esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results Analysis
### Prediction Scenario Design and Evaluation Metrics  
To evaluate model performance under various conditions, we designed 10 prediction scenarios including different sliding time window lengths and prediction scales, as shown in Table 1.  
For each scenario, we use two metrics to evaluate model performance:  
(1) Mean Absolute Percentage Error (MAPE):  
\[E(f;data)=\frac{100\%}{m}\sum_{i=1}^{m}\left|\frac{f(\mathbf{x}_{i})-y_{i}}{ y_{i}}\right| \tag{1}\]  
\(E(f;data)\): represents the Mean Absolute Percentage Error value of model\(f\) on dataset data \(m\): represents the total number of test samples  
\(f(\mathbf{x}_{i})\): represents the predicted output value of prediction model\(f\) for the \(i\)-th input sample \(\mathbf{x}_{i}\)  
\(y_{i}\): represents the actual observed travel time value corresponding to the \(i\)-th sample  
(2) Root Mean Square Error (RMSE):  
\[E(f;data)=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(f(\mathbf{x}_{i})-y_{i}\right) ^{2}} \tag{2}\]  
\(E(f;data)\): represents the Root Mean Square Error value of model \(f\) on dataset data \(m\): represents the total number of test samples  
\(f(\mathbf{x}_{i})\): represents the predicted output value of prediction model \(f\) for the \(i\)-th input sample \(\mathbf{x}_{i}\)  
\(y_{i}\): represents the actual observed travel time value corresponding to the \(i\)-th sample  
We divided the dataset (66,240 samples) into the following:  
(1) Training set (80% of the original dataset): used for shallow learning model training;  
(2) Test set (20% of the original dataset): used for testing all models;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sliding Time Window Length (min)** & **Prediction Scale (min)** \\ \hline
[60; 30] & [2; 10; 20; 30; 60] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction scenario design.  
Figure 2: Average speed distribution of 132 road segments.  
For deep learning models, we further divided the training set into training set (60% of the original dataset) and validation set (20% of the original dataset).  
After dividing the dataset, we reshaped the training, validation, and test sets into formats suitable for model input according to sliding time window length and prediction scale. Since there are 10 prediction scenarios, we constructed 10 sets of training input data, validation input data, and test input data with different dimensions.
# A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
###### Abstract  
This paper describes a simple, lossless and computationally efficient two-band single (s-) filter bank that creates an opposite band output by subtracting the primary filtered data from the original data. For computationally efficient and error-free s-filter bank achievement, a maximally flat (MAXFLAT) half-band filter with zero odd-order coefficients is characterized from a unique perfect reconstruction condition, and an explicit impulse-response formula (for non-zero integer coefficients of even order) is derived in a closed form of the filter. The examples are shown to provide a complete and accurate solution for the design of such s-filter banks. In addition, the effectiveness of the proposed s-filter banks is clearly verified by comparing the lossless 5/3 and lossy 9/7 filter banks (in the JPEG2000). The simulation results show that the s-filter banks lead to better performance than the JPEG2000 filter banks using two filters although allowing low computational complexity of less than 50%. This new approach is shown to provide significant advantages over existing lossless discrete wavelet transform (DWT) filter banks in both design flexibility and computational complexity.  
lossless two-band filter bank; wavelet transform; DWT filter bank; quadrature mirror filter
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 3 Classification System
### Support Vector Machine  
SVM has been used to some extent in bioacoustic species recognition with success [13; 26]. It discriminates the data by seeking the optimal hyperplane that separates the training data into two classes. However, the reptile call features are not lineally separable, so a non-linear kernel function has been used to divide the features in a higher dimensional space. For the experiments, a Gaussian or Radial Basis Function (RBF) kernel \(K(x,x^{\prime})=\exp(-\gamma\|x-x^{\prime}\|^{2})\) has been selected, where the parameter \(\gamma\) was optimized using a grid approach. For multiclass classification, the strategy \"one-versus-one\" [27] has been implemented, which trains a binary SVM classifier for each pair of classes. Therefore, for \(N\) different classes, \(N(N-1)/2\) binary classifiers are required to distinguish the samples, where \(N\) represents the number of reptile species. The SVM decision function is defined as in (4), where \(b\) is a numeric offset threshold and \(\alpha_{i}\) are Lagrange multipliers. The magnitude of \(\alpha\) is determined by the \(C\) parameter, which imposes a penalty on misclassified samples (\(0<\alpha\leqslant C\)).  
\[f(x)=\text{sign}\left(\sum_{i=1}^{l}y_{i}\alpha_{i}K(x,x^{\prime})+b\right),\; y_{i}\in\{1,-1\} \tag{4}\]
## 4 Results and Discussion
### Energy Consumption and Economic Analysis in Conventional HVAC  
Under the same conditions, the traditional method of increasing fresh air volume for oxygen supplementation and PSA oxygen generation is used to supply oxygen to the same air-conditioned room (two adult men are working in the room), and the energy savings and economic efficiency of these two oxygen supplement methods are compared.  
4.1 Energy Consumption in Traditional Method of Increasing Fresh Air Volume for Oxygen Supplementation  
According to the Design Code for Heating, Ventilation and Air Conditioning of Civil Buildings (GB50736-2012) [47], the fresh air volume per person in the working environment is determined to be 30 m\({}^{3}\)/h. The indoor and outdoor meteorological parameters in summer  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Oxygen Supply Method** & **Relation of Oxygen-Enriched Area (m\({}^{2}\))** \\ \hline
1\# & F = 0.4 + 0.383 Q \\
4\# & F = 0.237 + 0.8 Q \\ \hline \hline \end{tabular}
\end{table}
Table 6: The relationship between oxygen-enriched area and oxygen flow rate under air-conditioning.  
Figure 11: Oxygen-enriched area with different oxygen supply modes and oxygen flow rates under air-conditioning.  
Figure 10: Oxygen-enriched area with different oxygen flow rates and oxygen supply modes under air-conditioning (\(V_{\text{in}}\) = 0.85 m\(\cdot\)s\({}^{-1}\)) in the form 1\(\#\) and 2\(\#\). (**a**) 1\(\#\), (**b**) 2\(\#\).  
and winter in Beijing are shown in Table 7. The fresh air load is calculated according to Formula (6) [48].  
\[LQ_{\text{W}}=G_{\text{W}}\times(h_{W}-h_{N}) \tag{6}\]  
Through calculation, it can be concluded that the traditional air-conditioning system, with increased fresh air volume, consumes 0.502 kW/Day and 1.098 kW/Day in summer and winter, respectively. The air-conditioning system runs 24 h a day and runs for three months in summer and winter. The total energy consumption for the whole year is 3456 kWh. The annual operating cost of the traditional air-conditioning system with fresh air is 1728 RMB.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Experimental Settings  
#### 4.1.1 Dataset  
We obtain the data from the Administrative Examination and Approval Bureau of Wuhou District, Chengdu. The dataset contains 106,630 interaction histories (14,129 users and 361 items) spanning from October 2015 to August 2019. Each user is identified by a unique user ID. The service items including public services, administrative examination, and approvals of the citizens, are identified by unique item ID. Table 1 exhibits the implicit interaction information of the dataset. Each entry includes the user ID, item ID, interaction time, and auxiliary information such as the user's gender (Male = 1, Female = 0). The data indicates users' behaviors, for example, a 54-year-old man with ID \"001\" clicked the item with ID \"67540\" on 21 October.  
The recommendation system of personalized public service aims to predict a personalized top-K recommendation list for a user based on his interaction history. To simulate the real situation, we choose the leave-one-out scheme to split the train set and test set. For each user, we keep his latest action as the test set and train the remaining interaction histories. We follow the common strategy that randomly selects 100 negative items and then ranks the test item among the 100 negative items according to their predictive score [6; 38]. Meanwhile, we also sample from the remaining items without user interaction for training. To avoid sample imbalance, we adopt the ratio between positive and negative samples (1:1).
## 2 Materials and Methods
### Architecture of the Developed Smart Garment
The LTC3588-1 integrates a low-loss rectifying bridge and a configurable DC output voltage set to 3.3 V for the two piezoelectric harvesting stages, making it well-suited to collect energy from alternate sources (Figure 3a). To avoid reverse currents, Shottky diodes (type BAT86s, made by Vishay Co., Malvern, PA, USA) are used to aggregate the energy contributions delivered by each conditioning section (Figure 3b). They are featured by very low forward voltage (\(<\)300 mV), improving the efficiency of the harvesting system. Lastly, the voltage of the aggregated power is increased to 4.2 V using a low-power BQ25570 step-up converter (produced by Texas Instruments, Dallas, TX, USA), enabling charge accumulation in a 380 mAh LiPo battery. It has a very low quiescent current (300 nA), a wide input range (from 0.1 to 5.5 V), and high efficiency (up to 93%); additionally, the converter has a user-programmable overvoltage threshold and an internally set undervoltage discharge threshold to prevent shortening of the battery lifetime [45]. Nevertheless, no matching section was included in the harvesting architecture since relatively good impedance matching was already obtained for both photovoltaic and thermal subsystems. Indeed, the LTC3108 board shows an 11 \(\Omega\) input resistance for a 1:90 transformer, which fits well with the TEG impedance (i.e., for the series of three TEC1-12706 TEGs equal to 12.6 \(\Omega\) at \(\Delta\)T = 5 \({}^{\circ}\)C operating point). However, for future development, the design of an impedance matching network for harvesting sections has to be performed to optimize the charge extraction.
## 4 Experimental Validation
### Methods and Terminology
#### 4.2.2 Outdoor RGB Canopy Images  
The (semi-)manual segmentation of leaves requires more elaborated tools than a Grabcut method, due to the variation in illumination conditions, the presence of shadows, and the frequent occlusions by background objects or plants. Therefore, machine learning models are considered to label a subset of the training set through the iterative annotation of image parts.  
The subset of samples to label consists of 800 samples, corresponding to \(800\times 600\) images, extracted from full resolution images. Those samples are jointly considered by the Ilastik software [34], which trains a random forest (RF) model (100 trees, with a maximal depth of 50) based on foreground strokes that are interactively drawn by the user. This approach is denoted sRF, with the prefix's' referring to the fact that the whole set of images to manually label is jointly processed. As an alternative, the sRP method randomly samples patches in the same whole set and trains a CNN based on their annotation (see below for CNN parameters). Two more methods are considered to manually annotate the 800 image samples. They are interactive and proceed iteratively by adopting the corrective annotation protocol recommended in [35]. A small subset of samples is first annotated (based on the delineation of small patches corresponding to a single class), without referring to a model prediction. Those annotations are used to train an initial version of the model. Subsequent annotations correct the model predictions and are used to update the model. Two types of image segmentation models are envisioned, resulting in two distinct methods. The first one corresponds to the gradient-boosted decision trees [36] model, with default parameters. In that case, due to the experienced lack of generalization capabilities of GBT, one model is trained for each of the 800 images, and the method is denoted \(iGBT\), with the 'i' prefix referring to the fact that one GBT is trained for each individual image. The second one is a fully convolutional neural network (CNN). Its expressivity is sufficient to capture the knowledge associated to the entire set of 800 images. Therefore, a single model is considered for the whole set of manually labeled images. It is denoted \(sCNN\). The notation CNN*_\(X\) is adopted to refer to the data-reinforced convolutional network trained with pseudo-labels generated by model X.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 3 Materials and Methods
### Methodology
#### 3.5.2 Collective Anomalies--Trend Threshold  
One of the types of anomalies we seek to identify are collective anomalies, i.e., those that individually do not represent an anomaly but do so when considered as a sequence.  
To achieve this, a threshold is set on the trend calculated in the series decomposition. For both series, this threshold \(\delta\) is \(\delta=\tilde{x}\pm\sigma\), where \(\tilde{x}\) is the median of the original series and \(\sigma\) is its standard deviation. We consider a value anomalous only if it exceeds the defined upper threshold.
# Matched-Filter Thermography
## 3 Matched Filtering  
A matched filter (MF) is commonly defined as the optimal linear filter for maximizing the signal-to-noise ratio (SNR) in the presence of additive stochastic noise [27]. The methodology was introduced in the 1940s [28] and has since gained widespread adaptation in numerous fields such as telecommunication, radar design and medical imaging.
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides
### Template-Assisted Solution-Phase Synthesis  
For the past 20 years, template-directed synthesis has been a commonly used method for the fabrication of 1D nanostructures [74; 75; 76]. In 2011, [PERSON] et al. first reported a wet chemistry method for the controlled formation of ultrathin Te nanowires (with a diameter of several nanometers) using PVP as surfactant, which were subsequently employed as sacrificial templates to synthesize highly uniform Bi\({}_{2}\)Te\({}_{3}\) nanowires with diameter of 15-17 nm and length of tens of micrometers in triethylene glycol solution upon addition of Bi\({}^{3+}\) ions and hydrazine under heating (200 \({}^{\circ}\)C) at atmospheric pressure [68]. The synthesis of Bi\({}_{2}\)Te\({}_{3}\) nanowires was said to have involved the Kirkendall effect and Ostwald ripening (Figure 5a), as evidenced by experimental observations. Using a similar method, [PERSON] et al. later carried out a simple and efficient synthesis of n-type ultrathin Bi\({}_{2}\)Te\({}_{3}\) nanowires with an average diameter of 8 nm in ethylene glycol solution with high yield up to 93% (Figure 5b-d) [69]. The prepared Bi\({}_{2}\)Te\({}_{3}\) nanowires were compressed to bulk pellets by spark plasma sintering and exhibited a high ZT value of 0.96 at 380 K, which was attributed to the notable decrease in thermal conductivity due to the increased phonon scattering at the boundaries of the nanowires. Very recently, [PERSON] et al. also developed a large-scale one-pot solution-phase synthesis of ultrathin PbTe and Bi\({}_{2}\)Te\({}_{3}\) nanowires (\(<\)15 nm) using Te nanowires as templates with high yield (\(>\)80%) at lower temperature (120 \({}^{\circ}\)C) in ethylene glycol solution [77]. This facile and scalable solution-phase approach is a promising and reliable route for manufacturing uniform ultrathin metal chalcogenide nanowires for thermoelectric applications.
# The Specialist's Paradox: Generalist AI May Better Organize Medical Knowledge
## 3 Results
### Model-Specific Error Fingerprints  
A granular look at the misclassification list reveals distinct error patterns that are characteristic of each model's performance tier (Table 1). For the top-performing models--RoBERTa, MPNet, and BioBERT--the errors were not only few but also highly systematic. Their misclassifications were dominated by a single, recurring pattern: placing key dermatological and esophageal neoplasms (\"Basal Cell Carcinoma\", \"Melanoma\", \"Skin Neoplasms\", and \"Esophageal Neoplasms\") into the Oncology cluster, which cannot actually be considered an error. Their remaining errors were few and isolated, such as the misclassification of a single major Cardiology term.  
In stark contrast, the lower-performing models exhibited a much higher volume of errors that were widespread and more irregular. PubMedBERT, for instance, misplaced 19 terms, incorrectly mapping numerous Dermatology concepts to the Gastroenterology cluster and scattering Neurology terms across several unrelated specialties. The failure was most pronounced for BioClinicalBERT, which misclassified 27 of the 70 terms. Its error profile was characterized by a vast disorganization; terms from a single specialty like Dermatology were incorrectly assigned to four different clusters (Oncology, Endocrinology, Dentistry, and Gastroenterology).  
Figure 3: Frequency of the ten MeSH descriptors most often assigned to the wrong cluster across all six embedding models. Each bar represents the total number of misclustering events for a given term—summing errors from BioBERT, PubMedBERT, MPNet, MiniLM, RoBERTa, and Bio_ClinicalBERT.  
To provide a more direct and intuitive summary of performance, we calculated the overall number of misclassified terms, the corresponding error rate, and the final cluster purity for each model. This analysis offers a clear, high-level confirmation of the performance hierarchy established by the other validation metrics.  
The top-tier models demonstrated high fidelity in the clustering task (Table 4). MPNet and RoBERTa were tied for the best performance, each misclassifying only 5 of the 70 terms, which corresponds to a low error rate of 7.14% and a cluster purity of 92.86%. BioBERT also performed robustly, with only six misclassifications (8.57% error rate) and a purity of 91.43%.  
A stark drop-off in performance was observed for the subsequent models. PubMed-BERT and MiniLM were also tied, but for a much weaker performance, each failing to correctly classify 19 terms. This resulted in a substantially higher error rate of 27.14% and a correspondingly lower purity of 72.86%. Finally, BioClinicalBERT was confirmed as the definitive lowest-performing model, misclassifying 27 terms--more than a third of the entire corpus. This yielded a high error rate of 38.57% and a final purity score of only 61.43%. These straightforward metrics align perfectly with the qualitative analyses and external validation indices, clearly separating the models that generated a coherent semantic space from those that did not.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### DD Network Solutions
#### 4.3.2 Ambiguity Fixing Rate  
The ambiguity fixing rate of GPS, GLONASS, and GPS+GLONASS modes of the three networks is listed in Table 7. GPS has the lowest ambiguity fixing rate in the middle latitude network but the highest ambiguity fixing rate in the low latitude network. As there is no obvious difference in the GPS positioning accuracy of the three networks, we analyzed the types of receivers used. The receivers of the high latitude network were of 3 brands and 5 models, the receivers of the middle latitude network were of 3 brands and 6 types, while in the low latitude network, there were only 2 brands and 2 models receivers. This suggests that the different levels of ambiguity fixing rate could be related to the number of receiver types used in different networks. The different signal distortion biases between inhomogeneous receivers affect the GNSS data processing [31; 32]. Despite having the best receiver homogeneity, the ambiguity fixing rate of GLONASS in the low latitude network was only 26.4%, but 74.7% for GPS.
## 5 Conclusions  
In this paper, we proposed an electrically small and planar quasi-isotropic antenna and demonstrated its feasibility of improving the wireless link reliability under NLOS channel environments. The antenna had an electrical size \(ka\) less than 0.5 and showed a measured gain deviation around 5 dB, which is good enough to be considered as a quasi-isotropic antenna. Its performance in terms of the electrical size and gain deviation was compared with other planar quasi-isotropic antennas in the literature. Furthermore, from the wireless link reliability test in indoor NLOS channels, we demonstrated that PDR and RSSI can be enhanced by the simple use of the proposed antenna, in comparison to the use of omni-directional antennas. To the best of our knowledge, the usefulness of the quasi-isotropic antenna in real communication measurement was first demonstrated by this work. The exact analysis of the link reliability evaluation according to the different gain deviation values and the polarization dependency of the quasi-isotropic antennas remains a possibility for future work.  
Figure 14: Measured (**a**) PDR and (**b**) RSSI under NLOS at different floor levels of the building.  
**Author Contributions:** Conceptualization, S.M.R. and I.-J.Y.; methodology, S.M.R., M.J., P.P., and I.-J.Y.; software, S.M.R., M.J., P.P., and I.-J.Y.; validation, S.M.R., M.J., P.P., and I.-J.Y.; formal analysis, S.M.R., M.J., P.P., and I.-J.Y.; investigation, S.M.R., M.J., P.P., and I.-J.Y.; resources, S.M.R., M.J., P.P., and I.-J.Y.; data curation, S.M.R., M.J., P.P., and I.-J.Y.; writing--Original draft preparation, S.M.R.; writing--Review and editing, P.P and I.-J.Y.; visualization, S.M.R.; supervision, I.-J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Basic Research Laboratory of the National Research Foundation funded by the Korean government under Grant NRF-2017R1A4A1015744, and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00659).  
**Conflicts of Interest:** The authors declare no conflict of interest.