esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Voice Recording Technique and Study Corpus  
The audio database was compiled from clinical data recorded during the period of January 2014 to December 2019. All voice recordings were performed in the same place and in a sound-treated acoustic booth. Two different pieces of recording equipment were used during this period: (i) a desktop computer and (ii) a digital recorder. Computer recordings  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Men (_n_)** & **Women (_n_)** & **Total (_n_)** & **\%** \\ \hline \multirow{2}{*}{Vocal Hyperfunction} & \multirow{2}{*}{NPVH} & 3 & 6 & 9 & 29 \\  & & 7 & 15 & 22 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of patients according to gender and vocal hyperfunction condition.  
were taken using a monoarticular headset with a unidirectional condenser-type digital microphone (Satellite AE-216, frequency response range 20-15 kHz, sensitivity 64 \(\pm\) 3 dB) positioned at a 5 cm distance and a 45\({}^{\circ}\) angle from the mouth. Praat software [41] was used as an audio interface in a mono channel setting. For the cases with digital recorders, Zoom equipment (model H2n) was set at 90\({}^{\circ}\) MS, 4 to 6 mic gain, uncompressed wave, volume 40 to 60, and stereo channel. The recorder was placed at a distance of 15 cm and at a 45\({}^{\circ}\) angle from the mouth. The audio files were transferred from the digital recorder to the computer and stored.  
All recordings used a 44 kHz sampling rate and 32-bit quantization in.wav audio format. Procedures to calibrate voice SPL were carried to for both types of equipment separately by applying a comparison method, as described in [PERSON] et al. (2020) [35].  
All voice recordings contained at least a sustained vowel [a], the six CAPE-V sentences adapted for Brazilian Portuguese [42], and a spontaneous speech sample. The CAPE-V sentences were presented to patients on a card, and they were instructed to read the sentences with habitual pitch and loudness. When a patient had difficulty reading, the task was accomplished by repetition.  
Composition of Study Corpus  
CAPE-V sentences were chosen for analysis because they are commonly used for speech assessment and provide speech samples with a controlled phonetic environment. Four samples of the voiceless bilabial stop [p] were selected for this analysis. The target tokens are displayed in Table 2.  
Because the phonetic environment is expected to significantly affect acoustic measurements [24; 39], two phonetic features were controlled in this corpus: (i) vowel or diphthong [e, I, \(\circ\), a] and (ii) syllable stress, i.e., stressed or unstressed.  
For the purpose of analysis, an independent variable concerning the mode of speech task was created. As explained in item 2.4, the CAPE-V sentences could be performed by reading, preferable mode, or by repetition when the patient could not accomplish it by reading. The mode of delivery of speech task (reading versus repetition) was incorporated into the statistical model.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### MazF-mx  
_M. xanthus_ is a Gram-negative bacterium that forms a multicellular structure called a fruiting body. MazF-mx was originally shown to be responsible for altruistic cell death during the formation of the fruiting body; however, its direct involvement is currently under debate [46; 61; 62]. While many antitoxins of TA systems form an operon with their cognate toxins, the antitoxin gene of MazF-mx is distantly located on the genome. The antitoxin (MrpC) was identified through a yeast two-hybrid screening, and its antitoxin activity was subsequently verified [46].  
MazF-mx cleaves GU'UGC [46]. The first G-residue can be replaced by an A-residue. Based on the primer extension using MS2 RNA as a template, the enzyme has a preference for GAGU'UCCA over other sequences containing GUUGC. Higher concentrations of enzymes allow for the detection of additional cleavage sites, such as AUGU'CAGG, ACGU'AAUA and ACGU'AAAG.
# Throughput of Buffer with Dependent Service Times
## 6 Numeric Illustrations
### Dependence on Traffic Intensity  
In all the previous examples, the arrival rate was equal to the service rate, resulting in a traffic intensity of 1 in every scenario.  
Now, we will decrease the traffic intensity and check how this influences the throughput of the buffer. To achieve that, the arrival rate will be kept unaltered, i.e., 100 pkts/s, as follows from (73), but the service rate will be increased to \(r\cdot 100\), where \(r\geq 1\) is a parameter. To accomplish this, it suffices to use matrices \(rL_{0}\) and \(rL_{1}\) rather than the original \(\mathbf{L}_{0}\) and \(\mathbf{L}_{1}\) from (72) and (71), respectively. The resulting traffic intensity is then \(\frac{1}{r}\).  
In Figure 17, the dependence of the stationary throughput on the buffer size is shown. Four different values of traffic intensity are used, ranging from 0.4 to 1. In every case, a mild correlation of \(\rho_{1}=0.2\) is applied. As seen, when the traffic intensity is 0.6 or less, a buffer of size 150 suffices to achieve the maximal throughput. When the traffic intensity is 0.8, a buffer for 300 packets is needed. Finally, when the traffic intensity is 1, the buffer capacity must be greater than 500 to maximize the throughput.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model  
### Proposed Scenario  
We consider a flying backhaul/fronthaul network composed of UAVs that provides reliable connectivity to a 5G+ radio access network (RAN). The UAVs act as networked flying platform (NFP) nodes that can react to changes in weather or traffic conditions [34]. Its ground-to-air links are based on FSO technology with EH. This solution has four remarkable benefits: (i) its FSO links provide the required high data rates for the backhaul or fronthaul connections; (ii) FSO links do not cause interference to the 5G RAN; (iii) UAVs can adapt their location to the traffic and channel conditions; and (iv) EH extends the UAVs' service time and thus, improves the network performance.  
This approach is illustrated with Figure 1 where it shows the representative nodes and connections. The backhaul traffic represents the IP data transmissions between the 5G core network and the BSs in distributed RAN approaches [35]. Accordingly, the BSs fully implement the 5G RAN protocol stack, and thus they are source/destination of IP packets towards/from the core network.  
A recent approach to the classical distributed RAN arises with the cloud RAN (C-RAN) [36], where a base band unit (BBU) centralizes the RAN processing of many small cells. The signal transmission related to each cell is carried out by radio remote heads (RRHs) that are connected with the BBUs through fronthaul links. This latter approach offers two main benefits compared to distributed RAN. First, C-RAN reduces costs since each small cell can be implemented with a RRH, cheaper than a complete BSs. Second, C-RANs ease the implementation of coordinated mechanisms for interference mitigation.  
Coming back to Figure 1, it is straightforward to see the different backhaul links between the core network and the BSs through the FSO-UAVs based network. This scheme requires a FSO gateway that creates the link with a mother NFP node, i.e., a mother UAV that forwards the IP traffic towards the NFP node currently connected to the target BS. Analogously, the fronthaul links between the BBU and the RRHs also require a connection between a FSO gateway and the mother NFP node. Certainly, many applications beyond classical mobile networks can benefit from the proposed scenario shown in Figure 1 since it can provide backhaul/fronthaul connectivity to 5G V2X, or even future underwater communications [11; 12; 13; 37].
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 5 Experimental Results
Figure 14: MFT input and output voltages and currents (50% P\({}_{\text{nom}}\)): (**a**) Nanocrystalline, (**b**) Ferrite. Figure 15: MFT input and output voltages and currents at full load: (**a**) Nanocrystalline, (**b**) Ferrite. The power density in the ferrite MTF is 56.4% lower than in the nanocrystalline MFT. Regarding cost, the ferrite core is 50.82% lower compared to the nanocrystalline core, and a total transformer cost of 46.12% lower. Therefore, from the point of view of power density, for the upper corner of the medium frequency range, 20 kHz, it is higher in the nanocrystalline MFTs. However, regarding construction cost, the ferrite MFT is lower. This comparison result gives a new guideline to MFT designers useful to select the core material most suitable according to the application requirements, for instance, to obtain novel DC-DC converters of higher power density or low construction cost for fuel cell supplied systems, solid state transformers, electric vehicles, wind systems, and photovoltaic systems.
# Spatiotemporal Image Fusion in Remote Sensing
## 4 Conclusions  
Three categories of spatiotemporal image fusion methods have been discussed in this paper, namely reconstruction-based, learning-based and unmixing based methods. The most popular method for blending fine-spatial resolution with high-temporal resolution images is the reconstruction-based STARFM. Recently, new methods have been developed to fuse images acquired by other sensor products, e.g., Sentinel-2, Sentinel-3 or images acquired by microsatellites. Also, many reviewed methods account for land cover changes occurring during the observation period. The problem of cloud obscuration can be solved by fusing microwave data with optical images.  
Future efforts are required for generating spatiotemporal image fusion solutions which are: (1) generic enough to consider various sensors characteristics; (2) computationally efficient to be able to scale up to regional and global level; (3) robust to temporal and spatial variations specific to landscape of a different heterogeneity and complexity caused by different physio-geographical conditions, soil or land management practices, (4) flexible enough to consider phenological dynamics of vegetation or land cover changes caused by both external factors, such as natural hazards and anthropogenic activities, i.e., urban sprawl, and (5) testing and implementation of more sophisticated machine learning methods such as deep learning. It is unlikely that there will be a single optimal spatiotemporal method capable to address data blending needs of various remote sensing applications [70]. Therefore, an efficient and operational framework for benchmarking existing spatiotemporal image fusion methods similar to those developed by IEEE GRSS Data Fusion Contest is of paramount importance to help remote sensing community assess the performance of the spatiotemporal image fusion methods and the quality of the resulting output images.  
Both authors contributed to the development of the concept and manuscript writing.
# Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
## 3 Methods
### Segmentation Mask Comparison  
This study employs the Segment Anything Model (SAM), an advanced, large-scale segmentation model, to extract segmentation masks of pipes from two distinct sources, i.e., photos projected from BIM and photos captured on-site. SAM has demonstrated its robust object recognition and segmentation capabilities by excelling in identifying and isolating various construction elements within an image. Typical examples of these elements are pipes, brackets, and other associated structures. Remarkably, SAM achieves this without requiring additional training, making it a convenient and efficient solution for construction and engineering applications.  
This study employs SAM to obtain two segmentation masks for each image: one from the BIM model projection and one from the on-site photo. The masks represent a critical step in the proposed architecture, directly comparing the planned and as-built conditions. The BIM-based mask serves as a reference for the intended design, while the on-site mask reflects the actual state of the construction. SAM can handle complex segmentation tasks with minimal intervention. This is because, instead of requiring extensive manual labeling or retraining for specific contexts, it autonomously distinguishes the multiple objects in a scene, saving time and ensuring consistency and accuracy in the segmentation results. After generating masks using SAM, these will be further processed and analyzed to assess the alignment, discrepancies, and overall compatibility between the BIM design and the real-world implementation. This comparison is critical in evaluating construction accuracy, identifying potential issues, and ensuring that the as-built environment conforms to the original design specifications.
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 2 Related Works
### Problem Areas
#### 2.1.1 Full Repair Coverage  
According to the state-of-the-art analysis, we can state that some existing FRR mechanisms cannot provide full repair coverage. In other words, not all FRR mechanisms are able to repair and construct alternative backup paths for all possible failure scenarios. Furthermore, we can state that with the increasing repair coverage in most cases the complexity of the internal FRR algorithm also grows [48]. This may be an issue for complex and frequently changing network environment where routers with limited computation resources are installed, or low-priority FRR processes are used [48]. Therefore, one of the problem areas of current FRR solutions that provides full repair coverage is the complexity of an internal algorithm.
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
## 3 Results
### Grain Crushing Effect with Surface Roughness in the Ring Shear Test  
Particle breakage has a significant effect on soil behavior [22; 36]. According to [PERSON] [22], grain crushing is affected by many factors, e.g., particle shape, state of effective stress, void ratio, particle hardness, and presence of water. A relative breakage potential can be determined using a grain distribution analysis, but this was not considered in this paper. In addition, particle compaction in the ring shear test may result in potential grain crushing and strain localization [30; 37]. A grain size analysis was performed for the aquarium gravel using eight sieves before and after the tests. Figure 6 presents the grain size distribution curves with respect to surface roughness depending on the drainage condition and shear velocities of 0.1, 0.5, and 1 mm/s. As shown in Figure 6, under identical drainage conditions (Figure 6a-f) the grain crushing gradually increased with increasing shear velocity. In each curve, the area created before and after the test could be considered as a fragmentation effect through the artificial sliding plane. From the mechanical viewpoint, the roughness may cause increased friction in both drainage conditions. All tests showed large fragmentation using the rough surfaces compared to the smooth surface, although there was little difference between drained and undrained conditions. For clayey and sandy soils, it may have been easier to locate the shear surface in the test. However, it was much more difficult to visually compare the differences in shear zones obtained from drained and undrained conditions for granular materials because of the invisible slip surface in the ring shear test without using a transparent ring shear box [26]. For the gravel tested, the thickness of the shear zone (h\({}_{\text{s}}\)) was estimated as 3-6 mm, based on the concept of relative roughness, assuming that h\({}_{\text{s}}\) = (5 to 10)-D\({}_{50}\)[14]. In landslides, a great volume of fine-grained sediments from the entrainment process can affect landslide transformation. For example, [PERSON] et al. [20] reported that \"the fine soil layer carried the slidebody moving fast on the ground and decreased the integrity of the slidebody.\" Excess pore water pressure and the soft base effect may combine to create a possible mechanism for the transformation from slide to debris flow in landslides.
# PRISMA on Machine Learning Techniques in Smart City Development
## 5 Conclusions and Discussions
Limited synthesis of implications for policymakers regarding the adoption of machine learning in city governance and planning. Much research focuses on the technicalities without delineating practical takeaways for municipal authorities. This review extracts policy and planning-level insights. 4. Few works systematically identify challenges, limitations, and gaps to guide future research at the confluence of machine learning and smart cities. This review concludes by outlining recommendations to advance work in this nascent domain. 5. Lack of reviews centered on machine learning's capacity to create citizen-centric intelligent urban environments. This work highlights the human and social dimension, rather than solely technical capabilities. This article addresses the research gaps of lack of a multi-disciplinary integrative overview, implications for urban policymakers, and directions for future research at the intersection of machine learning and smart city development. It provides a holistic lens into this emerging field. The literature highlights the pivotal role of machine learning in optimizing transport systems and traffic flow within smart cities. Advanced algorithms demonstrate significant promise in traffic prediction, congestion management, route optimization, accident reduction, and lowering emissions. Graph neural networks are noted for effectiveness in real-time traffic speed estimation leveraging spatial and temporal data. Studies also showcase applications in predicting parking availability to mitigate urban congestion issues. Another prominent theme focuses on utilizing machine learning techniques to track the flow of people and events within smart city environments. This contributes to responsive mobility planning, security, surveillance, and overall efficient city management. Regression algorithms prove useful in predicting road closures, while CNN-LSTM models show potential in detecting abnormal crowd behaviors through surveillance systems. Sustainability is a crucial theme, with papers underscoring machine learning's contribution to energy efficiency, renewable integration, and environmental impact reduction in smart infrastructure. Use cases include drinking water quality monitoring, air pollution forecasting, managing electric vehicle charging loads, and mapping urban heat islands to mitigate climate change effects. Machine learning is noted for its increasing role in the security and cybersecurity aspects of smart cities. Studies discuss techniques like intrusion detection systems, network traffic analysis, credit card fraud detection, and electricity theft prevention in smart grids.
## 2 Methods
### Correction Technique of Displacement Signal Measured by UAV
#### 2.2.2 Fourier Transform and Inverse Fourier Transform  
The time domain signals can be converted into frequency domain signals with FT, which is a signal processing technique. For discrete data set \(X\), the expression of FT is as follows [33]:  
\[y_{k+1}=\sum\limits_{j=0}^{n-1}\omega^{jk}x_{j+1} \tag{4}\]  
where \(y_{k+1}\) is the new signal after FT, \(x_{j+1}\in X\), \(\omega=e^{\frac{-2\pi i}{n}}\) is one of the complex unit roots, \(i\) is an imaginary unit, \(n\) is the number of samples in \(X\), and the value ranges of \(j\) and \(k\) are both \(0\) to \(n-1\).  
The frequency domain signals can be converted into time domain signals with IFT, and its expression is as follows:  
\[x_{j+1}=\frac{1}{n}\sum\limits_{k=0}^{n-1}\omega^{-jk}y_{k+1} \tag{5}\]  
Figure 1: The process of EMD.  
The FT used in this paper is generally used for continuous signal processing. The displacement signal measured by the UAV includes the bridge true displacement and the false displacement, and the vibration frequency of the UAV is small relative to the vibration frequency of the bridge; therefore, a filter combined FT with IFT is used to filter out the low-frequency signal. The spectrum diagram is obtained from the displacement signal with FT, and the low-frequency part of the spectrum is removed. Then the frequency domain signal is converted into the time domain signal with IFT, so as to obtain the bridge true vibration signal.
# A Set-Theoretic Approach to Modeling Network Structure
## 3 Network Properties  
There are several scalar properties associated with every network \(\mathcal{N}\), including \(n\_nodes=|N|\), \(n\_links=|L|\) and \(density=|L|/|N|\). The average node degree over all nodes is \(2\cdot density\), since every link has two end nodes [1,2]. These are trivial to calculate given \(N\) and \(L\).  
The number of triangles [13] embedded in \(\mathcal{N}\) can be calculated by the count_triangles whose code is given in Pseudocode II.  
k_total = 0  for_each link {x, z} in L {  MEET = {x}.mbhd meet {z}.mbhd  {x, z}.k.count = cardinality_of(MEET)  k_total = k_total + {x, z}.k.count }  n_triangles = k_total/3  Pseudocode II, count_triangles  
Here, the \(k\_count\) of a link denotes the number of triangles for which the link \(\{x,z\}\) is one \"side\". Since that triangle has three links, \(n\_triangles=k\_total/3\). The computational cost of \(\{x\}.mbhd\cap\{z\}.mbhd\) is essentially constant, so the cost of count_triangles is linear, or \(O(L)\).  
Other scalar properties are dependent on the concept of shortest paths. Let \(x,z\) be two nodes in a connected network \(\mathcal{N}\). Because \(\mathcal{N}\) is connected, there exists a path \(\rho(x,z)\) of length \(n\). This may, or may not, be the shortest path (of minimal length) between them. We let \(\sigma(x,z)\) denote the (or all) **shortest path(s)** between \(x\) and \(z\). The path length \(|\sigma(x,z)|\) is also known as the **distance**, \(d(x,z)\), between \(x\) and \(y\)[1,2]. The \(diameter(\mathcal{N})\) of the network is the maximal distance, \(d(x,z)\) for all \(x,z\in N\). The _eccentricity_ of a node \(x\) is \(\varepsilon(x)=max(d(x,z))\) for all \(z\in N\). The _radius_, \(r(\mathcal{N})\), of the network is minimum eccentricity of any node \(y\)[2].
## 4 Results  
This section evaluates the performance of the proposed solution by means of system level simulations. After describing the considered scenario for the training and evaluation as well as the relevant parameters for both stages in Section 4.1, the evolution of training process in order to obtain the policy is presented in Section 4.2. Section 4.3 describes the considered benchmarking strategies for assessing the performance of the proposed MC strategy. Then, Sections 4.4-4.6 provide the obtained performance results considering different situations, namely UEs following different trajectories, UEs in fixed positions and situations in which the number of MC-capable UEs is increased.