esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Probe-Based Routing Algorithms
#### 3.2.2 During Data Packet Returning Phase  
When a router receives a data packet in basic CCN, it first checks to see if there is a matching PIT entry. If the _Name_ matches, the router forwards the data packet to all of the routers listed in the PIT entry before deleting it. Otherwise, because it implies that the same data packet has been received, the data packet is dropped. Meanwhile, the router may cache the data depending on its cache replacement policy and update FIB.  
At this phase, our approach improves basic CCN by updating FIB as described below.  
**FIB**: In the data packet, there are two _Names: data name_ and _probe name_. The _probe name_, in addition to the _data name_, is used to update FIB. If the _Names_ were already stored in FIB, the matched entry appends the router IDs to the data packets. Otherwise, a new FIB entry is created accordingly. Since the FIB entry's space is limited, a replacement policy is necessary. Generally, a time-based replacement policy, such as Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO), or a cost-based replacement policy, such as shortest path, QoS, is adopted.  
**SPT**: There are two types of networks: static and dynamic, based on network traffic. The transmission time between any two connective routers is identical in a static network, while the transmission time between any two connective routers is different in a dynamic network. In a static network, the hop count might be considered a cost. The SPT is sustainable as long as the network topology is static and the shortest paths between any two routers are fixed. In a dynamic network, the cost can be traveling time or other metrics that are impacted by network conditions (such as congestion). Many traditional network routing algorithms, such as Open Shortest Path First (OSPF) [14], can be used to optimize SPT. OSPF is a TCP/IP network routing protocol that is dynamic, hierarchical, and Link-State [39]. OSPF uses [PERSON]'s shortest path algorithm to find the best router [15]. In this paper, the network is static and the cost is the number of hops. The SPT is only updated when the network topology changes.
## 4 Results and Discussion
### Building Energy End Use Analysis
#### 4.2.4 Lillhardal  
A comparison of the breakdown of end-use energy consumption of the base model between NWC and PP0.3 was conducted. The highest energy consumption was in March where it was one of the coldest months of the year, while the lowest was in September which had the lowest heating and cooling loads. Figure 9 illustrates the percentage breakdown of the energy consumption by end use for NWC as compared to PP0.3.  
Figure 8: Percentage breakdown of annual energy consumption end-use comparison for Istanbul: (a) NWC; (b) HST0.75 + PP0.3.  
Figure 9: Comparison of percentage breakdown of annual energy consumption end-use comparison for Lillhardal: (a) NWC; (b) PP0.3.  
Space heating (12%) and heat-pump supply (26%) comprised about 38% of energy consumption followed by hot water (18%), ventilation (17%), miscellaneous equipment (15%), lighting (10%) and space cooling (2%). A greater part of the energy consumption in end use (about 38%) was related to heating needs. Space heat was reduced noticeably with the implementation of PP0.3. Space heating and heat-pump supply needs were 29.4 % and 43.2% higher, respectively, for NWC as compared with PP0.3, while other energy needs showed no significant variations.
# Research Trends in Collaborative Drones
## 4 Target Recognition
### Problem Description  
Drones are being used to explore terrestrial regions to discover specific or sought after features and characteristics, such as carrying out human or animal counts, diagnosing specific environmental situations, recognizing a given landmark or sign, or searching for a particular mammal, cf. [38; 39]. Additional technical studies also include [40; 41; 42].  
Tasks with low bandwidth requirements can be performed offline by participating ground stations interacting with drones using a cellular network. However, appropriate network bandwidth may not be available. It is more likely that a drone (or swarm of drones) would need to explore the environment, interpret the data collected, and make decisions based on its own online diagnosis. In this section, we ask the question:  
How can we accurately detect a (possibly) mobile target from a distance?  
We consider only altitude-based detection (see [43; 44; 45; 46]). Considerations could also be extended to speed-based detection (e.g., speed of data sampling) or a combination of altitude and speed.
## 3 Simulation System
### Propagation Modeling  
The database obtained from the ray-tracing simulation is discrete, while the actual target and UAV sensor location information is continuous. In this paper, continuous  
\begin{table}
\begin{tabular}{c c c} \hline \hline Rx & Antenna type & Isotropic \\  & Antenna height (m) & 50/75/100/125/150 \\ \hline  & Frequency (GHz) & 2.487 \\  & Bandwidth (MHz) & 5.00 \\ Tx & Transmit power (dBm) & 27.0 \\  & Antenna type & Isotropic \\  & Antenna height (m) & 2 \\ \hline  & Reflection & 6 \\ Iteration & Diffraction & 1 \\  & Penetration & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ray-tracing parameters.  
Figure 5: Simulation terrain model.  
Figure 6: Horizontal positions of the transmitter and UAV sensor candidate points.  
data were interpolated by applying a radio propagation model as explained below. The parameters of the model are calculated in a regressive manner from the data obtained by the ray-tracing simulation.  
In this paper, we consider the following propagation loss model with the elevation angle \(\theta_{e}\) between Tx and Rx as a variable in LoS and NLoS environments [18].  
\[\mathrm{PL}_{\mathrm{LoS}}(\theta_{e})=\mathrm{PL}_{\mathrm{FSPL}}(\theta_{e})+ \mathrm{PL}_{\mathrm{N}}(\theta_{e}) \tag{4}\]  
\[\mathrm{PL}_{\mathrm{NLLoS}}(\theta_{e})=\mathrm{PL}_{\mathrm{FSPL}}(\theta_{e })+\mathrm{PL}_{\mathrm{shadow}}(\theta_{e}), \tag{5}\]  
where \(\mathrm{PL}_{\mathrm{FSPL}}(\theta_{e})\) is the free space loss [18].  
\[\mathrm{PL}_{\mathrm{FSPL}}(\theta_{e})=20\log_{10}\biggl{(}\frac{4\pi f_{e}} {c}\frac{\Delta t}{\sin\theta_{e}}\biggr{)}. \tag{6}\]  
Here, \(\Delta t=|h_{\mathrm{rx}}-h_{\mathrm{tx}}|\), where \(h_{\mathrm{rx}}\) and \(h_{\mathrm{tx}}\) are the heights from the ground of the receiver and the transmitter, respectively.  
\(\mathrm{PL}_{\mathrm{N}}(\theta_{e})\) is the loss due to noise in the LoS environment, and \(\mathrm{PL}_{\mathrm{shadow}}(\theta_{e})\) is the loss due to shadowing and noise in the NLoS environment. \(\mathrm{PL}_{\mathrm{N}}(\theta_{e})\) and \(\mathrm{PL}_{\mathrm{shadow}}(\theta_{e})\) are assumed to follow a normal distribution, respectively [18].  
\[\mathrm{PL}_{\mathrm{N}}(\theta_{e})[\mathrm{dB}]=\mathcal{N}(\mu_{n},\sigma_{ n}) \tag{7}\]  
\[\mathrm{PL}_{\mathrm{shadow}}(\theta_{e})[\mathrm{dB}]=\mathcal{N}(\mu_{s}, \sigma_{s}), \tag{8}\]  
where \(\mu_{n}\), \(\sigma_{n}\) and \(\mu_{s}\), \(\sigma_{s}\) are determined regressionally from the results of ray-tracing simulations. They are expressed by the following equations using experimental parameters \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\)[14].  
\[\frac{\theta_{e}^{2}+\alpha\theta_{e}+\beta}{\gamma\theta_{e}+\delta}. \tag{9}\]  
The results of the regression are shown in Figure 7.  
Figure 7: Model parameter determination by regression (**top row**: \(\mu_{n},\sigma_{n}\) and **bottom row**: \(\mu_{s},\sigma_{s}\)).
## 4 Numerical Results and Discussions  
This section presents the performance of the proposed D2D-CRS-SM, in terms of BER, SE, and sum SE over independent and identically distributed Rayleigh fading channel. The channel coefficient of each link is assumed to be of zero mean and unit variance. Moreover, unless otherwise stated, the simulation parameters are summarized in Table 3.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
###### Abstract  
Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the evaluation of cardiac function, facilitating faster clinical assessments that prove advantageous for both practitioners and patients alike. Recent studies have predominantly concentrated on delineating structures on short-axis orientation, placing less emphasis on long-axis representations due to the intricate nature of structures in the latter. Taking these consideration into account, we present a robust hierarchy-based augmentation strategy coupled with the compact and fast Efficient-Net (ENet) architecture for the automated segmentation of two-chamber and four-chamber Cine-MRI images. We observed an average Dice improvement of 0.99% on the two-chamber images and of 2.15% on the four-chamber images, and an average Hausdorff distance improvement of 21.3% on the two-chamber images and of 29.6% on the four-chamber images. The practical viability of our approach was validated by computing clinical metrics such as the Left Ventricular Ejection Fraction (LVEF) and left ventricular volume (LVC). We observed acceptable biases, with a +2.81% deviation on the LVEF for the two-chamber images and a +0.11% deviation for the four-chamber images.  
cine-MRI; long axis; ENet; data augmentation; LVEF; heart segmentation; two chamber; four chamber +
Footnote †: journal: _Algorithms_  
1 IFIM, ICMUB Laboratory, UMR CNRS 6302, University of Burgundy, 21078 Dijon, France; [EMAIL_ADDRESS]  
2 CASS, 21800 Quetigny, France; [EMAIL_ADDRESS] (R.M.); [EMAIL_ADDRESS] (L.M.);  
[EMAIL_ADDRESS] (T.D.)
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 6 Conclusions and Future Plans  
By integrating IoT-enabled sensors and devices with advanced audio processing algorithms, healthcare providers can continuously monitor the acoustic environment of an infant. These sensors capture cry sounds, which are then analyzed using machine learning models to detect patterns and identify potential distress signals. This work aimed to distinguish between different types of generated infant cry sounds and determine the underlying causes of each cry. To this end, we proposed a novel cry classification approach. We carried out a study on cry sound analysis using various models and data preprocessing methods. Our auditing process involved removing unnecessary audio clips from the cry recordings, such that the dataset only included cry sounds essential to our categorization objective. By employing augmentation techniques, we added more diversity in time, noise, volume, speed, pitch, and other aspects of the dataset. Our classification algorithms are well-founded, mainly due to these preprocessing steps that effectively tackle the problems of insufficient data and class imbalance.  
Four approaches were implemented, each with its own architecture and features. Our findings highlighted each method's advantages and disadvantages and the accuracy range with which they could classify cry sounds. In contrast to the CNN, Multi-CNN, and ViT methods, our proposed method (_ViT-SPT-LSA_), _Vanilla Vision Transformer with enhancements_, achieved the most outstanding overall accuracy of **98.33%**. These results reveal valuable insights into the various classification tasks for which transformer-based systems can be used.  
Our plans involve testing the models on various datasets to address sampling bias and offer more insights into the behavior of the models. Examining the use of transfer learning strategies, such as pre-trained models on sizable audio datasets, could promote model convergence and classification performance and be another enhancement in the future. Combining multi-modal data for cry categorization could be the subject of additional research. Apart from audio, photos that depict babies' facial expressions throughout the crying phase can be supplied to classification models to enhance their performance and accuracy even more.  
Conceptualization, D.S.; Methodology, S.A.Y., D.S. and N.S.T.; Software, S.A.Y.; Validation, N.S.T.; Formal analysis, D.S.; Investigation, N.S.T.; Writing - original draft, S.A.Y., D.S. and N.S.T.; Supervision, D.S. and N.S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors assure that there are no conflicts of interest.
# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 4 Experiments
### Dataset and Settings  
We conduct experiments on three real-world datasets, two labeled and one unlabeled; the statistics of the datasets are reported in Table 1. Moreover, the experimental settings are also listed in this subsection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & & **\#Instance** & **\#Positive** & **\#Neutral** & **\#Negative** & **Sentiment Form** \\ \hline \multirow{3}{*}{ASTE-Data-V2-Rest} & Train & 2728 & 3490 & 241 & 1014 & \multirow{3}{*}{Aspect-Opinion-Polarity} \\  & Val & 668 & 841 & 76 & 248 & \\ \cline{3-6}  & Test & 1140 & 1497 & 120 & 376 & \\ \hline \multirow{3}{*}{MAMS-ASTA} & Train & 4297 & 3380 & 5042 & 2764 & \multirow{3}{*}{Aspect-Polarity} \\  & Val & 500 & 403 & 604 & 325 & \\ \cline{1-1}  & Test & 500 & 400 & 607 & 329 & \\ \hline \multicolumn{2}{c}{Yelp} & - & 1,160,546 & - & - & - & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the labeled and unlabeled datasets. Note that “Val” is short for “Validation”, the ASTE-Data-V2-Rest is labeled with aspect, opinion, and polarity, while the MAMS-ASTA is labeled with only aspect and polarity.  
Figure 6: Architecture of the classifier. This model incorporates 2D interaction representation and grid-formed tagging schema [28] to extract all aspect and opinion phrases in a sentence.
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Importance of Human-Centric Smart Manufacturing  
Human-centric smart manufacturing is a manufacturing approach that places human beings at the center of the manufacturing process. It involves the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and robotics to enhance manufacturing processes while focusing on the needs of workers. The importance of human-centric smart manufacturing can be seen in the following ways [33]:  
**Improved safety:** Human-centric smart manufacturing systems are designed to prioritize worker safety. By automating dangerous and repetitive tasks, they reduce the risk of accidents and injuries in the workplace;  
**Increased efficiency:** Smart manufacturing systems can analyze data in real time, enabling workers to identify and resolve issues quickly. This results in improved productivity, reduced downtime, and increased profitability;  
**Enhanced worker experience:** Human-centric smart manufacturing can improve the work environment, reduce physical strain, and enhance job satisfaction. By automating mundane tasks, workers can focus on more engaging and challenging work, leading to higher levels of job satisfaction;  
**Flexibility:** Smart manufacturing systems can adapt to changing production needs quickly. This means that workers can adjust to changes in demand, resulting in more efficient use of resources and reduced waste;  
**Improved quality:** Smart manufacturing systems can help reduce errors and defects in products. By monitoring the production process, they can identify issues early, enabling workers to make necessary adjustments to maintain quality.  
As a result, human-centric smart manufacturing is essential for creating safer, more efficient, and more satisfying work environments. By prioritizing worker needs and leveraging advanced technologies, smart manufacturing can help manufacturers achieve better outcomes while also benefiting their employees.  
Figure 8: Types of HCPS communications.
## 2 State-of-the-Art Overview and Problem Formulation
### State-of-the-Art in the Location-Aware Communications and Handover Schemes
Specifically, the authors of [41] presented a proactive handover method based on an assessment of the RSSI, the procedure of which remains rather similar to one of the handover methods considered in our work. However, the handover was considered between APs of the same RAT instead of a multi-RAT situation. In the conclusion, the authors also mention that such RSSI-based handover can be improved by considering the user mobility, which is another measure of the location awareness being considered in our work as well. Additionally, the authors of [42] have presented a WiFi/WiGig handover based on the RSSI, no other handover schemes were studied nor investigated therein. Furthermore, the authorsof [43] proposed handoff schemes for applications involving the mobility of robots and devices in a real industrial environment. By considering multiple metrics, such as the mobility awareness, RSSI and packet delivery condition, the study showed that the handoff can be triggered with high accuracy and reduced ping-pong effect. Admittedly, handover considering one metric may lead to inaccurate and frequent triggering, therefore, integrating various metrics results in accurate decision making for the handover. In our work, we demonstrate that when location awareness is accurate and reliable enough, the trigger timing for handover can be computed with high accuracy. It is noteworthy that integrating several metrics undoubtedly increases the algorithm complexity and latency in the overall handover process. Compared with the aforementioned works, our work differs in two major aspects, (i) instead of assuming perfect knowledge of the UE locations/trajectories, we develop a dedicated positioning algorithm (i.e., GBP) to achieve location awareness. In addition, the positioning results and their impact on the handover performance are analyzed and assessed by our simulations as well as practical experiments; (ii) unlike the works supported only by numerical simulation, a multi-RAT robotic platform is employed in our experiments to justify and assess the real-world feasibility of the LHO algorithm.
## 2 Materials and Method  
DMG ultrasonic 20 linear is employed in this study to conduct the experiments. It is a five-axis process in which both precision milling and RUM can be achieved (see Figure 1a). The main characteristics of the used machine are listed in Table 1. The schematic diagram of the used RUM is shown in Figure 1b.  
In order to remove the chamfer edges from the workpiece, face milling was previously conducted the surface flatness was measured by the dial gauge indicator (see Figure 2a). RUM tools provided by Schott Company, Germany are applied in the experiments as shown in Figure 2b. The used tools are nickel-based materials bonded with diamond abrasives with a mean diameter size of \(\approx\)44 \(\upmu\)m (D46, mesh size of 325/400), which are recommended by the Schott Company for machining smooth surfaces on ceramic-based materials. All specifications of the used tools can be found in Table 2. The size of the micro-pockets milled in this research was 6 mm (length) \(\times\) 6 mm (width) \(\times\) 0.3 mm (depth), as shown in Figure 1b. The material adopted in the current study was alumina (Al\({}_{2}\)O\({}_{3}\) 99.99%) from CeramTec, Germany. It is a polycrystalline high purity alumina, which is mainly used in medical applications [30]. Other applications of the used alumina ceramic can include hybrid circuits, high-strength ceramic tools, wear-resistant, corrosion-resistant parts, etc. [31]. Each of the alumina samples was under the form of block dimension \(50\times 50\times 10\) mm\({}^{3}\) in size. The specifications of the used Al\({}_{2}\)O\({}_{3}\) material are listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Value/Type** \\ \hline No. of axis & 5 \\ Linear Traverse path (X, Y, Z) & 200, 200, 280 mm \\ Rotational axis (A and C) & \(-\)10–30 and 360\({}^{\circ}\) \\ Feed range up to & 40 m/min \\ Frequency & 18–48 kHz \\ Amplitude & 5–80 \(\upmu\)m \\ Rotational speeds ultrasonic up to & 10,000 rpm \\ Ultrasonic generator & USG2000 \\ Coolant type (pressure) & Grindex \(-\)10 (0.4–40 bar) \\ Positioning accuracy & \(\pm\)2 \(\upmu\)m \\ Measurement accuracy & 0.25 \(\upmu\)m \\ Tool holder & HSK 32 E/S \\ DMG Control Panel & Siemens 840 D \\ Tool & Diamond/Nickel base \\ Workpiece measurement & Renishaw OMP 400 \\ Tool measurement & BLUM Laser P87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rotary ultrasonic machining (RUM) characteristics.  
Figure 1: (**a**) DMG ultrasonic machine used in this study and (**b**) experimental setup.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value/Type** \\ \hline Outer diameter & 2 mm \\ Inner diameter & 1 mm \\ Holder type & ER11 taper \\ Bonded material & Diamond \\ Base material & Nickel \\ Abrasive grain size & D46 (mesh size of 325/400) \\ Active milling length & 6 mm \\ Binding Type & GVD, (Galvanic coated) \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the RUM tool used in this study.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Artificial Intelligence and Machine Learning  
Traditional healthcare systems often give suboptimal health outcomes despite the best efforts of the clinicians and physicians. The incomplete knowledge of patients' health and the personal biases of clinicians account for most of these suboptimal health outcomes. Optimal results require such systems that are driven by scientifically- and statistically-validated data to provide benefit choices to the population. In the near future, it is inevitable that the majority of classical or traditional physicians' practices, prescription methods and monitoring system techniques will be replaced by intelligent devices, software toolboxes and testing procedures. Healthcare will mature into being more consistent and more scientific in terms of delivering a better quality of care at the expense of AI and data-gathering techniques. This will provide continual monitoring, more rigorous, personalized, precise, as well as logical care. This evolution from an exclusively human-based system to the intelligent and automated expert systems will provide a bionic assist to doctors by substantially complementing or enhancing their expertise. This will make even the average nurse or physician perform at the level of the best specialists [28].  
As an example of the advances in ML and AI, we point out the significant advancements the ML technique of deep learning has made, particularly in image processing and recognition. There is now growing hope that deep learning and other ML models will help improve diagnostic procedures by recognizing patterns that may be too subtle for the human eye [29]. Deep learning is already being used to detect skin cancer in images with almost the same number of errors as made by professional dermatologists [30]. Recently, a multidisciplinary research team of pathologists, geneticists, biomedical engineers and computer scientists at Stanford's School of Medicine succeeded in developing a deep learning algorithm that can detect lung cancer more accurately than human pathologists [31]. Healthcare systems based on AI, machine learning and probabilistic models have potential to provide therapeutic recommendations, prognosis learning, personalized real-time risk scoring, etc. There is now growing interest among doctors and entrepreneurs to largely deploy deep learning techniques.  
Figure 4: A general architecture of telemedicine with two locations.