esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas.  
sparse road networks; dynamic resilience assessment; two-layer topological model; geological disasters; emergency response
# Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation
###### Abstract  
To increase the reliability and accuracy of tolerance design, more and more research works are considering not only orientation and position deviations; they are also forming errors in tolerance modeling. As a direct cause of form errors in industrial mass production, the processing features of the machining system degrade over time. Under the Industry 4.0 paradigm, an assembly tolerance design method based on Skin Model Shape is proposed to take the effect of degrading processing features into consideration. A continuous-time multi-dimensional Markov process is trained through maximum likelihood estimation based on the nodal sampling point set on the machined surface. Degradation of the machined surface is modeled based on the joint probability distribution of nodal displacements. Assembly force constraints and assembly entity constraints are applied to spatial assembly simulations. Tolerance synthesis takes the manufacturing cost and assembling probability as design objectives. A design example of the rotary feed component in a five-axis machine tool is proposed for explanation and verification.  
computer-aided tolerance; processing features degradation; skin model shape; statistical tolerance analysis; tolerance allocation
# Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition
## 4 Simulation Results  
The experimental setup is based on a network echo cancellation scenario [36], as previously described in Section 2, related to the results reported in Figures 2 and 3. The experiments were performed using MATLAB R2018b (for programming and graphic representations), running on a GIGABYTE AORUS 15G XC device (Windows 10 OS) sourced by GYGABYTE, Taipei, Taiwan, which has an Intel Core i7-10870H CPU with 8 Cores, 16 Logical Processors (@2.21 GHz base speed), and 32 GB of RAM. As a performance measure, the normalized misalignment (in dB) is involved in all the following experiments. Summarizing, the main goal is to identify an impulse response \(\mathbf{h}\) of length \(L=512\) (corresponding to a network echo path), while the input signal \(x(t)\) is an AR(1) process. In this context, the reference signal \(d(t)\) is obtained based on (1), using a white Gaussian additive noise \(v(t  
expecting a good accuracy of the Wiener filter. Second, using \(\text{SNR}=10\) dB corresponds to moderate noisy conditions, while noticing the influence in reducing the accuracy of the solution (i.e., an increase in the misalignment). Finally, heavy noise conditions are considered when \(\text{SNR}=0\) dB, critically influencing the reliability of the Wiener filter.
## 2 Reconfigurable RFID Sensor for Detecting Solvent Vapors/Gas
### Reconfigurable Resonant Frequency of the Sensor  
Reconfigurable resonators offer the capability to change the resonator characteristics to adapt to dynamic wireless communication systems. The most significant characteristic is the resonant frequency, which is controlled by changing the capacitance of the resonator. Accordingly, different active RF elements such as PIN diodes [28], MEMS [29], and varactor diodes [30] can be used to change the capacitance of the resonator. In this paper, a varactor diode was selected because it could provide a continuous tuning of the resonant frequency of the sensor tag. Reconfigurability was used in order to obtain the optimal resonant frequency in which the sensor showed a maximum sensitivity to the environment. The varactor was placed into the resonant element and reverse biased through the WPT, which made the structure simple, since these types of feeding do not require wire connections. The resonant frequency of the resonator could be controlled by varying the reverse-bias voltage \(V_{\mathrm{r}}\), which was related to the variation in the diode capacitance. Figure 3 depicts the reconfigurable RCS of the chipless tag coupled to the loop and loaded with a Skyworks varactor diode (SMV1213) [33].  
Figure 3: Simulated RCS for different values of varactor capacitances \(C\).  
Figure 2: Simulated RCS of the chipless RFID tag with and without the sensitive layer.  
For different values of \(C=[1.9,\,3,\,6.4,\,30]\) pF that corresponded to \(V_{\text{r}}=[7.5,\,4.5,\,3,\,0]\) V, respectively, the frequency was tuned from 3.2 to 3.4 GHz, which offered a tuning range of 200 MHz, which corresponded to more than 5% when compared to the reference resonant frequency. The voltage \(V_{r}\) that controlled the capacitance of the varactor was obtained by rectifying the signal collected by the receiving loop from the received EM wave. The sensor was treated here in a simplified version without the active layer merely as a proof of concept.
## 6 Results and Discussion  
### Comparison and Evaluation of Prediction Models  
To verify the performance of the developed prediction model (PSO-LSSVM), the same training datasets and testing datasets were utilized. Furthermore, the root-mean-squared error (RMSE), mean absolute error (MAE), and correlation coefficient (\(r\)) were selected as the assessment indicators. The RMSE and MAE were negatively correlated with the performance of the models, while on the contrary for \(r\). The efficiencies of PSO-LSSVM, GA-BP, the unoptimized LSSVM model, and BP were compared by calculating and evaluating the performance indicators. As shown in Table 5, it can be seen that the hybrid models (PSO-LSSVM and GA-BP) outperformed the single prediction models (LSSVM and BP). In the single model, LSSVM seemed to exhibit a slightly better predictive performance than BP, with smaller error metrics (RMSE and MAE) and larger fit coefficients (\(r\)). Importantly, Table 5 shows that PSO-LSSVM had a more reliable performance compared with the remaining models.  
Additionally, a PPV profile between the predicted and the measured for training and testing is shown in Figures 9 and 10. Based on the graphical representation of the regression fit in Figures 9 and 10, the predicted values of PSO-LSSVM and GA-BP had a tendency to be close to the fit line. For instance, the predicted values of the single model had more discrete points, with a lower \(r\). Consequently, the PSO-LSSVM model was closest to the measured values with an \(r\) of 0.983 and 0.965 for the training and test sets.  
Thus, the three classical solutions (A, B, and C) are shown in Table 6. It is worth noting that the transformation of PPV in Sensitive Region c was weaker than the Br of the blasting fragmentation when the parameters changed. In order to validate the optimization results, a blasting scheme was designed using the blasting parameters extracted from Point B. Then, limited blasting field tests were carried out, and a total of three set dates were recorded, finally. Therefore, considering the B solution in the blasting scheme, the measured PPV values for Sensitive Area c were 0.752 cm/s, 1.00 cm/s, and 1.01 cm/s, and the Brs of the blasting fragmentation bulk were 11.02%, 9.06%, and 8.42%. Consequently, the actual measured values were similar to the optimized values, indicating that the optimization results were convincing.  
Figure 10: Regression curve of predicted and measured PPV for testing by different methods.  
Figure 9: Regression curve of predicted and measured PPV for training by different methods.
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 4 Experiments and Results
### Data Preparation  
In this work, the three sections of the progress notes _Subjective_, _Objective_ and _Assessment_ were included. To prepare the raw dataset for analysis, several pre-processing techniques were applied in this step. Lowercasing ensured consistent casing throughout the document. Censored pattern removal addressed personally identifiable information by replacing identifiers with empty spaces to avoid interference with processing tasks. Time format removal eliminated irrelevant expressions like \"HH:MM AM/PM\" in order to focus on essential information. Float number representation standardization ensured uniformity by standardizing numeric formats. Text replacements improved expressions like age, medical history, and gender to enhance readability. Line break and punctuation removal maintained text coherence and reduced noise, while stemming with the Lancaster stemmer normalized words to their base form to aid with vocabulary reduction and improve text processing efficiency.  
Vital signs are essential indicators of a patient's physiological status and play a key role in diagnosing medical conditions. Unlike previous systems, this work includes vital sign measurements such as \"TCO2\", which measures dissolved carbon dioxide in the blood, \"glucose\", which indicates blood sugar levels, and \"PLT\" for assessing platelet count, among others. Each vital sign was extracted from the _Objective_ section of the progress notes and was categorized based on specific thresholds according to multiple studies [46; 47; 48] to aid with interpretation and decision-making. Each category represents a range of values that are considered \"normal\", \"low\", or \"high\" or other specific conditions depending on the laboratory result.
## 4 Modeling and Results
### Datasets Used for Modeling
#### 4.1.1 CASIA-HWDB  
The CASIA-HWDB dataset consists of handwritten Chinese characters, alphanumeric symbols, and whole Chinese texts, widely used in various research studies focused on character and text recognition [23; 24; 25; 26; 27; 28]. This dataset is publicly available in [29]. For this work, we specifically employed the subset containing individual characters. The dataset is divided into the following three parts:  
* HWDB1.0: Contains 3866 Chinese characters along with 171 alphanumeric and special symbols. Among these, 3740 characters belong to the GB2312-80 level 1 set (which contains a total of 3755 characters). Each class includes roughly 420 samples, amounting to a total of 1,609,136 images;
* HWDB1.1: Includes 3755 GB2312-80 level 1 Chinese characters and 171 additional symbols. Approximately 300 samples represent each class, summing up to 1,121,749 images;
* HWDB1.2: Features 3319 Chinese characters distinct from those in HWDB1.0, alongside 171 alphanumeric and special symbols. Each class contains about 300 samples, with a total of 990,989 images.  
When combined, HWDB1.0 and HWDB1.2 offer 7185 unique characters, covering all 6763 Chinese characters in the GB2312 standard. For training purposes, only these 6763 classes were utilized. An 80/20 split was applied for training and testing (which is \(\sim\)240/\(\sim\)60 or \(\sim\)580/\(\sim\)140 samples for different characters).  
Preprocessing involved inverting the image colors (to match the format of the Kuzushiji-Kanji dataset, where kanji are white on a black background), resizing all the images to 64 \(\times\) 64 pixels, and converting them to tensors compatible with PyTorch 2.7. The images were then normalized to ensure the pixel values ranged between \(-1\) and 1.
## 3 Proposed Real-Time Data Fusion Method Based on a New Distance Metric  
Autonomous vehicles can exchange sensing data acquired from various sensors through V2V communication. In order to leverage sensing data from multiple sources in real-time, we require a data fusion system with low complexity and high reliability. In this section, we introduce an inter-vehicle data fusion method for identifying nearby objects.  
We classify the type of sensing data into two categories: \"_object_\" and \"_trajectory_\". The former is associated with the existence of an object and, given that the object exists, the class of the object. The latter is related with trajectory of an object such as position and velocity. According to these data types, we will adopt different data fusion schemes. We first discuss data fusion associated with \"object\" type data.
## 5 Further Discussion  
According to the results of the empirical analysis, it can be found that the introduction of the emotional tone of the MD&A does not provide more incremental information for the identification of financial crises of listed companies. However, it brings a certain amount of noise, which leads to a slight decrease in the overall mean value of AUC, ACC, SEN, and SPE. This is in line with some literature findings that management can manipulate and modify texts to increase the positive level of the emotional tone, which can be very disruptive to the early warning effect [42; 43; 44]. It is clear that texts disclosed internally by firms are vulnerable to manipulation by management. This paper further investigates external texts that are difficult for companies to influence, such as financial news texts. The introduction of the emotional tone indicator of financial news to the machine learning model results in a better early warning effect than that of MD&A, which provides a new research idea for financial crisis early warning models from the perspective of feature engineering. On the early warning effects of thirteen machine learning models, DBGT, AdaBoost, random forest and Bagging all have good prediction performance on all three sets of input feature variables, providing an empirical basis for following research and applications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **AUC** & **Rank** & **ACC** & **Rank** & **SEN** & **Rank** & **SPE** & **Rank** \\ \hline Logistic regression & 0.7215 & 11 & 0.6593 & 11 & 0.6433 & 12 & 0.6753 & 8 \\ Ridge regression & 0.7209 & 12 & 0.6587 & 12 & 0.6439 & 11 & 0.6734 & 9 \\ Lasso regression & 0.7217 & 10 & 0.6596 & 10 & 0.6420 & 13 & 0.6772 & 6 \\ DBGT & 0.7532 & 3 & 0.6879 & 3 & 0.6956 & 5 & 0.6802 & 4 \\ XGBoost & 0.7416 & 7 & 0.6710 & 7 & 0.6790 & 7 & 0.6630 & 10 \\ LightGBM & 0.7331 & 8 & 0.6645 & 9 & 0.6704 & 9 & 0.6587 & 11 \\ CatBoost & 0.7521 & 4 & 0.6817 & 5 & 0.7092 & 1 & 0.6543 & 12 \\ AdaBoost & 0.7579 & 1 & 0.6913 & 1 & 0.7037 & 3 & 0.6790 & 5 \\ SVM & 0.7232 & 9 & 0.6676 & 8 & 0.6581 & 10 & 0.6771 & 7 \\ BPNN & 0.7442 & 6 & 0.6814 & 6 & 0.6716 & 8 & 0.6913 & 1 \\ Decision tree & 0.7168 & 13 & 0.6519 & 13 & 0.7079 & 2 & 0.5958 & 13 \\ Random forest & 0.7557 & 2 & 0.6854 & 4 & 0.6839 & 6 & 0.6870 & 2 \\ Bagging & 0.7514 & 5 & 0.6898 & 2 & 0.6975 & 4 & 0.6821 & 3 \\
**Mean** & 0.7379 & & 0.6731 & & 0.6774 & & 0.6688 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean values and ranking of early warning effect evaluation indicators of 13 machine learning models based on three different sets of input feature variables.
## 3 Main Framework, Dataset, and Feature Extraction Overview
### Model Evaluation Index  
In order to facilitate the evaluation in the following article, we first give some definitions of evaluation indicators and cross-validation listed as below. Related indicators are introduced in the literature [22; 23; 24; 25].  
**Accuracy:** Accuracy is the ratio of the number of correct predictions to the total number of predictions. It reflects how often the classifier is correct across all classes.  
**Balanced accuracy:** Balanced accuracy measures model performance on imbalanced datasets. It is the average of the recall rates for each class, providing a more accurate reflection of the model's ability to recognize the minority class. Balanced accuracy values range from 0 to 1, with 1 being the best.  
\(F_{1}\)**-Score:** The F1-score is the harmonic mean of precision and recall, representing the overall effectiveness of the model in classification tasks. It balances the trade-off between precision and recall, especially in cases of class imbalance.  
**Specificity:** Specificity measures the proportion of actual negatives correctly identified as negatives. In multiclass classification, it is calculated for each class as the proportion of actual non-class samples correctly identified as non-class.  
**True negatives (TNs):** true negatives for class \(i\) are the count of actual non-class \(i\) samples correctly predicted as non-class \(i\).  
**False positives (FPs):** false positives for class \(i\) are the count of actual non-class \(i\) samples incorrectly predicted as class \(i\).  
Figure 9: Box plot of \(\Delta\) obtained using the \(QG\) method for groups \(A\), \(B\), \(C\), \(D\), and \(E\), with \(Q=30\) and \(k=4\).  
**Precision:** precision is the proportion of predicted positive samples that are actually positive, reflecting the accuracy of positive predictions.  
**Recall:** recall is the proportion of actual positive samples correctly predicted as positive, indicating the model's ability to identify relevant positive samples.  
**Matthews correlation coefficient (MCC):** MCC is a balanced classification metric ranging from \([-1,1]\), measuring the quality of binary classifications by considering true and false positives and negatives.  
**Cohen's kappa:** Cohen's kappa assesses agreement between the classifier and a random classifier, adjusting for chance agreement for a nuanced evaluation.  
**Hamming loss:** Hamming loss represents the proportion of incorrectly classified samples, measuring the average number of misclassified labels.  
**Jaccard index:** the Jaccard index, or intersection over union (IoU), evaluates classifier accuracy by comparing the intersection and union of predicted and actual sets.  
**Cross-validation:** Cross-validation involves dividing the dataset into \(k\) subsets, using \(k-1\) subsets for training and \(1\) for testing in each iteration. This process is repeated \(k\) times, with each subset serving as the test set once, and the results are aggregated for final evaluation.
## 2 Materials and Methods
### Sampling and Measurement Methods  
#### 2.3.1 CO\({}_{2}\) and CH\({}_{4}\) Emission Measurement  
The CO\({}_{2}\) and CH\({}_{4}\) emissions in the field were measured by static chamber-gas chromatography. The static chamber comprised a base (40 cm \(\times\) 30 cm \(\times\) 15 cm) and top box (40 cm \(\times\) 30 cm \(\times\) 40 cm). The base was made of stainless steel and it was sealed by a water-filled groove at the top. The top box was made of polyvinyl chloride sheet and it was wrapped with a layer of heat-insulating reflective material on the outside. A thermometer was placed at the top and a gas sampling pipe in the middle. The long side of the base was perpendicular to the planting row and it was positioned according to the proportion of each part of the planting pattern. Sampling commenced after fertilization and sowing, and it was conducted every half a month until the wheat was harvested. Sampling was always performed at 9:00-11:00 a.m. During sampling, the top box was fastened to the base and sealed with water. Gas samples were then extracted at intervals of 0, 10, 20, and 30 min using 50-mL syringes, and the temperature in the box was recorded. After sampling, the gas samples were analyzed as soon as possible using a GC-2010 Plus gas chromatograph (Shimadzu, Kyoto, Japan) with a flame ionization detector. The gas chromatograph was calibrated with standard gas before each test. The CO\({}_{2}\) and CH\({}_{4}\) emission fluxes were calculated by the following formula [43]:  
\[\mathrm{F=k\times(273.15/T)\times(V/A)\times(\Delta c/\Delta t)} \tag{1}\]  
where F is the gas emission flux (mg CO\({}_{2}\)-C m\({}^{-2}\) h\({}^{-1}\) or \(\mathrm{\SIUnitSymbolMicro g}\) CH\({}_{4}\)-C m\({}^{-2}\) h\({}^{-1}\)), k is the conversion factor (0.536 kg C m\({}^{-3}\) for both CO\({}_{2}\) and CH\({}_{4}\)), T is the average temperature inside the box during sampling (K), V is the volume of the sampling box (m\({}^{3}\)), A is the area of the bottom of the sampling box (m\({}^{2}\)), and \(\Delta c/\Delta t\) is the rate of change in the  
Figure 2: Schematic diagram of the ridge–furrow rainwater harvesting (RFRH) planting mode.  
gas concentration in the sampling box per unit time (CO\({}_{2}\): ppm h\({}^{-1}\); CH\({}_{4}\): ppb h\({}^{-1}\)). Linear regressions were performed based on the gas concentration and sampling time, and flux values were accepted when \(r^{2}>0.90\). Cumulative gas emissions were estimated by integrating the monthly average fluxes during the wheat growing season [44].
## 3 Results and Discussion
#### 3.1.5 Discussion of Key Studies Regarding the Research Question
However, these systems depend on network availability and battery life and face limitations in terms of power consumption. Additionally, studies such as [43; 45; 54; 69; 72] provide insights into communications and data transmission protocols. Some of them utilize BLE, RFID, LiFi, ZigBee, and MQTT, primarily focusing on interoperability and integration with hospital systems and home environments. Nevertheless, some compatibility issues exist with current medical systems (for hospital settings), alongside cybersecurity risks associated with wireless communication. Table 5 summarizes the studies related to each category presented in response to the question in Section 3.1. A detailed description of each listed work can be found in Table A1, which is organized by year of publication. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Technologies and Sensors Used** & **Applications** & **References** \\ \hline \multirow{6}{*}{Intelligent homes, Section 3.1.1} & Microcontrollers (ESP8266, ESP32, ATMega), IoT sensors, AI, LiFi, RFID, millimeter-wave radar, UWB radar, wearable sensors, cloud computing, MQTT, smart cameras & Home are monitoring, patient management, fall detection, health tracking, e-health monitoring, IoT-based monitoring, remote sensing, assistive health systems & [29; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] \\  & Smart clothing, wristbands, smart smartwatches, IoT sensors, ECG, TICE, inertial sensors, AI, i-gluture conductive material, body area network (BAN) & Health tracking, elderly monitoring, fall detection, emergency alerts, fitness tracking & [8; 40; 42; 49; 62; 63; 71; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of key studies for each presented category.