esa-sceva/satcom-chunk-collection huggingface dataset:
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Different IRT Modeling Strategies to Handle Local Dependence  
The IRT literature mainly deals with the issue of local dependence through three alternative strategies.  
First, in testlet IRT models, additional latent variables are included to model the testlet structure [19; 24]. Most frequently, the local dependence of all items within a testlet is modeled by an additional latent variable. This unidimensionality assumption might be too restrictive to fit item response data.  
Second, the dependency structure is covered by additional parameters. In this approach, a polytomous superitem is defined that decodes the combination of values of items within a testlet in their categories [25]. For example, if a testlet consists of three items, the superitem possesses \(2^{3}=8\) categories. The dependence is modeled by additional parameters [26; 27; 28]. The second approach has the disadvantage that the item parameters do not have a marginal interpretation. This issue also pertains to the first approach when using testlet models, although the item parameters can be transformed in order to obtain a marginal interpretation [29].  
The third approach starts with a marginal unidimensional IRT model and models the local dependence structure by means of copula models [21; 30; 31]. The latter approach is most attractive for the purpose of the marginal interpretation of item parameters. However, copula models require the specification of the type of copula distribution of the testlets and estimate testlet residual dependence parameters \(\delta\) along with item parameters \(\gamma\).  
In this article, we only consider modeling strategies that allow a marginal interpretation of the item response parameters.
## 4 Experimental Results and Analysis
### Result Analysis
#### 4.4.2 Ablation Experiment  
To validate the effectiveness and reliability of the model components, the Informer model was selected as the baseline for ablation experiments. The experimental results are shown in the last two rows of Table 6. As observed from the table, the introduction of the hybrid models led to a consistent improvement in all prediction metrics. Specifically, the MAE decreased from 0.32 to 0.29, with corresponding decreases in MSE and RMSE. These results demonstrate the effectiveness of the hybrid models in enhancing predictive performance. With the introduction of the DCSInformer, all metrics were further improved, highlighting the significant advantage of DCSInformer in capturing long-term dependencies and reducing errors in time-series forecasting tasks.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **RMSE** & **MAPE** & **IoA** \\ \hline LSTM & 0.4449 & 0.3835 & 0.6670 & 19.90\% & 0.8304 \\ \hline Transformer & 0.3265 & 0.3182 & 0.5714 & 16.96\% & 0.8880 \\ \hline Preformer & 0.2130 & 0.2350 & 0.4615 & 13.56\% & 0.9011 \\ \hline Informer & 0.2858 & 0.3217 & 0.5346 & 16.14\% & 0.9043 \\ \hline STL-Informer-ETS & 0.2599 & 0.2930 & 0.5098 & 15.27\% & 0.9288 \\ \hline STL-DCSInformer-ETS & **0.2092** & **0.2211** & **0.4574** & **13.33\%** & **0.9313** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model performance comparison.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & **Change in MSE** & **Change in MAE** & **Change in RMSE** & **Change in** & **Change in IoA** \\ \hline Seasonal feature & +0.011 & +0.012 & +0.015 & +0.23\% & \(-\)0.006 \\ \hline Holiday feature & +0.011 & +0.011 & +0.012 & +0.22\% & \(-\)0.006 \\ \hline Packaging type & 0.000 & 0.000 & +0.00 & 0.00\% & 0.000 \\ \hline Supply-chain delay & +0.002 & +0.002 & +0.003 & 0.01\% & \(-\)0.002 \\ \hline Rainfall days & +0.001 & 0.000 & +0.001 & 0.00\% & \(-\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sensitivity analysis of feature importance.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 2 Materials and Methods
### Modeling with Random Forests, XGBoost, and LightGBM
Note that a five-fold cross-validation was not performed for LED_RES, because the training and validation data were divided according to the explicit objectives. The model performance was evaluated based on the Pearson's correlation coefficient (COR), Nash-Sutcliffe coefficient (NSE) [26], and root mean squared error (RMSE) between the observed SPAD values and model output values. COR evaluates the correlation between the observed and predicted SPAD values of the model (Equation (1)). NSE considers the balance between the sum of the squared errors and the variance of observed data (Equation (2)). RMSE evaluates the error between the predicted and observed SPAD values at the dimension of the observed SPAD value (Equation (3)). \[\text{COR}=\frac{N\sum_{i=1}^{N}\left(Y_{o,i}Y_{p,i}\right)-\sum_{i=1}^{N}Y_{o, i}\sum_{i=1}^{N}Y_{p,i}}{\sqrt{N\sum_{i=1}^{N}Y_{o,i}^{2}-\left(\sum_{i=1}^{N}Y_{p,i} \right)^{2}}\sqrt{N\sum_{i=1}^{N}Y_{p,i}^{2}-\left(\sum_{i=1}^{N}Y_{p,i}\right) ^{2}}} \tag{1}\]  
\[NSE=1-\frac{\sum_{i=1}^{N}\left(Y_{o,i}-Y_{p,i}\right)^{2}}{\sum_{i=1}^{N} \left(Y_{o,i}-\overline{Y_{o}}\right)^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(Y_{o,i}-Y_{p,i}\right)^{2}} \tag{3}\]  
where \(Y_{o,i}\) and \(Y_{p,i}\) are the observed and predicted SPAD values for the data point \(i\) (\(=1,\ 2,\ \dots,\ N\)), \(\overline{Y_{o}}\) is the mean observed SPAD value, and \(N\) is the size of the dataset. Specifically, the NSE takes a value in the range \([-\infty,1]\), of which a NSE value of 1 means a perfect fit. For interpreting the model of the best score in all three algorithms, SHapley Additive exPlanations (SHAP) [27], Partial Dependence (PD), and Individual Conditional Expectation (ICE) were employed to visualize the model structures in this study. SHAP decomposes the difference between the predictions for a particular instance and the mean prediction into the contribution of each feature based on the Shapley value concept of cooperative game theory. However, it cannot explain \"how the predictions react to changes in feature values\", and thus, PD and ICE were used together to compensate this part of the interpretability. While SHAP evaluates the importance of the input variables in SPAD value prediction, PD and ICE can be used to interpret how the data-driven SPAD value prediction model responds to a different light environment.
# Teleolocation--Remotely Operated Legged Robots
## 1 Introduction
### Dynamic Locomotion
#### 1.3.1 DARPA Robotics Challenge  
The DARPA Robotics Challenge (DRC) aimed to develop teleoperated ground robots to complete complex tasks in dangerous, unpredictable task environments [38]. Due to the progression of wheeled or tracked robots to a new age of legged walking robots, mobility and balance were main areas of focus for the DRC [39]. Control of legged walking ranged from manual step planning [40], plan-and-execute trajectories with a balance controller, to full body humanoid control approaches [41]. Throughout the DRC trials, many robots fell en route towards the navigation goal, resulting in unrecoverable failures. Relying on predictable contact mechanics was ineffective, and more focus should have been placed on making, rather than merely planning, footholds [42]. The reader is directed to [43; 44] for detailed software and hardware implementations used at the DRC trials.
## 1 Introduction  
The concept of the industrial revolution is strongly linked to extraordinary growth and change in technology. The first industrial revolution (1750-1870) caused an increase in the application of science to industry and led to the rise of mechanized production. The second revolution (1870-1970) was caused by electrical motors powered by electricity and led to the rise of mass production and large-scale machine tools manufacturing. The third revolution (1970-2011) was characterized by the deployment of Programmable Logic Controllers (PLC) and Information and Communication Technology (ICT) derived by digital signals and led to production automation and human-controlled manufacturing. Finally, the ongoing fourth industrial revolution (2011-today), known as Industry 4.0, lays on autonomous manufacturing and connected businesses [1]. In particular, Industry 4.0 includes nine enabling technologies to support the implementation of the paradigm in industry, as in Table 1[2]. In the current production context, factors as efficiency, flexibility and responsiveness to the dynamic market demand are crucial for the survival of industrial companies [1; 2; 3; 4]. Modern production systems have to face with emerging factors such as shorter product lifecycles, the mass-customization and personalization and the reduction of the delivery time to the customers [5; 6]. The upcoming fourth industrial revolution can play a crucial role in managing such complexity, representing the global transformation of the overall industrial production through the integration of Internet of Things (IoT) and Information and Communication Technologies (ICT) with traditional production processes [7; 8]. In this context, embedded systems, semantic machine-to-machine communications and Cyber-Physical System (CPS) technologies connect the virtual space to the physical world [9]. Despite the increased levels of digitization and automation led by Industry 4.0, assembly systems are still based on manual labor because of several human skills, i.e., cognitive ability and problem-solving, are still irreplaceable [10; 11; 12]. However, despite such flexibility, manual assembly systems (MASs) lack in terms of:  
* Productivity; which is low compared to fully automated systems [13]. This aspect is crucial in regions characterized by high labor costs, i.e., the western countries, for its impact on the product full cost and market price [14];
* Accuracy of the performed tasks; the ability to perform continuously the same assembly tasks taking the same time and using the same tools is low in manual systems because of the variability of human nature [15; 16];
* Ergonomic work conditions; such factors, if not optimized properly, can further reduce productivity causing musculoskeletal disorders (MSDs) and other related factors, e.g., absenteeism, stress.
## 3 Results
### RQ5--What Technological Tools Have Been Used to Implement These Proposals?
Figure 6: Reported results of the deep learning models using the following metrics: (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) F1-score. In addition, recall (ability to detect real threats; see Figure 6c) measures a model's ability to correctly identify positive cases, thereby reducing false negatives. The mean recall is 96.06%, indicating that most models are highly effective in detecting attacks and critical events within the security context for which they were designed. The standard deviation is 6.09, showing some dispersion in values, with certain models achieving recall rates close to 100%, while others exhibit more limited performance. The best-performing studies in this metric are S19, S21, S36 (100%), S8 (99.9), and S18, S34 (98.9%), highlighting that methods based on Gradient Boosting, CNN, and LSTM effectively identify threats without overlooking critical events. Furthermore, 25 studies exceed the mean recall (S3, S7, S8, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22, S24, S25, S27, S28, S32, S33, S34, S36, S38, S43). However, models with the lowest recall, such as S42 (78.9%), S10 (81), and S39 (82%), suggest that they may be failing to detect important attacks, which is critical in security scenarios. Lastly, the F1-score (balance between precision and recall; see Figure 6d) is a metric that combines precision and recall, providing a balance between the ability to avoid false positives and the detection of real events. The mean F1-score is 96%, indicating that most models achieve an adequate equilibrium between both metrics. Nevertheless, the standard deviation is relatively high (6.08%), reflecting significant differences among models. The studies with the highest F1-scores are S19, S36 (100%), S21 (99.96%), and S8 (99.9%), suggesting that approaches such as DNN, Gradient Boosting, LSTM, and CNN-RNN-LSTM effectively capture complex patterns with high efficiency. Additionally, 23 studies surpass the mean F1-score (S3, S7, S8, S11, S12, S15, S16, S17, S18, S19, S21, S22, S24, S25, S27, S28, S32, S33, S34, S36, S38, S41, S43).
# Limit Equilibrium Analysis of Landfill Instability Based on Actual Failure Surface
## 3 Results
### Centrifugal Test Case Analysis
#### 3.1.1 Centrifugal Test without Berm  
The geometric parameters of the model are shown in Figure 5a, taking centrifugal test 3 in [28] as an example: \(\gamma=7\) kN/m\({}^{3}\), \(\gamma_{sat}=12\) kN/m\({}^{3}\). It can be obtained that the water level \(h/\)overall slope height \(H\) (water level ratio) \(h/H=0.79\) when the overall slip occurs, and the shear strength corresponding to 20% failure strain is taken: \(\delta_{A}=\varphi_{sw}=28.6^{\circ}\), \(\delta_{P}=12.8^{\circ}\), \(c_{A}=c_{sw}=24\) kPa, \(C_{P}=0\), and \(\beta=55.3^{\circ}\).  
In the traditional two-wedge model, according to the geometric model, the bulk density and pore pressure of the active wedge and passive wedge are obtained: \(W_{A}=898.8\) kN/m, \(W_{P}=4310\) kN/m, \(U_{A}=554.3\) kN/m, and \(U_{P}=3109.4\) kN/m. Then, these values are substituted into Equation (11) to obtain the \(FS\) of the slope, \(FS=1.29\).  
\(\alpha=78.7^{\circ}\) was observed during the centrifuge test, and its failure mode is shown in Figure 5b. Substitute the actual failure surface angle into the improved two-wedge model to calculate \(W_{A}=1158\) kN/m, \(W_{P}=4053.3\) kN/m, \(U_{A}=554.3\) kN/m, and \(U_{P}=2951.4\) kN/m. Substituting these data into Equations (16) and (21), we obtain \(FS=1.07\). Obviously, the theoretical model based on the actual failure surface is more accurate for evaluating the critical state of slope instability.
## 4 Results and Discussion
### Inference Time Evaluation  
The HARDenseRNN deep learning model outperforms the random forest-based pipeline in terms of inference time, as seen in Figure 8. Although random forest takes 441 ms to complete a pipeline with all 150 features, HARDenseRNN takes only 165 ms. Even after excluding feature computation time, HARDenseRNN's inference time remains significantly lower. For movement disorder detection, our model takes 45% less time compared to random forest, completing the task in 83 ms. In movement disorder classification, the time gap is even more substantial, with random forest taking 62% and 44% more time for the  
Figure 10: Performance metrics (accuracy, precision, and recall) for 12 movement disorder classification models, highlighting the comparative analysis between random forest and HARDenseRNN across different pre-processing configurations, using inertial data acquired from most affected upper limb via a GeneActiv smartwatch.  
full feature set and top 10 features, respectively, compared to the HARDenseRNN deep learning model.
# An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
## 3 Collaborative Coefficient-Triangle APIT Localization Algorithm
Calculate the mean and standard variation of the distances \(d_{Ai}\), \(d_{Bi}\), \(d_{Ci}\) for each valid triangle, and get \(E(d_{i})\), \(\sqrt{\text{var}(d_{i})}\), where \(d_{i}\) is the distance from the unknown node to anchor node \(i\). Compute mean weighting factor \(Q_{Ei}=\frac{1}{E(d_{i})}\) and standard variation weighting factor \(Q_{Vi}=\frac{1}{\sqrt{\text{var}(d_{i})}}\). Calculate two value pairs \((x_{E},y_{E})\), \((x_{V},y_{V})\) of the unknown node coordinates as the mean-weighted and variation-weighted coordinates of M valid triangles, as shown in Equations (10) and (11). Then, estimate the coordinates of the unknown node as a combination of \((x_{E},y_{E})\), \((x_{V},y_{V})\), as shown in Equation (12), where \(\alpha\) and \(\beta\) are the weighting coefficients of the mean and standard deviation respectively. \[(x_{E},y_{E})=\left( \begin{array}{c}\sum\limits_{i=1}^{M}Q_{Ei}x_{D_{i}}\\ \sum\limits_{i=1}^{M}Q_{Ei}\end{array} ,\frac{\sum\limits_{i=1}^{M}Q_{Ei}y_{D_{ i}}}{\sum\limits_{i=1}^{M}Q_{Ei}}\right) \tag{10}\]  
\[(x_{V},y_{V})=\left( \begin{array}{c}\sum\limits_{i=1}^{M}Q_{Vi}x_{D_{i}}\\ \sum\limits_{i=1}^{M}Q_{Vi}\end{array} ,\frac{\sum\limits_{i=1}^{M}Q_{Vi}y_{D_{ i}}}{\sum\limits_{i=1}^{M}Q_{Vi}}\right) \tag{11}\]  
\[(x,y)=\alpha\times(x_{E},y_{E})+\beta\times(x_{V},y_{V}). \tag{12}\]  
Step 8. Legality testing. Assume that node A is the located node and D is the un-located node. Calculate the distance d from node A to node D. If the distance is within 120% of the communication radius of the unknown node, the estimated coordinates are legal, and the coordinate values are recorded and set the identification of the node to \(\mu\_f\lg=2\); otherwise, the positioning error is considered to be too large, and the positioning result is invalid, discard the positioning information and set the identification of node \(\mu\_f\lg=0\). Step 9. After each iteration of the localization algorithm, judge whether the new unknown node is located or not. If yes, transform the located node (\(\mu\_f\lg=2\)) into the anchor node, transmit the power signal to the neighboring area, and broadcast its positioning coordinates, then proceed to Step 2 and participate in the next round of location calculation; if not, end the positioning and calculate the positioning error.
# A Taxonomic Survey of Physics-Informed Machine Learning
## 3 Discussion of Relevant Applications
### Neural Operator Learning  
One example of advancement in physics-data-driven machine learning worth highlighting for its consequent physics-model=driven adaptation is the neural operator. The ubiquity of the operator approach toward learning solutions to complex physical problems has led to incorporating physics-model based information into the operator learning process. The neural operator approximates latent operators which govern a mapping between input parameters and solutions. As noted by [PERSON] et al. [51], any point \(y\) in the domain of \(G(u)\), the output \(G(u)(y)\) is a real number. Hence, the network receives two component inputs, \(u\) and \(y\), and outputs \(G(u)(y)\). For operator learning, sufficiently numerous discrete underlying function sensor values are utilized for training. The neural operator abstracts complex multiphysics modeling problems as control function maps. Importantly, the introduction of the neural operator and the functional learning paradigm drastically change how model-driven inductive biases can be used in physics-informed machine learning. [PERSON] et al. [32] presents a framework for the use of Fourier transform layers in neural operator networks, applying the method to many examples, including Navier-Stokes, Burgers, and Darcy flow problems. The Fourier neural operator performs zero-shot super-resolution. [PERSON] et al. [33] conducted a study on the general applicability of Fourier neural operators to inverse problems governed by highly nonlinear differential equations. Another neural operator learning machine, the DeepONet, has received recent attention. [PERSON] et al. [34] studied the convergence of operator learning by branch and trunk networks in the context of Burgers equation and advection-diffusion problems. Several important theorems regarding the convergence of functional learning machines are also included. Most importantly, the neural operator learning paradigm exemplifies a predisposition to the introduction of learning, inductive, or observational biases with model and data drivers.
## 3 Results
### Dimensionality of the Spectral Data  
The spectral reflectance properties presented in Figure 2 also illustrate--while containing some information on LAI and WC--that large spectral ranges vary similarly with time and in response to nutrient and water conditions [47]. Thus, many of the spectral bands are inadequate or redundant in their information content when retrieving plant and soil characteristics such as LAI, chlorophyll or leaf and soil water content [48]. In order to get estimates of the dimensionality of our experimental data set, which is an important guidance on the minimum band/index number necessary for any retrieval algorithm--both linear and non-linear, local and non-local methods are used in the following.
## 4 Evaluation and Analysis
### Evaluation Metrics  
Evaluation techniques typically focus on precision-recall metrics [53]. Precision is defined as the ratio of accurate matches (i.e., true positives (TP)) to the total system detections (i.e., true positives plus false positives (FP)). Recall denotes the ratio of true positives to the total ground truth (i.e., the sum of true positives and false negatives (FN)). They can be defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
Based on this, we adopt the Precision-Recall (PR) curve to evaluate the relationship between these metrics. The curve closer to the top right has better performance.  
In addition, two other evaluation metrics are also adopted here: (1) the mean average precision (mAP), which refers to the coverage area under the PR curve as :  
\[AP=\int_{0}^{1}P(r)\;\mathrm{d}r \tag{19}\]  
where \(P(r)\) represents the PR curve and \(r\) is the recall. (2) the maximum recall rate at 100% precision. Note that the size of the area under the PR curve is positively correlated with the effectiveness of the LCD methods.  
Figure 6: The appearance variations of sample images from GardensPoint, Nordland, and St. Lucia datasets.