esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 1 Introduction
For instance, the use of a mobile sink can allow for a longer lifetime of sensor nodes avoiding multi-hop data collection [46]. Another important issue in H-WSNs is network overhead [47]. Routing protocols have to be adapted to limited time communication links [32; 48; 49]. A proposed solution is using position aware routing protocols [47; 50]. Another proposal is dynamic routing [8]. While using MANETs can be a comparatively simple solution [32; 50], the need for frequent topological updated undermines energy consumption [51]. Security and privacy are also concerns when using MANETs [52], as well as adaptability and scalability [53]. A more robust option is using H-WSNs, although the location of mobile and static nodes has to be carefully designed, particularly for the base stations. An alternative is employing mobile base stations or mobile sinks, and dynamic relocation [42; 54]. Optimization based clustering has been also proposed by several authors [46; 55; 56]. As for security and reliability, H-WSNs allow for reliable and timely data communication, for instance using dynamic congestion control schemes [57]. This paper presents the integration of a mobile node into a wireless sensor network for urban environments, constituting a hybrid wireless sensor network. The mobile node has a modular architecture, allowing different sensors to be included for every deployment, like gas, temperature, humidity or GPS location. The information provided by the sensors is stored in a local database, and synchronized with an external database located in a server. The synchronization reduces the need for multi-hop routing, thus ensuring reliable and timely data delivery. A Human-Machine Interface (HMI) allows for real-time monitoring of the information from the sensors, including location of the measurements. The main contributions of this paper are: a) the implementation of a mobile sensor node with a modular architecture which enables the installation of different sensors depending on mission needs; b) the integration of the mobile node into a static wireless sensor network, without the need for multi-hop routing or dynamic relocation methods, resulting in a hybrid wireless sensor network of improved range and flexibility respect to the original; c) experiments validating the mobile node and its integration, including real-time availability and presentation of the gathered information. This paper is structured as follows: after this introduction, Section 2 describes the original wireless sensor network serving as the basis for the hybrid wireless sensor network. Section 3 presents the mobile sensor nodes and its integration into the hybrid wireless sensor network. Section 4 is devoted to the experiments performed to validate the system. Finally, Section 5 contains the conclusions.
## 5 Results and Analysis
### Results
#### 5.2.3 Modeling Latency
Figure 7 shows the normalized prediction latency for all schemes under all ML/AI models in both DM000 and NM0007. In all situations, _Original_ was the slowest. Moreover, in most cases, the trend of latency was the same as that for training, and the prediction latency decreased as the attributes decreased. For the first group in DM000 and in CNN, _Pearson_, _Spearman_, _J-Index_, _Entropy_, and _Gini_ reduced the prediction latency by 60%, 61%, 56%, 63%, and 61%, respectively. When comparing the second group, some cases showed a different trend; for example, _Prs_Prs_ and _Prs_Sprm_ improved the latency by around 3% compared with _Pearson_ when using SVM in NM0007, and they reduced the latency by 5% in DM000. However, for most cases, the trend was the same as the training latency. Overall, for the proposed scheme, _Gini_Prs_ reduced the latency by up to 83% compared with the _Original_. This showed the high efficiency for our proposed scheme. For LSTM, the prediction latency for all schemes was similar, and they reduced the latency by 1-5%  
compared with the _Original_. The reason behind this was the same as for the training. In this case, the number of attributes was too small in all schemes; thus, the improvement in latency was not obvious.
## 1 Introduction  
### Background  
A Device-to-Device (D2D) enabled cellular network was proposed for improving spectral efficiency and throughput of the cellular network, with the aid of direct peer-to-peer communication between the proximity devices [1; 2; 3]. More explicitly, in a D2D-enabled cellular network, the closely located user devices may directly connect to each other and bypass the cellular base station (BS), for the sake of reducing the energy consumption and the end-to-end delay. However, enabling D2D communication in a cellular network generates several technical challenges. As one of the important technical challenges, the cellular spectrum-sharing scheme may be classified into two categories, namely underlay and overlay spectrum-sharing strategies [4]. According to the underlay cellular spectrum-sharing scheme, the D2D devices are capable of accessing the same spectrum with cellular users, provided that they do not substantially interfere with the transmissions of cellular users. Hence, when the cellular and D2D users are occupying the same spectrum, the interference between them must be invested for designing appropriate underlay cellular spectrum-sharing scheme [4]. Under the overlay cellular spectrum-sharing scheme, the D2D devices can only access the cellular channels which are not used by the cellular users. Compared to the underlay scheme, the spectrum efficiency of overlay cellular spectrum-sharing scheme may be limited by assigning a dedicated portion of the cellular spectrum to the D2D communication [4].
## 1 Introduction
### Motivation  
Several companies related to micro-factories were taken into account during the design process to understand the functionalities that this architecture should be compliant with. One of the major features required was the guarantee of trustability and traceability of data that flow between actors and the use of cloud technologies as enablers. Another highlighted functionality was the ability to analyze the performance not only locally at the shop-floor level but on several processes performed by different companies. Due to the nature of the micro-factories, modularity on such kinds of platforms is also a requirement; different clients have different needs, and at some points in time, it can be a necessity to enable the contribution of different stakeholders. For instance, a micro-factory producing plastic components might have to contract an external entity to make LCA assessments for one particular product for a specific client, and to do so, it cannot invest in a new infrastructure that will be used only on one small fraction of the production. Some works focus on information exchange such as [2], where some adoption barriers are identified such as trust, security, data control, and availability of resources but seem restricted to the manufacturing process and the collaborative networks paradigm. In [3], an information exchange architecture is presented with a web application as a mediator. The approach depends on specific integration libraries and the import and export of files through the web interface. From our analyses, it lacks scalability features and does not take advantage of the cloud paradigm. Also, trustability is not addressed in this architecture since it relies on the mutual trust of the organizations. Focusing on the particular issues of the automotive industry, in [4], another architecture for information exchange is presented. The implementation is based on a publish/subscribe mechanism, and the information is exchanged with JSON files. However, there is no mechanism to include external companies to exchange or validate the trustability of information flow that is focused on tracking machine data and mapping that information during the manufacturing process. A smart factory information service bus is presented in [5] with a focus on applications for several stakeholders, but it lacks an explanation of how the different actors can communicate with each other. Although this work seems complete, it does not have the trustability capacities to validate the message exchange and also does not highlight how it can be used in a cloud environment.  
From our research, there is no software architecture integrated with the cloud ecosystem that focuses on the information exchange between several stakeholders, that is modular, scalable, and trustable.
# A Goal-Driven Evaluation Method Based On Process Mining for Healthcare Processes
## 5 Case Study
### PM Analysis and Generating Answers to Questions
#### 5.5.1 Evaluation of the Results  
In this stage, an instance of the GQFI table is derived using the actual values of indicators, and the quantitative results are summarized in the form of answers to the questions 1-4.  
* Some performance problems were observed e.g., quite a few cases taking much longer than 21 days (up to 129 days, which is an outlier). It is necessary to determine the step(s) of the process that take(s) too much time, why, and for which patients or surgery.
* The most followed path covers 65% of all process instances, and there are many exceptional paths (with 29 process variants). These deviations show that the practicing of these processes needs to be divided into different paths for different groups of patients or surgery categories.
* Bottleneck points of the process are \"change service\" (60.9 h) and \"change department\" (4.2 days). This could be due to the problems related to preoperative operations or service resource allocation for these cases. During the average 40.3 h, the patients waited in the recovery room without being transferred to a service. This could be related to the health status of patients or the nature or success of surgery. The process map does not provide information about the reasons for these bottlenecks and the long waiting time since the root causes would differ and need to be investigated.
* The non-fitting process variants reveal that 6% of the patients were transferred to a service without being officially finished surgery, 3% of the patients had surgery without being officially admitted to the hospital, and six patients were not transferred to a service between two surgical operations. After filtering the non-fitting traces, concrete cases should be investigated with other attributes, such as department, surgery, or surgery category.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 4 Trusted Computing
### Trust Decision  
The Trust Decision is about deciding whether a platform can be considered trusted for an intended purpose or not. It builds upon the Authenticated Boot, can occur (at any meaningful time) during it of after it and implies the cryptographic verification of the loaded software components to identify them and verify that they are the expected ones. If the Trust Decision is taken during the bootstrap, the verification is performed by the platform itself (usually using the TPM) over the components loaded until the decision time: based on the result of the verification, the bootstrap process can be stopped. This portion of the Authenticated Boot(strap) is called Secure Boot(strap). If the verification is performed after the bootstrap completion, it is called Remote Attestation, as it requires a remote entity, the Attestor, that performs the verification with the support of the trusted component on the platform, i.e., the TPM. In our prototype, we implemented both the Secure Boot and the Remote Attestation.
## 4 Results
### Monit4 Healthy Platform Development
#### 4.2.1 Monit4 Healthy Platform Overview  
The Monit4 Healthy platform is designed to support two primary user roles--patients and medical professionals--each with specific, tailored functionalities. Following authentication, users are directed to their personalized dashboards, displaying real-time data. The patient dashboard presents the main health parameters of the patient, which include the pulse, SpO\({}_{2}\), temperature, movement stability, and fall detection status, while the medical professional's dashboard provides a detailed overview of patient data, allowing them to monitor patient health data and respond promptly to urgent events.  
The platform allows the analysis of historical data and graphical representations of medical information, intended to offer a clearer visualization of the data, and ultimately aid in proactive risk management. The platform's primary purpose is to integrate health information into a user-friendly interface, allowing for the monitoring of metrics, managing medical recommendations, and addressing potential health concerns.  
The Monit4 Healthy platform is designed with a Romanian-language interface, in order to support the accessibility of its primary users. To make navigation intuitive for both patients and healthcare professionals, all system functionalities, including dashboards, medical data visualizations, device management, and recommendations, are presented in Romanian. Throughout this section, the platform interface is illustrated alongside translated Romanian key terms to facilitate a better understanding of the interface.  
Figure 10: Electrical schematic of the EMG Blackbox device developed using Fritzing software. The schematic illustrates the integration of a MyoWare EMG sensor with the ESPRESSIF ESP32-WROOM-32E Wi-Fi module for wireless data transmission. Power supply is provided through a rechargeable 3.7V Li-Po battery, managed by a dedicated charging module.
## 4 Scenarios
### Extract Features with Handcrafted Methods
The observations from Tables 1 and 2 compared to Table 3 are:  
* LBP technique gives significantly improved classification accuracy compared to HOG in all databases, except for the JAFFE database;
* The highest classification accuracy for each technique and database is achieved with the same feature size; \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**KDEF**} & \multicolumn{2}{c}{**JAFFE**} & \multicolumn{2}{c}{**RaFD**} \\ \hline
**Cell Size** & **LBP** & **HOG** & **LBP** & **HOG** & **LBP** & **HOG** \\ \hline
4 \(\times\) 4 & 70.07\% & 59.65\% & 78.57\% & 84.21\% & 85.76\% & 73.82\% \\
8 \(\times\) 8 & 78.47\% & 69.92\% & 77.57\% & 84.71\% & 92.16\% & 77.67\% \\
16 \(\times\) 16 & 76.20\% & 61.08\% & 76.19\% & 85.71\% & 94.40\% & 78.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy with LBP and HOG methods applied on the images downsized by two. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**HOG** & **KDEF** & \multicolumn{2}{c}{**JAFFE**} & \multicolumn{2}{c}{**RaFD**} \\ \hline
**Cell Size** & **Feature Size** & **CA (\%)** & **Feature Size** & **CA (\%)** & **Feature Size** & **CA (\%)** \\ \hline
8 \(\times\) 8 & 233,496 & 55.57 & 34,596 & 80.95 & 384,048 & 69.84 \\
16 \(\times\) 16 & 56,304 & 59.65 & 8100 & 80.95 & 92,988 & 74.63 \\
32 \(\times\) 32 & 12,672 & 56.49 & 1764 & 83.33 & 22,320 & 76.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy and feature size with HOG method applied to the original image dimensions. * The downsizing of the images and cells (so that the feature is the same size) improves the classification accuracy up to downsizing by two for all databases. Specifically, the image reduction by a factor of two resulted in improved classification results with the HOG method for all databases. The results are improved only for KDEF and JAFFE databases with the LBP method. Figure 3 shows the highest classification accuracy achieved for each database with the techniques applied so far. In every case, the images are downsized by two. LBP method proved to be more efficient for KDEF and RaFD databases, while the HOG method for the straight pose images of JAFFE. For KDEF, the optimal cell size is \(8\times 8\) and for the JAFFE and RaFD databases is \(16\times 16\).
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 2 Materials
### Calibration Site and Targets  
#### 2.3.1 Baotou Calibration Site  
In 2013, the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Infrared and Visible Optical Sensors Subgroup (IVOS) established the Radiometric Calibration Network (RadCalNet), consisting of four international test sites located in the continental United States, France, China, and Namibia, providing automated in situ measurements and estimates of propagated top of atmosphere (TOA) reflectance [13]. The RadCalNet Baotou site (BTCN) is located in Inner Mongolia, China, about 50 km from Baotou city, as shown in Figure 2 (in red circle). It covers a flat area of approximately 300 km\({}^{2}\). The site is dominated by sand and bare soil. A series of targets and infrastructure has been built in the Baotou site to provide effective support for sensor aerial test flights, satellite sensor on-orbit calibration and performance evaluation, and remote-sensing product validation [14; 15].
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 3 Technical Approach
### Normalization of the Ear Data
#### 3.2.3 Ear Segmentation Based on General Ear Template  
From the aforementioned analysis, the vertices with higher LSV values, such as ear and hair data, are left after the LSV threshold processing step. In the second step, since most of the hair locates near the upper and lateral part of the auricle, the hair data will be eliminated as much as possible by setting a coordinate threshold if the ear gets an upright pose and a uniform coordinate position.  
A method of ear segmentation based on ear template is proposed in this paper. It should be noted that any two of the ear point clouds can be roughly aligned in pose and position using an ICP algorithm with a few iterations. As such, the pose and position of all the ears can be normalized by aligning with an ear template. A general ear template which is saved as a set of point clouds is created from an ear sample by rotating this ear to a vertical pose and translating the origin (0,0) of the coordinate system to the lower left corner of this ear. Furthermore, the non-ear data are mostly removed.  
As previously mentioned, all LSV threshold processed ear data is rough aligned with the general ear template using ICP algorithm of five iterations. After that, every ear has an upright pose and a uniform coordinate position. Then most of the hair data will be cropped out utilizing a coordinate threshold (\(0\leq\sigma_{x}\leq 35,0\leq\sigma_{y}\leq 60\)), leaving only the pure ear data for recognition. So far, the procedure of ear data normalization is accomplished automatically.  
Figure 5a,b shows the comparison between original and normalized ear data. It shows that the hair and skin data barely existed in the normalized ear data. Additionally, the data size is down to one third of the size of the coarsely segmented ear data size, so it will save plenty of time in the following ICP iteration. Experimental results show that this procedure gets satisfactory results on two different databases, which it might also be noted include different races.  
The average computational time of this ICP algorithm of five iterations is within 0.04 s. The time-error curve is illustrated in Figure 5c. It is shown that the registration error is reduced quickly after five iterations.
## 1 Introduction  
PbSe chalcogenide films are widely used as photosensitive elements in gas analysis devices, where high absorption in the IR spectrum from 1 to 4 \(\upmu\)m [1; 2; 3; 4] and low electrical resistance are important characteristics. Gas analysis devices are important for monitoring the environment and protecting it from emissions of various poisonous gases into the atmosphere. The use of these devices is a prerequisite for the safe operation of large oil refining, chemical and other enterprises, the production of which is associated with toxic substances that are harmful to nature and human health [5]. In addition, research and modification of the photosensitive materials' optical and electrical properties are becoming increasingly popular [6]. Changing optical and electrical properties and tuning them within small ranges of values is a very promising task for many applications. For example, optoelectronic systems, solar cells, and LED technologies [7; 8; 9] can be used as photoelectric sensors [10]. The optical and electrical properties of PbSe films are largely affected by the oxidation process, which in most technological processes is activated during heat treatment in an oven. This method of increasing the PbSe film's photosensitivity is technologically difficult to implement, which, in most cases, cannot be controlled and leads to a high percentage of defects at the photodetectors' manufacturing stage.  
An alternative to heat treatment in an oven is structural and property laser modification of such films [11; 12]. The use of laser irradiation makes it possible to carry out a local structure modification and, at the same time, a predictable change in the optical and electrical film characteristics. The film photothermal effect during laser irradiation leads to sharp and local material heating, followed by structural phase modification due to high temperature (gradient) and heating/cooling rate [13]. This structural modification results in photosensitivity laser correction of the material in a certain spectral range [14; 15].  
The development and implementation of this technology will significantly improve the sensitivity of gas analysis devices as well as change their design, making them more compact and less energy-consuming. In addition, laser processing of chalcogenide films, in contrast to heat treatment in an oven, can be fully automated, which will increase the yield of suitable products and reduce production costs. An equally important task is to evaluate the chalcogenide film quality after laser irradiation, taking into account changes in the material's optical and electrical characteristics. The solution to problem solution will make it possible to improve the functional elements of optoelectronics, photovoltaics, sensorics, and microanalytics [16; 17; 18; 19; 20].  
In the present work, the change in the sample's optical characteristics was carried out by modifying the structure of PbSe chalcogenide films with continuous laser radiation in the near UV range and pulsed IR radiation.