esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism
### Fault and Failure Models for the Design of PTRF  
In order to properly design a fault-tolerant system, first it is necessary to define its fault model and its failure model. The fault model includes the fault types that are assumed that may happen and, thus, the ones for which the system's fault-tolerance mechanisms are conceived to. On the other hand, the failure model includes the failure modes the system and its components are supposed to exhibit when suffering the faults included in the fault  
Figure 2: Example of a communication cycle in an output port of a device. The communication cycle is divided into a time-triggered, an event-triggered and a best-effort window.  
model. The failure mode of a system or a component refers to the incorrect behaviour that it may exhibit when it suffers from a fault.  
Normally, a given fault-tolerance mechanism is not designed to deal with all the types of faults and failure modes of a system. Instead, a given fault-tolerance mechanism is usually devoted to addressing a subset of faults and failure modes. Thus, the mechanism's fault model and failure model have to be defined accordingly.  
The fault type addressed by PTRF can be classified as temporary non-malicious operational hardware faults [24] that affect links, i.e., faults that last for a finite amount of time, that are not caused in a voluntary manner and that affect the hardware components of the system. Some examples of this type of fault are those caused by electromagnetic interference or temperature variations. These are the type of faults most commonly considered when designing communication subsystems.  
As concerns the failure model of PTRF, note that PTRF is designed to tolerate temporary faults in the links. Thus, its failure model is constituted by the failure modes of these components. Concretely, we assume that links only exhibit omission failures i.e., we assume that whenever a fault in a link affects a frame, if the frame becomes erroneous then it is eliminated by the communication port upon reception, manifesting as a frame omission. This assumption is reasonable as Ethernet frames count with a Cyclic Redundancy Check (CRC) code, which allows detecting virtually all the errors in a frame [25].  
We next describe the operation of the PTRF mechanism, the three different approaches and the most relevant aspects of our design.
## 5 Data Analysis of Gas-Liquid Separation Efficiency in Multiphase Flow
### Numerical Simulation of Two-Phase Flow and Multiphase Flow  
As shown in Figure 8, DGLS can meet the measurement requirements for the gas-liquid separation efficiency when the total flow is 20 m\({}^{3}\)/d, regardless of whether the medium is two-phase flow or multiphase flow. Therefore, the numerical simulation of gas-water two-phase flow can be used instead of oil-gas-water multiphase flow, which not only simplifies the model's settings but also produces the required effect in a shorter time.  
Figure 8: Gas–liquid separation efficiency curve at two-phase flow and multiphase flow.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Total Flow (m\({}^{3}\)/d)** & **20** & **30** & **40** & **50** & **60** \\ \hline \(\eta_{g}\)(\%) & 92.4 & 90.9 & 90.5 & 90.4 & 86.5 \\ \hline \(\eta_{oil}\)(\%) & 91.9 & 95.3 & 93.2 & 99.3 & 99.1 \\ \hline \end{tabular}
\end{table}
Table 5: Gas–liquid separation efficiency at oil–gas–water multiphase flow.  
Figure 7: Cloud diagram of oil phase distribution of gas–oil–water with total flow at (**a**) 20 m\({}^{3}\)/d; (**b**) 30 m\({}^{3}\)/d; (**c**) 40 m\({}^{3}\)/d; (**d**) 50 m\({}^{3}\)/d; (**e**) 60 m\({}^{3}\)/d.  
When the total flow is 30 m\({}^{3}\)/d-60 m\({}^{3}\)/d, the numerical simulation results for gas-water two-phase flow diverge from the numerical simulation results from the multiphase flow. In the process of gas-liquid separation, compared with the numerical gas-water simulation, adding the oil phase into the multiphase flow complicates the interaction between the equation describing the multiphase flow and the gas-liquid. It also increases the fluid flow state's complexity. Therefore, the numerical simulation results for gas-water two-phase flow cannot replace the oil-gas-water multiphase flow's numerical simulation results, especially when observing the gas-liquid separation gas volume. According to Equations (1)-(16), with the increase in the oil phase, the slippage effects between oil, gas, and water also increase, changing each phase's viscosity. With the gradual increase in flow, the slippage effect intensifies, leading to the stratification phenomenon in the gas, oil, and water phases. The oil phase's viscosity coefficient is greater than that of the water and gas phases, and when the gas enters the gas channel, the oil phase acts as a seal and blocks the water phase. At the same time, the exhaust hole's tiny diameter hinders the entry of the oil phase. Therefore, the gas-liquid separation efficiency of oil-gas-water three-phase flow is superior to that of gas-water two-phase flow.
## 2 Materials and Methods
### Loading Trained DNN Model  
This paper considers a TF DNN model trained for detection of ventricular fibrillation in a previous deep learning study [67]. To enable easy implementation in stand-alone AEDs, simplicity has been prioritized by using a minimal input of a single ECG lead, acquired at a low sampling rate (125 Hz) directly through the defibrillation pads, without pre-filtering or the use of additional sensors. Furthermore, the feature extraction and classification processis streamlined through fully convolutional operations. The convolutional architecture of the DNN model embedded in this study (Figure 2) includes five convolutional layers with rectified linear unit (ReLU) activation, five max-pooling layers, and a fully connected output layer with sigmoid activation. The output provides the probability of ventricular fibrillation (pVF) within a range of 0 to 1, where ventricular fibrillation is detected if the probability exceeds a threshold of 0.5.  
It is noteworthy that the hyperparameters of the trained DNN model presented in Figure 2 were determined through computationally intensive grid search optimization among more than 4200 architectures [67]. This process required substantial resources on  
Figure 1: Flowchart of the methodological steps, showing the conversion and implementation of DNN models in microcontroller boards. OS: operating system; IDE: Integrated Development Environment; DNN: deep neural network; TF: TensorFlow; LiteRT: Lite Runtime; ARM: Acorn RISC (reduced instruction set computer) machine; GPU: graphical processing unit.  
Figure 2: Architecture of the trained TF DNN model for detection of ventricular fibrillation, as selected from the optimization study [67]. The input layer processes a 10 s single-lead ECG signal sampled at 125 Hz, while the output layer consists of a single neuron presenting the probability of ventricular fibrillation (pVF). The hidden layer feature map dimensions are shaped by 1D convolution with valid padding, influenced by the kernel size. This model has a total number of 7521 parameters.  
the TensorFlow-enabled development workstation, taking significant computational power and extensive input data from public Holter ECG databases and out-of-hospital cardiac arrest AED databases, spanning over 10,000 training and validation samples. We selected a trained DNN model with the largest input size, corresponding to a 10 s ECG processing interval, to evaluate the maximum resource requirements for executing the best performing models on the target test platforms.
## Appendix B  
The total LOS path energy of the \(k\)-\(\mu\) distribution is now defined as \(P_{1}\) and the total NLOS path energy as \(P_{2}\). In the physical model of the \(k\)-\(\mu\) distribution, we have the relationship \(\sum_{1}^{n}{(p_{i}^{2}+q_{i}^{2})}/{\sum_{i}^{n}{(x_{i}^{2}+y_{i}^{2})}}=P_{1} /P_{2}\), the total received power \(E(r^{2})=P_{1}+P_{2}\). As a result, the fourth-order moment function of the \(k\)-\(\mu\) distribution (Equation (11)) can be reformulated as:  
\[\begin{split}& E(r^{4})=E(r^{2})^{2}\cdot\left(1+\frac{1+2k}{\mu(1+ k)^{2}}\right)=(P_{1}+P_{2})^{2}\Bigg{(}\frac{\mu(\frac{P_{1}+P_{2}}{P_{2}})^{2 }+1+2\frac{P_{1}}{P_{2}}}{\mu(\frac{P_{1}+P_{2}}{P_{2}})^{2}}\Bigg{)}\\ &=\frac{P_{2}^{2}\cdot\mu(\frac{P_{1}+P_{2}}{P_{2}})^{2}+P_{2}^{2 }\cdot 1+P_{2}^{2}\cdot 2\frac{P_{1}}{P_{2}}}{\mu}=(P_{1}+P_{2})^{2}+\frac{1}{\mu}(P_{2} ^{2}+2P_{1}P_{2}+P_{1}^{2})-\frac{1}{\mu}P_{1}^{2}\\ &=\frac{\mu+1}{\mu}U_{2}^{2}-\frac{1}{\mu}P_{1}^{2}\end{split} \tag{12}\]  
Similarly, Equation (11) in Appendix A, that is, the fourth-order moment function of the \(k\)-\(\mu\) distribution, can be reformulated as:  
\[\begin{split}& E(r^{6})=E(r^{2})^{3}\cdot\left(\frac{\mu(\mu+1)( \mu+2)+3k\mu(\mu+1)(\mu+2)+3(k\mu)^{2}(\mu+2)+(k\mu)^{3}}{(1+k)^{3}\mu^{3}} \right)\\ &=(P_{1}+P_{2})^{3}\cdot\left(\frac{\mu(\mu+1)(\mu+2)+3(\mu+1)( \mu+2)\frac{P_{1}}{P_{2}}+3\mu^{2}(\mu+2)\left(\frac{P_{1}}{P_{2}}\right)^{2}+ \mu^{3}\left(\frac{P_{1}}{P_{2}}\right)^{3}}{\mu^{3}\cdot\frac{(P_{1}+P_{2})^{ 3}}{P_{2}}}\right)\\ &=P_{2}^{3}\cdot\frac{\mu(\mu+1)(\mu+2)+3\mu(\mu+1)(\mu+2)\frac{P _{2}}{P_{2}}+3\mu^{2}(\mu+2)\left(\frac{P_{2}}{P_{2}}\right)^{2}+\mu^{3} \left(\frac{P_{2}}{P_{2}}\right)^{3}}{\mu^{3}}\\ &=\frac{3\mu+2}{\mu^{2}}P_{2}^{3}+3\frac{3\mu+2}{\mu^{2}}P_{1}P_{ 2}P_{2}^{2}+\frac{6}{\mu}P_{1}P_{2}P_{2}+(P_{1}+P_{2})^{3}\\ &=\frac{(\mu+1)(\mu+2)}{\mu^{2}}U_{2}^{3}-\frac{3(\mu+2)}{\mu^{2}} P_{1}^{2}U_{2}+\frac{4}{\mu^{2}}P_{1}^{3}\end{split} \tag{13}\]
## 2 Background
### Thermal Properties  
Thermal properties are important factors for analyzing the heat transfer phenomenon. Two thermal properties, thermal conductivity (\(k\)) and specific heat capacity (\(c\)), are needed for the non-steady state heat transfer analysis. This section discusses these two thermal properties for concrete and soil based on the existing experimental results from the literature.  
Concrete is a composite material with different components, such as cement, aggregates, water, and other additives, which have different chemical and physical properties. The thermal properties  
Figure 2: Thermodynamic processes of compressed air energy storage (CAES) (Modified from [7]).  
of concrete are affected by the thermal and physical properties of its components, as well as density, moisture, and temperature [19; 22; 23; 24]. For ambient moisture conditions and temperatures less than 150 \({}^{\circ}\)C, the thermal conductivity ranges from 1.0 to 2.3 W/m-\({}^{\circ}\)C for normal strength concrete according to Eurocode-2 [25], [PERSON] et al. [26], and [PERSON] and [PERSON] [27]; and from 2.0 to 3.2 W/m-\({}^{\circ}\)C for high strength concrete according to [PERSON] [28], [PERSON] et al. [29], and [PERSON] and [PERSON] [30]. On the other hand, the variation for the specific heat capacity is smaller compared to the thermal conductivity. For the normal strength concrete, the specific heat capacity ranges from 830 to 900 J/kg-\({}^{\circ}\)C according to Eurocode-2 [25], [PERSON] et al. [26], and [PERSON] and [PERSON] [27]. For the high strength concrete, the specific heat capacity ranges from 900 to 1000 J/kg-\({}^{\circ}\)C according to [PERSON] [28] and [PERSON] et al. [31]. Since both fiber reinforced concrete with normal strength and high strength concrete are considered as candidate materials for the energy storage pile foundation, this paper investigates a full range of thermal properties for the concrete.  
The thermal properties for soil are highly influenced by moisture content, density, porosity, and mineral components [19]. Typical thermal conductivity values of soils lie in a range from 0.5 and 3.2 W/m-\({}^{\circ}\)C according to [PERSON] [32] and ASHRAE [33]. Typical specific heat capacity of soils ranges from 800 to 1600 J/kg-\({}^{\circ}\)C for sand and 1200 to 2200 J/kg-\({}^{\circ}\)C for clay [34]. Since the primary focus of this paper is not on the soil, typical fine sand with a moisture content of 5% (\(k\) = 1.0 W/m-\({}^{\circ}\)C and \(c\) = 1220 J/kg-\({}^{\circ}\)C) is considered. The contact between the concrete and soil is not perfect since the soil is a granular material and the concrete has a rough surface, where thermal resistance can be developed between the gaps. [PERSON] and [PERSON] [35] recommend a thermal conductance value of 25 W/m\({}^{2\cdot}\)C for modeling the interface behavior.
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Lead-Acid Batteries  
After 150 years of development, the lead-acid battery has grown considerably in terms of theoretical research and product performance and is the most widely used chemical battery in small and medium-scale systems [57]. According to the Ministry of Industry and Information Technology data, China's lead-acid battery production is 227.36 million kVAh in 2020. Domestic lead-acid battery production reached 273.59 million kVAh in 2022. The anode is made of Pb, the cathode is made of PbO\({}_{2}\), and the electrolyte is sulfuric acid. It has high charge/discharge electrical efficiency (80%), low operation and maintenance cost, mature technology, and a small daily self-discharge rate (<0.3%) [21]. It has the advantages of safety and reliability. Lead-acid batteries can be used in stationary equipment, as backup power for data and telecommunication systems, and in energy management applications [58], being developed as a power source for fully electric and hybrid vehicles [57]. However, the drawback of the short life cycle of lead-acid batteries has not been solved for a long time (design life 8-12 years, actual operating life 3-5 years), as well as the waste of resources due to the hoarding of used batteries, which contain lead, sulfuric acid, and other wastes inside with strong polluting power and non-degradablecharacteristics [59]. At present, the research and development of lead-acid batteries are mainly focused on two aspects. On the one hand, it is to improve performance, such as extended cycle time and deep discharge capability. On the other hand, it is used in the application of battery technology to wind energy, photovoltaic power integration, and automotive applications. [PERSON] et al. [59] summarized a variety of fractional-order models for lead-acid batteries. Furthermore, the development history is from using fractional-order operators to describe the dynamic behavior of batteries to simplified fractional-order models. [PERSON] et al. [60] estimated the lifetime of lead-acid batteries in stand-alone photovoltaic systems. Their paper pointed out that many of the optimization results do not work as expected in practice because the lifetime metrics depend on the operating conditions of the batteries. However, using the Schiffer weighted model made a good result that is almost the same as the real lifetimes through the comparison of different lifetime prediction models.
Despite the advances in image analysis, manual or semiautomatic methodologies remain the most commonly used methods for analyzing corneal nerve tortuosity from IVCM images [37]. However, these techniques are subjective, time consuming and heavily dependent on the experience of the evaluator, which might not deliver reproducible  
Figure 1: Cornel nerve tortuosity. (**a**) High tortuosity. (**b**) Low tortuosity. outcomes [38]. These limitations show the need to create new automatic analysis systems that are able to provide reliable results. In 2004, [PERSON] et al. [12] were one of the first research groups that proposed an automated method for analyzing corneal nerve tortuosity in diabetic patients with neuropathy. Subsequently, in 2011, [PERSON] et al. [29] provided several algorithms for the evaluation of corneal nerve tortuosity. More recently, automated approaches to grade the tortuosity of corneal nerves based on deep learning methodology have been developed (see [34; 39] among others). However, many of these methods focus on computing the tortuosity of individual fibers instead of calculating the global tortuosity of the whole image. These measurements are combined using the average of several fiber tortuosity degrees or the weighted average by fiber length. This approach does not take into account the fact that nerve fibers could exhibit considerably different tortuosity characteristics in the same image, i.e., highly twisted nerves coexisting with other very straight ones, which is usually labeled as highly tortuous by clinicians [29]. This may explain why an underestimation has been noted when fully automated algorithms are used [40]. In this work, a new index to quantify sub-basal nerve tortuosity using an automatic analysis of corneal IVCM images is proposed. The method is based on the segmentation of the image in individual segments between extreme or intersection points instead of nerve fibers. Thus, each fiber may be characterized by one or more segments depending on nerve fiber branching and length. Global tortuosity is calculated by taking into account the curvature of each identified segment regardless of the location of each fiber. The fully automated method is implemented in a fast and easy-to-use R [41] tool. Finally, the new index is compared to a manual grading method in laser-assisted in situ keratomileusis (LASIK) surgery patients, because they usually show an altered sub-basal nerve plexus and automatic nerve recognition might be more challenging [40].
## 2 Sensor Design
### Electrodes Geometry  
The performance of a coplanar capacitive sensor is primarily determined by its geometry, which includes the size, shape, and separation distance between the electrodes [16]. They are the most important parameters which affect the electric field strength and the penetration depth of the probe [17]. A bigger electrode size provides a deeper penetration depth; however, it reduces the image resolution in imaging applications since it samples a bigger area of the specimen. The shape of electrodes can be a simple form such as square, rectangular, triangular shape, or complex shape, like a comb or spiral shape. Free space width between adjacent electrodes refers to the separation distance between the electrodes and has a significant impact on the measured output. Therefore, the geometry of the electrode influences the sensor performance in signal strength, depth of penetration and sensitivity of the measurement and need to be optimized and selected with care [9; 18].
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 5 Multi-Modality Data Representation and Event Detection
### Feature Fusion Based Methods  
Event detection from multi-modality data is a difficult task that combines text, images and video. To exploit the heterogeneous data and multiple feature modalities, different fusion approaches including early fusion and late fusion have been proposed. In early fusion, also known as feature level fusion, all the features including textual and visual features are concatenated into a single feature vector at feature level [79]. For multi-modality event detection with early fusion, a set of concatenated features is feed into a classifier for training. Thus, the trained classifier can separate test data into different event types. Late fusion combined the posterior probabilities obtained from the classifiers of each feature type. In this case, several models are trained for text, image and video and then combined at decision level [80]. Late fusion combines the confidence scores of different models by leverage feature modalities. The confidence score measures the probability of classifying the data into different event types. Different to early fusion, late fusion trained separate classifiers for the features and then several classifier outputs probabilities for the different types instead of predicted labels.  
In addition, the intermediate fusion methods are proposed. For example, some works represent different modalities and fuse them by extracting joint latent topics using a multimodal extension of LDA [81]. [PERSON] et al. [82] presents a novel probabilistic approach which can fuse multimodal metadata for event based home photo clustering. They incorporated these multimodal metadata into a unified probabilistic framework. Some more complex fusion methods are also proposed. These models used more learning stages to optimize the weights of each feature modality to the final fusion event detection models. In addition, many works have constructed event detection models by combining the early, late, and other fusion techniques [83; 84].  
Figure 6: Framework of multi-modality data representation event detection.
# A Review of Medication Adherence Monitoring Technologies +
## 5 Challenges and Future Trends
### Future Trends  
It is clear from this review that most solutions have some sort of limitation. As such, the developed system may harness the advancements of a combination of technologies to achieve the ultimate goal. However, overcoming the challenges that were previously mentioned can be achieved as follows. In order to precisely monitor patient adherence, fine-grained sensors such as load cells, motion sensors for detecting and classifying gestures associated with hand-to-mouth movement, and switch or capacitive sensors for cap opening and closure verification, are strong candidate technologies.  
The integration of sensors that consume very little energy with limited fidelity along with sensors that report much higher fidelity of activity but also power-hungry on a single platform and decide what sensor and when to have it on, is an example of collaborative sensing that can be harnessed for prolonging the lifetime of a battery-powered system [104; 166]. However, this requires sensor fusion algorithms that build a unified model based on different sensed and reported inputs--for example, Bayesian inference. In addition, since the wireless functionally in wireless-enabled systems constructs a bottleneck as it consumes a large portion from the battery energy, searching for low communication technologies is a must. An example of this can be the Transmit Only (TO) approach [167] that can be employed rather than WiFi or Bluetooth. The TO technique is a single hop communication that does not demand handshaking or acknowledgment, and thus it minimizes the energy consumed for packet transmission to only a few tens of micro joules [104]. Finally, user's acceptability and comfort might be achieved by carefully designing a pill container that is low-energy consuming, smart, and wireless.