esa-sceva/satcom-chunk-collection huggingface dataset:
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Emergency Medical Service Coordination
#### Dynamic Reassignment
In the recalculation of an existing assignment, ambulances that have been already dispatched to a patient, but did not reach the patient yet, may be deassigned from their patients or might be reassigned to other patients. This approach assures that the assignment \(AS\) is optimal, with regard to the average travel time to the existing patients, at any point in time.
## 6 Performance Evaluation
The interaction between all three factors accounts for only 0.4% of the variation and, therefore, is negligible. The assumption that the residuals are normally distributed is proven by the quantile-quantile plots for all three factors in Figure 11a-c. The visual tests show that the residuals fall evenly around the least squares line.
## 2 Materials and Methods
### Literature Review and Analysis on HRC Modeling Methods
#### 2.1.3 Systems Modeling Language (SysML)  
SysML is a standardized formal language, of which the main purpose is the modeling of systems engineering processes. It is based on UML but is more well-adapted for complex systems in engineering and automation.  
Researchers proposed SysML as a modeling language as it was thought to simplify robot programming through abstraction [42]. Since robots are increasingly being integrated into environments where humans are present, they should be made safer, with straightforward programming and interaction capabilities. To achieve this, SysML was used to represent manufacturing processes by graphical diagrams capturing the system's structure from various perspectives. While this work did not focus directly on modeling human-robot collaboration, it was one of the motives for the approach, and the capabilities of the system show promise to be adapted for HRC scenarios.  
While the literature specifically focusing on using SysML for HRC tasks is limited, SysML has been widely used for modeling general robotic workflows. [PERSON] et al. [43] proposed robot software design based on SysML. The language was chosen due to its benefits in terms of reusability and flexibility. [PERSON] et al. [44] aimed at achieving real-time observation and control using SysML modeling for architecture, components, and information flows. This was based on Industry 4.0 and the resultant requirements for adaptability, flexibility, and responsive communication. SysML is used to develop a graphical model for a wireless factory work cell.  
In summary, although SysML can be a useful tool for modeling certain aspects of HRC such as requirements, system architecture, and interaction with external systems, SysML is not the most suitable tool for modeling real-time interaction or human and complex interaction scenarios.
## 4 Sensor Packet Loss Compensation
### CARIMA Process Model  
In the design of a sampled-data control system, a nominal model of the process to be controlled will normally be identified by the control engineers at an early stage of the design process. An Auto Regressive Moving Average (ARMA) model is often used for this. However, for predictive purposes, the ARMA model does not include the effects of potential disturbances and can perform poorly in practical situations; in addition, the model parameters may not be accurately known or may \"drift\" with time. To overcome the first of these problems, a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model can instead be adopted:  
\[A(z^{-1})y(k)=z^{-d}B(z^{-1})u(k)+\frac{\
u(k)}{\Delta} \tag{1}\]  
Where \(z^{-1}\) is the backward shift (delay) operator, \(y(k)\) and \(u(k)\) represent the process output and input at sample index \(k\), \(\
u(k)\) is a zero-mean white sequence and the difference operator \(\Delta=1-z^{-1}\). \(A\) and \(B\) are both polynomials in \(z^{-1}\) having the following definitions:  
\[\begin{split} A(z^{-1})=&\,1+a_{1}z^{-1}+\ldots a_ {n}z^{-n}\\ B(z^{-1})=&\,b_{0}+b_{1}z^{-1}+\ldots b_{m}z^{-m} \end{split} \tag{2}\]  
In the model represented by Equation (1), \(d\) represents the integer part of the system time-delay such that \(b_{0}\) is non-zero and, assuming that a zero order hold (DAC) is employed for control purposes, will always satisfy \(d\geq 1\) (if the synchronized sampling/actuation strategy as depicted in Figure 2 is also employed, it follows that \(d\geq 2\)). The transfer function model described by Equations (1) and (2) is representative of many types of real-world physical processes, see for example [27; 28], and is employed for advanced control techniques such as the Generalized Predictive Control (GPC) method [28]. The integrated white noise sequence in the model corresponds to unknown disturbance terms entering the system input, and stochastic behavior (random steps at random times) is well captured. Note that all disturbances (e.g., those on an output) can be referenced to a system's input via a simple transformation [27]. In terms of creating accurate short-term predictions of the process output, after multiplying out the delta term in the model given by Equation (1), it may be solved for \(\Delta y\) and transformed into an appropriate difference Equation:  
\[\begin{split}\Delta\hat{y}(k)=&\,-a_{1}\Delta y(k -1)-\ldots-a_{n}\Delta y(k-n)\\ +& b_{0}\Delta u(k-d)+\ldots+b_{m}\Delta u(k-d-m)+ \
u(k)\end{split} \tag{3}\]
## 4 Experiment
### Speed and Accuracy Comparisons
We visualized the inference results of LDMNet and the benchmark methods BiseNetV2, DDRNet-s, and Segmenter in Figure 8. It is evident that LDMNet pays more attention to details in the treatment of boundaries compared to other methods, is more sensitive to edge features, and demonstrates strong fitting ability. Cityscapes: To verify LDMNet's applicability on different types of datasets, we applied LDMNet to Cityscapes in the autonomous driving field for validation. The experimental results are shown in Table 6. LDMNet still outperforms the methods listed in the table, achieving real-time detection with an mIoU of 80.7% on the Cityscapes validation set, outperforming SFNet and PP-LiteSeg-T2 by more than 5.8%. The speed is close to  
Figure 6: Visualization of the inference of PIDNet-s, UNetformer, CM-UNet, and LDMNet on the LaRS. We have marked the detailed differences with red boxes and red arrows. Figure 7: Comparison of the IoU of different methods in predicting various categories on the LaRS. Among them, the green solid line with circles indicates the mIoU difference between different models on LaRS. STDC2-Seg75 and HyperSeg-M, but the mIoU is 3.9% and 4.9% higher, respectively. We also compared it with the current most advanced multi-branch network PIDNet-s. Although the inference speed is slightly behind, LDMNet's mIoU is 3.6% higher than PIDNet-s, indicating the strong applicability of the Dual-branch Network across different types of datasets and its potential to excel in the field of water and land unmanned driving.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 3 Experimental Verification
### Result Discussion  
In Tables 4 and 5, the error between the measured length and the actual length is less than 2.5 cm, and the average relative error is also within 1.1%, which meets the actual requirements of the project. No direct relationship between the burial depth of the metal pile and the error rate is found in the column \"Error rate\" in both tables. However, the \"average error rate\" decreases as the length of the buried metal pile increases, which is consistent with the common sense that relative error will decrease when the base is larger. Compared with existing methods of extracting the stress wave reflection wave period, this algorithm comprehensively utilizes the time-frequency domain information of the signal. The method based on image processing and time-frequency analysis makes the signal interpretation more intuitive, and it can be widely applied in different fields.  
The algorithm has the following three advantages: (1) it makes full use of the time-frequency domain information with high accuracy; (2) the fully automatic processing algorithm makes the calculation results unaffected by human factors; and (3) the visual processing process will definitely bring a better experience to users. However, the proposed new algorithm may need big storage to store the measurement data and high computational power to perform FFT and image processing. These common issues of time-frequency analysis can be easily overcome by using recent computer systems. In future work, the algorithm will be simplified and transplanted into an embedded system to promote the algorithm for field tests.
## 6 Systematic Differences Between the LT-1 and Sentinel-1 Satellites
### MDDR of Different Satellites  
The magnitude of MDDR, measured in mm/d, represents the maximum displacement rate that can be detected along the LOS using InSAR technology over a period of 1 day [35]. According to Equation (8), the MDDR values of various commonly used SAR satellites can be calculated as shown in Table 4. The LT-1 satellite, with its STRIP1 and STRIP2 modes, both with a slant range resolution of 1.67 m, exhibits theoretical MDDR values of approximately 4.41 mm/d and 1.77 mm/d, respectively, under the current repeat cycle over China's mainland, assuming coherence is maintained. The MDDR of LT-1's STRIP1 mode is significantly higher than that of STRIP2. This difference arises because, with comparable  
Figure 11: The track of the LT-1 satellite observing the study area is shown in the left image, while the track of the Sentinel-1 satellite observing the study area is depicted in the right image.  
resolution, the shorter the satellite's revisit period, the more advantageous it becomes in terms of MDDR.  
In the context of L-band satellites, LT-1's STRIP1 mode demonstrates the largest MDDR, ranging up to 4.41 mm/d, which is considerably higher than the MDDR of Sentinel-1. This substantial difference underscores the impacts of the shorter repeat cycle and the high resolution on LT-1's STRIP1 mode, providing a significant advantage in MDDR, especially when high temporal resolution is required for monitoring ground deformation.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 5 Conclusions  
The TSC model proposed in this paper provides an innovative solution for tourism planning by integrating spatial and semantic multi-dimensional associations. Compared with traditional recommender systems that rely only on a single dimension (e.g., spatial proximity or popularity), the model ensures the accessibility of the itinerary through the spatial correlation network and, at the same time, captures the tourists' deeper needs for the theme, emotion, and experience of the attractions with the help of text mining technology to construct routes that conform to the logic of geography as well as tourists' psychological expectations. This multiple-constraints mechanism not only improves the scientific nature of the recommendation results but also provides a basis for attraction cooperation. Neighboring attractions can design linked products through complementary themes, forming a synergistic effect of regional tourism ecology.  
Figure 14: JS divergence comparison of recommended attractions.  
Figure 13: Road distance comparison of recommended attractions.  
Despite its contributions, the current study has several limitations that warrant further investigation. These include incomplete characterization of tourist attraction entity features, limited automation in association construction, and the absence of temporal dynamics in the TSC. Future enhancements could incorporate real-time data streams to enable dynamic weight adjustment of emotional chains, aligning with the growing demand for personalized and fine-grained tourism management. To address these challenges, future research should prioritize expanding data sources through multi-modal integration of scenic spot entities with rich human mobility and behavioral datasets. Enhanced incorporation of deep learning architectures and large language models could further improve feature extraction and semantic understanding. Particular emphasis should be placed on temporal modeling of scenic spot semantics to strengthen both the cultural sensitivity and time relevance of emotional chain representations.  
Conceptualization, R.L. and J.W.; methodology, B.L.; validation, B.L. and J.W.; formal analysis, B.L.; resources, R.L.; data curation, A.S.; writing--original draft preparation, B.L.; writing--review and editing, R.L. and J.W.; visualization, B.L.; supervision, R.L. and A.S.; project administration, R.L. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. U20A2091.  
The datasets used in this study are publicly available and have been cited appropriately in the References section. No new data were generated or collected during this study.  
The authors declare no conflicts of interest.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 4 Result Analysis
### Results for the LC Dataset  
Table 3 presents the Python code smell detection results, showcasing the six performance measurements using five ensemble learning methods with applied chi-square FST and SMOTE. Notably, the max voting method emerged as the frontrunner, attaining an impressive 0.96 accuracy and F-measure scores and an MCC score of 0.85. This achievement underscores the effectiveness of the proposed approach or the combination of techniques used in the max voting method. This high accuracy, F-measure, and MCC score highlight the method's strong predictive power and balanced performance in terms of precision and recall, especially for the LC dataset. By concentrating on the most pertinent features and reducing class imbalance, the improvement in the MCC score indicates that using FST and SMOTE improves classification. In contrast, the Bagging and XGBoost models recorded the lowest accuracy and F-measure scores of 0.93. The lowest scores indicate that these are less effective for this dataset compared to the max voting method. Additionally, Figure 3 visually compares the results among the five Ensemble methods for the LC datasets.  
Table 4 shows the result without applying chi-square FST and SMOTE. The Bagging method, a clear standout, obtained the highest 0.95 accuracy and F-measure scores and an MCC score of 0.8272, instilling confidence in the method's performance. This indicates that Bagging is particularly effective for this dataset, providing strong predictive power and balanced performance in terms of both precision and recall. In contrast, the Gradient Boost and XGBoost models obtained the lowest accuracy and F-measure score, 0.92. This indicates that while they are still performing well, they are not as optimal as Bagging for the LC dataset.  
The following results are observed from Tables 3 and 4:  
Chi-square FST and SMOTE have significantly enhanced classification accuracy. This improvement underscores the progress achieved in the field of ensemble learning models.  
When FST and SMOTE are used, the max voting model obtains better results than other ensemble models (Table 3) because it combines several individual ML models to make predictions and produce the optimum outcomes.  
The difference in performance between Tables 3 and 4 highlights the significance of preprocessing, especially for models that are otherwise sensitive to data imbalance.  
SMOTE prevents the classifier from being biased toward the majority class, which helps to address the class imbalance. Chi-square FST improves model efficiency by reducing irrelevant features, leading to enhanced generalization.
## 3 Results and Discussion
### Nutritional Profile
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composition** & **Units** & **Values** & **IOC 2019 \({}^{*}\)** \\ \hline Moisture & g/100 g fresh WOF & 62.08 \(\pm\) 1.10 & \\ Crude protein & g/100 g WOF dw & 5.22 \(\pm\) 0.15 & \\ Oil & g/100 g WOF dw & 21.44 \(\pm\) 0.25 & \\ Ash & g/100 g WOF dw & 2.61 \(\pm\) 0.03 & \\ Carbohydrates & g/100 g WOF dw & 66.48 \(\pm\) 0.35 & \\ \hline Fatty acids & g/100 g oil & & \\ \hline Myristic (C14:0) & 0.07 \(\pm\) 0.01 & \(\leq\)0.05 \\ Palmitic (C16:0) & 12.79 \(\pm\) 0.41 & 7.5–20.0 \\ Palmitoleic (C16:1) & 1.00 \(\pm\) 0.04 & 0.3–3.5 \\ Heptadecanoic (C17:0) & 0.08 \(\pm\) 0.01 & \(\leq\)0.3 \\ cis-10 Heptadecenoic(C17:1) & 0.24 \(\pm\) 0.01 & \(\leq\)0.3 \\ Stearic (C18:0) & 2.56 \(\pm\) 0.11 & 0.5–5 \\ Oleic (C18:1) & 71.55 \(\pm\) 0.22 & 55.0–83.0 \\ Linoleic (C18:2) & 8.26 \(\pm\) 0.08 & 2.5–20.10 \\ Linolenic acid (C18:3) & 1.38 \(\pm\) 0.06 & \(\leq\)1.0 \\ Arachid (C20:0) & 0.42 \(\pm\) 0.02 & \(\leq\)0.6 \\ cis-11-Eicosenoic (C20:1) & 0.26 \(\pm\) 0.02 & \(\leq\)0.4 \\ cis-11,14-Eicosadienoic (C20:2) & 0.59 \(\pm\) 0.01 & - \\ Henicosanoic (C21:0) & 0.10 \(\pm\) 0.01 & - \\ Eicosapentaenoic (C20:5) & 0.08 \(\pm\) 0.01 & - \\ Behenic (C22:0) & 0.20 \(\pm\) 0.01 & \(\leq\)0.2 \\ Tricosanoic (C23:0) & 0.07 \(\pm\) 0.01 & - \\ Lignoerci (C24:0) & 0.33 \(\pm\) 0.04 & \(\leq\)0.2 \\ Saturated fatty acids & 16.76 \(\pm\) 0.08 & \\ Monounsaturated fatty acids & 72.80 \(\pm\) 0.10 & \\ Polyunsaturated fatty acids & 10.81 \(\pm\) 0.09 & \\ \hline \hline \end{tabular} \({}^{*}\)established limits by International Olive Council for virgin olive oil.  
\end{table} 
Table 1: Nutritional profile and fatty acid composition of Greek WOF sample. for var. _sylvestris_ oil in earlier studies in Algeria [7], Tunisia [6], Pakistan [23], Spain [48], and Italy [49].
## 5 System Control
#### Online Adaptation of PD Controllers  
Offline tuning of the PD controllers [17] in the outer loop is unable to cope with aggressive maneuvers, such as short radius curves and sharp changes of direction, and neither provides adaptations to different lengths and weights of DLOs. Therefore, we proposed [18] and their online adaptive tuning following an Adaptive Fuzzy Modulation (AFM) approach, combining a gradient descent adaptation rule and a fuzzy membership function activation [67; 68]. The membership functions act on the PD controller parameters if a fuzzy logic expression is satisfied, following the Takagi-Sugeno controller design paradigm [69].  
The perceived error \(Pe\) (cf. Equation (2)) measures the relative error between the real position of the UAV \(Y_{real}\) and its reference \(Y_{ref}\) at each moment.  
\[Pe=\frac{Y_{ref}-Y_{real}}{Y_{ref}}\cdot 100, \tag{2}\]  
The proposed fuzzy tuning rules contemplate four error conditions dependent on \(Pe\) value modeled by corresponding four triangle-shaped membership functions \(\{\mu_{i}(Pe)\}_{i=1}^{4}\), for which its membership supports are provided by the following.  
\[D_{\mu_{1}}=(-2,-9),D_{\mu_{2}}=(-1,-5),D_{\mu_{3}}=(1,5),D_{\mu_{4}}=(4,9). \tag{3}\]  
The adaptation of parameter \(K_{p}\) is modulated by functions \(\mu_{1}(Pe(t))\) and \(\mu_{4}(Pe(t))\) through Equation (4), while the adaptation of parameter \(K_{d}\) is modulated by functions \(\mu_{2}(Pe(t))\) and \(\mu_{3}(Pe(t))\) with Equation (5):  
\[K_{p}(t+1)=K_{p}(t)+\alpha e(t)(\mu_{1}(Pe(t))+\mu_{4}(Pe(t))) \tag{4}\]  
\[K_{d}(t+1)=K_{d}(t)+\alpha e(t)(\mu_{2}(Pe(t))+\mu_{3}(Pe(t))) \tag{5}\]  
where \(\alpha\) is the adaptation factor, which takes a constant value between 0 and 1 during the entire experiment. The \(e(t)\) functions compute the instantaneous error relative to the desired values \(\theta_{d}\) and \(\phi_{d}\) of the angles that determine the motion in the \(XY\) plane. The online adaptation Equations (4) and (5) follow a stochastic gradient descent algorithm The convergence of the continuously adaptive process of the fuzzy logic control approach has been proven [18].
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 4 Planetary Defense: State-of-the-Art  
### Origins of Asteroids and Comets  
#### 4.1.1 The Main Asteroid Belt  
The asteroid belt is also called the main asteroid belt (MAB) or main belt, a torus-shaped region in the inner solar system located roughly between the orbits of Jupiter and Mars. It accommodates a large number of solid, irregularly shaped bodies, called asteroids or minor planets, as illustrated in Figure 12. With a total mass of approximately 4% of Earth's Moon, the asteroid belt is the smallest and innermost circumstellar disc in the solar system. Furthermore, about half of the mass of the asteroid belt stems from the four largest asteroids, namely, Ceres, Vesta, Pallas, and Hygiea [115], and Ceres is the only dwarf planet with a mean diameter of 939.4 km [116]. The main asteroid belt is believed to have formed from the very early-stage solar nebula as a group of planetesimals which are the smaller precursors of the protoplanets [117]. The gravitational perturbations from Jupiter, the most giant planet in the solar system, pervaded the protoplanets with such high orbital energy that prevented them from accreting into a planet [118].  
Figure 11: (**a**) The seal of NASA’s Planetary Defense Coordination Office (PDCO) [111]; (**b**) OSIRIS-REx in launch configuration [112]; (**c**) artist’s concept of the Wide-field Infrared Survey Explorer (WISE) spacecraft in its orbit around Earth [113]; (**d**) artist’s illustration of the NEOCam (NEO Surveyor) space telescope [114].  
The main asteroid belt is generally believed to be the source of most near-Earth objects. Authors in [120] have performed extensive orbital integrations for a representative set of main-belt things to locate escape routes into the near-Earth space. Their proposed method can identify all essential regions for NEOs and provide a specific distribution of asteroids that will enter the NEO regions. Moreover, another investigation [121] conducted by researchers from Europe and the United States discovered that approximately 470 million years ago, a 200 km sized asteroid was disrupted by a collision in the MAB, which generated many fragments into Earth's crossing orbits. During several millions of years following this significant cosmic catastrophic event, the meteorite production and cratering rate was significantly increased. For example, the 7.5 km wide Lockne crater in central Sweden is the consequence of the MAB event. Moreover, the authors provided evidence that the impact of a binary asteroid formed Lockne and its nearby companion, the 0.7 km diameter Malingen Crater [122].  
Furthermore, the aforementioned Chelyabinsk asteroid is also considered to have an origin in the MAB. Based on the calculation of the pre-impact orbit of the Chelyabinsk asteroid and the orbit of asteroid 86039 (1999 NC43), researchers concluded that the pre-impact orbit is consistent with an origin in the MAB, most probably in the inner main belt near the \(v_{6}\) secular resonance [85].