esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 4 UE Receiver PHY for CS
### SSS Search  
In SSS Search, the received 240 samples extracted from the third OFDM symbol of the detected SS are correlated with 336 reference SSS sequences to detect the \(PCI_{2}\in\{0,1,\ldots,335\}\). Instead of storing all reference sequences, we have used the SSS generator in Figure 4 to generate the desired sequence. Since PCI is not known, the scheduler is used to cycle through all possible SSS sequences. The sequential SSS search architecture is shown in Figure 8. Depending on the given resource and latency constraints, we can explore serial-parallel architecture by using multiple SSS generators and correlation blocks in parallel.
## 4 Security in 5G-Enabled D2D Communications Network
### Problem Definition  
5G promises to create new use cases, support vertical services, reduce backhaul traffic and push content closer to the users facilitated by D2D communications. However, security in D2D communications has been a serious concern due its wireless characteristics [41], omission of third-party and 5G's heterogeneous nature, which present several security vulnerabilities that put the whole network at risk from new and old threats [2]. D2D-based content distribution [9; 42] and traffic offloading will increase the vulnerabilities of entities and data. In addition, security for services and user's data at the edge is another concern.  
Security issues in D2D communications and CCN systems [43; 44; 45; 46] have been investigated separately, but not for an integrated system. The system can be affected by attacks such as eavesdropping, data fabrication, spoofing, content poisoning, privacy violation, denial of services, masquerading and linkability attacks [47; 48; 49]. Due to the self-management of D2D UEs, implementing security and privacy is difficult and more challenging in 5G as privacy violation increases when D2D users are accessing ProSe. The HN and visited network could also be curious and eavesdrop on D2D communications. These attacks require robust authentication methods for a secure communication between D2D users [16] and access control methods to secure service access and delivery [29; 30].  
After primary authentication methods such as 5G-Authentication and Key Agreement (AKA) [1] is used to authenticate the UE to the HN [50], the UE is usually granted static authorization, but, with edge computing, third-party services, increasing demand for multimedia contents and tactile Internet in 5G, access authorization is more complicated. Therefore, multi-level security measures must be used to allow interoperability between different security domains. MNO/SP should be able to restrict access to its services even when the UE is not in coverage. Thus, network access and service protocols must be used to allow the UE gain access to services provided by MNO/SP in HN or data network [39; 50] and enable secure service sharing between UEs.
## 4 Algorithms for TSOAP
### The MULTIFIT Algorithm  
In [10], the authors suggest a basic heuristic for offset assignment, achieved by sorting the list by non-decreasing period before application of a basic First-Fit (FF) algorithm under the assumption that each successive task is 'easier' to fit around the previous tasks. Given the analysis of the previous Section, it is clear that a better option would be to order the tasks by non-decreasing harmonic period; this leads to the First-Fit-Phase (FFP) algorithm. FFP first performs such a sort, then each task is assigned--one by one--the lowest indexed offset that will schedule it, until either all task are scheduled (returning TRUE) or one or more tasks couldn't be scheduled (returning FALSE). The LCS algorithm is used for the feasibility test, and to reduce the search space only offsets bounded by each tasks phase capacity _p'_ are considered. The MULTIFIT algorithm proposed in this paper uses FPP as a key component in a binary search; MULTIFIT for the classical multiprocessor scheduling problem was originally proposed in [13], and it has been proven that the resulting makespan can be no further than 22% away from the optimal makespan. The modified algorithm considered in this paper is very similar; a binary search is performed to find the minimum value of \(\alpha\) such that applying FFP schedules the task set. Since FFP (and hence LCS) are called multiple times, the _gcd_'s of each pair of tasks are computed once only and stored in an array, and sorting is performed once only. The initial upper bound is set to the sum of tasks execution times, and the lower to the largest execution time in the task set. The MULTIFIT algorithm proposed in this paper is shown in Figure 6.  
As mentioned, for fixed \(n\) LCS runs in polynomial time; hence for fixed \(n\), MULTIFIT has complexity _O(np' max)_, _i.e._, pseudo-polynomial. The second algorithm to be described is based upon a local-search version of the classical LPT algorithm described by [PERSON] [17].  
Figure 6: MULTIFIT algorithm, in pseudo-code.
## 3 Operating Modes of the Proposed Solar Tracking System
### Manual Operation Mode
#### 3.2.2 Manual Operation by Desired Angle  
This manual tracking mode can be used mainly for the assembly of the payloads of the STS since it can orient the STS to an angular position established in the HMI screen. Figure 5 shows the closed-loop on-off position control of this operation mode, which is the simplest, easiest and most inexpensive control method. First, the control error \(e\) is calculated by Equation (9), where \(r\) is the desired angular position or reference, and \(TP_{G}\) is the actual angular position of the solar tracker. At the same time, \(TP_{G}\) is calculated by \(TP_{G}=TP_{E}*k\), where \(TP_{E}\) is the position tracker measured by the encoders' values, and \(k\) is the inverse of the total gear reductions of the slewing drive: \(k=1/14632\). It is important to mention that for dual-axis solar tracking, it is required to duplicate the control diagram of Figure 5 and Equation (9), corresponding to the azimuth and zenith axis:  
\[e=r-TP_{G}=r-(TP_{E}\times k)=r-\frac{TP_{E}}{14,632}. \tag{9}\]  
Thus, the calculated error \(e\) is used to determine the control signal (\(u\)) through a closed-loop on-off control. In this control system, the \(u\) value switches abruptly according to three possible conditions as seen in Figure 6, where \(L_{T}\) and \(U_{T}\) are, respectively, the lower and upper threshold of the allowed tracking hysteresis, which is commonly the same but with opposite signs. This threshold prevents excessive changes in the control signal because of the noise in the solar sensor. In the presented STS, the hysteresis has a value of \(\pm 0.3^{\circ}\); however, the user can change it in the HMI. As observed in Figure 6, if the \(e\) value is greater than \(U_{T}\), \(u\) is positive, and, by means of the H-bridges, the motor turns clockwise on the azimuth axis and/or up in the zenith axis. On the contrary, if the value is lower than the \(L_{T}\), \(u\) is negative and the motor turns counterclockwise in the azimuth axis and/or downward in the zenith axis to reduce the \(e\). In the ideal case, when the \(e\) value is between the \(U_{T}\) and \(L_{T}\), \(u\) is 0, and the actuators do not move. Video 4 ([[https://youtu.be/fz_K2](https://youtu.be/fz_K2) xjqwj8]([https://youtu.be/fz_K2](https://youtu.be/fz_K2) xjqwj8), accessed on 20 January 2010) shows the manual operation mode by desired angle. In this mode, the STS remains in its HP until the desired angle or reference value \(r\) is entered via the HMI screen. The system then begins its journey until it reaches the desired position: until the \(U_{T}\)\(>e>\)\(L_{T}\). The HMI displays the current \(e\) value. Note that the feedback produced by the encoders generates a closed-loop control. There is no solar tracking, however, since the STS tracks an established angular reference.
## 4 Numerical Results  
In this section, we present simulation results to demonstrate the performance of the proposed EDA and MEDA. The impact of network parameters (e.g., number of SUs, number of relays) is also investigated.
## 4 Soil water and drought monitoring for early-warning applications
### Model Inputs
#### 4.1.3 Soils  
Another important model parameter for the crop water balance model is the soil water holding capacity. For global applications, the digital soils map of the world from FAO is used (FAO, 1988).
# Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China
###### Abstract  
As an emerging business form of tourism development, homestays also play an important role in China's rural revitalization and tourism transformation and upgrading, and has attracted increasing social attention. At present, Chongqing is the city with the largest number of homestays in China. Taking Chongqing as a case-study area, based on the homestay data of the Baidu map, this paper comprehensively uses the methods of spatial analysis, multiple regression and geographical weighted regression to thoroughly analyze the regional characteristics and influencing factors of homestay distribution in Chongqing. The results show that: (1) the nearest-neighbor index R of homestay distributions in Chongqing and all regions is one, which shows an obvious agglomeration type. (2) In addition to being highly concentrated in the central urban area, three secondary high-density areas are also formed in the surrounding areas of the central urban area, and there is a trend of concentration and contiguity. The spatial distribution densities of the two urban agglomerations in Southeast and Northeast Chongqing are very low, and the overall distributions are extremely uneven. (3) The factors, such as tourism resource endowment, economic development, service industry development, traffic location, consumption demand and social development conditions, have significant impacts on the distribution pattern of homestays, and the impacts of each factor on the layout of homestays has obvious spatial heterogeneity. Analyzing and revealing the temporal and spatial characteristics and dynamic mechanism of homestays has an important theoretical value and practical significance for better serving the new urbanization plan and implementing the strategy of urban-rural integration and rural revitalization.  
homestay; space agglomeration; distribution characteristics; influence factor +
Footnote †: journal: Applied Sciences
## 5 Analysis of Cracking and Damage in Wharf Structures under Impact Loading
### Damage Identification Based on Modal Flexibility Using Finite Element Simulation
#### 6.2.1 Modal Flexibility Fingerprint  
According to structural dynamic analysis theory, the flexibility matrix of a structure can be expressed in terms of modal parameters as follows [32; 33]:  
\[F=\sum_{i=1}^{n}\frac{1}{\omega_{i}^{2}}\varphi_{i}\varphi_{i}^{T} \tag{7}\]  
Figure 31: Schematic diagram of vertical distribution of dynamic strain amplitudes along piles.  
Figure 32: Distribution of dynamic strain amplitudes at different heights of the bend.  
where \(F\) is the structural flexibility matrix; \(\omega_{i}\) is the \(i\)-th natural vibration frequency; \(\varphi_{i}\) is the mode shape vector normalized with respect to mass at the \(i\)-th frequency; \(n\) is the degrees of freedom of the structure.  
From Equation (7), it can be inferred that due to the relatively small \(1/\omega^{2}\) values of higher-order frequencies, the flexibility matrix is primarily determined by the lower-order modes. Therefore, by measuring the lower-order modes of a structure, a highly accurate flexibility matrix can be obtained. This matrix is referred to as the modal flexibility matrix. Given the numerous elements in the modal flexibility matrix, representative values are selected for damage identification to facilitate the study of modal flexibility changes before and after structural damage. The most representative values are the diagonal elements of the flexibility matrix and the uniform load surface (_ULS_) flexibility. In this paper, _ULS_ flexibility is used for damage identification. For pile-like slender rods, the uniform load surface of a node refers to the cross-section where the node is located. This method uses the _ULS_ values of each node to represent the overall flexibility matrix for damage identification. The variation in _ULS_ values before and after damage represents changes in modal flexibility. The _ULS_ value for each node is defined as follows:  
\[U_{i}=\sum_{j=1}^{m}f_{i,j}\ \ \ (j=1,\ 2,\ \cdots,\ m) \tag{8}\]  
where \(U_{i}\) is the _ULS_ value for each node;\(f_{i,j}\) is the element of the flexibility matrix in the uniform load surface at that node; \(m\) is the number of structural nodes.  
Using Equations (7) and (8), the modal flexibility before and after damage is calculated. The undamaged flexibility matrix and modal flexibility are denoted as follows:  
\[F^{0}=\sum_{i=1}^{n}\frac{1}{\left(\omega_{i}^{0}\right)^{2}}\varphi_{i}^{0} \left(\varphi_{i}^{0}\right)^{T}\ \ \ (i=1,\ 2,\ \cdots,\ m) \tag{9}\]  
\[U_{i}^{0}=\sum_{j}^{m}f_{i,j}^{0}\ \ \ (j=1,\ 2,\ \cdots,\ m) \tag{10}\]  
The flexibility matrix and modal flexibility after damage are denoted as follows:  
\[F^{D}=\sum_{i=1}^{n}\frac{1}{\left(\omega_{i}^{D}\right)^{2}}\varphi_{i}^{D} \left(\varphi_{i}^{D}\right)^{T}\ \ \ (i=1,\ 2,\ \cdots,\ m) \tag{11}\]  
\[U_{i}^{D}=\sum_{j}^{m}f_{i,j}^{D}\ \ (j=1,\ 2,\ \cdots,\ m) \tag{12}\]  
The difference in modal flexibility for each node before and after damage is given by:  
\[\Delta U_{i}=U_{i}^{D}-U_{i}^{0}\ \ \ (i=1,\ 2,\ \cdots,\ m) \tag{13}\]
## 1 Introduction
We evaluated the proposed learning framework on three image classification benchmark datasets: MNIST [16], Fashion-MNIST [17], and EMNIST-LETERS [18]. We compared our framework's performance to a conventional collaborative deep learning framework that does not protect privacy. We used two neural network architectures: multilayer perceptron and convolutional neural networks [15]. Our experimental results show that our framework preserves privacy with some loss in accuracy. Also, it decreases the training time significantly compared to a conventional collaborative framework that does not protect privacy. The main contribution of this work is the proposal of a novel paradigm in which Restricted Boltzmann Machines are used to extract salient features from training data, resulting in stochastic representations or encodings. These encodings act as a proxy for real data during deep learning model training, achieving equivalent accuracy. Additionally, our technique incorporates Instance Reduction, allowing a training entity to selectively share a portion of its encoded training data. Our framework's importance goes beyond privacy concerns. Extraneous noise and irrelevant instances are effectively filtered out by IR, leading to significant improvements in computer efficiency, communication time, memory use, and overall classification accuracy. In contrast to the existing literature, our approach stands out for its inherent simplicity, providing privacy preservation while preserving deep learning model efficiency, all while reducing computing and communication costs. Our study is notable for its synergistic combination of RBMs and Instance Reduction Techniques, which presents a unique framework that has not been previously studied in the literature, as far as we know. Our use of Restricted Boltzmann Machines (RBMs) to ensure privacy and security represents a novel approach. To the best of our knowledge, there are no previous studies that have utilized RBMs for security or privacy protection purposes [8]. The article is structured as follows: Section 2 introduces the key techniques used in our framework--Restricted Boltzmann Machines and Instance Reduction Techniques. In Section 3, we review related works on privacy preservation in deep learning. The heart of our contribution is in Section 4, where we present our innovative framework. Sections 5 and 6 cover the evaluation and discussion of our results, respectively. Finally, Section 7 concludes our study and suggests directions for future research.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 2 Related Work
### Transmission Map Estimation Approach  
Drawing from the seminal work of [PERSON] et al. [4], advancements in the DCP approach to transmission map estimation have led to notable improvements. These refinements are geared towards enhancing the precision of transmission map prediction, consequently augmenting the efficacy of haze removal techniques.  
[PERSON] et al. [6] contributed to this refinement by introducing a novel boundary constraint for the transmission function and context regularization based on a weighted L1 Norm. Their approach treats the recovery of unknown transmission quantities as an optimization problem, yielding natural images with reduced ambiguity between color and depth and mitigating excessive contrast.  
Meanwhile, [PERSON] et al. [8] propose a non-local dehazing method employing line averaging to reduce morphological artifacts. They construct a multiscale dehazing image model utilizing the Laplacian pyramid of the hazy image and the Gaussian pyramid of the transmission map. However, the effectiveness of this method hinges on the proper selection of the parameter \(\eta\), as an inappropriate value may lead to suboptimal haze removal. Nonetheless, its advantages lie in artifact removal and simplicity, rendering it suitable for mobile device applications.  
On another front, [PERSON] et al. [13] present a single-image dehazing technique leveraging dual transmission estimation and gradient domain guided image filtering, enhancing brightness and ensuring fast computational times. However, this technique encounters challenges in effectively dehazing night-time images and may introduce halo artifacts.  
These studies collectively introduce diverse methodologies for single-image haze removal, each contributing technically to the field and holding the potential for enhancing image restoration quality in real-world scenarios. While methodologies like boundary constraints [6] and multiscale approaches [8] demonstrate effectiveness, challenges persist in boundary detection accuracy, parameter selection, and applicability under specific conditions, such as night-time haze and halo artifact mitigation, despite computational efficiency [13].
## 4 Observation Simulations of GMR, GeoSTAR, and GIMS
### Simulation of the Observed Brightness Temperature TA of GMR, GeoSTAR, and GIMS
#### 4.2.2 Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR)
Figure 6: The antenna array (**a,b**) (partial zoomed—in view) and resulting UV plane sampling pattern (**c,d**) (partial zoomed-in view) of GeoSTAR. The array factor (AF) of a SAIR is defined as Equation (7), which is also called the point spread function (PSF). \[\text{AF}(\xi,\eta)=\iint W(u,v)e^{i2\pi(u\xi+\eta v)}dudv \tag{7}\]  
The window function \(W(u,v)\) reduces the sidelobe level (SLL) of the AF and enhances the main beam efficiency, whereas, at the same time, it increases the 3-dB width of the main lobe. The AF in a SAIR has the same behavior as the antenna pattern in a traditional RA radiometer. Therefore, the angular resolution of SAIR is defined by the 3-dB beamwidth of the AF. Figure 5b,g shows the simulated 2-D and E-plane 1-D AF of GeoSTAR with a rectangular window at 50.3 GHz, and the corresponding 3-dB beamwidth and the first SLL are 0.084\({}^{\circ}\) and \(-\)7.6 dB, respectively. The AF with a Blackman window is shown in Figure 5c,h, the 3-dB beamwidth increases to 0.116\({}^{\circ}\), while the first SLL decreases to \(-\)13.7 dB. The simulation parameters of GeoSTAR are shown in Table 3. The simulation process of GeoSTAR consists of the following steps. First, similar to GMR, the coordinates of each pixel of the upwelling TB are converted from the latitude and longitude coordinate system to the direction cosine coordinate system of GeoSTAR to obtain \(TB(\xi,\eta)\). Second, the measured visibility function is calculated according to Equations (4) and (5), and the system noise \(\Delta T_{rms}\) is added. Third, the observed brightness temperature TA by GeoSTAR is reconstructed according to Equations (5) and (6), and the Blackman window is used. In addition, to mitigate the Gibbs oscillation effect, flat target transformation (FTT) is applied to the brightness temperature reconstruction process [45]. The simulated GeoSTAR observation TA images of Typhoon Hagibis for all 8 channels are shown in Figure 4a-h.
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
###### Abstract  
The category for continuous use of electrical devices has created greater demands for electricity along with more efficient transmission techniques. Energy from natural resources can be solar, thermal, vibration, friction, or Radio Frequencies (RF) signals. This state-of-the-art work provides a summary of RF energy harvesting techniques and can be used as a guide for the manufacture of RF energy scavenging modules. The use of Radio Frequency (RF) Energy Harvesting (EH) technique contributes to the development of autonomous energy devices and sensors. A recetenna system includes three main units: the receiving antenna, the impedance matching network, and the rectifier. We thoroughly analyze how to design a recetenna system with special emphasis given on the design of the rectifier. At the same time many works of the last 10 years are presented. This review article categorizes the used topologies depending on the type of antennas, IMNs, and rectifiers and comparatively presents their advantages and disadvantages.  
radio frequency energy harvesting wireless power transfer; antennas; impedance matching network; rectifier; voltage multiplier; power conversion efficiency; Dynamic Threshold MOSFET 2021  
26 February 2021  
19 October 2021  
1
## 5 Case Studies and Co-Simulation Validation
### Response to the Controller Parameter Modification  
Next, one of the control parameters of the active power control block, \(K_{\text{droop}}\), is changed using mosaik scenario-API to investigate how a parameter modification in the controller affects the wind turbine generator performance in the co-simulation framework. The dynamic response of the system is triggered by a step in the active power set-point from 0.85 p.u. to 0.5 p.u. The response of the wind turbine in the monolithic simulation and the mosaik co-simulation is shown for different droop parameters in Figure 15. It can be seen that by increasing the value of \(k_{\text{droop}}\), the active power control changes more quickly upon a change in the set-point as is expected. The mosaik co-simulation, being the coordinator between the grid-forming controller and the power system, can reflect successfully controller parameter modification effects on the dynamic response of the system.