esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, an analytical and accurate in-to-out (I2O) human body path loss (PL) model at 2.45 GHz is derived based on a 3D heterogeneous human body model under safety constraints. The bit error rate (BER) performance for this channel using multiple efficient modulation schemes is investigated and the link budget is analyzed based on a predetermined satisfactory BER of \(10^{-3}\). In addition, an incremental relay-based cooperative quality of service-aware (QoS-aware) routing protocol for the proposed I2O WBAN is presented and compared with an existing scheme. Linear programming QoS metric expressions are derived and employed to maximize the network lifetime, throughput, minimizing delay. Results show that binary phase-shift keying (BPSK) outperforms other modulation techniques for the proposed I2O WBAN systems, enabling the support of a 30 Mbps data transmission rate up to 1.6 m and affording more reliable communication links when the transmitter power is increased. Moreover, the proposed incremental cooperative routing protocol outperforms the existing two-relay technique in terms of energy efficiency. Open issues and on-going research within the I2O WBAN area are presented and discussed as an inspiration towards developments in health IoT applications.  
health IoT; in-to-out body channel; WBAN; QoS-aware; BER; routing protocol +
Footnote †: journal: Electronics
## 5 Simulation Result and Discussion
### Simulation Setup
#### 5.1.4 Testing and Evaluation  
We evaluated our proposed DF model and other baseline deep learning models. The testing dataset was fed into the developed model; then, we calculated the evaluation metrics. The first metric is the learning parameter, which refers to the number of weighted parameters learned and changed during the training process. The fewer the learning  
Figure 13: Examples of DFU thermal data training: (a) the thermal images of a negative patient, (b) the thermal images of a positive patient.  
parameters, the faster the training process is. The second metric is accuracy, which is formulated as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
in which \(TP\) is a true positive, \(TN\) is a true negative, \(FP\) is a false positive, and \(FN\) is a false negative.  
The third metric is specificity. It represents the ability of a technique to classify a positive patient correctly. Specificity is calculated as follows:.  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{5}\]  
The fourth one is recall or sensitivity. It represents the ability to classify a negative patient correctly. It is calculated as follows:  
\[\text{Recall}\,\text{or}\,\text{sensitivity}=\frac{TP}{TP+FN} \tag{6}\]  
Accuracy, specificity, and sensitivity are the main metrics used to evaluate deep learning performance. Other metrics are also used, namely, precision and F-Measure. Precision is computed using the following formula:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{7}\]  
Precision shows the level of a correctly classified positive patient from a totally classified positive. The last metric is F-measure (FM), which is the harmonic mean of recall and precision. FM is formulated as follows:  
\[\text{F-measure}=\frac{2*\text{Recall}*\text{Precision}}{\text{Recall}+\text{ Precision}} \tag{8}\]  
To assess the proposed method, we compared it with the origin of MobileNetV2, ShuffleNet, ResNet50, DFUNet [7], the combination of MobileNetV2 and ShuffleNet using the average pooling (MS+AP) [48], the combination of MobileNetV2 and ShuffleNet using max pooling (MS+MP), Local Binary Pattern (LBP) and support vector machine (SVM) classifier (LBP+SVM), Local Binary Pattern (LBP) and Naive Bayes (NB) classifier (LBP+NB), and Local Binary Pattern (LBP) and random forest (RF) classifier (LBP+RF). We used 1000 trees for RF classification. In SVM classification, Sequential Minimal Optimization (SMO) [49] was applied with a linear kernel. Other kernels, namely, Gaussian and Polynomial, resulted in significantly poorer performance.
## 4 Conclusions  
This paper presents a novel open-loop control strategy for improving the control accuracy and stability of quadrotor trajectory tracking. The proposed strategy, which combines the high-precision characteristics of open-loop control and the stability characteristics of closed-loop control, can effectively reduce the system oscillation and overshoot existing in the current technology. Firstly, a real-time control and acquisition platform with high-precision positioning, low-delay communication, and asynchronous distribution was built to collect the UAV real-time flight trajectory data and control the quadrotors asynchronously. Secondly, the quadrotor maneuverability model was presented as the key part of the control strategy to bridge between the flight control instructions and the desired acceleration. Finally, the composite control mode on the RNN-LSTM maneuverability model was verified by an actual UAV flight demonstration. In the square flight task, the results show the effectiveness of the proposed hybrid control strategy on the unity of time and space. During the flight, the great consistency between the preset and actual values of the UAV control variables (including velocity and acceleration) was demonstrated to verify the superiority of the model. However, the total flight distance error is approximately 8% due to the lack of objective position information in the model. In future work, we will improve the model accuracy by adding more flight data, and then use more preset tracks of different shapes to demonstrate the robustness of our model.  
Conceptualization, K.C., H.H. and C.Y.; methodology, C.Y. and L.J.; software, H.W., L.J. and F.Z.; validation, C.W. and H.H.; formal analysis, K.C.; investigation, L.J.; resources, C.Y. and C.W.; data curation, F.Z.; writing--original draft preparation, C.W.; writing--review and editing, H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 9: Coordinate curves of the actual flight and preset trajectories.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 4 Aeronautical Mobile Telemetry
### Channel Coding in the 20 th Century
Modulations with improved spectral efficiencies were adopted in two steps or \"tiers\". First were the modulations with twice the spectral efficiency of PCM/FM but with the same detection efficiency as  
Figure 21: A diagram of a simple PCM format comprising minor frames of \(N\) words and a major frame of \(Z\) minor frames [cf., Figure 2a]. PCM/FM using limiter/discriminator detection: [PERSON]'s patented QPSK version B (FQPSK-B) and shaped offset QPSK version TG (SOQPSK-TG). FQPSK-B is a proprietary version of FQPSK [166; 167; 168; 169] with a quasi-constant envelope. It was adopted in the IRIG 106 standard in 2000 [170]. An improved version of FQPSK-B, called FQPSK-JR [171], was closer to constant envelope and was adopted in the IRIG 106 standard in 2004 [172]. SOQPSK was described in 2000 by [PERSON] [173] and was truly a constant envelope. [PERSON] introduced two versions called SOQPSK-A and SOQPSK-B. SOQPSK-A had a slightly narrower bandwidth and worse detection efficiency than SOQPSK-B. In 2004, the Telemetry Group (TG) of the Range Commanders Council adopted a compromise version called SOQPSK-TG in the IRIG 106 standard [172]. SOQPSK-TG was a fully interoperable alternative to FQPSK-B [173; 174]. The second phase or \"tier\" identified modulations with three times the spectral efficiency of PCM/FM but with the same detection efficiency as PCM/FM using limiter/discriminator detection. The result was a non-binary partial response two-index continuous phase modulation (CPM) called ARTM CPM. ARTM CPM was adopted by the IRIG 106 standard in 2004 [172]. An important feature of the new modulations is that all are _digital_ modulations. Consequently, they are well-positioned to incorporate channel coding. As of this writing, PCM/FM, SOQPSK-TG, and ARTM CPM are included in the current telemetry standards IRIG 106-23 [163]. Despite the name of the second one, all three are variations of CPM [175; 176; 177]. The search for new modulations, at the time, was restricted to CPMs because they have constant envelopes. Constant amplitude signals are well-matched to RF power amplifiers operating in their most power-efficient mode: full saturation. These modulations formed the basis for the exploration of channel codes in the 21 st century.
###### Abstract
Since the methodology aims to be a reference for machine producers that want to offer a complete post-selling service to their clients, after customization, it can be applied to every component and every machine, regardless of the specific sensor data and the specific industrial sector. At the methodology level, the main contributions of this paper are the following:  
1. The identification of the stakeholders and the definition of their requirements and expectations. To the best of our knowledge, no existing PHM methodology addresses machine producers. 2. The definition of an edge-cloud-based PHM framework. This allows the real-time health assessment of industrial machines and the simultaneous collection of high-frequency data, low-frequency data, and event-data, from machinery installed in several clients' plants. At the architecture level, the main contributions of this paper are the following:  
1. The integration of novelty detection into traditional PHM architectures. This allows the discovery of unknown or different behaviors of the machines. 2. The distinction between system-level features and component-level features. The first set of features reveal the machinery operating condition (i.e., the machine setting parameters implemented by the client during production) and thus are of great interest for the machine producer to know the possible working conditions and collect labeled data. The second set of features reveals the health status and the degradation trend. Thus, it serves for anomaly detection, fault detection, and RUL prediction. The remainder of the paper is organized as follows. In Section 2, existing PHM methodologies, frameworks, and architectures are described. Here, techniques used to perform feature extraction, diagnostics, and prognostics are described, including anomaly and novelty detection algorithms. In Section 3, a novel framework and architecture for semi-supervised and partially online PHM applications are introduced. Based on the design methodology described by [PERSON], [PERSON], and [PERSON] [7; 8; 9], stakeholders' expectations and requirements are first defined; then, the proposed framework and functional and logical architectures are presented. Section 4 includes the description of a case study. The data were collected from an experimental platform built by the Department of Industrial Engineering of the University of Bologna. The study aims to demonstrate the benefits of the proposed framework and architectures, which include storage-saving and novel condition detection ability. Finally, conclusions, open issues, and the future direction of our research are presented.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 3 Deep Learning-Based Target Detection  
Currently, deep learning is the most common and efficient means to solve the target detection problem. The existing target detection methods are divided into two main categories: two-stage and single-stage [24]. The two-stage method is a deep convolutional network based on candidate regions. Possible candidate blocks containing the detection target are first generated, and then the candidate blocks are classified and corrected to obtain detection frames for target detection. The common algorithms are RCNN (Region CNN) [25] and others. These methods have a high detection accuracy but a low detection speed. Single-stage methods are target detection based on deep convolutional networks with regression computation using end-to-end target detection methods such as YOLO series [18; 19; 20] and so on. These methods have faster detection speeds and can meet real-time requirements. Considering the detection efficiency, we prefer the single-stage model to complete the signal-blind detection task. With the progress of recent research advances, the single-stage YOLO series methods can even achieve higher performance than the two-stage RCNNs for some tasks [10]. Therefore, the YOLO series of target detection models are very suitable for us.  
Figure 4: Block diagram of dataset generation and automatic annotation procedure.  
In this part, based on the standard YOLOv5 network structure, we propose a method to improve the target detection model using a weighted BiFPN in order to achieve simple and fast multi-scale feature fusion and introduce the EIOU loss function to achieve higher accuracy signal detection.
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 3 The Proposed C-DL-NOMA System Model
### Logistic Chaotic Map  
The logistic map is very basic and is often used as a normal chaotic map. It appears to be quite simple and predictable, but its dynamic behavior is extremely complicated. The logistic map is defined in [16] as:  
\[\chi_{n+1}=r\chi_{n}(1-\chi_{n}) \tag{9}\]  
where \(n=0,~{}1,~{}2,~{}3,~{}\ldots\) (\(0<\chi_{0}<1\) and \(0<r~{}\leq~{}4\)).  
Here, \(\chi_{0}\) and \(r\) determine the sequences generated with the logistic map (which is the initial value of \(\chi\)). If even one of the two values changes slightly, the result will differ significantly. The system will exhibit varied properties depending on deviation level [17]. When \(3.56994<r~{}\leq~{}4\) is reached, chaotic behavior occurs, as indicated in Figure 3 [18].  
Figure 2: Proposed C-DL-NOMA system model.  
**Encryption and Decryption Process**:  
1. Assume the following parameters: \(\chi_{0}=0.1\) and \(r=4\). When this is iterated, we obtain \(\chi_{1}\), \(\chi_{2}\),..., \(\chi_{n}\) for the \(N\) value. This is a chaotic one-dimensional sequence (\(A\)).
2. To encrypt the \(MN\)-size original image, a matrix of the same size must be created through transformation of the one-dimensional sequence (\(A\)) to a two-dimensional sequence (\(B\)), where \(B\) is the cipher.
3. The original image is encrypted using an XOR cipher.
4. The encrypted image is decrypted using the XOR cipher.  
**Encryption and Decryption Algorithm:**  
Input: original image, \(\chi_{0}\), and \(r\).  
Output: encrypted image.  
**Step 1.** Transform the original image of a size of \(M\times N\) pixels with an array of \(P=p_{i,j}\), where (\(1<i<M\)) and (\(1<j<N\)).  
**Step 2.** Next, convert the pixel values to unsigned integers in the range of 0 to 255, using mod operation \(P_{m}=\ mod\ \{P,255\}\).  
**Step 3.** Generate the n number of chaotic sequence \(A=\{\chi_{1}\,\ \chi_{3}\,\ \chi_{3}\,\ \chi_{3}\ \dots,\ \chi_{n}\}\) in the range of 0 to 1 using the logistic map in Equation (9), with an initial condition of \(\chi_{0}=0.1\) and taking the parameter of \(r=4\).  
**Step 4.** Transform the chaotic sequence, \(A\), to an array, B, of a size of \(M\times N\).  
**Step 5.** Next, convert B into unsigned integers in the range of 0 to 255, using mod operation \(B_{m}=\ mod\ \{B,255\}\).  
**Step 6.** Encrypted image \(=P_{m}\ \oplus\ B_{m}\).  
**Step 7.** The decryption process is identical to that of encryption in reverse order.
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 4 Results and Discussion  
### Flexural Strength  
The 28 day flexural strength results showed an interaction between the amount of activator in the mix and the type of admixture used. The average results of the flexural strength for all the series are shown in Figure 1. The best results were obtained in the series with a liquifying admixture based on stabilized polycarboxylates and an activator to fly ash ratio of 0.6. In comparison with control samples with the same activator to fly ash ratio, an approximately 11% increase was observed when an admixture based on stabilized polycarboxylates was used. Control samples are characterized by an increase in flexural strength with an increase in activator content of about 4% and 5%, respectively. Dependence was observed in the mortar results with different admixtures.  
Regardless of the activator content, the highest flexural strengths were obtained in mortars with the admixture based on stabilized polycarboxylates (AD1) (Figure 2). Changing the content of the activator did not cause much change when using the plasticizing admixture (AD2).  
The greatest decrease (Figure 2) was observed when the liquifying admixture based on modified polynaphthalenes was used, approximately 11.5% and 17.5%, respectively. Higher activator content when using the admixture based on stabilized polycarboxylates did not cause an increase in the tested mechanical property when changing from 0.5 to 0.6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series Code** & **Fly Ash** & **Sand** & **Activator** & **Admixture** \\ \hline AC/FA = 0.5 AD1 & 400 & 1600 & 200 & 3.2 \\ AC/FA = 0.5 AD2 & 400 & 1600 & 200 & 3.2 \\ AC/FA = 0.5 AD3 & 400 & 1600 & 200 & 3.2 \\ AC/FA = 0.6 AD1 & 400 & 1600 & 240 & 3.2 \\ AC/FA = 0.6 AD2 & 400 & 1600 & 240 & 3.2 \\ AC/FA = 0.6 AD3 & 400 & 1600 & 240 & 3.2 \\ AC/FA = 0.7 AD1 & 400 & 1600 & 280 & 3.2 \\ AC/FA = 0.7 AD2 & 400 & 1600 & 280 & 3.2 \\ AC/FA = 0.7 AD3 & 400 & 1600 & 280 & 3.2 \\ AC/FA = 0.5 & 400 & 1600 & 200 & - \\ AC/FA = 0.6 & 400 & 1600 & 240 & - \\ AC/FA = 0.7 & 400 & 1600 & 280 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geopolymer mortar mix proportions (g).  
The drastic decrease of the flexural strength by approximately 17% in geopolymer mortars containing the highest amount of activator was observed.
## 1 Introduction
proposed an architecture for implementing big-data-driven knowledge management systems [14]. A knowledge management system in a big data context must fully ensure the development process of knowledge including four stages: capture, organize, transfer, and apply. The study stays on the abstract level of KMS without any implementation. In order to overcome the above challenges, we propose to build a big-data-driven healthcare knowledge management system supporting the diagnostic decision in a parallel and distributed environment. The large-scale healthcare system ensures a complete and comprehensive knowledge development process, including knowledge exploration and knowledge exploitation. Additionally, the involvement of artificial intelligent and big data processing is to provide real-time diagnosis decision supports with the massive volumes of medical records for a reasonable response time. The proposed healthcare knowledge management system for supporting medical diagnosis includes four layers: a data layer, an information layer, a knowledge layer, and an application layer. An illustration of the proposed system is presented using machine-learning techniques in the knowledge layer to generate knowledge for hypertension and brain hemorrhage diagnosis. Data used in this system are collected from several hospitals and health-monitoring devices. Hypertension is one of the most leading causes of disability and death worldwide. According to the World Health Organization (WHO), an estimated 9.4 million deaths are caused by high blood pressure. This dangerous disease needs to be promptly detected and treated to limit the risks of death as well as disease complications. We use decision trees to generate knowledge for hypertension diagnosis and classification. Decision trees learn and generate simple rules from a complex decision-making process that is similar to the way of human thinking. In addition, we use deep-learning techniques to generate knowledge for brain hemorrhage detection and classification. A brain hemorrhage is a type of stroke that is caused by an artery bursting in the brain. Stroke is the second leading cause of death according to the World Health Organization. The diagnosis of the disease is based on cerebral CT/MRIimages; thus, we proposed to use deep-learning techniques for hemorrhage detection and classification. The trained model with Faster R-CNN Inception ResNet v2 achieves the mean average precision of 79% in classifying four types of brain hemorrhage. The structure of this paper is organized as follows. Section 2 presents the background related to knowledge management systems in a big data context. The proposed method in Section 3 provides in details our proposed architecture to build a knowledge management system supporting medical diagnosis decisions. The next section introduces a healthcare knowledge management system with the proposed architecture for high blood pressure and brain hemorrhage diagnosis. The conclusion of the paper is presented in the last section.
## 3 UAV 6G Networks
### 6G Application Requirements
#### 3.2.2 Remote and Real-Time Control (RRC)  
Unmanned aerial vehicles are operated through remote and real-time links and a continuous feedback from designated UAVs is received by establishing links through this media. Equipment status, location and other sensory data are received at ground stations from UAVs. In order to ensure seamless command and control of UAVs over wireless communication media, latency and data rate are pertinent considerations and specific required criteria must be fulfilled. In potential 6G networks, a bigger number of unmanned aerial vehicles can be operated and even these machines can accomplish different mission profiles in autonomous mode without direct operator control [73]. Integration of 6G communication infrastructure with satellites can result in vast coverage and required level of remote command and control. Moreover, a latency value of less than 1 ms is also promised in 6G networks, which can bring significant improvement in future UAV networks.  
Figure 9: Multiple layers of the airspace with high-precision positioning.
## 4 Experimental Results  
### Bandwidth Testing  
Bandwidth is the maximum data transfer rate or available capacity of a physical communication link. Figure 6 shows the value of bandwidth for three routing protocols in the outdoor scenario.  
Figure 6 demonstrates that OLSR outperformed the other two routing protocols, reaching 2.9 Mbps. This demonstrates the protocol's capability to handle routing efficiently in a dynamically changing network environment, which is crucial for maintaining reliability  
Figure 4: An indoor WMN testbed featuring orange nodes as endpoints and blue nodes as routers.  
Figure 5: Outdoor WMN testbed.  
in WMNs. BABEL achieved the highest bandwidth at 2 Mbps, followed by BATMAN recording the lowest at 1.1 Mbps.  
Figure 7 presents the value of bandwidth for three routing protocols in the indoor scenario, where the number of nodes varied from 2 to 11, enabling an evaluation of each protocol's scalability based on network size.  
Figure 6: Bandwidth (outdoor testbed).  
Figure 7: Bandwidth (indoor testbed).  
As seen in Figure 7, the Babel protocol initially demonstrated the highest bandwidth of 57.19 Mb/s with two nodes, but its performance declined to 7.16 Mb/s with five nodes and to 7.05 Mb/s with eight nodes, before improving slightly to 8.36 Mb/s with eleven nodes, indicating generally better performance with more nodes. The BATMAN protocol exhibited an initial bandwidth of 45.98 Mb/s at two nodes, which declined to 11.6 Mb/s at five nodes, to 9.81 Mb/s at eight nodes, and further to 6.48 Mb/s at eleven nodes. The OLSR protocol showed an initial bandwidth of 48.67 Mb/s at two nodes, reaching 29.1 Mb/s at five nodes before dropping to 5.58 Mb/s at eight nodes and to 2.57 Mb/s at eleven nodes, suggesting a significant increase in network load as the network size expands. Figure 6 shows that the Babel protocol generally outperforms the other two routing protocols, BATMAN and OLSR.