esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.8 Hyperparameter Optimization Techniques  
Hyperparameter tuning is another critical aspect of ML model development, as the choice of hyperparameters can significantly influence the performance. Several of the surveyed articles specify the hyperparameter optimization techniques employed in their studies. Figure 18 illustrates the relative prevalence of these techniques across the sur  
Figure 16: Percent of papers that use each error metric for classification tasks.  
veyed papers. The most used hyperparameter optimization technique was a Grid Search, followed by Bayesian Optimization (BO), Random Search, Manual Optimization, and OPTUNA Automatic Hyperparameter Tuning Software. Less frequently used methods included algorithms such as the Firefly Algorithm (FA) [21], Neural Network Intelligence (NNI) [33], Enhanced Reptile Search Algorithm (ERSA) [73], and Hidden Dipper Throated Optimization (HDTO) [78], each of which was mentioned in only one paper. While many studies focused on a single optimization approach, one paper [45] provided a comparative evaluation of multiple hyperparameter optimization methods. This study found that Bayesian Optimization outperformed techniques such as a Grid Search and Particle Swarm Optimization (PSO) in terms of its search effectiveness. By leveraging probabilistic models to guide the search process, Bayesian Optimization was able to reduce computational costs while identifying optimal hyperparameter configurations more efficiently.  
Figure 17: Percent of papers that use each error metric for regression tasks.  
Figure 18: Usage for each hyperparameter optimization technique.  
Having examined the performance of conventional model architectures, this study now shifts focus to hybrid models, which integrate multiple ML and DL architectures, and are a common approach to improving the predictive performance in time series tasks.
## 1 Introduction
The results show that the relative deviation of magnesium stearate was less than 4% and that of lactose was less than 15% [28]. [PERSON] et al. introduced learning vector quantization (LVQ) in LIBS for the first time to differentiate _Fritillaria cirrhosa_ from non-_Fritillaria cirrhosa_. The classification accuracy was 99.17% [5]. [PERSON] et al. used LIBS and the hyperspectral technique combined with partial least squares discriminant analysis (PLS-DA) to determine the species, origins and ages of ginseng samples. The classification accuracy was above 93%, 94% and 99%, respectively [29]. [PERSON] et al. applied LIBS with principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machine (SVM) to the spectral analysis of ginkgo leaves at eight different locations in Xi'an. The first 30 principal components extracted using PCA were used as new input variables for identification with LDA and SVM. The classification accuracy was 97.50% and 96.25%, respectively [30]. [PERSON] et al. used LIBS technology combined with the random forest (RF) algorithm to perform grade classification for Dendrobium. The classification accuracy was 96.46% [31]. However, to the best of our knowledge, there has not yet been any study that has investigated and analyzed chemically synthesized or naturally derived drugs using LIBS combined with machine learning. Considering penicillins as an example, we attempted to identify the different penicillin manufacturers. In this paper, we propose LIBS technology as a potential tool for identifying penicillin manufacturers for the first time. A total of 12 samples of three types of penicillin (each type produced by four manufacturers) were used in this experiment. LIBS was adopted with feature selection and three commonly used classifiers (LDA, SVM and ANN) to identify penicillin manufacturers. The purpose of this study was to test the feasibility of using LIBS combined with machine learning in penicillin manufacturer identification.
# Software-Defined Visible Light Communication for Internet of Things: A Low-Complexity Approach
## 2 Architecture of SD-VLC System for IoT Applications
The choice of the PYNQ platform is motivated by its integration of a processor and programmable logic on a single chip, enabling efficient hardware implementation of digital baseband functions, such as modulation and equalization, while maintaining flexibility through software-defined control. This architecture simplifies development and reduces overhead compared to conventional SDR setups that require separate computing and radio hardware. PYNQ thus offers a practical balance between hardware efficiency and reconfigurability, making it particularly suitable for SD-VLC systems in IoT environments. The baseband processing (BBP) unit, as illustrated in Figure 2, is the key aspect of this study, and the following sections will explore low-complexity modulation and equalization techniques, which are critical for optimizing VLC system performance. The transmitter (Tx) path consists of channel coding followed by CAP modulation, generating the transmitted signal \(s_{m}\) from the input bit sequence \(b_{m}\). The receiver (Rx) path includes CAP demodulation and an equalization stage based on the SDLMS algorithm, processing the received signal \(r_{n}\) to recover the original bit sequence \(b_{n}\). Figure 1: Conceptual diagram of the proposed SD-VLC system for IoT applications.
# A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
## 4 Experimental Results and Analysis
### Training Curve Comparison Graph  
According to the set parameters for training the rebar dataset, Figure 14 shows the comparison curves of accuracy, recall, average precision (mAP_0.5 and mAP_0.5:0.95), bounding box loss, confidence loss of YOLOv5s, and the improved algorithm during the training process. In the figure, \"yolov5s\" is the training curve of YOLOv5s, \"yolov5s+c\" indicates the addition of CA attention, \"yolov5s+c+s\" denotes adding CA attention and using SD_IoU loss, and \"yolov5s+c+s+m\" denotes adding CA attention, using SD_IoU and loss, using the multi-scale method on the network.  
From Figure 14a-d, the values of P, R, mAP0.5, and mAP0.5:0.95 of the improved algorithm are higher than those of the original YOLOv5s. From Figure 14e,f, the confidence loss values of the improved algorithm on the training and validation sets are always smaller than those of the YOLOv5s, which indicates that the prediction box of the improved algorithm RebarNet is closer to the true box.  
The \(F_{1}\) score considers both the precision and the recall, allowing both to be maximized and a balance to be achieved. Figure 15 shows the graph of \(F_{1}\). The horizontal coordinate is the value of the confidence, and the vertical coordinate is the value of \(F_{1}\).  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Model** & **K-Maens++** & **CA SD\_IoU** & **Multi-Scale** & **Precision** & **Recall** & _[EMAIL_ADDRESS] & **[EMAIL_ADDRESS]:0.95** & **Parameters** & **Weight** \\ \hline
**YOLOv5s** & & & & & 95.2\% & 79.5\% & 88.9\% & 58.9\% & 7,022,326 & 14.4 MB \\
**Improvement1** & \(\surd\) & & & & 94.9\% & 79.6\% & 89.4\% & 59.6\% & 7,022,326 & 14.4 MB \\
**Improvement2** & \(\surd\) & \(\surd\) & & & 94.7\% & 79.8\% & 89.8\% & 59.1\% & 7,211,814 & 14.8 MB \\
**Improvement3** & \(\surd\) & \(\surd\) & \(\surd\) & & 90.4\% & 91.4\% & 94.2\% & 62.3\% & 7,211,814 & 14.8 MB \\
**Improvement4** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 96.3\% & 96.6\% & 97.9\% & 65.2\% & 7,862,352 & 17.0 MB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ablation experimental results.  
Figure 14: Training curve comparison graph. (**a**) Precision, (**b**) Recall, (**c**) mAP_0.5, (**d**) mAP_0.5:0.95, (**e**) train/obj_loss, and (**f**) val/obj_loss.  
Figure 15: \(F_{1}\) graph.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 2 Related Research
The algorithm was improved based on the Fairness scheduling algorithm, which can simultaneously consider multiple different types of DAG scheduling requirements, including factors such as arrival time and priority [23]. In addition to algorithms that improve fairness, other algorithms can also solve multi-DAG scheduling problems. [PERSON] et al. proposed an online task scheduling optimization method for DAG-based requests and modeled the scheduling procedure as a Markov decision process. A temporal-difference learning-based mechanism was adopted to learn an optimal task allocation strategy at each decision stage [24]. [PERSON] et al. proposed a fault-tolerant DAG task scheduling algorithm to minimize the response latency experienced by tasks. After formulating the DAG task scheduling problem, a context-aware greedy task scheduling algorithm was proposed. Then, to address edge server failure events, a dependency-aware task rescheduling algorithm was designed [25]. [PERSON] et al. proposed a multi-DAG scheduling algorithm based on reinforcement learning. They first utilized a convolutional neural network with graph attention to fully process and encode scheduling information. Then, a fully connected neural network selected appropriate nodes from the DAG application for execution. Finally, a heuristic approach was employed to repeatedly allocate processors to the selected nodes based on the earliest completion time [26]. In summary, current multi-DAG task scheduling primarily focuses on task execution time and resource utilization. However, unlike ground networks, satellite networks equipped with edge computing capabilities face challenges of limited computational resources and constrained energy (the energy consumption is directly related to the satellite's lifespan). Therefore, when integrating multi-DAG task scheduling with satellite networks, factors such as task latency and energy consumption should be considered. Furthermore, there is currently a lack of research on the multi-DAG task scheduling problem in the context of integrated SDN satellite networks.
## 2 Materials and Methods
### Material Characterization
#### 2.4.6 Barrier Measurements  
Water vapor permeability (WVP) of the films was determined using the ASTM 2011 gravimetric method. To this end, 5 mL of distilled water was placed inside Payne permeability cups (\(\O\) = 3.5 cm, Elcometer Sprl (Hermalle-sous-Argenteau, Belgium) and the films were placed in the cups so that on one side they were exposed to water (100% RH), avoiding direct contact with water. Thus, the cups containing the films were secured with silicon rings and stored in desiccators containing Mg(NO3)2 over-saturated solution (53% RH) in a chamber with temperature control at 25 \({}^{\circ}\)C. Identical cups with aluminum films were used as control samples to estimate the vapor loss through the sealing. The cups were weighed periodically using an analytical balance of \(\pm\)0.0001 g accuracy (ME36S, [PERSON], Fisher Scientific, Hampton, NH, USA). The water vapor permeance was calculated considering the water vapor transmission rate (WVTR), which was determined from the steady-state permeation slope obtained from the regression analysis of weight loss data per unit area versus time, where the weight loss was calculated as the total cell loss minus the loss through the sealing. The water vapor permeance was corrected for permeant partial pressure and the film thickness to obtain the WVP values.  
Limonene permeability (LP) was measured following the same procedure described above for water vapor, replacing water with 5 mL of D-limonene inside the Payne permeability cups. The permeability cups containing the films were placed at the controlled conditions of 25 \({}^{\circ}\)C and 53% RH and cups with aluminum films were used as control samples to estimate solvent loss through the sealing. Limonene permeation rate (LPR) was obtained from the steady-state permeation slopes and weight loss was calculated as the total cell loss minus the loss through the sealing, which was thereafter corrected for permeant partial pressure and the film thickness to yield LP. Both vapor permeability measurements were performed in triplicate.  
Oxygen permeability (OP) of the films was determined using an oxygen permeation analyzer (OxySense(r) Model 8101e, Systech Illinois, Thame, UK) at 25 \({}^{\circ}\)C and 53% of RH, according to ASTM D3985-05. The exposed area of the films was 50 cm\({}^{2}\) and the oxygen transmission rate (OTR) was obtained every 15 min until equilibrium was reached. The gas permeability measurements were carried out in duplicate.
## 2 Proposed Framework and Experimental Methodology
### Experimental Environment and Setup  
Experiments utilized three NVIDIA GPUs--GTX 1080 Ti (Pascal) (NVIDIA, Santa Clara, CA, USA), RTX 2080 Ti (Turing), and RTX 3080 (Ampere)--and multi-core Intel/AMD CPUs (AMD, Santa Clara, CA, USA) as baselines (Table 1). Python-based benchmarking scripts were executed with \"timeit\", and NVIDIA's \"nvprof\" profiled GPU workloads. Statistical analyses employed NumPy/SciPy libraries (scipy.stats.linregress), ensuring reliability via repeated measurements (10 iterations each). All the codes, libraries (CUDA (Compute Unified Device Architecture), cryptographic), and datasets are available upon request for reproducibility.
## 5 Load Segmentation and Aggregation
### EWH Segmentation and Clustering  
The characteristics of actual EWH classes in the residential segment are quite different: tank size, weather conditions, energy conversion technology, inhabitants and load service, and individual or common use. It seems necessary to perform some segmentation and classification in the WH population to assess strategies and to match controllable EWH best suited to each DR policy, finding the size of QHCGs. Moreover, developing methods to match customer loads to control policies and markets could increase the overall benefits and relevance of DR portfolio. The proposed classification in this paper is based on WH demand profile. This end-use demand can be achieved by aggregators through submetering or NIALM from representative customers (geographical area, income, kind of dwelling...).  
ERWH and HPWH with different water draws and rated power (from 250 to 350W in the case of HPWH and from 700 to 2200 W in the case of ERWH) but with similar tank  
Figure 9: Elemental and aggregated demand service and profiles: (**a**) A typical WH water flow profile, q(t), used to determine the energy requirements of WHs according to EU standard [34]; (**b**) Aggregated WH demand (40 loads, 1200 W/load, simulation output each minute) with the previous water demand profile.  
sizes (from 75 to 100 L, around the average tank size in Spain) have been monitored and included for clustering. All the demand profiles were normalized to avoid the influence of the amplitude of demand in the segmentation. The clustering method was carried out by means of the R package \"TSClust\", using the integrated periodogram distance [35] and the average linkage. Figure 10 shows the results of the clustering method applied to our example, where four different groups can be detected. Figure 11 depicts the demand profile inside each cluster. Note that the distance measure used focuses on the frequency domain instead of the time domain.  
Figure 11: Some representative WH demand profiles for each cluster: (**a**) cluster 1; (**b**) cluster 2; (**c**) cluster 3 and (**d**) cluster 4 (the color corresponds to clusters defined in Figure 10).  
Figure 10: Classification of EWH profiles (each column represents WH daily load profile, pp, in the format dd/mm/yy). Each box defines a cluster (1 to 4).
## 3 Flight Control
### Tracking Differentiator (TD)  
Since the input commands are usually abrupt, resulting in a large and persistent error before the state catch up with the command. If there is a differential term in the controller, this abrupt change will often cause saturation of the controller output, so that the system overshoot occurs with high probability. Therefore, a reasonable transient trajectory of the command that the system can follow is needed. This paper adopts the TD proposed by [27], TD's discrete form is as follows:  
\[\left\{\begin{array}{l}\dot{v}_{1}(t)=v_{2}(t)\\ \dot{v}_{2}(t)=\text{than}(v_{1}-v,v_{2},r,N_{0}h)\end{array}\right. \tag{10}\]  
where \(v\) is the command as the input of TD, \(v_{1}\) is the desired trajectory, \(v_{2}\) is the derivative of \(v_{1}\). \(N_{0}\) is the filter factor of TD, which can be tuned according to the smoothness of convergence, \(h\) is the sampling period. \(\text{than}(x_{1},x_{2},r,h)\) is a time-optimal solution in discrete  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Term** & **Definition** & **Value** & **Unit** \\ \hline \(J_{xx}\) & the moment of inertia about the \(X\) axis & 364 & g\(\cdot\)cm\({}^{2}\) \\ \(J_{yy}\) & the moment of inertia about the \(Y\) axis & 294 & g\(\cdot\)cm\({}^{2}\) \\ \(J_{zz}\) & the moment of inertia about the \(Z\) axis & 343 & g\(\cdot\)cm\({}^{2}\) \\ d & the distance between the point O and the & 6 & cm \\  & CoM & & \\ \(\left[0,I_{y},I_{z}\right]\) & the coordinates of the Cop (left) in the flapping plane coordinates & \(\left[0,7.289,4.5\right]\) & cm \\ m & vehicle mass & 29.6 & g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the FMAV’s dynamic model.  
Figure 6: The block diagram of the attitude and altitude controller.  
form that guarantees the fastest convergence from the desired trajectory \(v_{1}\) to the command \(v\). The expression of this function can be found in [27]. \(r\) is a key parameter, equivalent to the maximum acceleration of the transient trajectory, which directly determines the speed of convergence. In this controller, each control channel has a TD, which outputs a reasonable desired value according to the input command.
## 3 System Model
### Model for Energy Consumption  
The generalized energy model that is used to calculate the energy needed to deliver a packet (energy/packet) between two nodes separated by distance \(d\) is given by [19]  
\[E_{d}=E_{t}(d)+E_{r}(d) \tag{11}\]  
where \(E_{t}\) is the transmission energy/packet and \(E_{r}\) is the reception energy/packet. Both components can be expressed in terms of other specifics parameters, as seen in the next two equations  
\[E_{t}(d)=L\ \big{(}E_{elec}+\varepsilon_{amp}\big{)}+P_{t}\frac{L}{\alpha B(d )}, \tag{12}\]  
\[E_{r}(d)=L\ \big{(}E_{elec}+E_{DA}\big{)}+P_{r}\frac{L}{\alpha B(d)}, \tag{13}\]  
where \(L\) is the number of bits in a packet; \(P_{t}\) and \(P_{r}\) are the transmission and reception power, respectively, both independent of the distance; \(E_{elec}\) is the electronic energy required to process one bit of message; \(\varepsilon_{amp}\) is the energy/bit consumed by the amplifier; \(E_{DA}\) is the energy/bit required for data aggregation; \(\alpha\) is the modulation efficiency; and finally \(B(d)\) is the available bandwidth, which could depend on the distance \(d\).
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 2 Related Work
### AI/ML Adoption in RAN-Architecture  
The usefulness and applicability of ML-based methodologies in future deployments of mobile networks have been recognized by mobile network operators (MNO) and the industry overall. An architecture that organizes intelligence in RAN, studied by 3 GPP in [10] has been developed according to Figure 4. The study has been concluded with a recommendation to define technical specifications according to the baseline, which will become 3 GPP Release 18 [10].  
Figure 4: Functional framework for RAN intelligence [10].  
Figure 3: Model of Artificial Neural Network.  
The architecture for RAN implementing ML groups RAN entities into different functions:
* Data collection that provides input data to model training and model inference functions. The data collection plays the role of ML algorithm-specific data preparation;
* Model training that performs the AI/ML model training, validation, and testing which may generate model performance metrics as part of the model testing procedure. The Model Training function is also responsible for data preparation;
* Model inference that provides AI/ML model inference output (e.g., predictions or decisions);
* An actor that receives the output from the model inference function and triggers or performs corresponding actions. The actor may trigger actions directed to other network entities or to itself.  
The functional framework realization in RAN needs to be compatible with radio interface procedures, as depicted in Figure 5. The data collection function can be split into the UE and RAN nodes. If UE measurements are needed by a gNB for a certain task, they are triggered by a RAN command requesting the UE to perform measurements (e.g., radio signal level and experienced delay). In addition, optionally, the measurements can be also performed by the gNB itself (marked as data collection* in Figure 5). Based on a joint input (the UE indicated measurements and the gNB generated data), the gNB conducts ML model training. The combined data training in a task-driven operation in RAN requires continued observation for a particular period, which typically is followed by an updated instruction on data collection. The typical strategy of such a learning process from data collection is to achieve a predictive analysis and an intelligent decision. This is realized by the model inference and leads to an action. These high-level principles guide a solution to be worked out as a normative standardized method in 3 GPP Release 18.