esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 3 System Architecture
### System Overview  
We begin by collecting pedestrian-crossing data, including images and videos captured at various times of the day and night. Video data are converted into individual frames to facilitate subsequent deep learning training. These images are then manually annotated with pedestrian-crossing features and supplemented with additional contextual information. To address the imbalance of training data under specific conditions, a CycleGAN-based image generation module is employed to synthesize a limited number of images representing scenarios such as nighttime, rain, and glare. Both real and synthetic images are used to train the first version of the pedestrian-crossing detection model using YOLO technology.  
Next, the initial detection model is integrated with previous manual annotation functions to develop an automatic labeling module capable of detecting and labeling pedestrian crossings at scale. While the system substantially reduces manual effort, a small degree of human verification is still required to eliminate incorrectly labeled instances. Additionally, this automatic labeling module is used to filter out low-quality synthetic images produced by CycleGAN as inconsistencies in generated image quality may otherwise degrade the clarity of crossing features during training. To further enhance model generalization, the dataset is gradually expanded by continuously collecting new pedestrian-crossing images from a variety of environments. Beginning with the area surrounding Feng Chia University, data collection efforts are extended to diverse backgrounds, such as urban centers, school zones, commercial districts, and suburban areas.  
A large volume of raw and labeled image data are stored using Amazon Web Services (AWS) cloud storage solutions. This approach offers greater flexibility for subsequent analysis and significantly reduces storage costs compared to traditional local data warehousing methods. As the training dataset grows, second and third iterations of the pedestrian-crossing detection model will be developed. Each updated model is fed back into both the automatic labeling module and the front-end vehicle assistance system, forming a feedback-driven learning loop. Ultimately, the goal is to realize a pedestrian-crossing detection system capable of continuous self-improvement through both external and internal cyclic learning processes. The overall system architecture is illustrated in Figure 1.  
The data collection module comprises dashcams and smart mobile devices. The image generation module, based on CycleGAN, is used to synthesize pedestrian-crossing scenes under various environmental conditions. YOLO serves as the core detection module for training pedestrian-crossing recognition. The trained detection model is then integrated with rule-based logic to construct the automatic labeling module, which facilitates large-scale annotation of pedestrian-crossing features.
## 2 Materials and Methods
### Numerical Modelling
The experimental and computational results, along with the calculated errors, for the boiler's operation in a condensing regime at nominal thermal load, are presented in Table 1.  
By analyzing Table 1, it may be noticed that there is a good correlation between measured and calculated values, the error ranging between \(\pm 1\%\) for the total and sensible heat and for thermal efficiency. The 3% error range for the condensing thermal load is acceptable, consisting of approximately 50 W, which falls in the precision range of the measuring facility. The thermal efficiency does not include the pump and automation system operation and it is calculated as the ratio between useful thermal load and the fuel heat input, calculated by respect to the net calorific value. By reporting to the net calorific value, the thermal efficiency value increases above one unit. For the corrugated pipe coil boiler, the computational model validation was also realized by direct comparison between computational results and experimental results, and is plotted in Figure 4:0 The results were consistent with those obtained for the finned pipes boiler model validation. The program calculates every time the boiler efficiency is in a purely convective functioning regime and makes the necessary corrections for the combustible flow. Thus, the combustible flow will be modified until the heat and mass transfer loops are satisfied in the imposed errors, and the boiler's convective heat transfer load is the one set by input. We considered, mainly due to the fact the condensing process was the aim of this study, that it is best to set as a constant, the convective useful thermal load of the boiler for each case. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Measure Unit** & \begin{tabular}{c} **Calculated Results** \\ **(Modelling Program)** \\ \end{tabular} & \
\begin{tabular}{c} **Measured** \\ **Values (Experimental Stand)** \\ \end{tabular} &
\begin{tabular}{c} **Relative Error** \\ **\%** \\ \end{tabular} \\ \hline Inlet water temperature & [°C] & 31.2 (input) & 31.2 & \\ \hline Outlet water temperature & [°C] & 51.6 (input) & 51.6 & \\ \hline Combustible flow & [m\({}^{3}\)/h] & 2.503 (input) & 2.503 & \\ \hline Excess air at the burner & [-] & 1.26 (input) & 1.26 & \\ \hline Flue gas exit temperature & [C] & 55.5 & 47.9 & 0.46 * \\ \hline Total useful thermal load & [kW] & 26.34 & 26.53 & \(-\)0.72 \\ \hline Sensible useful thermal load & [kW] & 24.42 & 24.56 & \(-\)0.57 \\ \hline Condensing useful thermal load & [kW] & 1.92 & 1.97 & \(-\)2.54 \\ \hline Thermal efficiency (by respect to the Net calorific value) & [\%] & 105.8 & 106 & \(-\)0.23 \\ \hline \hline \end{tabular}  
* Error related to the entire temperature span for the flue gases: theoretical burning temperature to chimney temperature.  
\end{table} 
Table 1: Experimental vs. computational results for finned pipes boiler.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.3 One-Way Delay  
One-way delay (OWD) is the time taken by the packet to arrive from sender to receiver. It gives knowledge of network congestion level to the sender. According to our solution, the receiver will send the received time of a data packet to the sender and sender will use the sent time of that data packet to calculate the one-way delay (\(d_{ow}\)) as:  
\[d_{ow}=t_{r}-t_{s} \tag{1}\]  
To calculate OWD, we consider reference clock. In our solution, if the reference clock is absent, the packet's received time noticed by the receiver will be according to its local clock which may not be the same as that of the sender's clock. Thus, with this information sender's calculation (Equation (1)) may not end up inaccurate results. Thus time values used in Equation (1) are supposed to be according to the reference clock.
## 2 Materials
### Data Set  
The data sets used in this study included satellite images, water level records and pre-processed auxiliary data.  
Figure 1: Location and distribution of layer cultivation regions in the central Jiangsu coast. (**a**) The location of the central Jiangsu coast; (**b**) optical image with standard false coloring of the central Jiangsu coast taken by HJ-1B CCD on 1 January 2013 and the layer aquaculture regions in 2013; (**c**), (**d**) the layer aquaculture regions observed from Google Earth and field investigations; (**e**)–(**h**), (i)–(**l**) the aquaculture regions observed from optical images with low and high water levels taken in spring, summer, autumn and winter, respectively. The datum of water levels is the mean sea level.
## 3 Results and Discussion
### CV and Trend of NPP
The NPP trends, the CV (Figure 5**d**), and the relative annual rate of change showed strong spatial consistencies, indicating that the NPP in the northeast, middle, and southwest regions of the study area changed dramatically from 2000 to 2015. Figure 5: The NPP variation trends (**a**), F significance test (**b**), CV of NPP (**c**), and relative annual rate of change in NPP (**d**).
## Towards Efficient Mobile M2M Communications: Survey and Open Challenges  
**[PERSON] * and [PERSON]**  
Instituto de Telecomunicacoes, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +351-225-081-400.  
External Editor: [PERSON]  
_Received: 10 May 2014; in revised form: 10 September 2014 / Accepted: 11 October 2014 / Published: 20 October 2014_  
**Abstract:** Machine-to-Machine (M2M) communications enable networked devices and services to exchange information and perform actions seamlessly without the need for human intervention. They are viewed as a key enabler of the Internet of Things (IoT) and ubiquitous applications, like mobile healthcare, telemetry, or intelligent transport systems. We survey existing work on mobile M2M communications, we identify open challenges that have a direct impact on performance and resource usage efficiency, especially the impact on energy efficiency, and we review techniques to improve communications. We review the ETSI standard and application protocols, and draw considerations on the impact of their use in constrained mobile devices. Nowadays, smartphones are equipped with a wide range of embedded sensors, with varied local and wide area connectivity capabilities, and thus they offer a unique opportunity to serve as mobile gateways for other more constrained devices with local connectivity. At the same time, they can gather context data about users and environment from the embedded sensors. These capabilities may be crucial for mobile M2M applications. Finally, in this paper, we consider a scenario where smartphones are used as gateways that collect and aggregate data from sensors in a cellular network. We conclude that, in order for their use to the feasible in terms of a normal depletion time of a smartphone's battery, it is a good advice to maximize the collection of data necessary to be transmitted from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways.  
**Keywords:** Machine-to-Machine communications; mobile communications; gateways; cellular networks; wireless networks; wireless sensor networks; resource usage efficiency; smartphones; CoAP; MQTT
## 3 Use of GNSS Carrier Phase Observations in Various Fields of Science
Therefore, PPP may be widely used in many fields, such as precise navigation or surveying.
# A Computational Study on the Aeroacoustics of a Multi-Rotor Unmanned Aerial System
## 3 Results and Discussion
### Sound Pressure Level
#### 3.1.2 Effect of Pitch  
Figure 8 compares the SPL spectra on the microphone located downstream (\(\Phi=180^{\circ}\)) for the isolated propeller case and CRC-3 at several pitch angles. The results for some of these pitch angles were not presented in Figure 7 for the sake of brevity. As previously shown in Figure 2, while the inflow velocity angle changes to evaluate the pitch effect, the relative position of microphones and propeller-3 (see Figure 1) remains unchanged. The isolated propeller experiences little to no change at BPF, while it experiences a slight increase in broadband noise. The rate of increase decreases at large \(\theta\). In contrast, for CRC-3, the fluctuation on propeller loads increases substantially with \(\theta\), which results in gradual increase in the noise level from \(\theta=0^{\circ}\) to \(\theta=75^{\circ}\). The additional peak at \(f=\text{BPF}/2\) in the spectrum of CRC-3 exists in all pitch angles, and the rate of change in its magnitude follows the same trend as the one occurring at BPF. In fact, at both harmonics, the noise level increases with \(\theta\), while its growth rate decreases. Such a similar growth rate at the two aforementioned harmonics confirms that the change in the CRC-3 noise level with \(\theta\) does not originate solely from the propeller-tip vortices.  
Figure 9 demonstrates change in the directivity of SPL at 1 st harmonic and the overall SPL with change in pitch angle for the two cases. The trend of directivity at BPF is almost the same as the one shown in Figure 8. For the isolated propeller, the difference between trends of SPL and OASPL is minimum. For CRC-3, the difference between the OASPL and SPL at small pitch angle is much larger than that of the larger pitch angle, indicating that the ratio of the noise at higher harmonics to the one at BPF decreases with \(\theta\), which may also be seen in the spectra depicted in Figure 7. This confirms that the contribution of propeller-propeller interaction to the overall noise decreases with increase in pitch, since it is the main source of noise at harmonics larger than BPF.
## 4 The Proposed Routing Protocol: HPA-SR  
### Basic Concept of the Proposed Routing Protocol  
In this subsection, we present the basic concept of the proposed routing and clustering protocol. The proposed clustering (ASCS) protocol considers the speed and direction of the node to elect the cluster head while cosine similarity and cosine distance for forming the clusters (to decide the cluster members) are underlying structures to support stable connectivity between nodes.  
The proposed routing protocol (HPA-SR) utilizes hybrid auction to establish the secure route from a source node to a destination node. As we can see in Figure 1, when the node receives various route information from the neighbor nodes, this node utilizes the second price auction to avoid the sinkhole attack. Therefore, the proposed HPA-SR protocol can establish the secure route such as S-CH\({}_{1}\)-CH\({}_{3}\)-\(\cdots\)-CH\({}_{k}\)-D, which can avoid sinkhole node.  
The route establishment process consists of two steps which can be summarized as follows:  
* **Step 1 (Clustering):** In the first step, we perform a clustering process in which all nodes in the network are divided into clusters by using cosine similarity method. We use the position, direction, and speed as parameters to make a cluster form that works as underlying structure. This work is a development of a paper that has been done by the author in [33]. The ASCS clustering protocol considers the gateway node to improve the route connectivity and reduce the control overhead.
* **Step 2 (Routing):** After the clustering step, using the hybrid auction method, a source node broadcasts the RREQ packet to find a destination node. When the intermediate nodes receive the RREQ packet, they update their routing table and re-broadcast the RREQ packet. When the RREQ packet arrives at the destination node, the destination unicasts the RREP packet. In addition, the sink hole node also unicasts the RREP packet. If the intermediate nodes receive the RREP packet from the different way, the node utilizes the second price auction to avoid the sinkhole attack. Otherwise, the intermediate nodes employ the first-price auction.
## 6 Conclusions  
The primary objective of this work was to analyze the timbral acoustic features to improve the classification accuracy of emotions in the valence dimension. To accomplish this, timbre acoustic features that consisted of spectral shape and harmonic features were extracted using a timbre toolbox [30]. To find the best feature subset among the timbre acoustic features, the SFS was applied. MFCC, energy, and their first-order derivatives were also analyzed in order to compare the results. All acoustic features were divided into four groups, namely baseline (MFCC, energy and their first-order derivatives), timbre all, timbre selected and a combination of baseline and timbre selected acoustic features.  
The classification was performed for binary valence and classification of categorical emotions using SVM and LSTM-RNN on the EMO-DB and IEMOCAP emotional speech databases. The average accuracy rates of 24.06% and 18.77% were improved for binary valence and discrete emotions as compared to the results using the baseline acoustic features for the EMO-DB. Although the improvement of the average classification rate for the IEMOCAP database was not high, the classification accuracy of happy and neutral emotions was improved considerably using timbre all acoustic features. The LSTM-RNN gave better results than SVM for the IEMOCAP.  
In conclusion, timbre features showed their effectiveness in the classification of positive and negative emotions, as well as the classification accuracy of happy emotion improved considerably. For future work, timbre features should be analyzed for classification of fear and boredom emotions, which are also a challenge to discriminate. Moreover, timbre features need to be analyzed with other types of acoustic features to find an acoustic feature that complements timbre features.  
Conceptualization, A.T. and S.K.; methodology, S.K.; software, A.T.; validation, A.T. and S.K.; formal analysis, A.T. and S.K.; investigation, A.T. and H.-S.P.; resources, A.T.; data curation, S.K.; writing--original draft preparation, A.T. and S.K.; writing--review and editing, S.K. and H.-S.P.; visualization, A.T.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K.  
This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00189, Voice emotion recognition and indexing for affective multimedia service).  
The authors declare no conflict of interest.
## 3 Results  
### Simulation Performance  
The eddy correlation technique is the most direct method used for measuring CO\({}_{2}\) exchange and water and energy fluxes between the atmosphere and ecosystems and has been widely used in carbon cycle studies of terrestrial ecosystems. Carbon fluxes include ecosystem respiration (ER) and net ecosystem CO\({}_{2}\) exchange (NEE). Net ecosystem productivity (NEP) and NEE have the same value and opposite sign. The observations at the Xishuang Banna station were derived from the ChinaFLUX ecosystem carbon and water fluxes and key meteorological elements dataset ([[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/)), accessed on 16 May 2021), including daily and annual-scale flux data from 2003 to 2012.  
Overall, the FORCCHN model reproduced the carbon fluxes of tropical seasonal rainforests on a daily scale (Figure 3a), with the maximum value of _Corr_ occurring in ER (0.85), followed by GPP and NEP. FORCCHN slightly underestimated ER and GPP, and their corresponding mean deviations were \(-\)0.43 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) and \(-\)0.21 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\), respectively, and the simulation results overestimated NEP (0.19 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)). The RMSEs of ER, GPP, and NEP were 1.77, 1.96, and 1.39 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\), respectively.  
The simulated curves were very similar to the observed curve fluctuations, indicating that the model could reproduce the seasonal variation in tropical seasonal rainforests (Figure 3b). The model had a small underestimation of ER and GPP for tropical rainforests, and the simulated values were lower than the observed values by 6.83% and 3.56%, respectively. However, the model overestimated NEP by 3.50% above the observed value. It was able to reproduce the patterns of carbon sources (NEP \(<\) 0) and sinks (NEP \(>\) 0) in tropical seasonal rainforests throughout the year, with tropical rainforests being carbon sources from June to November and carbon sinks from December to May, which was consistent with in situ observations [73], but unfortunately, the model was numerically biased.  
Analyzing the interannual variation and deviation of carbon fluxes in forest ecosystems from 2003 to 2012 (Figure 3c), the results showed that the FORCCHN model had the ability to simulate the trend of interannual variation in carbon fluxes in forest ecosystems, but there were differences in the magnitude of the values of the annual distance, and the averageannual deviation rates of ER and GPP and NEP in tropical rainforests were \(-7.29\%\), \(5.50\%\), and \(14.90\%\), respectively.
# Modeling the Energy Performance of LoRaWAN
## 5 Evaluation
### End-Device Current Consumption
#### 5.1.2 Acknowledged Transmission
DR2, DR3 and DR5 are not shown in the figure for the sake of illustration clarity. We next evaluate end-device average current consumption for non-zero BER in acknowledged transmission, for BER values up to \(10^{-3}\), and for DR0 and DR5. Since for DR5, retransmissions may reduce the DR (see Section 3.3.2), and for the sake of a better comparison, for DR5 we consider the same payload size as that of DR0. As shown in Figure 11, for a given DR, a non-zero BER increases end-device average current consumption (up to one order of magnitude for the range of BER values considered), since the end-device performs retransmissions. This effect increases as \(T_{\textit{Notify}}\) decreases, since sleep current consumption then becomes less significant. Note that for a given BER, current consumption for DR5 is lower than that when DR0 is used. For DR5, the lower duration of intervals related with transmission and reception reduces current consumption. This phenomenon is emphasized as BER decreases, since then the number of retries decreases as well. Figure 11: Impact of Bit Error Rate (BER) on the average current consumption of the end-device in acknowledged transmission, as a function of \(T_{\textit{Notify}}\) for different DR settings and for \(p_{\textit{call}}\) = 0. Figure 10: Comparison of the end-device average current consumption with 1-byte and maximum-sized frame payload in acknowledged transmission, as a function of \(T_{\textit{Notify}}\) and for different DR settings, for BER = 0 and \(p_{\textit{call}}\) = 0. Only DR0 and DR5 are shown in the figure for the sake of illustration clarity. Finally, we evaluate impact of collisions on the average current consumption of an end-device, for DR0 and DR5, and for \(p_{coll}\) values up to 0.3 (Figure 12). As expected, collisions increase the average current consumption in acknowledged transmission. Increasing \(p_{coll}\) has a slightly greater relative impact on current consumption for DR5 than for DR0. In fact, the contribution to energy consumption of states related with processing and wait times is relatively greater for DR5 (with smaller transmit and receive state duration) than for DR0. Influence of collisions decreases with \(T_{Notif}\), since sleep current becomes dominant.
## 2 Materials and Methods
### Vocal Fold Cell Phenotyping
#### 2.1.1. Vocal Fold Surgical Model  
A total of 160 male Sprague-Dawley rats (four to six months old, 450-500 g) were used in the study. Vocal fold injuries were performed in 140 rats using an established protocol. Twenty rats were used as uninjured controls [35]. Regarding the surgical protocol, animals were anesthetized, and their vocal folds were bilaterally injured using a 25-gauge needle to strip the vocal fold mucosa until the thyroarytenoid muscle was exposed [35]. At each end-point of the study, animals were euthanized via CO\({}_{2}\) asphyxiation, and larynges were removed immediately following euthanasia [35]. For anesthesia induction, the rats were injected with isoflurane (2% to 3% delivered at 0.8 to 1.5 L/min) followed by maintenance with an intraperitoneal injection of ketamine hydrochloride (90 mg/ kg) and xylazine hydrochloride (9 mg/kg). Rats with injured vocal folds were euthanized at each of seven post-surgery time points: Days 1, 2, 3, 5 and Weeks 1, 2, 4. Due to the unexpected death of the animals, between 16 to 19 animals survived at each assigned time point for laryngeal harvest and the subsequent flow cytometry analysis.