esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Envelope Transformation in Singing Voice for Advanced Pitch Shifting
## 3 Analysis of Parameters and Their Variation along Pitch
### Formant Analysis  
Among the parameters involved in our proposed spectral envelope model, we found significant evidence of their variation with the pitch only in the central frequency of the three first formants. We did not find evidence of any noticeable modification in the case of other parameters, like gain or slope depth. Moreover, we found no meaningful relationship between the bandwidth of the different resonators in our model and the pitch, although previous works could make us hypothesize the opposite [14]; therefore, the variation of the bandwidth of the resonators has not been considered in our model.  
The analysis has been carried out for the harmonic and the residual component of each audio sample separately. This has been accomplished by making use of the algorithms described in [23; 24]. Each component has been analyzed independently; however, the process and even the results obtained are very similar for both cases, so we will focus our explanation on the harmonic part. Whenever it is necessary to distinguish between the two cases, specific descriptions will be provided.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### Corresponding Studies to Hyperledger Fabric
#### 4.7.1 Fabric++  
Two simple experiments recognised a number of issues with Hyperledger Fabric's simultaneous transaction processing [51]. These problems limit parallel transaction processing, the main issue being the abortion of very high numbers of transactions caused by serialisation conflicts. If the number of successful transactions is to be increased, two necessary modifications must be done. The ordering and abortion of transactions must be further developed. The ordering of transactions is developed through the use of an advanced transaction reordering mechanism whilst implementing the concept of early abort in multiple phases is a solution to transaction abortions.  
**Transaction Reordering:** Firstly, the conflicting transactions must be identified. It is challenging to determine these transactions as they are executed in isolation. Thus, it is not possible for the transactions to see one another's later problematic and conflicting modifications. Transactions must be ordered such that the reading of the given key occurs before the given key is written. Cycles of conflicts can occur. As a result, the suggested mechanism for transaction reordering deletes certain transactions to form a cycle-free subset. The transactions present in the highest number of cycles are aborted first until a cycle-free state is reached. The remaining transactions are integrated into a serialisable schedule.  
**Early Transaction Abort:** To reduce the number of broadcast transactions in the network, the transactions that were removed in the mentioned reordering mechanism can now be aborted in the order phase rather than later in the validation phase. A form of early abort is also accessible to reduce performance issues. Eventually, this concept of early abort places only those transactions in the block that have a likely possibility of commit. To further reduce the number of problematic and invalid transactions in the pipeline, abortions are also attempted in the execute phase.  
To allow for simultaneous validation in the execution and validation phase within a peer, Fabric's concurrency control mechanism must be made more efficient. With this further developed concurrence mechanism in the execute phase, it is possible to recognise stale reads and abort the corresponding transaction during execution. In the order phase, early abort is similar to the previously described execute phase. The primary aim is to have two transactions read the same version of the key within the block.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Interaction in AR Applications  
Creating intuitive and easily understandable interaction in Augmented Reality is challenging and depends on the device used. If using a hand-held device for Augmented Reality applications, user interaction must either rely on the user's one hand, since the other hand will be holding the device, or on the movement of the device itself. Moreover, it is expected that the interaction will be somewhat familiar to the user who uses a mobile device or tablet regularly.  
In our application, the Sarajevo 5D Tour Guide, which is a tracker-based application, additional information is presented to users on-demand. When the user scans the image mark, apart from the cultural heritage object bound to that target, the interactive pin is shown, as can be seen in Figure 5. When the user touches the screen in the place of the pin, additional content about the object is shown. This interaction is standard for all mobile phones and tablets, which is why it is easily understandable for the user. In addition, pins are commonly used to present points of interest on maps. A possible problem with this approach occurs if the user is holding a map on the other hand since it can then be inconvenient to click on the pin while holding a phone with the same hand.  
On our second Augmented Reality application, Roman Heritage of Sarajevo, similar interaction principles are used. On this application, the user scans a map with tracker  
Figure 5: Clickable pin to show additional information about an object on the Sarajevo 5D Tour Guide.  
images, and 3D reconstructions of objects are then shown, together with different types of content. These include an HD video, 3D models, 360\({}^{\circ}\) video, text, and audio, as shown in Figure 6. HD video and audio content are started by clicking on the play icon that is commonly used, so the users did not have any problems with it. Informative text is shown on scroll-able panels to make it more visually appealing while not overwhelming the scene with a lot of text. To scroll through the text, a swipe motion is used, which is again widely used on mobile applications, only this time it is placed in 3D space. For movement in 360\({}^{\circ}\) video, the swipe motion in all directions is used. As an alternative to this, we could also use the phone movement.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.2 Algebraic Approaches  
The algebraic analysis of groups of images to calculate spatial information has a long history. Due to advances in both the quality and availability of camera hardware and image analysis software [90], most digitized models are now produced using photogrammetry. A well-established method is the 3D modelling of architectural heritage from current native digital photographs or video content using photogrammetric algorithms. The development of various feature matching methods [97; 98] and their integration into the structure-from-motion (SfM) workflow has led to their widespread use [77; 99; 100; 101; 102; 103]. Today, a wide varietyof image spatialization algorithms are available [104; 105; 106], and photogrammetry is used in various fields such as landscape surveying [107], underwater archaeology [108], and monument recording [109]. While current algorithms achieve remarkable quality for natively digital (sequential) images, they often fail with unordered historical images due to the high quality and number of input images required [100; 110; 111]. As a result, current approaches often achieve a low quality or fail to process historical non-native digital imagery due to sparse sampling, incompleteness, missing metadata and a problematic radiometric quality [98]. These are not only technical but fundamental barriers, and despite much research, current photogrammetry dealing with historical images [94; 112], such as re-photo approaches [113; 114], still requires a lot of manual processing. Initial attempts to advance photogrammetry from historical images using video [115] or--invented by our group--architectural-feature-based photogrammetry [116] and a combination of algebraic and machine learning approaches [117] seem promising avenues for further automation, but algebraic processes still require a lot of prior optimization and multiple images of similar views.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 3 The Algorithms to Realize ASPF  
This section presents the algorithms to realize ASPF. We first propose an improved _k-means_ clustering method to divide the data into several groups. Then, LARS is presented to find the most relevant variables in every group. Followed by this, we discuss the compensation network for each group. We also present the feedback and updating mechanism.
# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 2 Literature Review and Case Study
### New Techniques of Rock Masses Classification  
Apart from the regular RMR system in Korea, which treats the upper and lower benches of the tunnel face as equal in weight of their RMR values, there are new attempts to apply the Weight (W)-RMR classification system, which is illustrated in Figure 4. The W-RMR categorises RMR values into two distinct groups based on associated levels of risk. To obtain the weighted RMR of the entire tunnel section in this W-RMR system, it is concluded that 80% weightage is reasonable for the upper section of the tunnel, whereas the lower section has the remaining 20% weightage, according to the site demonstrations  
Figure 3: Selecting the support type by RMR classification.  
carried out by Korea Express Corporation [11]. This differential weighting approach allows the engineers to have a more nuanced understanding of the structural integrity and risk factors present in different sections of the tunnel face. This approach is justified as the rock quality of the upper-half section has a much greater impact on the stability of the tunnel.  
Furthermore, [PERSON] [8] proposed the Geological Strength Index (GSI) rock classification method, which emphasises the assessment of joint set numbers and the conditions of discontinuities, and Table 2 indicates the correlation between RMR\({}_{\text{b}}\) and GSI values [12; 13]. Unlike the RMR method, which is primarily qualitative, the GSI enables a detailed classification of rock masses that exhibit very poor quality. Specifically, the RMR system is incapable of further differentiating rock masses with an RMR value of 23 or below. In contrast, the GSI classification method allows for a more refined delineation of support patterns for these poor-quality rock masses, thus enhancing the effectiveness of engineering interventions (Figure 5).  
Figure 4: Implementation case of weighted RMR.  
Figure 5: Classification of support patterns using GSI under extremely poor rock quality.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
###### Abstract  
The purpose of this study was to evaluate the stability and therefore suitability of available fifth generation (5G) and long-term evolution (LTE) signals for positioning navigation and timing (PNT) purposes with particular focus on answering questions around the time-scale-dependent stability of these sources, which, to our knowledge, has not been addressed in the context of the numerous publications within the PNT community to date. The methodology used directly measured the over-the-air signal phase stability to one or more of the cellular signal sources that were visible from the lab environment simultaneously while using a local atomic clock or differential measurements to isolate the time stability of the observable cellular downlink signals. This approach was taken since it does not require subscription or association with the networks under test. Instead, it exploits a'signal of opportunity' (SoP) approach to signal use for PNT purposes. The somewhat surprising result is that the time domain instability of the sources was highly variable, dependent on the implementation choices of the operator, and that the stability of even the modernized towers was generally best at interrogation intervals of approximately 0.01 s, which indicates that the existing exploitation of these signals within the PNT community has substantial room for improvement through simple changes to the selected update rate used.  
navigation; timing; 5G; LTE; cellular; signals of opportunity +
Footnote †: journal: _Sensors_  
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Footnote 1: institutetext: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), O. S. Bragstads Plass 2D, 7034 Trondheim, Norway; [EMAIL_ADDRESS]  
2
Footnote 2: institutetext: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), O. S. Bragstads Plass 2D, 7034 Trondheim, Norway; [EMAIL_ADDRESS]  
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Footnote 3: institutetext: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), O. S. Bragstads Plass 2D, 7034 Trondheim, Norway; [EMAIL_ADDRESS]
## 3 Method
### Data Pre-Processing  
In this experiment, we use the OpenMIC-2018 dataset [31], an open-source, multilabel music instrument annotated database. This dataset does not control for specific physical parameters, such as subglottal pressure, vocal fold geometry, or recording equipment settings, as the dataset was designed to facilitate general audio analysis rather than studies requiring tightly controlled experimental conditions.  
The dataset contains over 20,000 ten-second audio clips, each annotated with multiple instrument labels, providing a diverse and extensive collection of musical instrument samples for analysis. The dataset includes 20 different musical instruments, and all recordings are real audio recordings capturing the authentic characteristics of various instruments in natural settings.  
We used the OpenMIC-2018 default train-test split, which includes 14,914 training samples and 5084 testing samples. During the training process, we further divided the training set by defining a validation split of 20%, with a random shuffle applied to ensure balanced representation across classes. This setup allows for consistent evaluation of model performance on unseen data while using a portion of the training data to monitor and fine-tune the model during training.  
Figure 1 presents various audio feature representations extracted from a musical instrument sample, along with the effects of combining these features at different scales. Panel (a) shows the individual spectrogram types: the Log-Mel spectrogram has 128 Mel frequency bins, the Chroma feature is divided into 12 pitch classes, the Spectral contrast feature has 7 frequency bands, and the Tonnetz feature consists of 6 bins representing harmonic intervals. This is the default input dataset configuration used in the experiment, with no scaling adjustments applied to the spectrograms.  
Using OpenCV [36], we resize the Chroma, Spectral contrast, and Tonnetz (CST) features to match the Log-Mel spectrogram's dimensions, as Log-Mel has shown superior performance in musical instrument classification research. In panel (b), all CST features are scaled to match the Log-Mel's size exactly. Panels (c), (d), and (e) illustrate alternative scaling approaches where CST features are resized to 1/4, 1/2, and 3/4 of the Log-Mel size, respectively. This scaling comparison explores the impact of different size ratios on feature representation and classification performance.
# Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids
## 3 Deep Q-Learning for EV Energy Scheduling
```
Inputs: S--state space, \(A\)--action space, \(CS\)--the set of charging stations, \(EV\)--the set of electrical vehicles, \(T\)--the time slots, \(N\)--the number of epochs for model training, \(E_{target}(T)\)--DR program target curve Outputs:\(\theta\)--the weights of the TQ-network Q-network parameters: \(\theta\)--the weights of the network, \(\varepsilon\)epsilon parameter, \(D\)--replay memory, \(m\)--target network update frequency, \(\eta\)--learning rate, \(\gamma\) discount factor Begin
1. \(Q-network\), \(TQ-network\)\(\leftarrow\) Initialize parameters
2. for epoch \(=1\) to \(N\) do
3. \(S^{initial}\)\(\leftarrow\) generate \(\big{(}E_{target}(T)\), \(CS,EV\big{)})\)
4. \(S^{current}=S^{initial}\)
5. \(\varepsilon=1\), done \(=\) False
6. while \((\
eg done)\) do
7. if \((random\ (0,1)<\varepsilon)\) do
8. \(action=select\_random\_action\)\((A)\)
9. else
10. \(action=argmax\big{(}Q-network\big{(}S^{current}\big{)}\big{)}\)
11. endif
12. \(S^{next}=[done=take\_action(action)\), reward \(\big{(}S^{current}\), action\big{)}]\)
13. \(D.append\big{(}S^{current}\), action, reward, \(S^{next}\), done)
14. \(S^{current}=S^{next}\)
15. endwhile
16. if \((|D|>batch\_size)\) then
17. \(mini\_batch=sample\_batch\)\((D\), batch\_size)
18. \(TQ-values=compute\)\((mini\_batch,\gamma\), \(TQ-network)\)
19. \(Q-values=compute\)\((mini\_batch,\gamma\), \(Q-network)\)
20. \(loss=mean\_squared\_error(TQ-values,\,Q-values)\)
21. \(\theta=\theta-\eta*\
abla Loss(\theta)\)
22. if \((epoch\%\)\(m==0)\) then
23. \(TQ-network\)\(\leftarrow\)\(copy\)\(Weights(Q-network)\)
24. end if
25. \(\varepsilon=\varepsilon*\varepsilon_{decay}\)
26. endfor
27. return \(\theta\) of \(TQ-network\)
```  
**Algorithm 1** Deep Q-Learning for EV Scheduling  
Then, for the training process, we randomly extract a mini-batch sample of transitions from the replay memory \(D\) of the size of the batch (lines 16-24). This step is only performed once there are enough transitions in the memory to cover the batch size. The Q-values from the TQ-network are determined from stored transitions from the previous step, and the Q-values from the Q-network are determined based on the original network (18-19). Afterward, we determine the loss by using the mean squared difference between the TQ-values and Q-values and calculate the loss gradients relative to the Q-network weights (lines 20-21). The new weights of the model are determined considering \(\eta\): a hyperparameter representing the learning rate of the network in which the weights are updated. The weights of the Q-network are updated based on the calculated gradients, and after each \(m\) episode, the weights of the current Q-network are copied to the target Q-network to improve the accuracy of the target network for future training (lines 22-23). Finally, the value of the \(\epsilon\) parameter is updated via a constant decay rate, and the TQ-network parameters are returned.
## 2 Related Works and Research Problem
Increasing resource usage while guaranteeing the minimum QoS requirements for the usual slices in the 5G C-RAN has not been the focus of any previous research project to the best of our knowledge. C-RAN-based 5G cellular networks have limited number of available resources to serve the growing number of users with different QoS requirements. The concept of network slicing was introduced in C-RAN to support various 5G services, such as eMBB, URLLC, and mMTC [18; 19]. Although there is a massive amount of data traffic in C-RAN, the research on C-RAN slicing is still limited. Efficient resource allocation algorithms for the network slices in C-RAN are essential to increase resource utilization and improve the QoSrequirements for users. While the 3 GPP used static resource allocation in C-RAN, the need for a scalable system that accommodates changes in the network environment led to a focus on dynamic resource allocation [20]. Unlike previous studies in the literature, our proposed system will combine dynamic resource allocation with admission control and slice isolation to provide better QoS requirements and maximize resource utilization. Our research approach can enhance the methods for developing resource allocation in 5G C-RAN slicing. In addition, the developed system and results can be widely used by researchers attempting to solve comparable resource allocation challenges in communication systems. This research study will focus on resource allocation for network slices in the 5G C-RAN network. Previously, static resource allocation was used to address the allocation problem in C-RAN [27; 28]. However, more recently, dynamic resource allocation has also been used. The primary goal of this paper is to develop a system that uses dynamic resource allocation to satisfy the various requirements of network slices. Moreover, it considers the admission control and isolation between the network slices to increase resource utilization. Toward these ends, the proposed study has the following objectives:  
* Investigate the current allocation techniques in 5G C-RAN, such as static and dynamic resource allocation algorithms. * Design a system for resource allocation at the slice level and then implement a system that has combined functions, such as admission control, resource allocation, and slice isolation. Initially, the system will implement admission control to admit the users to existing slices. Then, the system will dynamically allocate the RBs to the different slices, such as eMBB, URLLC, and mMTC, in the C-RAN based on the priority and the QoS of the slices. The system will also implement isolation between the network slices to protect the minimum QoS requirements for the slices while increasing system utilization. * Implement a performance evaluation and develop a simulation model to evaluate the performance of the proposed system against the static slice allocation (SSA) system. We will compare the result to the SSA system by considering some performance measures such as resource utilization, average waiting time, and the total blocking probability (TBP).
## 3 Results  
### Effects of the Neural Networks  
Neural network 1 (based on the speed and positioning time, with 98.56% accuracy) performed better in identifying the position states of the vessels than neural network 2 (based on the heading and positioning time, with 51.44% accuracy) (Tables 2 and 3). The identification accuracy of casting, hauling, and sailing states based on neural network 1 were 98.33%, 100.00%, and 97.33%, respectively. In contrast, the identification accuracy states based on neural network 2 were significantly lower, at 67.00%, 41.00%, and 46.33%, respectively.