esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 3 Methodology
### Proposed Model Framework  
Our proposed method synergistically combines TimeDis-CNN and LSTM deep learning tools designed to receive a sequence of input data and static output with a number of frames extracted from a video as input to predict a single label of the driver behavior class. As shown in Figure 1, the proposed system takes a series of five consecutive images as an input to the convolutional network. Then, the spatial feature information of each input image is extracted by the CNN. To facilitate faster training and combat the overfitting issue, we deployed a pre-trained CNN that can extract the spatial feature of each driver image input. Specifically, we used the VGG16 [32] architecture, as it has been shown to generalize on our datasets better than a few other experimental pre-trained models (see Section 4.1). VGG16 is a pre-trained architecture originally designed to classify 1000 categories on very large-scale ImageNet [38] datasets that contain over one million images.  
The original VGG16 architecture has nearly 140 million parameters, of which the fully connected layer has the most since it consists of a large number of parameters to be trained. However, this inevitably leads to high computational costs and a long training time. Therefore, in this proposed model, the fully connected layer is excluded and the LSTM layer is concatenated to perform end-to-end fine tuning. Then, LSTM uses the extracted sequence images to further explore the temporal dynamic information related to each driver activity. Afterward, the LSTM layer transforms the abstract feature vectors outputted from the time-distributed convolutional layer into an understandable representation for the fully connected dense layer. Thereby, a fully connected dense layer gathers the LSTM outputs and learns the driver's behavior representation. In the end, the n-class, where n denotes the labeled driver behavior class, of the softmax layer is applied, which chooses the most probable labeled class among the nine driver behavior classes. More details of the unified network working principles are introduced in the following sections.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Results and Discussion  
#### 5.4.1 Evaluation and Comparison Regarding Accuracy  
The goal of this experiment is to analyze which recommendation criteria make the system more accurate when we rely on preference, rule items content or both. Thus, we have carried out several performance experiments using the proposed approach by observing five criteria:  
1. Explicit Preferences (EP).
2. Implicit Preferences (IP).
3. Both Explicit and Implicit preferences (EIP).
4. Contextual-based Bayesian (Cx-Bayesian).
5. All criteria are considered (EIP and Cx-Bayesian).  
Comparisons of accuracy across criteria (explicit preference, implicit preference, rule item content and situation context) are reported in Figure 14. The proposed approach reaches a promising precision based on all criteria as compared with other possible combinations of criteria of the proposed approach with 95.23% on rules repository. Besides,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Size** \\ \hline Number of situations rules items & 100 \\ Number of daily activities & 80 \\ Number of health and security rules & 20 \\ Number of profiles configurations & 3 \\ Number of available devices & 10–1000 \\ Weights Explicit Preference (WEP) & 0.35 \\ Weights Implicit Preference (WIP) & 0.35 \\ Weights Naive Bayes (W\({}_{\text{S}}\)) & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Configuration setup.  
Figure 13: Implicit weights for rules history.  
the proposed approach achieves 96.42% recall and demonstrates a higher F1 score rate of 95.82%. In addition, even if implicit context content is extracted from the smart-domain dataset, the proposed approach performs better than other models.
## 2 State of the Art  
A significant portion of the scientific community has been active in evaluating the risk of radon exposure and identifying locations with high concentrations, both locally and globally, by conducting studies in various countries. Several initiatives to construct radon potential maps have been completed, and more are underway [15; 16; 17; 18; 19; 20].  
The most fundamental geostatistical techniques are based on two phases known as variogram fit and kriging [21]. The variogram provides a quantitative estimate of the spatial autocorrelation of a sparse dataset. Based on this autocorrelation, kriging can interpolate the provided dataset, producing predicted values on a finer grid with a predetermined resolution.  
The desired geographic resolution and the specific application necessitate the selection of the mapping technology.  
Kriging is the best estimator for spatial analysis because it is non-polarized. There are several varieties of kriging, including Simple Kriging (SK), Ordinary Kriging (OK), Universal Kriging, and CoKriging (multivariate geostatistics). Furthermore, alternative methodologies for this research, such as fractal and multi-fractal analysis, Bayesian spatial quantile regression, and standard multivariate statistical algorithms, have been presented in the literature [22; 23; 24; 25; 26; 27; 28]. Although this information is useful for effective radon management, the uncertainty associated with simulations is often not fully considered for interpretation purposes [29].  
Finally, other studies have applied logistic regression models to estimate the probability that the indoor Rn concentration exceeds a certain fixed value (for example the maximum reference level for the law in force) [30]. The major requirements are data uniformity and robustness, especially if the dataset employed is not designed for a certain technique.  
Correct application of these approaches, and the correct interpretation of the results, provides knowledge about the system's underlying variability structure. This is appropriate for any type of spatial study aimed at modelling the trend in variability and developing appropriate spatial models. Several studies have established that geological data and indoor radon levels are sufficient factors for obtaining indoor radon maps [29; 31].
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 4 Methods for Order Quantification
### Order Quantification by Fourier Transform
(**e**) Diffraction of normally incident laser beam transmitted through a uniform hexagonal latex monolayer using a 4 mm diameter beam spot (top) and reducing the beam spot to 0.25 mm (bottom) which changes the diffraction pattern to six symmetrical spots (i); graphical representation of Debye ring obtained using a violet laser pointer (406 nm, 2.2 mm spot size) to illuminate the 2 DPC at normal incidence (ii). Reproduced from Ref. [102] with permission from the 2004 American Chemical Society and from Ref. [103] with permission from the 2017 Royal Society of Chemistry, respectively. points of the 2 DFT should show two very smooth and high peaks. [PERSON] et al. exploited this characteristic to quantify the regularity of pore arrangement in TiO\({}_{2}\) nanoporous surfaces [97]. To do so, they defined a regularity factor (R) as the ratio of the peak height in the 2 DFT profile to the width of the peak at the half-maximum (Figure 6b). Because the nanoporous structure analyzed could have different interpore distances, the same image area could include a different number of pores, which would affect the peak intensity in the 2 DFT profile. To overcome the influence of pore numbers on the analysis, the average regularity factor calculated from the 2 DFT profiles was multiplied by pore density (n). By following this procedure, the authors could compare the regularity of samples fabricated by applying different potentials in the electrodeposition of TiO\({}_{2}\) nanoporous structure.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
###### Abstract  
The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer's identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.  
smart meter; smart metering; real-time services; load shifting; demand response; distribution system operator (DSO); in-home device (IHD); Internet of Things; Time-of-Use; Real-Time Pricing; energy awareness; home automation Article
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 1 Introduction
### The Millimeter-Wave Spectrum for Time-Critical Applications  
Emerging time-critical applications in entertainment, the automotive sector, Industry 4.0 (i4.0), and healthcare require low communication latency, summarized in Table 1. Two types of latency are addressed, depending on the use case: end-to-end (E2E) latency, the communication latency between the application layers of two devices and round-trip time (RTT), the E2E delay with the addition of a response.  
Compprised of interactive and immersive applications, extended reality (XR) requires a large throughput. Rendering scenery in life-like detail, and depending on external factors such as video resolution and compression, the requirements range from tens of Mbps [5] to several Gbps [11; 12]. While XR devices can compensate low data rates with elastic services [13], having a human in the loop means they have to always comply with a certain latency constraint. The latter ranges roughly from 100 to 1 ms, depending on the user mobility [14; 15], XR device [16], and physiological parameters [17]. However, most of the XR applications have either got a 20- or 10 ms latency constraint. Two examples are virtual reality (VR) entertainment and telesurgery using augmented reality (AR) or mixed reality (MR).  
Following the growing amount of real-time data sharing fuelled by the pursuit for the widespread edge- and fog computing adoption, mmWave frequencies are increasingly being considered by other emerging applications. Among them are vehicle-to-everything (V2X), unmanned vehicle (UV) and drone communications. Their further dissection yields specific allowed latency regions, for instance, in platooning, remote control, cooperative driving, and collective information sharing scenarios as shown in Table 1.  
The same applies to i4.0 where, for example, numerous collaborating robots on a factory floor can leverage highly directive mmWave data links to avoid interference. Digital twins and real-time control, on the other hand, are examples of applications requiring a high throughput. Whether XR, V2X, or i4.0, all the above-mentioned applications can benefit from mmWave communication systems, given they provide low-latency operation.
# Study of Systems of Active Vibration Protection of Navigation Instrument Equipment
## 5 Numerical Analysis
When constructing vibration isolation systems for navigation equipment based on elastic suspension on flat springs, the use of additional load of the moving part along the flat springs leads to a significant reduction in the natural frequency of the suspension, an increase in the efficiency of vibration isolation and an increase in the operational reliability of navigation equipment by reducing the errors caused by low-frequency horizontal vibration. The specificity of the inertial dynamic vibration isolator, which, in some cases, is associated with the implementation of anti-resonance conditions, can lead to the fact that resonance frequencies can arise on both sides of the tuning frequency of the vibration isolator. Therefore, the disorder of the system with the vibration isolator can lead to the appearance of dangerous vibrations. The sensitivity of the system can be determined by the dependence of the natural frequencies of the vibration isolator system on the system parameters. In order to obtain a low natural frequency of the suspension, it is necessary to ensure the closeness of the actual and limiting masses of the moving body. At the same time, the natural frequency of the suspension tends to zero, that is, the oscillating system approaches the limit of stability. The developed technique allows for a wide range of applications in practice [3; 5; 7; 11; 26; 27; 28; 29], thanks to the provision of the desired natural frequency and the largest possible amplitude of oscillations of the moving body, provided that the strength of the spring and the stability of its shape are preserved. Under kinematic disturbance, the effectiveness of vibration isolation systems is determined not only by the vibration isolator's own dynamics, but also by the formation mechanism of the external disturbance itself, i.e., the right-hand side of the differential equation of the vibration isolator. The given calculation methods can be used in the design of anti-vibration systems of navigation equipment, as well as in the design of elastic suspensions of mechanical vibration parameters, meters or stands for vibration reproduction.
# Advances in Valveless Piezoelectric Pumps
## 2 Configuration of the Valveless Piezoelectric Pump
### Bionic Valveless Piezoelectric Pump  
Inspired by the swimming of fish, [PERSON] et al. [29; 65] employed the bending vibration of piezoelectric bimorph to simulate the swing of a fish, and then the unidirectional flow of liquid was promoted to realize the function of a pump. However, [PERSON] et al. only imitated the structure of a fish tail, and did not conduct in-depth research on this structure. On this basis, [PERSON] et al. [66] proposed a valveless piezoelectric pump imitating caudal-fin, where the flow rate is as much as 560% of that of experiment results carried out by [PERSON] et al., and its working principle is shown in Figure 6. [PERSON] et al. [67] studied the solid-liquid coupling vibration characteristics of piezoelectric bimorphs in piezoelectric underwater acoustic devices. [PERSON] et al. [4] studied piezoelectric materials, and he designed a piezoelectric pump based on macro fiber composite, which could achieve the flow rate of 28.7 mL/min under the frequency of 15 Hz. In 2019, [PERSON] et al. [30] proposed a piezoelectric pump with built-in compliant structures. This pump can achieve the integration of \"valve-based\" and \"valveless\" in one body. These research results provide a new idea for the research of valveless piezoelectric pumps. Table 5 shows the summary of properties for the bionic valveless piezoelectric pump.  
In summary, a bionic valveless piezoelectric pump mainly realizes one-way flow of fluid by imitating part of the structure of a biological body. Bionic valveless piezoelectric pumps have the advantages of simple structure, easy miniaturization, low cost, and can also solve the backflow problem of valveless piezoelectric pumps. Moreover, the pump flow of this kind of pump is usually relatively large.
## 5 Practical Issues  
Regarding new drone registration, we created an efficient way to register new drones using mainly MAC functions. Additionally, the registration process is distributed, which means other network participants can register new devices in parallel with the assistance of the base station.  
When considering the communication cost, using the P-256 Weierstrass curve and HMAC-SHA-256, the space complexity is s \(384+i*208\) bytes cost, where \(i\) is the number of drones. The lengths of the variables are the following: IDs are 16 bytes, an HMAC is 32 bytes, and P-256 consist of 32-Byte x values and 32-Byte y values each. Assuming a swarm of 10 drones, our proposed protocol requires only 2.4 kilobytes of data for new drone registration. We conducted the analysis, which includes the number of operations and the execution time of each. We utilized a PC, a Raspberry PI 4 and an ESP32 for the analysis. The PC we used had an AMD Ryzen 5 5600X processor with 16 GB of RAM. The Raspberry PI is a widely popular, small on-board computer (SBC) that comes equipped with all the necessary software for basic computing. Although the manufacturer recommends Raspbian as the official OS, numerous other operating systems are available as well. In our case, we used a RasPi that features a Broadcom BCM2711, Quad core Cortex-A72 1.5 GHz processor and 2 GB RAM. In the tables, \(i\) depends on the number of registered drones. Tables 2 and 3 show the results. Furthermore, as the number of drones was increased, we measured the registration times, which are shown in Figure 8 for the different devices.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Operation** & **Raspberry** & **ESP32** & **PC** \\ \hline AES-GCM & 0.0000016 & 0.00155 & 0.0000033 \\ HMAC & 0.0000059 & 0.0082 & 0.0000011 \\ EC scalar mult. & 0.0205 & 0.2945 & 0.002880 \\ \hline \end{tabular}
\end{table}
Table 2: Execution time of operations in the registration.  
We compared the efficiency analysis with the solutions in our contribution, considering the operations in the protocol steps (vor, concatenation, and random generation are ignored), summarizing the results in Table 4. Since the protocols to be compared are not all scalable, we performed the comparison for one drone registration and key exchange. In addition to the number of operations, we also considered the number of interactions, the amount of data sent and the size of the data stored on the drone. The motivation behind the latter aspect was that drones have limited computing and storage capacity, and the number of interactions can be important for protection against DoS and other attacks. Analyzing the other papers, the proposals use the SHA-1 hash algorithm, which has 20 bytes output; however, it does not provide collision resistance [31]. Therefore, we used a standard HMAC with 32 bytes output in our implementation, which increased our communication costs but provided collision resistance. Table 4 indicates that our protocol is cost-effective in terms of communication and storage. Additionally, it requires fewer interactions or operations in most cases.
## 3 Materials and Methods
### Distributed Deep Learning
#### 3.3.1 Distributed Training Frameworks  
To carry out the training process in a distributed manner, especially in the context of DDL, specialized applications or frameworks that facilitate coordination and communication between multiple computing devices must be used. These frameworks are designed to distribute the workload, synchronize model parameters, and efficiently use resources in a distributed infrastructure to achieve the best results in the shortest time.  
PyTorch and TensorFlow share similarities that support distributed training, including efficient communication, the ability to parallelize processes across single or multiple computing nodes, and integration and compatibility for installation in a distributed computing infrastructure.  
Specifically, to perform distributed training, TensorFlow uses various strategies, such as MirroredStrategy, TPUStrategy, MultiWorkerMirroredStrategy, CentralStorageStrategy, and ParameterServerStrategy. These strategies are designed to perform data-parallel training, typically implementing synchronous training that supports all-reduced operations through the parameter server architecture [45].  
Similarly, PyTorch uses libraries that implement distributed strategies such as Distributed Data Parallel (DDP), which enables data parallelism by allowing multiple batches of data to be processed simultaneously across multiple devices, with the goal of maximizingperformance. Similar to TensorFlow strategies, DDP supports all-reduce operations to compute gradients and synchronize values across devices [46].  
In this study, the DDP and MultiWorker-MirroredStrategy strategies from PyTorch and TensorFlow, respectively, are used to evaluate the performance of the three datasets described above, allowing the analysis of which library is more optimal on an LSDS-HPC infrastructure such as the one proposed. Additionally, it is important to mention that in this implementation, both PyTorch's DDP and TensorFlow's MultiWorker-MirroredStrategy lack fault tolerance settings. This implies that if a node fails during training, the resulting gradients may be lost. To avoid this, the model training results are typically saved every certain number of iterations or epochs during experimentation.
# Emergency Evacuation Simulation Study Based on Improved YOLOv5s and Anylogic
###### Abstract  
With the development of the social economy and the continuous growth of the population, emergencies within field stations are becoming more frequent. To improve the efficiency of emergency evacuation of field stations and further protect people's lives, this paper proposes a method based on improved YOLOv5s target detection and Anylogic emergency evacuation simulation. This method applies the YOLOv5s target detection network to the emergency evacuation problem for the first time, using the stronger detection capability of YOLOv5s to solve the problem of unstable data collection under unexpected conditions. This paper first uses YOLOv5s, which incorporates the SE attention mechanism, to detect pedestrians inside the site. Considering the height of the camera and the inability to capture the whole body of the pedestrian when the site is crowded, this paper adopts the detection of the pedestrian's head to determine the specific location of the pedestrian inside the site. To ensure that the evacuation task is completed in the shortest possible time, Anylogic adopts the principle of closest distance evacuation, so that each pedestrian can leave through the exit closest to him or her. The experimental results show that the average accuracy of the YOLOv5s target detection model incorporating the SE attention mechanism can reach 94.01%; the constructed Anylogic emergency evacuation model can quickly provide an evacuation plan to guide pedestrians to leave from the nearest exit in an emergency, effectively verifying the feasibility of the method. The method can be extended and applied to research related to the construction of emergency evacuation aid decision-making systems in field stations.  
public safety; emergency evacuation; Yolov5s; Anylogic