esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 8 Conclusions and Future Directions  
In the research field of acoustic scene classification, a rapid increase of scientific publications has been observed in the last decade. This progress was mainly stimulated by recent advances in the field of deep learning such as transfer learning, attention mechanisms, and multitask learning, as well as the release of various public datasets. The DCASE community plays a major role in this development by organizing annual evaluation campaigns on several machine listening tasks.  
State-of-the-art ASC algorithms have matured and can be applied in context-aware devices such as hearables and wearables. In such real-world application scenarios, novel challenges need to be faced such as microphone mismatch and domain adaptation, open-set classification, as well as model complexity and real-time processing constraints. As a consequence, one future challenge is to extend current evaluation campaign tasks to evaluate the classification performance, as well as the computational requirements of submitted ASC algorithms jointly. This issue will be first addressed in this year's DCASE challenge task \"Low-Complexity Acoustic Scene Classification \".  
The general demand for deep learning based classification algorithms for larger training corpora can be faced with novel techniques from unsupervised and self-supervised learning, as is shown in natural language processing, speech processing, and image processing. Another interesting future direction is the application of lifelong learning capabilities to ASC algorithms [99]. In many real-life scenarios, autonomous agents continuously process the sound of their environment and need to be adaptable to classify novel sounds while maintaining knowledge about previously learned acoustic scenes and events.  
This work has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement. No 786993 and was supported by the German Research Foundation (AB 675/2-1).  
The author would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for valuable discussions and proof-reading, as well as the anonymous reviewers whose comments greatly improved this manuscript.  
The author declares no conflict of interest.
## 6 Software Architecture of the Multi-Sensor Platform
### Setup Phase  
The core of the sensor platform is the STM32F4 series microcontroller. After powering the platform, the controller first initializes the system with the corresponding system and peripherals clocks, and the system interrupts. The controller is programmed to operate at 168 MHz generated by the internal PLL clock. It also generates 48 MHz for USB and 25 MHz for the Ethernet PHY chip. The microcontroller's peripherals are SPI, I2C, UART ports, Ethernet PHY, and GPIO. Their corresponding DMA channels are initialized. These DMA channels enable data from the sensors to be directly written to the memory without the controller's intervention. This enables the controller to be effectively used for other essential controls. The external and onboard sensors are connected to the microcontroller via the SPI and I2C interfaces. The UART ports enable the communication channel to the SARA-R5 module for setup and receiving the timestamps. In addition, another port is enabled to act as a debug interface.  
The SARA-R5 is set up to establish a connection to the base station and enable the _CellTime_ feature to receive timestamps. After successful peripheral setup, network configuration occurs, whereby the controller initializes the TCP/IP stack for networking. The controller uses a lightweight TCP/IP stack (LWIP). It initializes the stack and enables the DHCP connection to receive the IP address for the module over the Ethernet. After successful DHCP acknowledgment, the controllers open the socket communication. It opens two ports: one for UDP data transmission and the other port for receiving the reconfiguration over the LWM2M protocol. UDP is used for transmission, as it has a low overhead and is fast compared to other protocols. The LWM2M is used for remote reconfiguration of sensor platform parameters. The Wakaama implementation of the LWM2M stack is customized and scaled down to have a low stack and memory usage while working alongside a non-blocking UDP communication with multiple ports.
## 2 Materials and Methods
### Methods
#### 2.2.4 Hybrid Approach Based on FCMs, SOGA, and ANNs  
The hybrid approach for time series prediction is based on FCMs, the SOGA algorithm, and ANNs [68]. This approach consists of two stages:  
1. Construction of the FCM model based on the SOGA algorithm to reduce the concepts that have no significant influence on data error.
2. Considering the selected concepts (data attributes) as the inputs for the ANN and ANN learning with the use of backpropagation method with momentum.  
This hybrid structure allows the decision-maker to select the most significant concepts for an FCM model using the SOGA algorithm. These concepts are used as inputs for the ANN model. Such a hybrid approach aims to find the most accurate model for time series prediction problems.
## 3 Theoretical Framework
### Stochastic Impacts by Regression on Population, Affluence, and Technology Model (STIRPAT Model)  
The STIRPAT model extends the IPAT and ImPACT frameworks, which are limited to capturing the non-monotonic or non-proportional effects of influencing factors. In contrast, the STIRPAT model has been effectively used to examine how various driving forces contribute to different environmental impacts [27]:  
\[I_{i}=aP_{i}^{b}A_{i}^{c}T_{i}^{d}e_{i} \tag{1}\]  
The model retains the multiplicative structure of the equation \(I=PAT\), where environmental impact (\(I\)) is determined by population (\(P\)), affluence (\(A\)), and technology (\(T\)).  
After taking logarithms, the model takes the following form:  
\[lnI_{it}=a+b(lnP_{it})+c(lnA_{it})+d(lnT_{it})+e_{i} \tag{2}\]  
In this study, \(P\) is the population density, \(A\) is the gross regional and provincial product per capita (GPP) and industrial density, and \(T\) is the energy consumption per capita. The energy consumption per capita is used as technology (\(T\)) proximity in the STIRPAT model that follows [18].  
Table 3 presents three EKC patterns: the monotonic rising curve, the inverted U shape, and the U shape. It also includes studies on various EKC patterns related to air pollution issues and the independent variables that influence the dependent variable. In this study, the independent variables from the table are used as reference variables. Additionally, we reviewed further research to confirm the relationship between the dependent and independent variables in the context of air pollution issues.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EKC Patterns** & **Authors** & **Dependent Variables** & **Independent Variables** \\ \hline \begin{tabular}{c} Monotonic \\ rising curve \\ \end{tabular} & [28; 29; 30; 31; 32; 33] & Annual emissions of CO\({}_{2}\) & \begin{tabular}{c} Gross Regional Product (GRP) per capita and \\ square, Energy Consumption, Output, Foreign \\ Direct Investment (FDI), Transport energy \\ consumption, Labor Force, Exports and Imports \\ \end{tabular} \\ \hline \begin{tabular}{c} Inverted \\ U shape \\ \end{tabular} & [34; 35] & Annual emissions of NO\({}_{2}\), SO\({}_{2}\), suspended particulate matter & \begin{tabular}{c} GRP per capita and square, Population density, \\ Industry shares in GRP, \\ Trade intensity \\ \end{tabular} \\ \hline \
\begin{tabular}{c} U shape \\ \end{tabular} & [32; 36; 37; 38; 39; 40] & Annual emissions of CO\({}_{2}\), SO\({}_{2}\), suspended particulate matter &
\begin{tabular}{c} GRP per capita, Population growth, Spatial \\ intensity of economic activity, Energy \\ consumption, FDI, Transport \\ energy consumption \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: A review of the relationship between economic growth and pollution.
## 2 Results and Discussion  
### Volatile Compounds of Sweet Orange Determined by GC-MS  
A total of 49 volatile compounds were identified and quantified after the GC-MS analysis of orange juice samples, by identification of retention index (RI), odor descriptors with authentic standard, as shown in Table 1. There were 35, 31, 30, 28 and 29 volatile compounds corresponding to the different orange samples, respectively. Hydrocarbons were predominant in the headspace gas of orange juice, followed by esters, aldehydes, alcohols ketones and acids. It has been reported that sweet oranges displayed higher levels of monoter, especially d-limonene [18]. Our results generally agreed with that data; limonene was the most abundant monoterene hydrocarbon tested in this study, followedby o-cymene, p-cymene, myrcene and valencene. Limonene was the main constituent of all samples. ZG samples contained the highest percentage of limonene (24.4 mg/L). ZG and MS samples contained a high percentage o-Cymene and 4-isopropyltoluene, and a high percentage of myrcene was present in ZG and YX samples. Of the esters, a large amount of ethyl butyrate was present in QY. In addition, the ZG and QY samples contained a large amount of acetaldehyde; additionally, ZG and MS samples also contained a large amount of linalool. Other studies revealed that linalool was the most abundant alcohols in Archi citrus juice [(21)].
## 4 Experiments, Results, and Discussion
### Production of the RG Switchgear  
The third technological process considered is the production of the RG switchgear, which, unlike the previous processes, is implemented in the production company of the electrical industry. Compared to the processes discussed above, the technological process of the switchgear is much more extensive and the longest. (For these reasons, the experiments were performed on a small number of orders). The execution time of most technological operations is strongly related to the selected configuration of production resources, where the difference in the implementation time of some operations between the best and worst employees can be as much as 54%. However, because they do not need to use energy-consuming machines (only simple hand tools) and there is a low consumption of auxiliary materials, the selection of an employee has little influence on the cost of implementing a production operation; the main factor that differentiates the costs is the employee's hourly wage, which is usually balanced by the difference in employee performance.  
The conducted research indicates a very high increase in the quality of the schedule, where the quality of the schedule is understood as the orders' set completion time, if the variable conditions (selected resources) for the execution of production operations are taken into account. It can be seen in Figure 10 (the green curve) that in the case of the technological process of manufacturing the RG switchgear, the average relative profit is more than two times greater than for the best results obtained so far. The RG switchgear manufacturing process is characterized by the greatest dependence of the time required to perform individual technological operations on the selected configuration of production resources (exactly, the selection of employees), which resulted in obtaining the greatest profit from the use of the proposed methodology while optimizing the completion time. The information obtained from the technologists shows that in the case of the analyzed production process, the employee's long-term experience is of great importance.  
However, the improvement of the cost measure does not exceed 0.5%, and in the extreme case, it amounts to approx. 0.85%, which results from the fact that technological operations do not use machines and, to a small extent, additional materials. On the other hand, shorter lead times are usually associated with a higher hourly wage for a more experienced employee. We will deal with such limited opportunities to reduce costs by optimizing the schedule for any purely assembly production process. Such a situation is especially favored by the low risk of defects in a given assembly process.
## 2 Experimental and Analytical Procedure
### Samples  
As reported in Ref. [16], the samples used to test the strain field mapping were made by growing a GaAs\({}_{0.991}\)N\({}_{0.009}\) film on a GaAs buffer. The film was grown by molecular beam epitaxy at 500 \({}^{\circ}\)C. Thickness was 200 nm. The buffer was deposited on a [001] GaAs substrate at 600 \({}^{\circ}\)C. A radio-frequency plasma source was employed for N\({}_{2}\) cracking. X-ray diffraction measurementsallowed the characterization of the film regarding thickness and nitrogen concentration. The GaAsN surface was covered with a 50 nm thick film of titanium, which is not transparent to hydrogen. Electron beam lithography was used to pattern the Ti film. A 100 eV ion beam produced hydrogenation with a H dose \(\approx 3\times 10^{18}\) ions/cm\({}^{2}\). Such dosage allows the full passivation of an unpatterned GaAsN layer of the same thickness. Finally, chemical etching produced the removal of the titanium masks. It must be noted that a sharp GaAsN/GaAsN:H interface can be accomplished between GaAsN wires and the hydrogenated barriers [29], now exhibiting a \"GaAs-like\" behavior. In fact, hydrogen diffuses In GaAsN with a steep front-line of 10 nm/decade, or less [30].  
The hydrogenation process that was used to create the vertical GaAsN/GaAsN:H interfaces defining the wires is illustrated in Figure 1. The figure shows the concentration of electronically active nitrogen atoms following hydrogenation when a 10 \(\upmu\)m wide titanium mask is used. In the examined sample of composition GaAs\({}_{0.991}\)N\({}_{0.009}\), wires of different width w (w = 0.5, 1, 2, 5, and 10 \(\upmu\)m) and parallel to the [110] (or equivalent) direction were interposed with hydrogenated barriers (10-20 \(\upmu\)m wide).
## 6 Conclusions  
This paper proposes the SQL-CBRP, a new routing strategy for satellite self-organizing networks, and tests the performance of this routing strategy through a satellite self-organizing network simulation platform based on OMNeT++. SQL-CBRP combines the cluster-based routing and greedy algorithm, selecting the next hop by the queue lengths of neighbor nodes, which can reduce delay, packet loss rate, and the signaling overhead in the whole network. The results show that for the three types of services with specified transmission rates, the packet loss rate and delay performance of SQL-CBRP are generally better than Dijkstra and GPSR. Furthermore, this paper also tests the performance of the  
Figure 8: Packet loss rate and delay of Dijkstra, GPSR, and SQL-CBRP when the number of heavy load clusters is 4, 8, and 12. (**a**) Packet loss rate of different numbers of heavy load clusters; (**b**) Delay of different numbers of heavy load clusters.  
SQL-CBRP under different packet-sending probabilities. The results indicate that under conditions of high packet-sending probability and imbalance load, the performance of the SQL-CBRP algorithm is superior to the other two algorithms, confirming the superiority of SQL-CBRP in terms of performance under higher and imbalanced network loads.  
Conceptualization, G.W. and J.Z.; methodology, G.W. and J.Z.; software, G.W.; validation, G.W. and C.L.; formal analysis, G.W.; investigation, G.W. and J.Z.; resources, J.Z.; data curation, G.W. and Y.Z.; writing--original draft preparation, G.W.; writing--review and editing, J.Z. and C.L.; visualization, Z.C. and Y.Z.; supervision, J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the Fundamental Research Funds for the Central Universities\", grant number 2023 ZCJH09.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Towards Client Selection in Satellite Federated Learning
## 5 Simulation
### Experiment Results  
We measured the accuracy and convergence speed in different scenarios, that is, we compared the impact of server deployment on the GS or the satellite, and FedSat [15] and FedSpace [16] were applied as two benchmark methods. We compared the impact of CS mechanisms on these SFL methods.  
Firstly, we considered the impact of the CS mechanism on both methods when the PS is located is the GS. As shown in Figure 9, in 200 rounds of iterations, the algorithms equipped with a CS mechanism not only exhibited a faster convergence speed but also significantly improved the accuracy of the model. Amid the stringent visitation conditions, both FedSpace and FedSat, devoid of CS, faced arduous endeavors in reaching model convergence within a span of 48 h. Consequently, the final precision obtained stood at 38.4% and 37.5% for each, respectively. Fortunately, the CS mitigated this situation and significantly improved the convergence speed and accuracy rate; both methods achieved more than 80% model accuracy within 48 h.  
What we wanted to illustrate next is that, in SFL, deploying the PS on the satellite is a better choice. We used the same training parameters and configurations as on the GS, but changed the location of the PS to a satellite at a height of 500 km. As shown in Figure 10, the benchmark method significantly improved the convergence speed and accuracy rate. This reveals the potential of inter-satellite FL. Compared to the traditional methods, we achieved more than a 30% improvement in accuracy. Moreover, using CS can shorten the model convergence time to less than 16 h, with FedSpace achieving an accuracy of 85.5% and FedSat achieving an accuracy of 79.8%. A detailed comparison of the results is shown in Table 2.  
We attempted to further validate the effectiveness of the algorithm on the CIFAR-100 dataset. As shown in Figure 11, although the increase in dataset variety indeed presents challenges for satellite federal learning, our method was still able to achieve convergence within 24 h and attain accuracies of 82.3% and 77.4%.  
Figure 10: The result of experiment in inter-satellites scenario with Fashion-MINIST.  
Figure 9: The result of experiment in satellite–GS scenario with Fashion-MINIST.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 1 Objective measurement of joint movement  
The system uses electronic goniometers to accurately measure the range of motion and joint angles. These data can be utilized to monitor changes over time in strength, flexibility, and coordination.
## 4 Non-Parametric Methods
### Support Vector Machines  
Support Vector machines (SVMs) are traditionally supervised learning classifiers that require labelled training data to map samples into one of two classes [44]. These types of SVM models are called binary or two-class linear models. Binary SVMs can be adapted to work with non-linear data by mapping them into a high dimensional feature space using the Kernel method. Structural risk minimisation (SRM) is achieved when a maximum margin hyperplane can be found. In cases where unsupervised learning is required, which is normally the case in anomaly detection schemes because of the lack of training data, one-class SVMs can be used. In one-class SVMs the only class is the \"normal\" class and anything which deviates from the norm is classified as an anomaly.  
The use of a sliding window-based Least Squares SVM for online outlier detection was proposed by the authors in [45]. The algorithm uses a Reproducing Kernel Hilbert Space (RKHS) with Radial Basis Function (RBF) kernel. This kernel requires the system that produces the readings to be in steady state otherwise the detection performance deteriorates. The proposed algorithm addresses this issue by using the norms of the differences between samples and Least Squares Estimates instead of Euclidean norm. The modified scheme was shown to significantly outperform the original but is much slower.  
An anomaly detection scheme based on SVM and Linear regression models is proposed for medical applications by the authors in [46]. The SVM detects anomalies and the linear regression model is used to find the abnormal sensor measurements. A patient's vital signs can sometimes deteriorate suddenly and seemingly without warning but this model is able to distinguish between those cases and erroneous sensor readings. The system was tested using real patient data and was found to have a higher true positive and lower false negative rate than state-of-the-art techniques.  
As already mentioned the lack of training data is a serious drawback of binary SVMs. The same is true for multi-class SVMs because they are an extension binary SVMs that allow for sample categorisation into multiple classes. Moreover, should the training data be available SRM remains a major issue in these schemes and as such new emerging anomalies are difficult to detect. A multi-classSVM for anomaly detection in large scale deployments that seeks to address this issue is proposed in [47]. It is capable of classifying data into multiple known classes and uses the unknown class to detect anomalous data. The scheme is an extension conic segmentation SVM (CS-SVM) which is known as a true multi-class SVM as it does not use a combination of binary SVMs to achieve multi-class classification. The added functionality of the CS-SVM is achieved by combining it with the one-class SVM. The proposed scheme was tested using several benchmarking datasets and had favourable results.
## 2 HSI Procedure
### Preprocessing
#### 2.2.5 Auto Scaling (AS)  
Auto Scaling is a normalization method that standardizes spectral data by normalizing each variable against its standard deviation to achieve a mean of zero and a standard deviation of one. This extends mean centering by considering data variations and transforming the data into user-specific ranges, such as between \(-1\) and \(1\) or \(0\) and \(1\). AS works well when splitting spectral data into training and validation datasets, maintaining better consistency during model development. One major disadvantage of AS is that it inflates small spectral values and is highly sensitive to outliers. Applications of AS in food quality assessment are rare [16; 63].  
Although applications of AS in food quality assessment are still limited, [PERSON] et al. (2024) showed the potential of AS to predict protein content in dried laver. In their study, a variant of AS in combination with SNV and OSC as preprocessing steps enhanced prediction accuracy, as reflected in the high coefficients of determination and low root mean square errors obtained from their SVR models [61]. In the case of limit studies, the feasibility demonstrates AS's promise for non-destructive food quality assessment though further studies will be required to generalize its applicability across different food products, such as different types of algae, in predicting different quality parameters.