esa-sceva/satcom-chunk-collection huggingface dataset:
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 3 Preliminaries
### Pointcheval-Sanders Signatures  
The multiple-message Pointcheval-Sanders (PS) signatures [34] consist of the following PPT algorithms:  
* \(\mathsf{PS}.\mathsf{Setup}(1^{\lambda},q)\to pp\): On input of a security parameter \(1^{\lambda}\) and a positive integer \(q\), this algorithm generates a bilinear group \(\mathcal{BL}=(\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T},e,p,g,\widetilde{g})\) and outputs public parameters \(pp=(\mathcal{BL},q)\).
* \(\mathsf{PS}.\mathsf{KeyGen}(pp)\to(sk,pk)\): On input public parameters \(pp\), this algorithm selects \((x,y_{0},\ldots,y_{q})\stackrel{{ R}}{{\leftarrow}}\mathbb{Z}_{p}^{p}\) and computes \(\widetilde{X}=\widetilde{g}^{x},\widetilde{Y}_{i}=\widetilde{g}^{y_{i}}0 \leqslant i\leqslant q\). It outputs a secret key \(sk=(x,y_{0},\ldots,y_{q})\) and a public key \(pk=(\widetilde{X},\widetilde{Y}_{0},\cdots,\widetilde{Y}_{q})\).
* \(\mathsf{PS}.\mathsf{Sign}(sk,\{m_{j}\}_{j\in[0,q]})\to\sigma\): To sign messages \(m_{0},\cdots,m_{q}\) (\(m_{j}\in\mathbb{Z}_{p}^{*},0\leqslant j\leqslant q\)), the signer selects \(r\stackrel{{ R}}{{\leftarrow}}\mathbb{Z}_{p}\), computes \(\sigma_{1}=g^{r}\), \(\sigma_{2}=g^{r(x+\sum_{j\in[0,q]}m_{j}y_{j})}\) and outputs the signature \(\sigma=(\sigma_{1},\sigma_{2})\).
* \(\mathsf{PS}.\mathsf{Verify}(pk,\{m_{j}\}_{j\in[0,q]},\sigma)\to 0/1\): To verify the signature \(\sigma=(\sigma_{1},\sigma_{2})\) on messages \(\{m_{j}\}_{i=0}^{q}\), this algorithm checks two equations, \(\sigma_{1}\
eq 1_{\mathbb{G}_{1}}\) and \(e(\sigma_{2},\widetilde{g})=e(\sigma_{1},\widetilde{X}\prod_{j\in[0,q]} \widetilde{Y}_{j}^{m_{j}})\). If both equations are satisfied, it outputs 1; otherwise, it outputs 0. PS signature achieves EUF-CMA secure status under the PS assumption [34].
# Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
## 4 Experiment Results
### Data Sets  
The object detection data sets PASCAL VOC (07+12) and MS CO-CO2017 were trained and evaluated for network training. The data sets were divided into sets that were used for training, validation, and testing. The train set was used for training. Meanwhile, the validation set did not participate in the actual weight training and could check the training state, setting network parameters. The test set was used in the training process. The PASCAL VOC data set used for evaluation has a total of 20 classification categories and used 8324 images for the train set, 11,227 images for the validation set, and 4952 images for the test set. Moreover, the MS COCO data set has a total of 80 classification categories, and 118,287 train images, 5000 validation images, and 4952 images were used for evaluation. The characteristics of the data set may not be reflected well when training was done without using data augmentation during training, degrading the network generalization performance. Therefore, random data augmentation was applied as shown in Figure 5.  
The data was augmented with random probability during the training process rather than physically expanding them before training. The augmentation method that modifies image color information and adjusts the image scale or shape of an object was used. Additionally, it is important to change the image color information because convolution filters are greatly affected by image pixel values. In the case of objects, small or large data are not well distributed in the training set. Therefore, the data imbalance problem was alleviated by cropping the object area or reducing the image scale ratio.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 5 Simulation Results
### Hbf-Mo-Fd-R  
In this subsection, simulation results for the proposed robust HBF scheme for the FD relay system is presented. For notation convenience, \"HBF-MO-FD-R\" represents our proposed method, \"HBF-FD-SIC\" denotes the HBF-MO-FD-R algorithm with SIC-NP replaced by the SIC method in [30], \"HBF-FD-NSIC\" stands for the proposed method without SIC-NP, while \"HBF-MO-HD\" denotes the proposed HD algorithm with imperfect CSI. Additionally, it is assumed that \(a_{0}=b_{0}=\psi=0\)[15]. Unless otherwise stated, the parameters related to channel estimation errors are set as \(\alpha=0.6\), \(\beta=0.4\) and \(\sigma_{e}^{2}=0.5\). Meanwhile, the Interference to Noise Ratio (INR) is defined as \(\text{INR}=\)\(10\lg\left(P_{\text{th}}/\sigma_{\text{R}}^{2}\right)\)[30; 35].  
In Figure 7, we show the convergence performance of HBF-MO-FD-R with respect to iterations at SNR = 6 dB and INR = 10 dB, where the maximum number of iterations is set to 15. It can be observed that SE converges to a maximum of above  
about seven iterations. On the other hand, MSE decreases to a minimum of almost 0.18 after about five iterations.  
Figure 8 compares the SE performance of four different algorithms. As expected, SE of all approaches improves monotonically with increasing SNR. It can be obviously seen that both the HBF-MO-FD-R algorithm and the HBF-FD-SIC algorithm obtain higher SE by suppressing the LOS SI in contrast with the method without SIC. Additionally, the performance of the two methods is still affected by the residual SI because the NLOS channel cannot be accurately estimated. As is shown in the figure, with the increase in INR, the SE of HBF-MO-FD-R and HBF-FD-SIC decreases, but that is still higher than the non-SIC method. Meanwhile, we find that our proposed algorithm outperforms the HBF-FD-SIC approach. The average gap between two approaches is about 0.913 bit/s/Hz when INR = 10 dB and 0.791 bit/s/Hz for INR = 20 dB. Moreover, the non-robust algorithm can obtain more SE than the non-SIC algorithm when SI is severe (INR = 20 dB). It is noteworthy that the value of INR has no impact on the performance of HBF-MO-HD because there is no SI channel in HD mode.  
Figure 8: SE versus SNR for four HBF algorithms with different INR settings.  
Figure 7: Convergence of SE and MSE for the HBF-MO-FD-R algorithm.  
Figure 9 depicts the SE performance with various CSI errors. We analysis how the SE performance is affected by estimation error variance with \(\alpha=0.6\), \(\beta=0.4\) and INR = 10 dB. As we can see, imperfect CSI will lead to performance degradation in all approaches, whereas our algorithm still has the best performance, which illustrates the effectiveness of the robust design and SIC.
## 3 Results
### Dynamic Biogas Process Modeling
#### 3.4.1 White Box Biogas Process Models
The simulation resulted in a reduction of 30 to 35% of liquefied petroleum gas substituted by biogas production made of biowaste at the campus site. The \"Dynamic Biogas plant Model\" (DyBiM) was introduced as a MATLAB(r) Simulink simulation in [91] to connect the model of a biogas plant with an electrical energy grid. The AD process was included as an ADM1 with its parameters from literature to describe different types of substrates. Cattle manure and sugar beet were used as reference substrates in this work. Regarding the plant aggregate, a dynamic mass balance of a perfect mixed gas storage was modeled to obtain a homogenous gas mixture depending on different types of feedstock. Compared to the dynamic behavior of the AD, the CHP unit was modeled as a balance equation without any dynamics but is affected by the gas storage levels, which leads to an increasing CHP energy production at higher gas storage levels and vice versa. Operation under Swedish conditions showed that adaptive feeding management due to flexible energy demands and, thereby, flexible CHP production rates led to a reduced gas storage capacity under varying gas ratios, calculated by the broad AMD1. In order to control this kind of flexible biogas energy production, Reference [92] introduced a MATLAB(r) biogas model with a proportional and integral gain error (PI) controller which regulates the substrate feed input comparing the methane production rate. The ADM1 was modified to a version called ADM1 xp, which allows the use of a Weende analysis of the feedstock to enable the utilization of well-known analytical substrate characteristics. The PI controller could regulate the substrate flow into the digester to cover the energy demand by connecting to a virtual energy demand as a load profile of the region of Emden (Germany). Some differences remained as controller error, however, the methane production covered the demand 79.4% of the time. The PI controller was originally designed for fast systems and could not react to the slow digester behavior to cover a higher methane demand due to instabilities in the simulation.
## 5 Conclusions  
This paper proposes and presents a novel packet scheduling algorithm, which utilizes the mechanism of multiple CCs aggregated for transmission, for efficient downlink transmissions in 4G/5G cellular systems. The simulation results detailed above indicate that, as long as \(\eta\) and \(t_{\text{th}}\) are properly adjusted, the proposed scheduler design can cope well and simultaneously with both RT and NRT traffic. In addition, the scheduler also ensures a certain level of fairness among all the users because it is based on the PF criterion. Compared with a baseline algorithm in which multiple CCs are not aggregated for transmission, the proposed algorithm was able to achieve significant improvements in system throughput, mean delay, and fairness performance. In particular, the proposed algorithm provided substantial improvement in terms of the delay performance under the condition of high system loads. Meanwhile, because it flexibly and effectively utilizes all CCs, the proposed packet scheduling algorithm strongly supports the timely delivery of RT traffic and NRT traffic in a simultaneous manner. As a result of this efficiency in its design, the proposed algorithm can also improve overall energy efficiency to some degree. We therefore believe that the proposed scheme constitutes an excellent solution for the packet scheduling of downlink transmissions in multiple-CC cellular systems. Relatedly, we recommend that the cellular industry consider applying the proposed algorithm in developing 5G cellular systems, given the fact that such systems must take both spectral efficiency and energy efficiency into account. In future studies, it would be worthwhile to consider the QoS requirements of various real-world traffic patterns. Further investigation may be required in order to determine how to adjust \(\eta\) and \(t_{\text{th}}\) in the scheduling process in an adaptive manner based on the fluctuation of RT and NRT packet traffic loads so as to achieve the best overall system performance possible.  
**Acknowledgments:** The author wishes to express his appreciation for the financial support provided by the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., under the Contracts MOST 104-2221-E-019-053-, MOST 104-2218-E-019-004- and MOST 105-2628-E-019-002-MY3.  
**Conflicts of Interest:** The author declares no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Evaluation Methodology  
As accuracy is not generally considered the best measure of the performance of a model, the study employed two main metrics i.e., precision and recall, which are not biased by imbalanced class distributions. However, accuracy was also calculated for the sake of comparison with existing approaches. It is important to note here that the metrics were applied for each class separately. In this way, the case of abnormal behavior (three classes) versus _none_ was not considered as it will be inconsequential to mark a case as true positive where, for example, fall has been predicted as loitering (note both are abnormal events).  
The metrics are briefly described in the following.  
_Precision:_ Precision is the measure of the proportion of positive identifications of a class that was actually correct. In other words, it measures what proportion of the predictions made for a class (fall, loitering, violence, none) was correct. Precision \(P\) is defined as follows:  
\[P=\frac{TP}{TP+FP} \tag{3}\]  
where _TP_ and _FP_ refer to true positives and false positives, respectively.  
_Recall:_ It is also known as sensitivity. It is a measure of the proportion of actual positives that were identified correctly. In our case, recall measures what proportion of the behavior labeled as one of the four classes was correctly predicted. Recall \(R\) is defined as:  
\[R=\frac{TP}{TP+FN} \tag{4}\]where _TP_ and _FN_ refer to true positives and false negatives, respectively.  
_Accuracy:_ The accuracy \(A\) of the model can be calculated as follows:  
\[A=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
where _TP_, _TN_, _FP_, and _FN_ are true positives, true negatives, false positives, and false negatives, respectively.  
**F\({}_{1}\) Score:** The \(F_{1}\) score is the most used metric of a more general parametric class known as the F-measures. It is defined as the harmonic mean of precision and recall and can be calculated as given below:  
\[F_{1}=\frac{2.TP}{2.TP+FP+FN}=2.\frac{precision.recall}{precision+recall} \tag{6}\]  
where _TP_, _FP_, and _FN_ are true positives, false positives, and false negatives, respectively, and precision and recall are the metrics defined above.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 7 Simulation--Real Topology
### Methodology for Finding Repeaters Position  
The methodology for finding repeater position is based on methods used in the real grid with BPL. There are three kinds of BPL nodes in the network hierarchy:  
* Head End node, also called the Master. The position of the Head End node is always in the transfer station (node n1).
* Repeater node, which behaves as a slave for the Master and a Master (Time Division Repeater (TDR)) for the slaves.
* Customer Premise Equipment (CPE), also called the slave node. General description according to real grid network hierarchy with BPL:  
1. The HE master sends continuous invitation. When a CPE detects an access network, it will start the Access Protocol in order to obtain access to the network (to find master). The master will select the best CPE according to certain criteria (throughput and SNR).  
Figure 9: Real topology.  
Once a CPE is connected to a master, it can re-evaluate its status to the TDR, and can establish a new connection to a new CPE (which is not visible thanks to the distance to the HE master), according to certain criteria (throughput and SNR). TDR units have the main function of extending the coverage over a longer distance.
3. TDR sends continuous invitation and CPE starts the Access Protocol in order to obtain access to the network (to find master or TDR).
4. Repeated for the whole topology to build network hierarchy.
5. The second step takes place after the auto-configuration process and every period (e.g., three hours).  
The proposed methodology in our approach for finding repeaters according to the General description above:  
1. The throughput and SNR are evaluated between the HE node (n1) and the following nodes in the topology (nx) until the throughput does not drop below 1 Mbps or SNR below 3.
2. The nx node is set-up as a new repeater (TDR).
3. The throughput and SNR are evaluated between the TDR node (nx) and the following nodes in the topology (nxx) until the throughput does not drop below 1 Mbps or SNR below 3.
4. Repeated for whole topology after the CPE is found.
5. Optimization of positions of repeaters according to knowledge of throughput and SNR of all possible communication paths.
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method
### Algorithm for Image Encryption  
#### 3.2.1 5D Gauss Generator  
The Generator creates a 5D Gauss Map throughout this procedure. By the virtue of Equations (2)-(6), and starting values of \(x_{1}=0.3250\), \(y_{1}=0.4250\), \(z_{1}=0.5250\), \(w_{1}=0.4350\), \(s_{1}=0.5350\), \(a=0.0135\), \(b=0.0177\), \(c=4.9\), and \(d=-0.58\), it creates chaotic sequences. Any value between zero and one for the initial values of the variables can be chosen here.
# Spectrum Demand Forecasting for IoT Services
###### Abstract
In addition to new bands, an important amount of research has been put recently on spectrum sharing and technologies coexistence [15]. This is a result of the need to use shared spectrum and smarter radio technologies more aggressively. One tangible result of the shared spectrum trend is the CBRS alliance (Citizens Broadband Radio Service), where a powerful group of telecommunications stakeholders has demonstrated the possibility of shared use in the CBRS band (3550 MHz to 3700 MHz) that used to be of exclusive use for military radars in the USA. According to the FCC, mobile operators will be able to deploy 5G networks without acquiring licenses for this band. This 150 MHz will be shared between three types of users: incumbents, priority access licensed (PAL), and general authorized access (GAA). Some telecommunications vendors like Qualcomm have rushed into developing technologies for the CBRS band or other bands of similar characteristics that may be approved in the future. MulteFire and LAA (Licensed Assisted Access) are examples of LTE-U and Wi-Fi/5G spectrum sharing [16; 17; 18; 19]. Never before have wireless communications permeated so deeply in all industries and economic sectors. The main technologies, networks, and services of the IoT might not exist yet, such as a network for the huge business of vehicle communications (V2X). Therefore, it is crucial to correctly forecast the spectrum needs, which bands should be used for which services, and the economic potential of their utilization. All telecommunications operators and manufacturers in the world are concerned about what should probably be the most important spectrum planning framework of the century. Without correct spectrum allocations and pricing policies, manufacturers and stakeholders will not risk their investments and will not develop technologies for future services. Consequently, this research provides an example of a complete study on IoT spectrum forecasting from a government regulator perspective divided into two phases:  
* Section 3: A market study specifically tailored for IoT. Based on available bands, incoming technologies and their possible market penetration, regional telecommunications industry influence, and local adoption capabilities. * Section 4: A forecasting model accounting for new spectral efficiency gains, and new techniques improving the overall system capacity in terms of rate (such Multiple Antenna, MIMO, systems) and in terms of access efficiency (such Non-Orthogonal Multiple Access, NOMA). The model has been developed independently for each group of services and technologies that use a specific frequency band in a shared area of coverage. Additionally, Sections 5 and 6 show the application of the methodology and the spectrum forecasting results in Colombia. All steps are documented from the market study to the final adjustments. Section 7 elaborates on the main learned lessons and draws conclusions.
## 1 Introduction  
Micro autonomous underwater vehicles (\(\mu\)AUVs) are currently a prominent research topic and are expected to gain more importance in the future, especially for applications in confined liquid-filled tanks, e.g., in process engineering. However, current self-localization approaches for \(\mu\)AUVs pose great challenges and do not provide satisfactory and reliable performance yet, which is a key capability for autonomous operations.  
Advances in the area of microelectronics are continuously leading to the miniaturization and cost decrease of hardware, such as motor controllers and sensor suits. This enables groundbreaking possibilities for the development of novel \(\mu\)AUVs. Underwater vehicles are usually termed \(\mu\)AUVs if their characteristic length is less than 50 cm. Recent examples are the Avexis submersible [1] and the HippoCampus robot [2]; the latter is shown in Figure 1. Micro AUVs are becoming increasingly interesting for industrial applications where they are operated in confined liquid-filled tanks. Examples include nuclear storage ponds [1], tanks in process engineering or wastewater treatment. Figure 2 illustrates a potentially large fleet of \(\mu\)AUVs operating in a liquid-filled tank. Tank environments considered in this work have length scales of approximately 3 m to 25 m. While the mentioned robotic platforms could in principle allow for autonomous operations as depicted in Figure 2, the current main challenges lie in the self-localization of the vehicles. No available localization approach provides satisfactory results for autonomous operations of \(\mu\)AUVs in confined tanks so far. Any kind of useful autonomous \(\mu\)AUV behavior requires the \(\mu\)AUVs to know their absolute positions. In the last few years, a very promising underwater localization framework based on the attenuation of electro-magnetic(EM) carrier waves in water was introduced in a series of papers [3; 4; 5; 6; 7]. In principle, the approach provides a satisfactory solution for the localization problem in confined tanks; however, it is not of immediate use for \(\mu\)AUVs. The method utilizes a signal analyzer that is too large and expensive to be included in a \(\mu\)AUV. This paper presents an embedded low-cost version of the EM localization principle for global self-localization of \(\mu\)AUVs.
## 2 Related Work
### Compliance Checks
#### 2.2.1 Source of Data  
Data about production can come from various sources, including human and IIoT devices. Human data can be recorded through mobile apps and desktop applications.  
While there are still producers relying on manual paper-based data recording, even that data can be converted to digital information. However, the data are mainly structured, i.e., specific data are recorded for specific purposes. The following are some categories of data in the case of agriculture:  
* _Sensors_ collect real-time numeric data on soil moisture, temperature, plant health, and other factors [55; 56]. This is a well-established technology;
* Pest control measures with traps are still mostly manually recorded, although IoT devices are being developed for this as well [57; 58]. On-site detection of pesticide residues, such as through biosensors and colorimetric sensors, is also mostly recorded manually [59; 60];
* Drones and satellites capture high-resolution images of fields, allowing farmers to assess crop health, identify pest or disease outbreaks, and monitor irrigation effectiveness [61; 62; 63];
* QR code, RFID, and NFC offer a secure and easy way for consumers to access product information with their smartphones [64; 65; 66; 67];
* Several advanced technologies that can directly generate digital data in the form of images or text are being developed to uniquely identify farm products, such as DNA barcodes and unique chemical makeup [68; 69; 70; 71];
* Compliance checks in the industry can involve a variety of methods or work, including document review, field inspections, and laboratory testing, if necessary [72]. These will ultimately create documents that are usually in a structured or semi-structured format.