esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we discussed several novel ideas based on our recent novel formulation of the problem of synthesizer control as matching the two latent spaces defined as the _audio perception space_ and the _synthesizer parameter space_. To solve this new formulation, we relied on VAEs and Normalizing Flows to organize and map the auditory and parameter spaces of a given synthesizer. We introduced the _disentangling_ flows, which allow to obtain an invertible mapping between two separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation.  
We showed that our approach outperforms all previous proposals on the seminal problem of _parameters inference_, and that it is able to provide an interesting approximation to any type of sound in almost real-time, even on a CPU. We showed that for sounds that are not produced by synthesizers, our model is able to match the evolution of the spectral shape quite well, even though the local timbre is not well approximated. We further showed that our formulation also naturally introduces various original and first-of-kind tasks of _macro-control learning_, _audio-based preset exploration_, and _semantic parameters discovery_. Hence, our proposal is the first to be able to simultaneously address most synthesizer control issues at once, while providing higher-level understanding and controls. In order to allow for usable and creative exploration of our proposed methods, we implemented a Max4 Live interface that is available freely along with the source code of all approaches on the supporting webpage of this paper.  
Altogether, we hope that this work will provide new means of exploring audio synthesis, sparking the development of new leaps in musical creativity.  
Conceptualization, P.E. and A.B.; Data curation, N.M. and R.D.; Formal analysis, P.E. and A.C.-R.-S.; Funding acquisition, P.E.; Investigation, P.E., N.M., R.D. and A.C.-R.-S.; Methodology, P.E., N.M., A.B. and A.C.-R.-S.; Project administration, P.E.; Resources, A.B.; Software, P.E., N.M. and R.D.; Validation, A.C.-R.-S.; Visualization, N.M.; Writing--original draft, P.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the MAKIMOno project funded by the French ANR and Canadian NSERC (ANR:17-CE38-0015-01 and NSERC:STFG 507004-17) and also the ACTOR Partnership funded by the Canadian SSHRC (SSHRC:895-2018-1023). This work was also supported by an NVIDIA GPU Grant and GPU Center grant.  
The authors declare no conflict of interest.  
Figure 7: _FlowSynth_ interface for audio synthesizer control in Ableton Live. The interface wraps a given VST, and allows to perform direct parameters inference, audio-based preset exploration and relying on both semantic and unsupervised macro-controls learned by our model.
## 3 Results and Discussion  
### The Mean Annual Cycle of Precipitation  
The mean precipitation annual cycle from the GPP (Figure 2a-c) reveals a high correspondence with observations for the subregions defined in Figure 1. For the western sub-region and the Caribbean as a whole, the GPP represent well the bimodal pattern of the rainfall, including the MSD (Figure 2a,b). There is strong coincidence between the GPP that overestimate or underestimate observations in the western Caribbean and the Caribbean as a whole. This may reflect the fact that the larger proportion of stations fall in the western Caribbean (45 out of the 60 stations). In the Eastern Caribbean (Figure 2c), where the MSD is absent and the land masses are smaller, the GPP similarly capture the main features of the observed annual variation pattern including the occurrence of the maximum in November. However, differences from the observations tend to be greater than for the western Caribbean and some GPP a such as PERSIANNCDR, CFSR, and CHELSA are even displaying a displaying the opposite sign in total precipitation particularly in the wet season months (not show). The implication is that a GPP that does well at capturing mean climate in the western Caribbean or the Caribbean as a whole may not necessarily be equally good in the eastern Caribbean.  
For the eastern sub-region, four gauge only/gauge-based GPP (CHIRPS, PRECL, GPCC025, and GPCC05) are among the best performers, having the lowest absolute differences. In comparison, the reanalysis products tend to underestimate the monthly mean precipitation, with ERAi producing the best representation of observations (smallest monthly deviations). GPP with largest deviations are CPCGLOBAL, ERA5, PERSIANNCDR and MSWEP2 (underestimates) and CRU, MSWEP and HERREAULT (overestimates). These GPP use different methodologies in their development and different sources of data; for example, whereas CPCGLOBAL and CRU use only rain-gauges, PERSIANNCDR and CHIRPS merge rain-gauges and satellite information, while ERA5 is a reanalysis product. We suggest that the relatively poorer performance of most of the GPP over the eastern Caribbean may result from: (i) the scarcity of rain-gauges in this subregion, and (ii) the poor representation by the reanalysis-based GPP of the precipitation processes that occur in the small islands (e.g., with areas \(\leq\) 1500 km\({}^{2}\)).  
The GPP' performance is significantly better in the western Caribbean, though substantial departures exist between observations and some GPP e.g., CPCGLOBAL and ERA5. We note that the better performance by some GPP with respect to the entire domain reflect their ability to minimize differences with ground stations in both subregions. Over the entire domain, GPCC025, GPCC05, CHIRPS, and CHELSA stand out as the better performing GPP with respect to the annual precipitation cycle features in the Caribbean.
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 4 Experimental Assessment
### Baselines  
To demonstrate the performance of our proposed model framework, we compare with the following speech sentiment analysis baselines.  
**CNN+BiLSTM**: [PERSON] et al. [11] proposed a CNN+BiLSTM framework model, and the key contribution lies in the use of normalization techniques to enhance the use of features.  
**K-Mean+DBN**: [PERSON] et al. [24] proposed a method combining deep belief networks with K-Mean clustering to improve the efficiency of speech emotion recognition tasks.  
**DCNN**: [PERSON] et al. [42] introduced a novel framework that extracts some acoustic features from speech signals as input for emotion recognition and compared to previous work, it takes raw audio samples and does not convert to visual features.  
**DSCNN**: [PERSON] et al. [15] proposed an AI-assisted Deep Convolution Neural Network (DSCNN) architecture that use a common network approach to learn outstanding and discriminative features from the spectrogram of the voice signal, which are enforced in the previous steps and thus behave better.  
**Bag-SVM**: [PERSON] et al. [43] proposed a bagged ensemble consisting of support vector machines and Gaussian kernels, from which a combination of spectral features was extracted, further processed and reduced to the desired feature set.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Utterances** & **Participation (\%)** \\ \hline Anger & 192 & 13.3 \\ Happiness & 192 & 13.3 \\ Sad & 192 & 13.3 \\ Neutral & 96 & 6.9 \\ Calm & 192 & 13.3 \\ Disgust & 192 & 13.3 \\ Fear & 192 & 13.3 \\ Surprise & 192 & 13.3 \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of different emotions in the dataset RAVDESS.  
**ConvLSTM+GRUs**: [PERSON] et al. [17] proposed the ConvLSTM model, which performs local feature learning by layering, and combines GRU for global feature learning to improve the accuracy of the model.  
**HA-ACNN**: [PERSON] et al. [44] proposed a multi-head attention-based convolution neural network model, and in order to improve the model accuracy, the authors also studied the impact of noise intensity and time-shift effects on the accuracy.  
**LSTM-Transformer**: [PERSON] et al. [45] proposed a hybrid model architecture of a hybrid long short-term memory network and transformer encoder, which is able to preserve hidden states and learn long- and short-term dependencies using a multi-head attention mechanism.
## 3 Results
### Themes and Categorization
It highlights the potential benefits of integrating SARS into hospital routines, though further research is necessary to refine their application and improve outcomes. & The study suggests that SARS have potential in alleviating depressive symptoms among older adults, particularly through companionship and communication. However, the evidence is not yet strong enough to make definitive clinical recommendations, and more research is needed to assess long-term effects and effectiveness. & The review indicates that SASRs may improve emotional support, reduce agitation, anxiety, and loneliness, and enhance the overall quality of life in older adults. However, the lack of high-quality studies calls for further RCTs to better understand the impact of SASRs on older adults’ health and well-being. \\ \hline \multirow{9}{*}{[22]} & This scoping review explores the use of SASRs in adult psychiatry, focusing on their impact on mental health conditions like schizophrenia, autism spectrum disorder, and intellectual disability. The study suggests that SASRs can reduce symptoms, improve functioning, and provide insights into these conditions, with positive user feedback on their acceptance. & This study highlights the potential of SASRs to improve mental healthcare, particularly in adult psychiatry, where their use is currently limited. It suggests that further research with larger, randomized trials could establish more concrete evidence of their benefits. \\ \hline \multirow{9}{*}{[24]} & This meta-analysis examines the effect of AI socially sensitive robots (SAR) on the cognitive function of older adults. Nine studies were included, and the results suggest that AI SAR in anthropomorphic form can improve cognitive function. & The study shows that AI SAR can effectively support cognitive function in older adults, offering a non-pharmacological intervention with the potential to enhance the quality of care and reduce caregiver burden, although challenges in implementation remain. \\ \hline \multirow{9}{*}{[25]} & This systematic review investigates non-pharmacological interventions for pain management in people living with dementia. It highlights interventions like ear acquressure, music therapy, and personal assistive robots as effective options for managing pain. & The findings suggest that non-pharmacological interventions, including SASRs, can play a role in pain management for people with dementia, offering safer alternatives to pharmacological treatments. However, more rigorous studies are needed for validation. Table 4 presents a selection of studies on SASRs, categorizing them into clusters based on their design and intended application. Each study is grouped into one of the following clusters: emotional and social support, therapeutic robots for special needs, and cognitive support. The table also provides a justification for the classification of each study, offering insight into the focus and objectives of the robot interventions discussed.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 4 Performance Evaluation  
The simulation experiment platform is ONE, and the RATP is compared with Greedy protocol, CHBPR protocol and GPCR protocol. To accurately evaluate our protocol, we selected smaller areas from the 250 \(\times\) 250 km trajectory area because the simulation of 200,000 nodes makes the simulation experiment extremely slow. The simulation experiment scene model uses 5 \(\times\) 5 Manhattan model, and the simulation scene is 2000 \(\times\) 2000 m. All nodes were equipped with 802.11b wireless radio interfaces. The communication distance between nodes was 10 m and all the broadcasts were set to the same channel frequency. In this paper, through the results of data training and simulation experiments the weight values of each factor were set to \(\tau_{a}=0.8\), \(\tau_{b}=0.1\), \(\tau_{c}=0.1\) in order to ensure the association between nodes and data forwarding probability, which can significantly improve the performance of experimental results.
# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 3 Applications of Cognex Vision Systems
### Identification  
Machine vision technologies enable the reading of codes and alphanumeric characters. A machine vision system for part identification and recognition can read barcodes (1D), matrix codes (2D), direct part markings (DPMs), and printed characters on parts, labels, and packaging. An Optical Character Recognition (OCR) system reads alphanumeric characters without prior knowledge, whereas an Optical Character Verification (OCV) system confirms the presence of a character string. Additionally, machine vision systems can identify parts by locating a unique pattern or by recognizing objects based on color, shape, or size [29].  
DPM applications involve marking a code or string of characters directly on a part. Manufacturers across various industries commonly use this technique for error proofing, enabling effective packaging strategies, process control monitoring, and quality control metrics. DPM also helps to identify problematic areas, known as bottlenecks, within a facility. Tracking through direct part marking improves asset monitoring and part authenticity verification. It also provides unit-level data to enhance technical support and warranty service by documenting the genealogy of parts within the subassemblies of the final product [29].  
Conventional barcodes have gained wide acceptance in retail checkouts and inventory control. However, tracking information requires more data than a standard barcode can hold. To increase data capacity, companies have developed 2D codes, such as Data Matrix, which can store more information, including manufacturer details, product identification, batch numbers, and even unique serial numbers for virtually every finished product [29].
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 1 Introduction
### Related Work  
To solve both the ideal and the realistic version of the problems, a great variety of models and algorithms have been proposed in the literature in the last decade, each exhibiting a different performance. In particular, most of them can be broadly classified into two categories: those representing the timetable as an array and those representing it as a graph (see e.g., Reference [1]). Two of the most successful (and effective) examples of the array-based model are the Connection Scan Algorithm (CSA) [4] and the Round-bAsed Public Transit Optimized Router (RAPTOR) [5]. In CSA, all the elementary connections of a timetable are stored in a single array, which is scanned only once per query. An elementary connection represents a vehicle driving from one stop to another without intermediate stops. The acyclic nature of the timetables is then exploited to solve the earliest arrival problem. In RAPTOR, on the other hand, the timetable is stored as a set of arrays of trips and routes, that is suitably defined sets of elementary connections. This representation is used by a dynamic programming algorithm that operates in rounds and extends partial journeys by one trip per round to solve the problem. Several variants of RAPTOR, either incorporating heuristical improvements or considering more refined modeling strategies, have been presented and experimentally analyzed in the last couple of years (see, e.g., References [6; 7; 8]).  
The graph-based models, instead, store the timetable as a suitable graph and execute known adaptations of [PERSON]'s shortest path algorithm to compute optimal routes [2; 9; 10; 11]. Alternatives to both plain array-based and graph-based models have been also recently considered [12; 13; 14]. Some of them, like the one in Reference [12], directly operate on the timetable. In details, trips are labeled with the stops at which they are boarded and a precomputed list of transfers to other trips is scanned during a query. Newly reached trips are labeled and for a trip reaching a desired destination, a journey is added to the result set. The algorithm terminates only when all optimal journeys have been found.  
Some others, like those in References [13; 14], combine a graph representation of the timetable with the notion of graph labeling to achieve extremely low query times. In this paper, we focus on this latter category of approaches, since they are the ones that offer the smallest average query times and are hence suited for modern applications of the journey planning problem.
## 4 Experimental Investigations
### Subjective Experiments for Discrimination Degree of Given Azimuths  
#### 4.2.1 Experimental Protocols  
To investigate the effectiveness and superiority of the individualized HRTFs in perceiving the spatialization cues, a subjective experiment was conducted to compare the directional discrimination degree of sound sources. This experiment tested two HRTF species: individualized HRTFs (obtained from the presented method) and non-individualized ones (measured from KEMAR dummy head). Eight Chinese adults (four males and four females) aged between 23 and 27 were employed. They were postgraduates and doctoral student majoring in acoustics with self-reported normal hearing and former subjective experiment experience. All the participants were remunerated for their participation. Prior to the experiment, the reference features selected in Section 3.2 of each participant were measured to synthesize their individualized HRTFs. For body features of x\({}_{1}\), x\({}_{2}\), x\({}_{3}\), x\({}_{4}\), x\({}_{5}\), x\({}_{6}\), x\({}_{7}\), x\({}_{8}\) and x\({}_{14}\), they were measured directly by a ruler, for head and ear features of x\({}_{9}\), x\({}_{10}\), x\({}_{11}\), x\({}_{12}\), x\({}_{13}\), they were measured from digital photographs as shown in Figure 8. On the basis of Inverse Fast Fourier Transform, HRTFs were transferred into HRIRs. Then testing stimuli were produced by convolving the HRIRs with an original double-channel notify signal whose waveform in time domain is shown in Figure 9. The testing stimuli were presented to the participants through Hi-Fi headphone [Sennheiser HD560].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Individualization Method** & **Left Ear** & **Right Ear** & **Average** \\ \hline Proposed method & 7.2537 & 6.9303 & 7.0920 \\ GRNN based method & 7.7385 & 7.1026 & 7.4206 \\ Database-matching method & 7.8314 & 8.2359 & 8.0337 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average spectral distortion between the synthesized and the measured HRTF (dB).
## 3 Materials and Methods
### Pose Estimation for Detecting the Start of Anchoring in Archery  
In this study, the Mediapipe framework [20] was used to automatically detect the start of the anchoring phase in order to measure the shooting time of archers during competitions. Mediapipe is an open-source framework developed by Google, which, as shown in Figure 2, estimates the coordinates of 33 key body landmarks in real time. For this study, three Mediapipe landmarks were used to calculate the angle of the athlete's right arm. The landmarks corresponding to the right shoulder, right elbow, and right wrist, with indices 12, 14, and 16, respectively, were utilized as shown in Figure 2.  
Mediapipe landmarks can experience jitter due to noise from factors such as lighting conditions and occlusion. To accurately detect the athlete's shooting state, a low-pass filter was applied. This method compares the landmark coordinates of the current frame with those of the previous frame; if the difference between the two is small, the coordinates of the previous frame are retained. However, if the difference exceeds a certain threshold, the current frame's coordinates are updated, assuming actual movement has occurred. In this study, the threshold was empirically set to 10. By applying this approach, noise and jitter in the landmarks caused by various factors were reduced, allowing for more accurate detection of the anchoring start time through the stabilized landmarks.  
To detect when the athlete has started the anchoring phase, the filtered landmarks from indices 12, 14, and 16 were used to calculate the angle (\(\theta\)) of the arm through the dot product of the vectors between the right shoulder and elbow, and between the elbow and wrist, as shown in Figure 3. This method was applied to video clips containing the full sequence of drawing, anchoring, aiming, and release, with the arm angle calculated for each frame. To account for unexpected variance, this study computed the angle difference between each frame and calculated the average angle difference for the last 10 consecutive frames. The average angle difference was larger in the draw phase, but smaller in the anchoring and aiming phases. Based on this characteristic, this study empirically determined that the athlete was in the anchoring phase when the average angle difference was less than \(1^{\circ}\) and the arm angle was less than \(25^{\circ}\).
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 3 Results and Discussion
### Diurnal Variations of Forecasting Models
Figure 8: Same as Figure 6, but during storm condition for the year of solar maximum (2015) at days 76–79 and 173–176. The top panels show the variation of the magnetic Dst index during the two storms. It turns out that the LSTM model forecasting results compared well with the observed values. Of particular interest is the response of the LSTM model forecasting to the sudden drop in foF2 value during the magnetic storm of day76-79 in 2015. Thus, the LSTM model in forecasting foF2 could be used to capture storm events on a global scale, which will be one subject for our future studies. Figure 9: Absolute deviation of foF2 forecasting in Figure 8. The top panels show the variation of the magnetic Dst index during the two storms.