esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2012**, _12_, 704-731; doi:10.3390/s120100704  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors.  
_Article_  
**Underwater Sensor Networks: A New Energy Efficient and Robust Architecture**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Institut ITACA, Universitat Politecnica de Valencia, Edifici 8G, Valencia 46022, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.V.C.); [EMAIL_ADDRESS] (J.J.S.)  
\({}^{2}\) Pervasive Systems, University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-96-387-7000 ext. 88251.  
_Received: 30 November 2011; in revised form: 5 January 2012 / Accepted: 9 January 2012 / Published: 10 January 2012_  
**Abstract:** The specific characteristics of underwater environments introduce new challenges for networking protocols. In this paper, a specialized architecture for underwater sensor networks (UWSNs) is proposed and evaluated. Experiments are conducted in order to analyze the suitability of this protocol for the subaquatic transmission medium. Moreover, different scheduling techniques are applied to the architecture in order to study their performance. In addition, given the harsh conditions of the underwater medium, different retransmission methods are combined with the scheduling techniques. Finally, simulation results illustrate the performance achievements of the proposed protocol in end-to-end delay, packet delivery ratio and energy consumption, showing that this protocol can be very suitable for the underwater medium.  
**Keywords:** underwater sensor networks; energy efficiency; fault tolerance; routing; scheduling and retransmission
## 5 Experimental Results
### Experimental Test Scenario  
Experiments were carried out in the presence of multiple static objects, as well as moving ones that were walking around the excavator to represent a collision scenario.  
In Figure 10, one monitored static object \(T2\), and two moving objects, \(T1\) and \(T3\), are indicated. The objects present in the ROI carry their unique labels. If an object leaves the ROI, its corresponding label is deleted, and a new label will be assigned upon returning to the ROI. Therefore, some labels in Figure 10 are missing because the objects that had left the ROI did not present in the ROI anymore. As the object enters the ROI and continues to move closer to the excavator, object detection, tracking, and safety evaluation are performed.  
Figure 9: Sensors and test platform: (**a**) Velodyne VLP-16 LiDAR; (**b**) modified mini hydraulic excavator; (**c**) LVDT stroke sensor on the excavator; (**d**) rotatory encoder.  
Note that some points in Figure 10 are not clustered at the current time frame due to the issue of processing time. Therefore, these points may be in a cluster formed after a few frames.
## 2 Materials and Methods  
### Experimental Setup  
The aim of the study was to explore the possibility of protecting wood from fire and biological damage (caused by insects and fungi) using the crystallization of cementitious materials in wood veins. Presumably, such microcrystals decrease the heat transfer into the deeper layers of wood materials, and, in addition, an endothermic reaction takes place with the release of CO2, which cools the fire front and blocks the access of atmospheric oxygen. As the cementitious phases do not provide sufficient protection from rot fungi and wood-damaging insects, we added humates extracted from the peat into the impregnation solution.  
The experimental setup was as follows:  
1. Test specimens were cut from various species of wood.
2. The test specimens were dried at 105 \({}^{\circ}\)C for 48 h.
3. Hunate solutions were prepared. Humates were extracted from black peat using ammonia solution, and a highly concentrated humate solution (exceeding 3%) was obtained.
4. Colloidal oil shale ash, colloidal silica, and humate were mixed, and the test specimens were placed into a mixer using special holders. The system was vacuumed to remove air veins in the wood. The air was slowly reintroduced into the system while the impregnation solution was kept moving by a mechanical mixer.
5. Test specimens were rinsed with water and dried.
6. Cuts of the test specimens were made, and SEM studies were performed to determine the quality of impregnation and the chemical composition of the crystals formed.
7. Another part of each test specimen was placed into a termite colony.
8. The third fraction of each test specimen was used in the fire-resistance tests using a cone calorimeter.  
Upon completing the experimental cycle, we noted varying levels of impregnation and crystallization among different wood samples, and, in some instances, even across different sides of the same sample. This discrepancy stemmed from inadequate liquid flow in the commercial equipment utilized. To ensure uniform impregnation across all wood samples, equipment guaranteeing consistent, continuous liquid flow around the samples is essential. However, the current grant did not allocate sufficient financial resources for this purpose.
## 2 Methods
### Thermography Data Processing Algorithms  
A typical vibration-based thermography procedure results in a sequence of IR-images reflecting the evolution of temperature in time: \(T(x_{i},y_{j},t_{k})\), where \(x_{i}y_{i}\) are the surface coordinates of the pixels and \(t_{k}\) are the discrete time steps. Mathematically, such a sequence of IR-images can be regarded as a 3D matrix \(T_{ijk}\) of temperature data, where \(i\) (row), \(j\) (column) are pixels of the 2D IR-image and \(k\) (frame) is the discrete measurement time. Data processing algorithms for IR-image data can be classified into 1D algorithms, being applied to pixel-based temperature evaluations in time \(T_{k}\), or 2D algorithms, being applied to single 2D IR-images \(T_{ij}\) without considering the change over time [23]. Due to the lack of information resulting from the 2D single image algorithms, pixel-based methods that consider the evolution of temperature over time are commonly used. These 1D pixel-based \(T_{k}\) methods reach from very simple ones like averaging algorithms [23; 24] to more advanced ones like the principal component analysis (PCA) method [23; 25; 26; 27]. The PCA method is commonly used to reduce data dimensions and extract main features and patterns from the IR thermography image sequences. This is done by using either selected principal components or PCA scores of the original images as features with improved properties, for example the ability to detect deep defects. Another commonly used thermography data processing algorithm to suppress slow uneven heating phenomena caused by environmental conditions and enhance the visibility of damages like delaminations that are located within the investigated structure is the discrete Fourier transformation (DFT) [23; 28].
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Time-Driven Model
A large-scale WSN includes many sensor nodes and thus faces issues in terms of data analysis and gathering; it has some limitations regarding data redundancy, sensor energy, and network lifetime. [PERSON] et al. [89] propose an open-pit mining data aggregation technique for heterogeneous clustering sensor networks. Heterogeneous sensor data aggregation is difficult as compared to homogeneous data. The packet ID is used to identify different packets created by several sensors and various applications. The mining pit technique captures as several similar packets as needed, aggregate them strategically and dynamically, and then transmits them to the base station node. To reduce transmission and consume less energy, packets are sent by single hop near neighboring nodes with the help of the mining pit method.
## 4 Experiment  
### Datasets  
To evaluate the effectiveness of the proposed method in this paper, we employed two public datasets, FB15k-237 and WN18 RR, as well as a Tibetan Thangka dataset specifically constructed for Thangka knowledge graph completion.  
FB15k-237 is a dataset based on the Freebase knowledge graph, containing 14,951 entities, 237 relations, and 310,116 triplets. It is a subset of FB15k and is designed to achieve a more balanced dataset by removing some low-frequency relations. The dataset provides three parts: a training set with 272,115 triplets, a validation set with 17,535 triplets, and a test set with 20,466 triplets.  
WN18 RR is a dataset based on the WordNet knowledge graph, consisting of 40,943 entities, 11 relations, and a total of 90,343 triplets. The triplets are divided into training, validation, and test sets. The training set contains 86,835 triplets, the validation set contains 3034 triplets, and the test set contains 3134 triplets. This dataset is commonly used to evaluate the performance of knowledge graph completion algorithms. WN18 RR is a simplified and revised version of the WN18 dataset, designed to increase the difficulty of training models and better reflect real-world applications.  
The Thangka domain knowledge graph, constructed by our project team, describes the history, figures, events, era, style, genre, material, and craftsmanship of Thangka, a special form of painting art. Due to the semi-automatic and manual construction process, there are issues such as incomplete entities, insufficient relationships, and limited knowledge. Therefore, we propose the CAKGC knowledge completion method and validate it on the Thangka dataset. The dataset consists of 3491 triplets, including 1399 entities and 13 relations. It is divided into a training set (2793 triplets), a validation set (349 triplets), and a test set (349 triplets) in a ratio of 8:1:1.
## 4 Implementation Method for Improving Accuracy
### Selection of Calculation Method  
In the climbing stage, the smaller GC is, the larger the difference is in the three-point sampling and the smaller the result error is. If GC is too small, the sampling may reach the stable stage after climbing, the difference between the three sampled voltages will be too small, and the resulting error will increase. The accuracy is the highest only when the three sampled voltages are in the climbing stage of the voltage variation curve. Equation (6) shows that when the difference of the three sampled voltages is close to zero, the calculated value of \(\Delta E_{1}\) is nearly infinite because of signal interference in  
Figure 4: Proposed three-point climbing algorithm.  
the actual application unlike in the ideal situation. This condition seriously affects the measurement result. A positive constant is set as \(C_{a}\) to determine if GC is too small by using the following rule.  
\[\left\{\begin{array}{l}v_{11,2}-v_{11,3}<\mathrm{C_{a}}\\ v_{22,2}-v_{22,3}<\mathrm{C_{a}}\\ v_{12,2}-v_{12,3}>-\mathrm{C_{a}}\\ v_{21,2}-v_{21,3}>-\mathrm{C_{a}}\\ \end{array}\right. \tag{13}\]  
If the three sampled voltages satisfy Equation (13), then GC is too small to be non-negligible, and the traditional sampling method is adopted. If the three sampled voltages do not satisfy Equation (13), then GC is non-negligible, and the proposed three-point climbing algorithm is adopted.
###### Abstract
As can be seen from the published dataset, it simply labels the cracks around the intersection points as distinct objects, which does not satisfy the factual situation. Therefore, the instance segmentation of complex cracks that cross longitudinally and horizontally has yet to be studied in depth. Based on the above research, we can make the following conclusions: the main difficulties in the adoption of bridge cracks include both data and algorithms. The difficulty at the data level lies in the lack of calibration methods used for describing crack morphology. Due to the characteristics of complex cracks intersecting longitudinally and horizontally, a simple closed graph cannot be used to describe its morphology. Therefore, a new technical standard for crack annotation is urgently needed. The difficulty at the algorithm level lies in the absence of targeted optimization for cracks in the instance segmentation. At the same time, the data and algorithm are mutually influential. The crack-counting standard not only affects the difficulty and accuracy of the crack segmentation dataset, but also influences the post-processing of previous output and management decisions. The lack of an optimized instance segmentation algorithm is contrary to the requirements of subsequent evaluation, especially for the detection of cracks with long and thin shapes. Breakpoints in cracks are one of the embodiments of an unoptimized instance segmentation model, resulting in counting errors and wrong dimension-estimation. To this end, this paper uses cameras and UAVs to collect high-resolution images of concrete bridge cracks. A criterion for calibration and the counting of concrete cracks under complex conditions, called relative breakpoint annotation, is proposed. The proposed criterion is used to create a high-quality segmentation of concrete bridge crack examples using Photoshop, LabelMe and Eiseg. The dataset was manually calibrated with information on the true size of the crack targets in the dataset. In order to verify the improvement of crack recognition accuracy with this dataset format, several instance-segmentation algorithms with different design architectures (Mask RCNN and Yolact) were trained and tested. The proposed method aims to propose a normalized crack counting rule and boundary determination rule to standardize the annotation of a crack instance segmentation dataset. Based on the proposed method, the proposed dataset will contain 787 images, including 3794 crack objects, which will greatly improve the accuracy of crack instance segmentation detection at the dataset level. Based on the proposed dataset production method, a set of lightweight crack-recognition methods is implemented according to the proposed dataset production method. This paper begins by introducing the two classic deep learning algorithms in Section 2, including Mask RCNN and Yolact, which are employed to validate the proposed crack annotation method. Following this, Section 3 presents the proposed crack annotation method and a comprehensive analysis in detail. The evaluation work is conducted in Section 4, followed by a section of concluding remarks.
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Method Suggested by [PERSON] et al. [4]  
[PERSON] _et al._[4] have proposed a method to differentiate a legitimate received signal, called clean signal, _c(t)_ from a signal received from a jammer, called jammed signal, _j(t)_ based on the standard deviation of the clean signal, \(\sigma\) and the mean difference between the clean and jammed signals, \(\vec{d}\), averaged over a period of time \(t\) from \(t=a\) to \(t=b\), where, \(a\) and b are chosen constants. They first compute the root mean square value of the clean signal, \(C_{rms}\), and then use it for other computations.  
If \(\ \vec{d}\\) is greater than \(\sigma\), they conclude that it is a jamming signal; else, it is a clean signal. As evident, the calculations have to be done by the WSN node over a period of time (from \(t=a\) to \(t=b\)), very frequently, almost all the time, to keep differentiating the clean and jammed signals. This is a great disadvantage of this method, if used for jamming detection in the WSN scenario. Also, it cannot discriminate a jamming signal, if the jammer uses the same power in the jamming signal as that in the clean signal.
## 5 Case Study
### Operation Mode and Control Analysis  
The thermal system is designed to supply the heating and DHW demands since, according to the climate data and placement of the buildings, no cooling is required for summer conditions. In Vitoria-Gasteiz, to summarize, summers are not very hot, winters are cold and it is partly cloudy all year. During the year, the temperature generally varies from 1 \({}^{\circ}\)C to 26 \({}^{\circ}\)C and rarely drops below \(-4\)\({}^{\circ}\)C or rises above 34 \({}^{\circ}\)C; being the average temperature of 12 \({}^{\circ}\)C.  
The generator units of the HVAC system are two high-performance natural gas boilers and two micro-cogeneration engines working between 80/60 \({}^{\circ}\)C temperature range. The boilers are programmed to work in cascade, and modulate their power from 320 to 500 kW; their energy efficiency varies from 92% (at full load) to 95% (at 30% load). Each of the combined heat and power engine can provide 5.5 kW of electricity, with a nominal electrical efficiency of 27%, and 12.5 kW of heat, with a nominal thermal efficiency of 61%.  
The activation and deactivation of the micro-cogeneration engine circuit is muffled by a 3000-L tank connected to a heat exchanger [34]. The water heated by boilers and the cogeneration units is afterwards separated in heating and DHW circuits. The first circuit is connected to the corresponding heaters of each dwelling while the DHW one is connected to a 3000-L storage tank. In addition, the  
Figure 3: Buildings of the case study in Vitoria-Gasteiz (Basque Country).  
building also has a series of photovoltaic panels on the south facade with 33.5 kW peak power [35] (This work focuses only on the heating and DHW thermal demands so, as electric demand is not considered, the implications of photovoltaic (PV) panels are not taken into account. Nevertheless, the cogeneration engines produce simultaneously electricity, which is used as fuel of hydraulic pumps of the facility. Therefore, such electricity would be considered during the analysis).  
The control system is programmed in such way that the priority is given to cogeneration engines, which are able to cover 90 % of the DHW demand. They turn on at any time when there is heating or DHW demand, since they are the priority units as they simultaneously produce electricity and heating. When the demand is not satisfied and more heat is required, the boilers are turned on.  
The simplified hydraulic scheme of the installation appears in Figure 4, in which all the equipment and flows are labelled. The legend, in the upper and right part, includes the selected components and their assigned abbreviations.
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
## 1 Introduction
Inorganic materials, like silicon oxide and amorphous silicon, are generally used as sacrificial materials [11; 12], but their etching requires F-based vapors that could be a serious challenge in combination with micromachining technologies for structures containing aluminum or silicon oxide. Organic materials, which can be easily removed with oxygen plasma etching, are interesting candidates for sacrificial layers [7; 10] in fabrication processes at low temperature (<200 \({}^{\circ}\)C). In this work, encapsulating caps were fabricated on conductive coplanar waveguides (CPWs), in view of their application for packaging of RF MEMS in fifth generation (5G) and modern telecommunication systems. Capping layers in the thin multilayer SiN\({}_{x}\)/aSi/SiN\({}_{x}\) were deposited by a mixed frequency PECVD. The technological novelty aspect of this work is that a barrel etching was used to pattern the capping layers as an alternative to RIE and ICP processes. This allows an entirely chemical process with no ion bombardment to be applied to the device. Moreover, the use of the barrel etcher for both the pattern and release of the capping layer offers the advantage of reducing fabrication costs; since the encapsulating layer is sequentially etched and released in the same chamber, the plasma chemistry only needs to be changed from fluorine to oxygen-based chemistry. The removal of the sacrificial layer was investigated as a function of the percentage of cap perforated area as well as the time (t) and the power (P) of the barrel etching. Finally, a full three-dimensional (3D) finite element method-based (FEM) simulation model was used to predict the RF performance of uncapped and capped CPWs.
# Secure Physical Layer Network Coding versus Secure Network Coding +
###### Abstract  
When a network has relay nodes, there is a risk that a part of the information is leaked to an untrusted relay. Secure network coding (secure NC) is known as a method to resolve this problem, which enables the secrecy of the message when the message is transmitted over a noiseless network and a part of the edges or a part of the intermediate (untrusted) nodes are eavesdropped. If the channels on the network are noisy, the error correction is applied to noisy channels before the application of secure NC on an upper layer. In contrast, secure physical layer network coding (secure PLNC) is a method to securely transmit a message by a combination of coding operation on nodes when the network is composed of set of noisy channels. Since secure NC is a protocol on an upper layer, secure PLNC can be considered as a cross-layer protocol. In this paper, we compare secure PLNC with a simple combination of secure NC and error correction over several typical network models studied in secure NC.  
secrecy analysis; secure communication; untrusted relay; network coding; physical layer security; cross-layer protocol Article
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 3 Proposed Methodology
The reason behind choosing VGG16, a CNN (convolutional neural network), and MobileNet for this study was that these machine learning models are supported by a mix of architectural adaptability and have demonstrated success in image-based tasks, as mentioned below. * Architectural Diversity: Each of these models exemplifies a different architectural paradigm, demonstrating architectural diversity. With 16 weight layers and a reputation for simplicity, VGG16 serves as a starting point for comparison. In contrast, a CNN is a flexible architecture that is frequently utilized in picture classification tasks because its convolutional layers can effectively record spatial hierarchies. MobileNet is incredibly effective for embedded and mobile applications due to its lightweight, depthwise, separable convolutions. * Depth and Complexity: Although VGG16 needs more processing resources because of its deeper architecture, it can capture complicated features. While not as in-depth as VGG16, a CNN manages to combine complexity and effectiveness. MobileNet is shallower but computationally efficient because it was created for contexts with limited resources. * Benchmarking Performance: VGG16 and CNNs have proven adept at picture classification tasks. These models are appropriate for comparison analysis because they have been used as benchmarks in many research investigations. * Robustness: The objective is to create models that are robust and effective at generalizing to new data in addition to performing well on training data. In a variety of image recognition domains, VGG16, CNNs, and MobileNet have demonstrated strong generalization capabilities. * Availability: These models are easily accessible in well-known deep learning frameworks (such as TensorFlow and PyTorch) and frequently include pretrained weights in huge image datasets like ImageNet. The performance of the models is increased by using these pretrained weights as a starting point. * Resource Constraints: The selection of MobileNet acknowledges situations when computing resources are constrained, such as edge devices or Internet of Things applications. Its lightweight architecture guarantees real-time inference without considerably lowering accuracy. The VGG16, CNN, and MobileNet models function as a carefully chosen synthesis of models, balancing depth, efficiency, and performance. This well-planned choice gave tools for systematically assessing and analyzing the effectiveness of different models in the context of detecting driver drowsiness, illuminating their relative strengths and applicability for various real-world applications.