esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 2 Methods
### Cyclical Learning Rates (Clrs)  
Cyclical Learning Rates (CLRs) eliminate the need to identify the optimal value of the initial learning rate and learning rate schedule for CNN training [39]. Despite learning rate schedules, where the learning rate is being constantly decreased, this technique allows for the learning rate to oscillate between reasonable limits. CLRs give us the opportunity to have more freedom in the selection of our initial learning rate. CLRs lead to faster neural network training convergence with fewer hyperparameter updates.  
Saddle points are points in the loss function where the gradient is zero, but they do not represent minima or maxima. The authors in [40] found out that the efficiency of CLR methods lies in the loss function topology, and showed that saddle points have a worse impact on minimizing the loss function than poor local minima. One cause for getting stuck in saddle points and global minima can be a learning rate that is too small. CLR methods help to fix this issue adapting the learning rate between a minimum value and a maximum value iteratively. Another reason for the efficiency of CLR methods is that the optimal learning rate is somewhere between the lower and upper bound, so the training is performed with near-optimal learning rates.  
There are three main CLR policies: _triangular_, as shown in Figure 6, _triangular2_, and _exponential range_. The _triangular_ policy is a triangular cycle: the learning rate starts from a lower limit, increases the value to the maximum in half a cycle, and then returns to the base value at the end of a cycle. The difference between _triangular_ and _triangular2_ policy is that the upper bound of a learning rate is decreased in half after every cycle. Training with a _triangular2_ policy provides more stable training. _Exponential range_ policy, as its name suggests, includes an exponential decay of a maximum learning rate [39].
###### Abstract  
Defect detection is still challenging to apply in reality because the goal of the entire classification assignment is to identify the exact type and location of every problem in an image. Since defect detection is a task that includes location and categorization, it is difficult to take both accuracy factors into account when designing related solutions. Flaw detection deployment requires a unique detection dataset that is accurately annotated. Producing steel free of flaws is crucial, particularly in large production systems. Thus, in this study, we proposed a novel deep learning-based flaw detection system with an industrial focus on automated steel surface defect identification. To create processed images from raw steel surface images, a novel method was applied. A new deep learning model called the Parallel Attention-Residual CNN (PARC) model was constructed to extract deep features concurrently by training residual structures and attention. The Iterative Neighborhood Component Analysis (INCA) technique was chosen for distinguishing features to lower the computational cost. The classification assessed the SVM method using a convincing dataset (Several: Steel Defect Detection). The accuracy in both the binary and multi-class classification tests was above 90%. Moreover, using the same dataset, the suggested model was contrasted with pre-existing models.  
Keywords:steel surface defects; PARC model; classification +
Footnote †: journal: Applied Sciences
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications
### The Key SON Applications Leading to Full Automation
#### 3.2.4 Mobility Load Balancing (MLB) and Traffic Steering (TS)  
In a similar manner, Mobility Load Balancing (MLB) or traffic steering (TS) is one of the key optimization activities applied either independently, or in case multiple radio access technologies coexist. The target of MLB is to forward voice and data traffic to the most appropriate frequency/carrier or hierarchy cell layer or radio technology according to the radio parameters policy followed by the network operator. MLB can be applied either in idle or in connected mode states of a UE (inter-/intra-frequency or Inter-RAT scenarios).  
In GSM/UMTS networks, static load balancing procedures usually take place while in LTE and 5G mechanisms for MLB are embedded in 3 GPP standardized SON functions, leading to autonomous operation based on network counters' feedback [57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. Basic parameters that affect traffic steering/load balancing concern neighbor cell level parameters related to intra-/inter-frequency or inter-system relations or cell parameters that concern only a specific cell [22; 23]. Currently, key mechanisms for traffic steering include the tuning of inter- or intra-frequency handover or reselection parameters, absolute priorities (AP) (the UE is informed in idle mode about the priority of a candidate camp frequency or candidate RAT) and Basic Biasing (BB) (triggering cell selection or reselection in idle mode).  
As a definition, network load can be described by the following scenarios with the first one being the most common [8; 22; 23; 64; 73]:  
* Radio resource load related to blocking due to a lack of resources.
* BTS/NodeB/eNodeB/gNB hardware resource load.
* Transport/backhaul network resource load.
## 5 Experimental Details
### Experimental Procedure  
First, regarding data preprocessing, where data are standardized to scale the values of different features to the same range, the data normalization is aimed at eliminating the influence of dimensions and accelerating the training speed and convergence speed. By scaling the weights of the same feature within the same scale range, the fairness of the model is ensured, and the interpretability of the features is improved. In this report, we used the Z-score standardization method. Our input variables include Year (X1), Month (X2), Week (X3), Average price of hogs at slaughter (X4), Average price of piglets (X5), Average sow price (X6), Corn (X7), Wheat bran (X8), and Fattening pig compound (X9), and the output variable is Pork prices (Y).  
1. Calculate the mean of the original data \[\mu=\frac{1}{\text{N}}{\sum_{i=1}^{\text{N}}{\rm{x_{i}}}}\] (13) 2. Calculate the Standard Deviation \[\sigma=\sqrt{\frac{1}{\text{N}}{\sum_{i=1}^{\text{N}}({\rm{x_{i}}}-\mu)^{2}}}\] (14) 3. \[\text{Z-score standardization}\] \[\text{z}_{\rm{i}}=\frac{{\rm{x_{i}}}-\mu}{\sigma}\] (15)  
Secondly, feature selection occurs. In regression problems, compared to other machine learning models, the decision tree model has the greatest advantage of having its own feature selection. Therefore, in our study, we did not choose other feature engineering methods for feature selection. Instead, we used mean squared error (MSE) between the parent and leaf nodes as the criterion for feature selection.  
Thirdly, the first step is to set some basic parameters of the optimization algorithm when using the SSA algorithm to optimize the CART model, and we set the population size of the SSA algorithm to 50, the maximum number of iterations to 100, the hyperparameters' LU to 0, and the BU to 30; dimension space \(D\) is 2. Furthermore, we fitted the Fittrree function to the decision tree regression model and optimized the hyperparameter of \"MinLeafSize\". Next, we calculated the partitioned validation set true data and the training set forecast values, and returned the population with the highest fitness. If the maximum number of iterations is not reached, the hyperparameter is updated and trained again, until the maximum number of iterations is reached. At last, the optimal hyperparameter 'MinLeafSize' was returned with a value of four.  
Finally, the model is trained using the optimized hyperparameters and test set. The evaluation metrics MAE, MAPE, MSE, RMSE, and R\({}_{2}\) are utilized to assess the feasibility and efficacy of the SSA-CART model. Compare the predictions of a single decision tree and the optimized decision tree of the well-known SA algorithm and WSO algorithm; the predicted results are shown in Table 5.  
According to Table 5, the outcomes for each model are provided. As can be observed, when merely utilizing the conventional decision tree model, the test results for R\({}_{2}\) are 7.36 \(\times\) 10\({}^{-1}\), which is not ideal. However, after using other optimization techniques, the MAE, MAPE, MSE, and RMSE are significantly decreased, and the R\({}_{2}\) has somewhat improved.
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 3 Three-Dimensional Numerical Modelling
### Model Calculation Theory
#### 3.2.3 Theory of Coupled Temperature and Seepage Fields  
In the model boundary, the head difference is set to produce unidirectional seepage. When the seepage flow occurs in the porous media, it will produce a heat exchange phenomenon. Heat will diffuse with the seepage flow, resulting in uneven distribution of the temperature field. When the temperature field changes, the permeability coefficient of the porous media and other physical parameters will change with the temperature, and temperature constitutes a functional relationship so that the temperature field has an inverse effect on the seepage field to achieve the bi-directional coupling, the temperature field coupled with the seepage field to achieve. The coupling of the temperature field and seepage field is the process of dynamic adjustment of heat and fluid in porous media. In this study, the Heaviside function is used to describe the coupling relationship between the permeability coefficient and the temperature in the process of ice-water phase transition, and the unit leap order function is established as:  
\[H(T-T_{\text{d}},\text{d}T)\]  
\[K(T)=(K_{\text{u}}-K_{\text{f}})H(T-T_{\text{d}},\text{d}T)+K_{\text{f}}\]  
where: subscripts u and f are unfrozen and frozen soil, respectively; \(K\) is the permeability coefficient; \(T\) is the soil temperature, \(T_{\text{d}}\) is the critical temperature of ice-water phase transition; d\(T\) is the ice-water phase transition gap.
## 3 Possible Mobility Scenarios in 6 LoWPAN  
Figure 1 shows a hospital building where each room hosts a PAN. The PANs are comprised of SNs and a GW that connects the PAN to the outer world. A patient can have a sensor node attached, which gathers his/her vital heath parameters like temperature and pulse rate etc. The sensor node then communicates these parameters to the GW, by transmitting the information to its nearest SN. The SN  
then forwards the information to the GW via other SNs in the PAN. The following lists three possible categories of the mobility types in this scenario:  
1. _Intra-PAN mobility_: When a MN moves within a PAN, Intra-PAN mobility occurs. In Figure 1, this kind of mobility occurs when a patient moves within a room.
2. _Inter-PAN mobility:_ This kind of mobility occurs when a MN moves from one PAN to another. For example, in Figure 1, a patient moves from one room to the other, where each room hosts one PAN.
3. _WPAN mobility_: In this case, the whole PAN changes its point of attachment. This case is similar to NEMO.  
In this paper, we limit our discussion to the Intra-PAN mobility category only. In order to support mobility in the resource constrained devices; the IETF specifies some mobility requirements in 6 LoWPAN. These include [3]:  
1. Providing fast handoff detection.
2. The MNs should be addressable by any CN, irrespective of its whereabouts.
3. The signaling is also required to be minimized by considering the resource constraint characteristic of the LoWPAN devices.
4. Reduced Function Devices (RFDs) should be kept out of the mobility-related signaling.
The Usefulness of Modified Mallampati Score and CT Upper Airway Volume Measurements in Diagnosing OSA among Patients with Breathing-Related Sleep Disorders  
[PERSON]  
1 Chair and Department of Dental Prosthetics, Pomerarianian Medical University, 70-110 Szczecin, Poland; [EMAIL_ADDRESS] (B.D.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (E.S.)  
[PERSON]  
1 Chair and Department of Dental Prosthetics, Pomerarianian Medical University, 70-110 Szczecin, Poland; [EMAIL_ADDRESS] (B.D.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (E.S.)  
[PERSON]  
2 Student Research Club of Dental Prosthetics, Pomerarian Medical University, 70-204 Szczecin, Poland; [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (A.K.)  
[PERSON]  
2 Fusko Syrico  
in the upper respiratory tract, i.e., an elongated soft palate and/or uvula, palatine tonsil hypertrophy, nasal septum curvature, nasal turbinate hypertrophy; endocrine disorders such as hypothyroidism and acromegaly; mandibular development disorders (micro-, macronathia); use of sedatives, hypnotics; alcohol consumption (especially at bedtime) and smoking [17]. Underdiagnosed or untreated sleep apnea may lead to the development of secondary hypertension (in more than 50% of patients), ischemic heart disease and myocardial infarction, arrhythmia and conduction disorders, heart failure, stroke, and transient ischemic attacks, pulmonary hypertension, and upper airway resistance syndrome, gastroesophageal reflux disease, depression, impaired glucose tolerance (type II diabetes), metabolic syndrome, and even sudden death [18].  
To avoid these life-threatening complications and for financial reasons, it is crucial to identify patients with high risk for OSA at an early stage. The primary care physician or dentist should use simple and repeatable screening tools for OSA risk assessment and subsequently refer them to more expensive and time-consuming polysomnography as the gold standard for OSA diagnosis [19]. The aim of this study was to estimate the usefulness and value of combined diagnostic tools like modified Mallampati scores (MMP) and upper airway volume (UAV) in diagnosing high risk OSA among patients reporting breathing problems during sleep treated in the Prosthetics Outpatient Clinic, Pomeranian Medical University in Szczecin, Poland. Additionally, we aimed to develop the simplest and fastest possible screening method that could be used routinely to identify high risk-OSA patients.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 2 Related Work
### Single-Modality-Based Approach  
Content features refer to features that can be extracted directly from published information. Content features mainly include text features and visual features [15].  
In the early research, people's processing of text content is mainly through manual extraction of text features and the use of machine-learning classification algorithms to detect false information. The method based on text features detects posts by extracting features such as text length, word frequency, part-of-speech and word vectors, morphological and syntactic features from the text information of a post. [PERSON] [16] et al. built a fake news detector based on linguistic features. The authors extract N grams, punctuation, psycholinguistic features, features indicating text intelligibility and grammar into five sets of linguistic features from the text and finally use the SVM classifier and five cross validations for detection. [PERSON] [17] used structural information and linguistic features to capture the multimodal phenomenon of rumor propagation using three classification models: support vector machine, random forest classifier and decision tree. In the early days of the multimedia age, fake news began to use multimedia content with pictures to attract readers to spread false information. [PERSON] [18] et al. demonstrated the importance of images in fake news detection. Due to lack of expertise, it is difficult for traditional machine-learning-based false information detection models to obtain handcrafted features.  
With the widespread application of deep learning, [PERSON] [19] et al. used a recurrent neural network to obtain the hidden features of text, which were used to detect rumors. [PERSON] [20] et al. used an attention mechanism to dynamically fuse the features of the image frequency domain and pixel domain for false information detection. Although deep learning can automatically extract features, it is often affected by noise, which degrades its model performance. Inspired by generative adversarial networks (GAN), [PERSON] [21] et al. proposed a model based on GAN network, which can remove noise and irrelevant features through adversarial training to obtain more discriminative features.  
Although the detection of false information using text features or visual features alone has been proved to be effective, with the advent of the multimedia era, the existing form of information has changed from the form of plain text in the past to the form that contains text, pictures, videos and other multimodal data at the same time, so if false information is detected only from the perspective of text or pictures, not only are the information utilization ratio and detection accuracy low, but it is also difficult to adapt to the multimedia era that had arrived.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.7 Data Persistence  
Data persistence is essential to support infrastructure resilience. Message queuing results in lower power usage, and allows for the possibility to send minimized data packets, and efficiently distribute information to one or many subscriber. Still, most protocols analysed lack support for data persistence.  
AMQP relies on queuing, while NDN integrates into its design in-networking caching, aspect which becomes relevant in the context of guaranteeing low latency interactions in comparison to cloud computing [27].
## 5 Performance Analysis and Simulation Results
### Delay of Forwarding Data
At the same time, the performance of the delay for forwarding data of the fixed duty cycle strategy, the dynamic duty cycle strategy and PNLDC approach are compared. With the increase of the distance between the node and sink, the total delay for forwarding data also increases, which is obvious. It can also be obtained that the greater the duty cycle, the smaller the delay for forwarding data. This is because the greater the duty cycle, the more time the node is in the active state, that is, the shorter the time that sender needs to wait. Compared with the fixed duty cycle strategy, the delay for forwarding data is reduced by 8.9\(\sim\)26.4% in the PNLDC approach. Therefore, in terms of delay for forwarding data, the PNLDC approach performs better than the fixed duty cycle strategy. And compared with the dynamic duty cycle strategy, the PNLDC approach has the advantage that the delay for forwarding data is smaller in the region far away from the sink. This is because in the non-hotspots far away from the sink, the duty cycle of the nodes in the dynamic duty cycle strategy is smaller than that in the PNLDC approach.
## 3 Efficient Energy Aware Routing Protocols: R-EERP and S-EERP
### Radio Model  
We use the same radio model as stated in [8] for the radio hardware energy dissipation, which is shown in Figure 3. This radio model has been widely adopted in several studies [13,14,17,18] and the simulation parameters are listed in Table 2. Transmitting (_ETx_) and receiving energy costs (_ERx_) are calculated as follows:  
\[E_{Tx}\textit{(k,d)}=kE_{elec}+kE_{eiss}d^{2}:d<d_{crossover}\]  
\[E_{Tx}\textit{(k,d)}=kE_{elec}+kE_{two-ray}d^{d}:d\geq d_{crossover} \tag{2}\]  
For the receiving cost:  
\[E_{Rx}\textit{(k)}=k.E_{elec} \tag{3}\]  
where:  
\(E_{elec}\) is the transmitter circuitry dissipation per bit,  
\(\epsilon_{\textrm{amp}}\) is the transmit amplifier dissipation per bit per square meter.  
\begin{table}
\begin{tabular}{|l|l|l|} \hline x & y & z \\ \hline
1 & 1 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: XOR Truth Table.  
\(k\) is the length of the message in bits, \(d\) is the distance between transmitter and receiver node, and \(d_{\textit{crossover}}\) as the distance threshold value to decide on the radio propagation model. If \(d<d_{\textit{crossover}}\), the free space model is adopted, otherwise if \(d\geq d_{\textit{crossover}}\), the two-ray model is adapted (see Figure 3 and Table 2).
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.3.2 Combination of Audio and Contextual Data
In contrast, studies categorized under feedback-focused and intervention classification continue to predominantly employ statistical approaches, likely reflecting their closer alignment with the principles of transparency and the generation of actionable feedback. Figure 8: Sankey diagram illustrating the relationship between the analytical methods employed (deep learning, machine learning, and statistical analysis) and the presence and type of explainability techniques. This visualization highlights how interpretability varies across methodological families, showing which types of explainability (if any) are applied in each case (RQ4). Figure 9: Relationship between RQ1 categories and RQ4 techniques used (RQ4). For the purposes of this review, we distinguish machine learning algorithms from deep learning models using two objective criteria. First, traditional ML methods (e.g., support vector machines, random forests, and XGBoost) operate on carefully engineered feature vectors (e.g., MFCCs, prosodic statistics, and diarization parameters) and generally contain on the order of 10,000 trainable parameters or fewer. In contrast, deep-learning models ingest raw or minimally processed audio representations, such as spectrograms or learned embeddings, and learn hierarchical features end-to-end. These architectures typically surpass several million trainable parameters and achieve high performance without relying on manual feature selection or preprocessing.