esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Works  
Multiple antennas and the related technologies can be employed to improve the NOMA system performance in various metrics by exploiting the spatial dimensions [3; 4; 12; 13; 14; 15; 16; 17; 18; 19]. Even the security of the NOMA network can be improved [20] by multiple antenna technologies. In the multiple input multiple output (MIMO) NOMA, both the access point (AP) and the UTs are equipped with multiple antennas, the clustering of UTs can be carried out, the signals pertaining to different clusters can be separated, and the UTs of each cluster form an independent NOMA channel [13; 14; 15]. Such useful clustering is not possible in multiple input single output (MISO) NOMA systems, where only the AP is equipped with multiple antennas, and the AP beamforming can serve only single channel NOMA UTs [12; 21; 22]. Optimal beamforming for this MISO-NOMA system is considered in [21]. Some of the NOMA UTs close to AP can relay the signal for the other UTs far away from the AP in the cooperative NOMA system. The relaying UTs help the AP transmission [23; 24; 25; 26; 27; 28] to improve the diversity performance and the power efficiency at the expense of additional spectral resource. The relaying UTs in the cooperative NOMA may operate either in half-duplex (HD) mode or in full-duplex (FD) mode. In HD mode [25; 26; 27], the AP signals and the UT relaying signals are transmitted in separate spectral resources, which simplifies the design with extra spectral resources. On the other hand, the FD mode relaying [23; 24] is spectrally efficient since the two signal transmissions are overlapped in the same spectral resource, though the handling self-interference (SI) at the relaying UTs becomes more challenging. One method to avoid this challenge and to enjoy the FD advantage of efficient spectral efficiency is adopting successive relaying (SR), where two HD relaying UTs relays the AP signal in alternating fashion [28]. In this way, two SR cooperative relaying UTs can emulate the FD operation and save additional wireless resource without generating SI.
## 4 Numerical Analysis and Results
### Performance Comparison  
The MU-FTN-DCSK system is compared to newly introduced DCSK-based schemes in order to evaluate its effectiveness as a newly proposed design. In terms of BER performances, the system lags due to the impact of the interferences caused by the functioning of the filter in the FTN regime. For instance, for a multi-path Rayleigh fading channel, a difference of 5 dB is observed in comparison with the CIM-MC-M-DCSK MISO-SWIPT system [31]. However, the complexity is greatly reduced since the latter system uses many additional signal processing transforms to accomplish the given gain. Table 1 gives a detailed comparison of the complexity of the two systems. Moreover, the BER performance of MU-FTN-DCSK is comparable to the performance of the GCI-DCSK SWIPT for the AWGN and Rayleigh fading channels. The MU-FTN-DCSK also presents an overall better BER relative to the spreading factor for the single user scenario.  
In the addition to the lesser complexity, the MU-FTN-DCSK offers a better spectral efficiency given the same configuration as the CIM-MC-M-DCSK MISO-SWIPT system. For example, for the CIM-MC-M-DCSK MISO-SWIPT system and for a number of antennas \(N=1\) and a modulation order \(M=2\), the spectral efficiency is lower than that of the MU-FTN-DCSK system for any value of the sampling parameter meeting the condition \(\rho_{(p)}<0.65\). In addition, the accumulative spectral efficiency gained from all users transmitting in the FTN regime can be higher than systems having very advanced designs. Furthermore, the results above indicate that using FTN in combination with additional signal processing methods can result in much higher spectral efficiency gains.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 3 System Model and Network Controller Design
### Network Architecture
#### 3.1.2 Functional Architecture  
In order to achieve the goal of autonomous decision-making, unlike the conventional method, which deploys an SDN controller on the ground station, we deploy a network controller that includes one UAV flight controller and one SDN controller in the core layer of the UAV network. The functional architecture of the UAV network is shown in Figure 2. The separation of the data plane and the control plane of the SDN architecture provides a solid foundation for the autonomous control of the UAV network. The control plane consists of the network controller deployed in a UAV at the core layer, and the data plane consists of all the edge-layer UAVs and other UAVs in the core layer. The network controller chooses the optimal strategy based on the state information, including GPS data, transmission rate data, network delay data, and SINR data collected from the data plane. The UAV flight controller is responsible for UAV track planning and flight attitude control, such as heading angle adjustment, location control, and energy control. The SDN controller is responsible for transmission channel configuration and data packet forwarding control, including routing control, congestion control, and power allocation control. The relationship between the UAV flight controller and the SDN controller is a collaborative relationship. Specifically, the flight of the UAV swarm is usually controlled by the flight controller. However, the SDN controller can fine-tune the UAV track when the network is subjected to a jamming attack.  
Figure 1: Topology of a software-defined network (SDN)-based unmanned aerial vehicle (UAV) network under a smart jamming environment, and the dotted lines in the figure indicate the wireless channel between the UAVs or between UAVs and Ground Station. GPS: Global Positioning System.
## 2 Model Structure
### LSTM Network
Specifically, the old cell state \(c(t-1)\) is scaled by the forget gate \(f(t)\), and new information is added from the input gate. This ensures that only useful information is retained or updated in the cell state, as shown in Equation (3). Output Gate: The output gate decides which values to output based on the cell state. It determines what part of the cell state should be outputted to the next hidden state and the subsequent layers of the network. This gate controls the contribution of the cell state to the next step's output, as represented by Equation (4). Hidden State: The hidden state \(h(t)\) is updated based on the output gate and the cell state. The new hidden state is a product of the output gate's value and the \(\tanh\) of the updated cell state, as described in Equation (5). This hidden state carries the information forward to the next time step and is crucial for the LSTM to maintain long-term dependencies. LSTM networks, a type of recurrent neural network, address the problems of gradient vanishing and explosion caused by long-term dependencies by using memory cells and three control gates: the forget gate, input gate, and output gate. These gates enable LSTMs to store and utilize past contextual information, allowing the model to better capture dependencies in sequence data. This capability makes LSTMs suitable for tasks requiring long-term memory and contextual information preservation, such as traffic demand prediction in satellite communication systems. By effectively managing long-term dependencies through a sophisticated gating mechanism, LSTM networks maintain stable and accurate long-term predictions, which is crucial for improving performance in these systems.
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 4 Controller Design
### Flc  
Fuzzy logic (FL) is an extension of multivalued logic. It is related to the theory of fuzzy sets, which refers to classes of objects with blunt boundaries in which membership is a matter of degree. It emulates, unlike crisp data, the ability to reason and uses approximate data for finding solutions. FLCs are knowledge-based controllers that consist of linguistic if-then rules, which can be constructed by using the knowledge of experts in a particular field of interest. FLCs have shown their ability in many applications, especially in complex nonlinear systems, for which analytical modeling is difficult [39; 40]. Figure 3 illustrates the block diagram of a traditional FLC system, which consists of four components: fuzzification interface (FI), rule-base (RB), inference mechanism (IM), and defuzzification interface (DI). The FI classifies the inputs into suitable linguistic values/sets. The RB is comprised of fuzzy sets (database) and fuzzy control rules. The IM is the kernel of the FLC, which is capable of simulating human decision making based on fuzzy concepts and inferring fuzzy control actions using fuzzy relations and rules of inference. The DI makes conclusions from the IM and gives an output, i.e., input for the plant [41; 42].  
Figure 4 presents the model of an FC with a fuzzy logic controller in MATLAB/Simulink. The output voltage is measured and is compared with the reference value, and thus, an error is generated. Two quantities, namely error \(e\) and derivative of the error \(\Delta e\), are generated as the inputs for the fuzzy logic controller. The fuzzy logic controller processes the quantities based on membership functions (MFs) and fuzzy rules to generate an output, which is the duty cycle \(d\). This signal is compared with a 100 kHz triangular waveform to generate duty cycle pulses for the gate of MOSFET \(S\).  
Input MFs are presented in Figure 5a,b. Five MFs were assigned to each input variable for the universe of discourse of \(-24\) to \(24\). The output MFs of a Sugeno type FIS are constants or linear functions. In this case, constants were selected. Finite values were assigned to each MF, as shown in Figure 5c. FLC works based on fuzzy rules. Table 2 illustrates the fuzzy rules.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\Delta e\) & **Neg. Big** & **Neg. Small** & **Zero** & **Pos. Small** & **Pos. Big** \\ \hline Neg. Big & 0 & 0 & 0 & 0.25 & 0.5 \\ \hline Neg. Small & 0 & 0 & 0.25 & 0.5 & 0.75 \\ \hline Zero & 0 & 0.25 & 0.5 & 0.75 & 1 \\ \hline Pos. Small & 0.75 & 0.5 & 0.75 & 1 & 1 \\ \hline Pos. Big & 0.5 & 0.75 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rules for the FLC.  
Figure 3: Fuzzy logic controller (FLC) block diagram.  
The 3D graphical representation of the rule surface relating output value for any combination of the two inputs is presented in Figure 6.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### The System Model  
In our proposed model, searchable encryption involves four different parties, as illustrated in Figure 1: the data owner, the authorized user, the server S and the cloud servers S1, S2,..., SN. The data owner encrypts data before uploading it into the cloud as well as authorizes users to retrieve it. After authorization, the user can retrieve the corresponding cipher-text files according to keywords. The server S is dedicated to store the index after the data owner encrypts it while providing partial retrieval services to the user. The cloud servers S1, S2,..., SN store the cipher-text files uploaded by the data owner and the cipher-text index by the server S after re-encryption while providing retrieval services to the user.  
Assume that the cloud servers are honest-but-curious, i.e., they will carry out search operations correctly but, nonetheless, are considered to pose threats to data security and privacy. Also assume that the number of authorized users is n, i.e., U = {u1, u2,..., un}, and the number of files uploaded into the cloud is k, i.e., F = {f1, f2,..., fk}. To provide the functionality of protecting the security of  
Figure 1: The system model.  
the data, a set of keywords W = {w1, w2,..., wp} are extracted from the set of files before they are uploaded. In addition, fingerprint S\({}_{i}\) of keyword w\({}_{i}\) is used to construct an index I through the keyword fingerprint generation algorithm based on the MinHash technique. The set of encrypted files and the index I are eventually uploaded onto the cloud servers. The set of encrypted files are divided into N portions according to certain rules and stored on the servers S\({}_{1}\), S\({}_{2}\),..., S\({}_{N}\), respectively.  
An authorized user would get the key of trapdoor provided by the data owner, generate the fingerprint of the keyword for a query and then construct the keyword trapdoor T\({}_{w}\) before presenting it to the cloud server. To improve the accuracy of search, the cloud server needs to sort the search results. To save bandwidth, an authorized user can upload an integer k together with T\({}_{w}\) to the cloud server for it to return the most relevant top-k cipher-text files. Finally, the authorized user can use the key to decrypt the obtained cipher-text documents.
## 2 Literature Review and Research Hypothesis
This in turn promotes the rapid flow of production factors such as capital, technology, labor, and information, while also expanding and deepening the scope and frequency of intercity connections, improving the efficiency of resource allocation, creating economies of scale in the inflow destinations, and prompting the continuous agglomeration of economic factors and industries [22; 23]. The opening and improvement of high-speed transportation networks in urban agglomerations improve the integration of transportation in urban agglomerations and accelerate the pace of urban industrial agglomeration overall. However, there are differences in the opening time of high-speed rails in different cities, individual differences between cities and enterprises [24], and heterogeneity in the effect of promoting industrial agglomeration. As the construction cycle of a high-speed railway and related supporting facilities is long, and the diffusion, learning, digestion, and absorption effects of innovation factors generated by trans-regional flow also take time, industrial agglomeration has heterogeneity in different periods leading to the year of high-speed railway opening [25]. In the urban agglomerations of the Yangtze River Delta, which is close to 20% of the national economic aggregate, there may be heterogeneity in the industrial agglomeration effect of different central cities after the opening of high-speed rail owing to the geographical location of the central cities and the industrial structure and economic vitality of the urban agglomerations. Therefore, this paper proposes the following research hypotheses. **Hypothesis 1 (H1). **_The integrated development of urban agglomeration promotes industrial agglomeration. Urban heterogeneity exists in the impact of transportation integration on industrial agglomeration._  
The enhancement of transportation integration in an urban agglomeration improves the intercity accessibility within the agglomeration [26; 27; 28] and links economic activity across regions as a whole [29]. As the boundaries of cities and urban agglomerations continue to spill over, it becomes easier for industrial firms to relocate. To reduce labor costs, industrial firms in cities with higher labor costs may move to cities with lower labor costs. Moreover, the networking of transportation infrastructure in urban agglomerations, especially the opening and operation of intercity high-speed railways, pushes up wages in the region. Under the pressure of operating and production costs, industrial firms may make relocation decisions, thereby reducing regional industrial agglomeration. **Hypothesis 2 (H2). **_With the improvement of the transportation integration level in the urban agglomeration, the rise in local labor costs restrains the level of local industrial agglomeration, while the rise in labor cost in other areas has a positive effect on the level of local industrial agglomeration._
# Towards a Counting Point Detector for Nanosecond Coherent X-ray Science
## 2 Experimental Setup  
Time-resolved measurements at synchrotrons utilize the pulsed nature of X-ray radiation that originates from the time structure of electron pulses that circulate around the storage ring. Typically, a series of multiple electron pulses is filled into consecutive radiofrequency (rf) buckets that are evenly spaced. As shown in Figure 1, our demonstration was performed at the 1C PAL-KRISS beamline at PLS-II, which generates hard X-ray pulses with down to 2 ns pulse spacing and approximately 50 ps (FWHM) in pulse duration. The 2 ns minimum spacing is set by the frequency of the storage ring rf cavities of 500 MHz. The storage ring revolution period is 1 \(\upmu\)s, corresponding to 500 rf buckets per revolution. The monochromatized X-ray beam with the central wavelength of 10 keV and an energy bandwidth of (dE/E) of \(10^{-4}\) is delivered from cryogenically cooled double Silicon (111) crystals to the detector that is positioned downstream of aluminum foil filters (See Figure 1). In our experiment, we make use of combinations of multiple aluminum filters with varying thicknesses to control the transmitted X-ray intensity. The thickness of the filters in the measurement were 4, 8, 16, 32, 64, 128, 256, 512, 1024, and 2048 \(\upmu\)m. The transmitted intensities at different filter settings were calibrated by using ion chambers before and after the attenuators. Using these filter settings, we are able to perform the experiment with less than 0.2 percent of the initial intensity. Here, a crucial component of this detection scheme is the capability of resolving a single X-ray pulse. For the X-ray detection, we used the Menlo systems optical avalanche photodiode (APD-210, 0.5 mm detector size) with its front glass windows removed to increase detection efficiency. Real-time, single-shot oscilloscope-trace measurement provides an accurate way to monitor X-ray photon counting. The signal of the APD is fed to a high-bandwidth oscilloscope (LeCroy WaveRunner 604 Zi) that is triggered by a 1 MHz rf-revolution clock. Given the manufacturer's specified intrinsic rise time of 500 ps for the APD and the bandwidth (BW) of 400 MHz for the oscilloscope that translates to a rise time of \(\frac{0.35}{\mathrm{BW}}=875\) ps, we calculate an effective rise time of our acquisition system to be \(RT_{eff}=\sqrt{(875\,\mathrm{ps})^{2}+(500\,\mathrm{ps})^{2}}=1.007\) ns. This value is consistent with our result shown in Figure 1, where the direct X-ray beam is attenuated using calibrated filters so that the detector receives less than a single photon event within 2 ns time-interval. Figure 1 (inset) shows the oscilloscope trace of a single X-ray pulse collected by the APD, which displays a full recovery within 2 ns time-interval. Assuming that the rise and fall time responses are the same as shown in Figure 1, we estimate the dead time of 2 ns. We note that a single X-ray photon can arrive at the detector at any time during the 50 ps duration of an electron pulse.
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 4 Results  
### Experimental Environment and Evaluation Indicators  
In order to evaluate the quantization parameter decision scheme proposed in the paper, the algorithm is compared using the HEVC reference software HM-16.20. The resolution of all the test sequences is 4K, and their names are: AerialCity, DrivingInCity, DrivingInCountry, and PoleVault. The sampling format is 4:2:0, bit depth is 8, and all-intra encoding mode is used. The test sequence information and the relevant parameters of the DE algorithm are shown in Tables 1 and 2, respectively. All the test sequences of the experiment can be downloaded from the website Common Test Condition (CTC) [50], and differential evolution algorithm was implemented on MATLAB 2020A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Sequence** & **Resolution** & **Frame Rate** & **Frame Number** \\ \hline AerialCity & 3840 \(\times\) 1920 & 30 & 100 \\ DrivingInCity & 3840 \(\times\) 1920 & 30 & 100 \\ DrivingInCountry & 3840 \(\times\) 1920 & 30 & 100 \\ PoleVault & 3840 \(\times\) 1920 & 30 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test sequence information.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 50 \\ Iteration number & 50 \\ Mutation factor & rand (0.1, 0.9) \\ Crossover rate & 0.5 \\ Terminal condition & max iteration number \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related parameters of the proposed algorithm.  
Figure 4 shows the basic characteristics of the four test sequences. It can be seen from the figure that most of the upper part of the 360-degree panoramic video is the sky or the cloud, and most of the lower part is the ground. Therefore, a differential evolution algorithm can be used in these background areas to deduce higher QPs, which makes this part of the region coarse quantized. For regions closer to the equator, the algorithm can be expected to derive a smaller QP for fine quantization.  
BD-rate and BD-PSNR are important parameters for evaluating the performance of video-coding algorithms [51], indicating the changes in the bitrate and peak signal-to-noise ratio (PSNR) of videos encoded using the new algorithm compared to the original algorithm. In video coding, a low bitrate indicates a large amount of compression, and a high PSNR value indicates good objective quality. Therefore, if the compressed video bitrate is reduced and the PSNR value is increased, then the algorithm has good performance. However, usually video coding algorithms will lose compression quality while increasing the compression amount. That is, when the bitrate decreases, the PSNR value also decreases. In this case, BD-rate and BD-PSNR need to be used for measurement.
# Animal Sound Classification Using Dissimilarity Spaces
## 2 Proposed System  
The system presented here for spectrogram classification extends that proposed in [43] and is schematized in Figure 1, which illustrates the approach using one SNN. The pseudocode in Algorithms 1 and 2 that correspond to Figure 1 are detailed in the remainder of this section.  
The process begins by generating a similarity space during the training phase via a learning distance measure \(d(x,\,y)\) from a set of prototypes \(P\,=\,p_{1},\,\ldots p_{k}\). The distance measure is learned by four SNNs trained (1) to maximize the similarity between pairs of spectrograms belonging to the same class and (2) to minimalize the similarity for pairs of spectrograms belonging to different classes. The set of prototypes generated in this phase are the \(k\) centroids of the clusters produced by a clustering approach. What results is a feature vector \(f\,\in\,R^{k}\) that represents training sample \(x\) in the dissimilarity space, where a given \(f_{i}\) is the distance between \(x\) and the prototype \(p_{i}:\,f_{i}\,=\,d(x,\,p_{i})\). Once these features have been calculated, they are used to train an SVM classifier.  
In the testing phase, each unknown pattern is represented by its projection in a dissimilarity space: the descriptor is obtained by calculating the pattern distance to the set of prototypes \(P\), and the resulting feature vectors of the input images are then classified by SVM. In our experiments, we test both spectrograms and HASC [48], a 2D descriptor extracted from the spectrogram, as the input of the classification process. The method for extracting HASC is outlined in Section 2.5.  
```
Input: Training images (_imgsTrain_), training labels (_labelTrain_), number of training iterations (_trainIterations_), batch size (_trainBatchSize_), number of centroids (_k_), and clustering technique (_type_). Output: Trained SNN (_tSNN_), set of centroids (C), and trained SVM (_svm_).
1:_tSNN_\(\leftarrow\)trainSlameses(_imgsTrain_, _labelTrain_, _trainIterations_, _trainBatchSize_)
2:p\(\leftarrow\)Clustering(_imgsTrain_, _labelTrain_, \(k\), _type_)
3:F\(\leftarrow\)getDissSpaceProjection(_imgsTrain_, \(P\), _tSNN_)
4:_tSVM_\(\leftarrow\)trainSvm(_labelTrain_, _F_)
```  
**Algorithm 1** Training phase  
Figure 1: Generalized schematic of the proposed approach.