esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Result and Analysis
### Quantitative Evaluation  
As compared to more common performance metrics (e.g., accuracy), AUC or ROC were more suited metrics imbalanced datasets because they helped to identify over-fitting [54]. Higher AUC or ROC values represented better separability between the MIR and NMIR classes. Figure 7 depicts the qualitative scores with ROC/AUC for the Agency_records, Employee_records, and Customer_records. The scores presented in Figure 7 were averaged across the Agency_records, Employee_records, and Customer_records. Transformer based models (BERT, RoBERTa, and ERNIE 2.0) and XGBoost performed best with a 93% ROC score for the Agency_records dataset. For the Employees_records and Customers_records, CNN performed best at 95% and 74% ROC. Overall, Catboost achieved minimum ROC scores of 69%, 50%, and 50%. This was expected as Catboost struggled in unknown categories and needed to build in-depth decision trees for high cardinality features [55].  
As indicated in Section 3, our datasets were highly imbalanced. Therefore, we resampled our dataset and re-ran our prediction pipeline. The results are depicted in Figure 8. ERNIE 2.0 achieved a 95% ROC for Agency_resampled. BERT and ERNIE 2.0 achieved the highest scores, 97% for Employee_resampled and ERNIE 2.0 at 98% ROC for Customer_records.  
Figure 6: Average tokenizer/class for training and test data across (**a**) Agency_records, (**b**) Employee_records and (**c**) Customer_records.  
tomer_resampled. NB achieved 71%, 53%, 59%, and 54% ROC for the Agency_resampled, Employee_resampled, Customer_resampled and Combine_All, respectively, which was the lowest among all the classifiers. A recurring issue with the NB model was that with no occurrences of a class label and a particular attribute value, the frequency-based probability estimation would be zero. A large dataset was required for reliable predictions of each class's probability.  
We also experimented with our synthetic data curation (SMOTE). For the Agency_smote and the Employee_smote, XGBoost performed best with 94% and 86% ROC scores, respectively. GB performed best for the Customer_smote with an 85% ROC value. Figure 9  
Figure 8: Quantitative ROC/AUC results across (**a**) Agency_resampled, (**b**) Employee_resampled (**c**) Customer_resampled, and (**d**) Combine_All for state-of-art methods.  
Figure 7: Quantitative ROC/AUC results across (**a**) Agency_records, (**b**) Employee_records, and (**c**) Customer_records for state-of-art methods.  
shows that the DL models (Bi-LSTM, GRU, CNN) performed relatively poorly on all the synthetic subsets. This was largely contributed to the synthetic data profile generated by the SMOTE technique. SMOTE created more extensive and less specific decision boundaries that increased the generalization capabilities of the ML classifiers, which were more suited for one-hot encoding. In contrast, DL models used tokenizers, causing extensive feature spaces in NLP and resulting in the DL models falling quickly into high sparse dimensions.
## 3 Results and Discussion
### Development of HPLC-FD
#### 3.2.2 Optimization of Chromatographic Analysis Conditions  
The fluorescence spectra (excitation and emission) of both DUV and SEL (Figure 9) indicated that DUV has two wavelength maxima for excitation (\(\lambda_{\rm ex}\)) at 273 and 344 nm, and a wavelength maximum for emission (\(\lambda_{\rm em}\)) at 407 nm. SEL spectra show three \(\lambda_{\rm ex}\) at 239, 263 and 290 nm, and a \(\lambda_{\rm em}\) at 349 nm. Fluorescence of both DUV and SEL were detected at 370 nm after their excitation at 280 nm. These excitation and emission wavelengths were selected for detection of both DUV and SEL because both are excitable at this \(\lambda_{\rm ex}\) and also both emit fluorescence at this \(\lambda_{\rm em}\) (Figure 9). Additionally, one molecule could be used as an internal standard in the determination of the other.  
Optimization of chromatographic conditions were designed to establish the most appropriate mobile phase, flow rate, and reversed phase column. The chromatographic separation was initiated in isocratic elution mode on a C18 column (150 mm length \(\times\) 3.9 mm i.d., 5 \(\upmu\)m particle diameter). The temperature of the column was maintained at 25 \(\pm\) 2 \({}^{\circ}\)C. Acetonitrile and water were used as the constituents of the mobile phase for separations, with a flow rate of 1.5 mL/min. Upon applying these initial conditions, a fairly long run time (-20 min) was required to achieve elution of DUV and SEL from the column. This long run time does not meet the requirements of high throughput analysis in QC. Due to both DUV and SEL's high lipophilicity, this delayed elution can be addressed. Accordingly, it was decided to switch to a more polar cyano (CN) column and adjust the  
Figure 9: The fluorescence (excitation and emission) spectra of DUV (1 and 2, respectively) and SEL (3 and 4, respectively). The concentrations of DUV and SEL were 380 and 10 \(\upmu\)g/mL, respectively. Both solutions were in methanol.  
mobile phase's ratio to 40:20:40 acetonitrile: methanol: water. These conditions reduced the run time (\(\sim\)8 min); however, the resolution of DUV and SEL was not adequate. In order to achieve better resolution, variable flow rates and various mobile phase compositions were examined. When a mobile phase composed of acetonitrile: acetate buffer, pH 4.5 (40:60, \(v/v\)), and flow rate of 1.7 mL/min were used, the best resolution and peak shape were obtained. Another reversed phase column (Hypersil(tm) Phenyl, 250 mm length \(\times\) 4.6 mm i.d., 5 \(\upmu\)m particle diameter; manufactured by Phenomenex, Torrance, CA, USA) was also evaluated, and it gave the superlative chromatographic resolution; thus, it was designated for all the subsequent work. Increasing the flow rate to 2.2 mL/min significantly reduced the run time to 5 min without affecting the resolution. Under these best resolution conditions, DUV and SEL were eluted as sharp and narrow symmetric peaks at 3.25 and 4.20 min, respectively (Figure 10).
# Computational Accountability in MAS Organizations with ADOPT
## 4 The ADOPT Accountability Protocol
### Verifying ADOPT
#### 4.3.4 Liveness and Nested Commitments  
The nested commitments in ADOPT also deserve some special attention. In the role-adoption phase, the following nested commitments are created: C\({}_{1}=C(Ag,Org,accept\_proposal(adopt\_role\)\((Ag,R))\), \(\bigwedge_{pwr_{k}\in P_{R}}C_{2,k}\)); where, as before, \(C_{2,k}=C(Ag,Org,C(Ag,Z,pwr_{k}),pwr_{k})\) for every power \(pwr_{k}\in P_{R}\). In this case it is interesting to verify whether whenever \(C_{1}\) is detached by \(Org\), \(Ag\) has the chance to satisfy \(C_{1}\) by creating the commitments \(C_{2,k}\). That is, whether there exists at least one run of the protocol where these commitments will be actually created. This corresponds to verifying the _liveness_ of the protocol by asking whether something \"good\" will eventually happen. The following CTLC formula serves this purpose: \(\texttt{AG(detachedC1}\to\texttt{EF(createdC2k))})\), where detachedC1 is an atomic proposition that becomes true when \(Org\) sends message _propose(adopt_role(Ag,R))_, whereas createdC2k is another proposition that becomes true when \(Ag\) sends the message _inform(done(adopt_role(Ag, R)))_. Indeed, looking at the sequence diagram in Figure 1, it is apparent that once the commitment \(C_{1}\) is detached, there exists at least one run of the protocol along which the message _inform(done(adopt_role(Ag, R)))_ is actually sent by the agent. The formula is thus satisfied, and the protocol enjoys the liveness property.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Study of FL-LoRaMAC
#### 6.2.1 Model Performance of FL-LoRaMAC in the Presence of Channel Loss  
We now present the effect of packet loss on the performance of the proposed FL-LoRaMAC framework. We assume that the ambient traffic in the entire network leads to packet losses on uplink transmissions, which depend on the traffic volume. It is assumed that packet loss does not affect network joining and all end devices are successfully configured according to the specifications mentioned in Section 4.2. Also, the end nodes are supplied with the training data (representing locally sensed data in a real scenario). The application server transmits the untrained global model as downlink messages to the end devices taking part in the training. This transmission of the global model is acknowledged by the end devices. Hence, all fragments of the global model are received by all the end devices without loss.  
The end nodes train the model based on the local data and the corresponding gradient updates are transmitted to the application server as fragments. Each end device is assumed to transmit one fragment every 60 s. We assume that these fragments are 28 bytes long. Assuming that all devices use SF-7, this leads to the time on air for each packet being 62 millis. During this transmission, fragments may suffer collisions with either data fragments from other participating devices or data from non-participating devices. We use a Poisson analysis for evaluating the probability of successful transmissions under these conditions. The system treats lost fragments from collisions according to the specifications in Section 4.3. Due to this loss, the model performance deteriorates in terms of making predictions on the testing dataset. Evaluation metrics calculated for varying loss rates from the implementation, each averaged from 100 simulation experiments, are depicted in Figure 12.  
Figure 11: Computation time for machine learning (100 epochs) and federated learning (3 communication rounds and 100 epochs) approach.  
the transmissions of a certain model parameter is received by the server, the averaging algorithm preserves the notion of that parameter in the updated global model. Hence, the end devices receive the averaged value instead of obtaining a 'zero' for such cases. This helps the model to converge faster and reap the benefits of federated learning. Once packet loss exceeds a threshold, i.e., not even a single transmission for some parameters is able to reach the server, the model may need more communication rounds to converge or may not converge at all.
## 6 Conclusions  
In this study, we investigated indoor localization performing simultaneous floor classification, landmark detection, positioning, and map learning. The study was divided into three topics: (i) feature extraction from Wi-Fi RSSIs for floor classification and landmark detection, (ii) mobile fingerprinting and pseudolabeling for positioning, and (iii) mapless localization.  
In the first part, characteristics of Wi-Fi RSSIs were determined by pattern recognition, using a semisupervised discriminant analysis. The proposed algorithm extracted the features from the noisy Wi-Fi signal data by reducing them from a high to a low dimension. During this process, worthless elements were removed to obtain clustered data points according to the different floors and different landmarks. At the same time, by investigating the distance between a test point and the training data on the reduced signal space, we successfully detected the floor and landmark changes.  
The second part addressed the efficiency of fingerprinting. Compared to conventional static fingerprinting, our algorithm improved the efficiency for collecting the training data because a user could be mobile during the collection without manually labelling the position at every grid point in the area of interest. The proposed pseudolabeling algorithm based on the semisupervised regression aimed to obtain accurate pseudolabeled positions for the unlabeled training data. Considering both the spatial and temporal aspect, we formalized the optimization based on a graph Laplacian and H-P filter. Further, the optimization provided a closed-form solution, which enabled fast computation.  
In the last part, we considered the situation where the true map information is not available. The key idea was crowdsourcing, from which we can obtain trajectory samples. From the experimental results, as more participants joined in, the learned map was updated more accurately. The experiments  
Figure 8: Positioning results with the trajectory learning (**a,b**) and without the trajectory learning (**c,d**).  
conducted in this study involved floors in a multifloor office building. Many people participated in validating our algorithm. The integration of all the parts led to accurate localization.  
[PERSON] designed and developed the learning based localization algorithm, and implemented and validated the experiments; [PERSON] reviewed and edited the paper.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1F1A1057516).  
The authors declare no conflicts of interest.
## 1 Introduction
The fault-detection performance of this model attained F-1 scores ranging from 0.92 to 1.00 across various types of sensor faults. [PERSON] et al. proposed a fault-detection method based on a generalized regression neural network (GRNN) model to detect system and sensor faults within an air handling unit (AHU) [12]. The GRNN model compared newly acquired and previously learned data to generate residuals. Faults were detected based on whether the residuals exceeded a specified threshold. This study categorized various potential fault types within the AHU and simulated cases to evaluate the fault-detection performance of the GRNN model. Through case-specific simulations, the GRNN fault-detection model was deemed beneficial for real-time fault detection. [PERSON] et al. proposed a dual-coupled artificial neural network (ANN)-based fault-detection strategy to detect sensor faults in the supply air temperature control loop of HVAC systems [13]. Primary and auxiliary ANNs were developed through control and variable correlation analyses by combining the two ANNs, and the performance was evaluated in the cases of fixed offset, drift offset, and complete sensor failure. This fault-detection method exhibited false alarm rates (the ratio of normal conditions incorrectly identified as faults) ranging from 0.6% to 7.5% and missed detection rates (the ratio of faults incorrectly identified as normal) ranging from 0% to 8.3% across all fault occurrence scenarios. The research related to sensor fault detection within HVAC systems using the data-driven method are summarized in Table 1.  
The following issues from the literature review must be considered:  
* A chiller operates continuously in response to outdoor and indoor environments, and it is difficult to distinguish between sensor faults based on the acquired data. Furthermore, situations in which a chiller operates under sensor faults are uncommon, and it is difficult to obtain fault data. Accordingly, the imbalance of the acquired data adversely affects the performance of the fault-detection models. * There has been a lack of research on detecting faults in SCWT sensors and evaluating indoor thermal comfort and energy consumption by correcting sensor faults. Therefore, this study developed a virtual sensor for detecting faults in the SCWT sensor. Subsequently, a case study was conducted using EnergyPlus to simulate scenarios of SCWT sensor faults. This approach facilitated the evaluation of the fault-detection performance and effects resulting from sensor fault correction. Detailed information about the methodology of this study and development of the virtual sensor is presented in Sections 2 and 3, respectively.
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 1 Introduction
Although the inherent Gaussian pruning method in the 3D Gaussian Splatting model limits the number of Gaussians to some extent, our experiments have shown that inherent pruning is incomplete, leaving a substantial number of redundant Gaussian points. Some existing research works, such as Compact3 DGS [20] and LightGaussian [21], have contributed to the compression of 3D Gaussian models by implementing pruning and compression techniques. These methods, when handling under-reconstruction rendering regions, often rely on the cloning approach of the original 3D Gaussians Splatting to improve the efficiency of the Gaussian distribution. However, this full-parameter cloning approach does not specifically address the issue of managing redundancy in high-density areas with large local variations or maintaining rendering quality. When dealing with localized scene variations caused by scale or rotation properties, the traditional cloning method merely copies the parameters related to spherical harmonic coefficients without adapting to these changes. According to the rendering principle of the splatting method, since the Gaussian points are purely cloned, during the splatting stage along a certain ray, the Gaussian points will overlap, causing the front-order opacity weight to be too large, thus causing color errors. In contrast, our opacity update technique dynamically adjuststhe spherical harmonic coefficients of newly generated Gaussian points, ensuring that the rendering quality is preserved by maintaining the consistency of pixel-wise renderings. Based on the above characterization of the storage structure of the 3 DGS model, it is evident that, aside from a small amount of data for constructing the global scene, the storage size is primarily determined by the product of the number of Gaussian points and the number of parameters within each point. Therefore, to effectively reduce the model size, two approaches can be pursued: (a) prune redundant Gaussians to reduce the number of Gaussian points, and (b) compress Gaussian parameter representations to shrink the product of the Gaussian model. Inspired by existing work [20; 21; 22; 23], we improve the applicable 3 DGS model compression method. The main contributions of this paper are as follows:  
1. Propose a principle-level opacity-updating strategy to assist in pruning redundant Gaussian points;
2. Propose a proportional pruning method of Gaussian contribution values combined with trainable Gaussian masks;
3. Validate the effectiveness of a method combining teacher-student models and vector quantization for compressing spherical harmonic coefficients. By employing these effective 3D Gaussian model compression methods, the memory requirement of the Gaussian model can be significantly reduced, which is of great significance for the application of the 3D Gaussian model in real scenarios.
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 2 Physical Model  
The problem of determining the location and intensity of a radiation source from detector measurements requires the solution of the Boltzmann equation describing the transport of neutral particles--i.e., particles with no electrical charge--which is computationally intractable to solve in real time. To address this problem, we make the assumption that gamma rays that suffer collisions before reaching a detector are never detected. Under this assumption, we obtain the model  
\[\Omega\cdot\
abla\psi(r,E,\Omega)+\frac{1}{\lambda_{T}(r,E,\Omega)}\psi(r,E, \Omega)=\frac{l_{0}}{4\pi}\delta(E-E_{0})\delta(||r-r_{S}||_{2}) \tag{1}\]  
describing gamma transport [15]. Here, \(\psi\) denotes gamma intensity per unit area--particle scalar flux--at location \(r\) and \(l_{0}\) denotes the intensity of the source, which is located at the position \(r_{S}\) and emits gamma particles with directional unit vector \(\Omega\) and energy \(E\). The Dirac density \(\delta(\cdot)\) on the right-hand side isolates the source energy and position to \(E_{0},\ r_{S}\). Finally, \(\lambda_{T}\) is the mean-free path, which is the mean distance traveled between collisions.  
Solving Equation (1) for total flux at a certain point \(r_{i}\), which we take to be the location of the \(i\)th detector, yields  
\[\hat{u}_{i}(q)=I_{0}\Delta t_{i}e_{i}\frac{A_{i}}{4\pi||r_{i}-r_{S}||_{2}^{2}} \text{exp}\bigg{(}-\int_{r_{S}\to r_{i}}\frac{1}{\lambda_{T}}dr\bigg{)},\ i=1, \ldots,d, \tag{2}\]  
where \(u\) is the expected number of gammas observed by a detector at location \(r_{i}\), given the parameters \(q=[r_{S},\ l_{0}]\in\Omega\) corresponding to the source location, \(r_{S}=(x_{0},y_{0})\) and intensity, \(I_{0}\). Here, the path of the gammas from the source to the detector is represented by \(r_{S}\to r_{i}\), which represents an arbitrary parameterization of the curve from \(r_{S}\) to \(r_{i}\); e.g., \([r_{S}\to r_{i}](\tau)=r_{S}+(r_{i}-r_{S})\tau\) where \(\tau\in[0,1]\). Here, the \(i\)th detector has face area \(A_{i}\), a dwell time \(\Delta t_{i}\), and efficiency \(\epsilon_{i}\). The full derivation of this equation is provided in Ref. [5].
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 5 Evaluation of the Proposed Scheduling Heuristic
### Comparison of Results of the Heuristic Concerning the Priority Rules  
The results obtained with the proposed heuristic in Section 4 are compared with the results of the priority rules TTE and ETT. When due times are not considered, the range of values for the necessary energy is [725; 785]. The heuristic presented in Section 4 provides a solution after 18 iterations; the obtained scheduling is displayed in Figure 10.  
This solution has a total energy cost of 725 with a make-span of 65, while the due times are respected for each job.  
Figures 11 and 12 present the corresponding Gantt diagrams, and Table 9 presents a comparison of their performances.  
Figure 11: Gantt diagram of TTE solution.  
Figure 10: Gantt diagram of HET heuristic solution.  
In the considered case study, the proposed heuristic reaches the optimal performance concerning total energy, while due time constraints are satisfied for all the jobs. The TTE priority rule reduces the make-span of the considered production plan (from 65 to 62), but this is achieved at the expense of an increase in the energy cost, which is 33.3% of the total energy variation range [785,725]. Finally, the ETT priority rule achieves the same energy performance as the heuristic, but the two jobs do not comply with their due dates.
# Review of Professionally Applied Fluorides for Preventing Dental Caries in Children and Adolescents
## 2 Materials and Methods  
This paper aimed to examine and summarize the results of controlled clinical trials, systematic reviews, and meta-analyses regarding professionally applied fluoride for the prevention of dental caries in children and adolescents. A web search was conducted for English papers published between 2000 and 2021, using different digital resources (Pubmed, Google Scholar, Cochrane Library, and ResearchGate). The keywords used were \"professionally applied fluoride\", \"fluoride gel\", \"fluoride varnish\", \"fluoride foam\", \"fluoride mouthrinses\", and \"non-cavitated caries lesions\", refined through hand-search. Inclusion criteria:  
Participants: children and adolescents, treated in a dental care setting;  
Intervention: professionally applied fluorides (gel, varnish, foam, mouthrinse);  
Comparator: no professional treatment or other preventive treatments;  
Outcomes: clinical effectiveness (e.g., caries reduction, tooth remineralization);  
Study design: randomized controlled trials, systematic reviews, meta-analyses;  
Publication period: 1 January 2000-31 December 2021.  
Exclusion criteria: clinical studies about home-use fluoride products, discussion papers, in-vitro studies, case reports, non-English articles, and studies with unclear methodology.  
This study is based on data from previously published literature and publicly available information. Therefore, the study did not require ethics approval and was conducted according to the principles of the Declaration of Helsinki.  
Some reviews and meta-analyses measure the effect of the selected methods using preventive fraction (PF). PF is a percentual ratio computed as the difference between increment or prevalence in the control sample and study sample divided by the increment or prevalence in the control sample.