was measured to be 5 usec and the execution time for the receiver-side processing was found to be 9 usec. Given that the wireless signal propagation delay is negligible, compared to the fiber transmission delay (5 us/km for standard single mode fiber (SSMF)), the fiber transmission length was the main contributing factor to the total delay budget of the experiments. As a result, even for ultra-reliable and low-latency communication (URLLC) applications with up to 100 usec of latency requirements for one way transmission delay [36], optical propagation distances of up to 18 km can be supported by our  
Figure 2: A-IFoF transceiver developed on state-of-the-art Xilinx RFSoC board, interconnected with 10 GHz electro–optic units. presented solution. Furthermore, the power consumption of the technology solutions that are involved in A-IFoF-based transport implementations is a key feature. As such, the specific power consumption details of each stage of the transceiver are presented in Table 1 in which it is apparent that the conversion of the digital streams to analog signals and vice versa was the most power consuming process of the RFSoC, whilst the RF amplifications that were connected to the electro-optic components also contributed to a fair share of the power consumption.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods
### TF-F: IF1213 PPP Model Ignoring the PIFCB  
The TF-F model is an IF1213 PPP model that disregards the time-varying part of the phase hardware bias of the receiver and satellite. The effect of the time-varying part of the phase hardware bias of the receiver can be ignored, owing to its small magnitude [34]. In this case, the precise satellite clock including only the satellite code hardware bias is expressed as follows:  
\[dt_{IF12}^{s,Q}=dt^{s,Q}+d_{IF12}^{s,Q} \tag{11}\]  
The code and phase hardware delay from the receiver and satellite were fully absorbed by the receiver clock, ionosphere, and ambiguity parameter [35]. Combining Equation (11) with Equation (6) and considering the consistency of the receiver clock difference, the TF-F model was derived by correcting the satellite DCB product \(\widetilde{p}_{r,IF13}^{s,Q}=\widetilde{p}_{r,IF13}^{s,Q}+\beta_{12}\cdot DCB _{12}^{s,Q}-\beta_{13}\cdot DCB_{13}^{s,Q}\) and then correcting the precise satellite clock product. TF-F is expressed as follows:  
\[\left\{\begin{array}{l}p_{r,IF12}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{ r}+m^{s,Q}\cdot Z_{r}+\varepsilon_{r,IF12}^{s,Q}\\ l_{r,IF12}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q}\cdot Z_{r}+ \overline{N}_{r,IF12}^{s,Q}+\varepsilon_{r,IF12}^{s,Q}\\ \widetilde{p}_{r,IF13}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q} \cdot Z_{r}+IFB_{r}+\varepsilon_{r,IF13}^{s,Q}\\ l_{r,IF13}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q}\cdot Z_{r}+ \overline{N}_{r,IF13}^{s,Q}+\varepsilon_{r,IF13}^{s,Q}\end{array}\right. \tag{12}\]  
where  
\[\left\{\begin{array}{l}IFB_{r}=\beta_{12}\cdot DCB_{r,12}-\beta_{13}\cdot DCB _{r,13}\\ \overline{\overline{N}_{r,IF12}^{s,Q}}=\overline{N}_{r,IF12}^{s,Q}+d_{IF12}^{ s,Q}-d_{r,IF12}\\ \overline{\overline{N}_{r,IF13}^{s,Q}}=\overline{N}_{r,IF13}^{s,Q}+d_{IF12}^{ s,Q}-d_{r,IF12}\end{array}\right. \tag{13}\]  
and \(IFB_{r}\) is the inter-frequency bias (IFB) between IF12 and IF13. All the estimated parameters in the TF-F PPP models include the following:  
\[X=[x\quad d\overline{t}_{r}\quad Z_{r}\quad IFB_{r}\quad\overline{\overline{N} _{r,IF12}^{s,Q}}\quad\overline{\overline{N}_{r,IF13}^{s,Q}}] \tag{14}\]
## 2 Materials and Methods
### Machine Learning Models  
We evaluate a large number of important CNN architectures (Figure 1) for the classification of breast cancer stages from histopathological images. These architectures include VGG, EfficientNet, ConvNeXt, RegNet, and variations of ResNet models, including ResNet18, ResNet50, Wide ResNet101, and ResNet152. VGG stands out for its depth and use of numerous small-receptive-field filters that capture fine details. EfficientNet scales CNNs using a compound coefficient for balanced efficiency. ConvNeXt adapts Transformer principles for convolutional architectures, often enhancing performance. RegNet optimizes network structures for favorable performance/complexity ratios.  
While we explored the possibility of training more modern model architectures, particularly Vit and Swin-Vit, on this dataset, our early attempts did not yield satisfactory results. This is likely due to the inadequacy of samples present in the dataset, which renders highly parameterized models ineffective, as highlighted by [PERSON] et al. (2016). We therefore did not pursue such architectures in our analysis.  
Our Slide Level Classifier, depicted in Figure 2, is tailored specifically to biomedical image data. We used Clustering-constrained Attention Multiple Instance Learning (CLAM). This weakly supervised method employs attention-based learning to automatically identify sub-regions of high diagnostic value to classify the whole slide. CLAM uses instance-level clustering over the representative regions identified to constrain and refine the feature space ([PERSON] et al., 2015). After retrieving features, we added two fully connected layers, with the first layer mapping the feature inputs to a 512-node hidden layer with ReLU activation. The second layer transforms the representation to the number of target classes. The classifier is further enhanced with feature pooling methods--average and max pooling--to synthesize information from the tile-level data of the slide images into a cohesive feature vector, which is then used for classification.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Train** & **Validation** & **Test** \\ \hline Biopsies & 800 & 100 & 100 \\ Images & 8847 & 967 & 1032 \\ White & 483 & 66 & 61 \\ non-White & 317 & 34 & 39 \\ Biopsies with stage & & & \\
0 & 18\% & 16\% & 18\% \\ I, II & 58\% & 60\% & 66\% \\ III, IV & 23\% & 24\% & 16\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data distribution of training, validation, and test sets for the multi-class classification formulation.  
Figure 1: We used CNN models for image feature extraction and classification. We then applied post-processing strategies in an attempt to reduce bias. Finally, we evaluated the models using traditional algorithmic fairness metrics.  
We also construct an Ensemble model, integrating the averaged predictions from all other models to produce a final outcome.
## 1 Introduction  
### Electrification in Developing Countries and Mini-Hydropower Plants  
The use of Mini Hydro-Power Plants (MHPP) constitutes one of the most efficient solutions for the problem of energy access in remote rural areas, especially in the context of developing countries [1]. These small installations are capable of exploiting the potential energy of a natural water flow to generate electricity with minimal environmental impact and simple equipment. Nevertheless, the context of poverty of these emplacements conditions the design strategies, which are usually based on the personal experience of local technicians and the use of hand rules. For this reason, the development of efficient and robust design strategies can play an important role in the application of these technologies to combat energy poverty.  
An MHPP basically consists of extracting a fraction of the water flow from a natural course and conducting it downhill through a long pipe, called pensstock, at the end of which the water interacts with a generation unit and transforms its kinetic energy into electrical energy (see Figure 1). It is relevant to note that the water is returned to its natural course, and thus the environmental impact is almost zero. The potential and cost efficiency of an MHPP is determined by the correct use of the terrain or, in other words, the optimal emplacement of its elements and of the pensstock layout to achieve the maximum height difference with the shortest pipe length (as pipe friction lowers the efficiency). When the price of the equipment is low, the costs of the deployment can become relevant with respect to the overall cost, and thus the civil works involved become a variable of interest. This,together with the penstock bending and the arbitrary profiles of the terrain and the river, increases the complexity of the problem and motivates the use of numerical and heuristic optimization approaches.
# Leveling Control of Hillside Tractor Body Based on Fuzzy Sliding Mode Variable Structure
## 3 Leveling System Model and Controller Design
### Design of Fuzzy Sliding Mode Variable Structure Controller
#### 3.2.2 Fuzzy Rule Design  
The next step is to design the fuzzy rules of the switching gain, \(K(t)\).  
The condition of existence of a sliding mode is \(s\dot{s}<0\).  
When the system reaches the sliding surface, it maintains a hold on it using the gain \(K(t)\) to ensure that the system motion can reach a sliding mode surface whose value is sufficient to eliminate the influence of uncertain items and ensure that the sliding mode existence condition, \(s\dot{s}<0\), is established [20; 21]. The two fuzzy rules are as follows.  
If \(s\dot{s}>0\), \(K(t)\) will increase;  
If \(s\dot{s}<0\), \(K(t)\) will decrease.  
Using these fuzzy rules, a fuzzy system can be designed based on the relationship between \(s\dot{s}\) and \(\Delta K(t)\), where \(s\dot{s}\) is designated as the input and \(\Delta K(t)\) as the output.  
The fuzzy set of system input and output is defined as follows.  
\(\dot{s}\dot{s}=\) [NB NM ZO PM PB];  
\(\Delta K=\) [NB NM ZO PM PB].  
where NB is a large negative value, NM is a medium-sized negative value, ZO is zero, PM is a medium-sized positive value, and PB is a large positive value.  
The input and output membership functions of the fuzzy system are shown in Figures 5 and 6.  
The fuzzy rule design is as follows in Table 1.  
Figure 5: Membership function of fuzzy input variables.  
Figure 6: Membership function of fuzzy output variables.  
The upper bound of \(\overset{\wedge}{K}(t)\) is estimated using the integral method.  
\[\overset{\wedge}{K}(t)=G\int_{0}^{t}\Delta Kdt \tag{14}\]  
where \(G\) is the proportional coefficient.  
Replace \(K(t)\) in the formula (11) with a \(\overset{\wedge}{K}(t)\), the control law becomes  
\[u=\frac{1}{b_{0}}[(c_{1}-a_{2})e_{2}+(c_{2}-a_{3})\,e_{3}+\overset{\wedge}{K}( t)\text{sgn}(s)] \tag{15}\]