esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Beamforming Design for BIOS-FD Systems
### SIC-WMMSE Approach
\begin{split} f_{\text{D}}&=\text{Tr}(\mathbf{A}_{ \text{D}}\mathbf{E}_{\text{D}})-\log\text{det}(\mathbf{A}_{\text{D}})\\ &=\text{Tr}\Big{(}\mathbf{F}^{H}\hat{\mathbf{H}}_{\text{D}} \mathbf{A}_{\text{D}}\mathbf{U}_{\text{D}}^{H}\mathbf{U}_{\text{D}}\hat{ \mathbf{H}}_{\text{D}}^{H}\mathbf{F}\Big{)}-2\operatorname{Re}\Big{(}\text{Tr} \Big{(}\mathbf{A}_{\text{D}}\mathbf{U}_{\text{D}}^{H}\hat{\mathbf{H}}_{\text{ D}}^{H}\mathbf{F}\Big{)}\Big{)}\\ &\quad+\text{Tr}\Big{(}\mathbf{A}_{\text{D}}\sigma_{\text{D}} \mathbf{U}_{\text{D}}^{H}\mathbf{U}_{\text{D}}\Big{)}+\text{Tr}(\mathbf{A}_{ \text{D}})-\log_{2}\lvert\mathbf{A}_{\text{D}}\rvert\\ &=\text{Tr}\Big{(}\zeta_{\text{F}}\mathbf{F}^{H}\mathbf{H}_{\text {TB}}^{H}\mathbf{\Phi}_{1}^{H}\mathbf{G}^{H}\mathbf{\Phi}_{2}^{H}\mathbf{H}_{ \text{D}}\mathbf{A}_{\text{D}}\mathbf{U}_{\text{D}}^{H}\mathbf{U}_{\text{D}} ^{H}\mathbf{U}_{\text{D}}^{H}\mathbf{\Phi}_{2}\mathbf{G}\mathbf{\Phi}_{1} \mathbf{H}_{\text{TB}}\mathbf{F}\Big{)}+\text{Tr}\Big{(}\mathbf{A}_{\text{D}} \sigma_{\text{D}}\mathbf{U}_{\text{D}}^{H}\mathbf{U}_{\text{D}}\Big{)}\\ &\quad-2\operatorname{Re}\Big{(}\text{Tr}\Big{(}\sqrt{\zeta_{ \text{F}}}\mathbf{A}_{\text{D}}\mathbf{U}_{\text{D}}^{H}\mathbf{H}_{\text{TB}}^ {H}\mathbf{\Phi}_{2}\mathbf{G}\mathbf{\Phi}_{1}\mathbf{H}_{\text{TB}}\mathbf{F }\Big{)}\Big{)}+\text{Tr}(\mathbf{A}_{\text{D}})-\log_{2}\lvert\mathbf{A}_{ \text{D}}\rvert,\end{split}  \tag{20}\]  
\[ \begin{split} f_{\text{U}}&=\text{Tr}(\mathbf{A}_{ \text{U}}\mathbf{E}_{\text{U}})-\log\text{det}(\mathbf{A}_{\text{U}})\\ &=\text{Tr}\Big{(}\mathbf{U}_{\text{U}}\mathbf{A}_{\text{U}} \mathbf{U}_{\text{U}}^{H}\Big{(}P_{\text{U}}\hat{\mathbf{H}}_{\text{U}}\hat{ \mathbf{H}}_{\text{U}}^{H}+\mathbf{H}_{\text{SI}}\mathbf{F}\mathbf{F}^{H} \mathbf{H}_{\text{SI}}^{H}+\sigma_{\text{U}}^{2}\mathbf{I}\Big{)}\Big{)}\\ &\quad-2\operatorname{Re}\Big{(}\sqrt{P_{\text{U}}}\text{Tr} \Big{(}\hat{\mathbf{H}}_{\text{U}}\mathbf{A}_{\text{U}}\mathbf{U}_{\text{U}}^{H }\Big{)}\Big{)}+\text{Tr}(\mathbf{A}_{\text{U}})-\log_{2}\lvert\mathbf{A}_{ \text{U}}\rvert\\ &=\text{Tr}\Big{(}\mathbf{U}_{\text{U}}\mathbf{A}_{\text{U}} \mathbf{U}_{\text{U}}^{H}\Big{(}\zeta_{\text{F}}\mathbf{U}_{\text{TB}}\mathbf{ H}_{\text{BB}}^{H}\mathbf{\Phi}_{1}\mathbf{G}^{H}\mathbf{\Phi}_{2}\mathbf{H}_{ \text{U}}\mathbf{H}_{\text{U}}^{H}\mathbf{\Phi}_{2}^{H}\mathbf{G}\mathbf{\Phi} _{1}^{H}\mathbf{H}_{\text{BR}}\\ &\quad+\Big{(}\mathbf{H}_{\text{TR}}+\sqrt{\zeta_{\text{F}}} \mathbf{H}_{\text{BR}}^{H}\mathbf{\Phi}_{1}\mathbf{H}_{\text{TB}}\Big{)}\mathbf{ F}\Big{)}\mathbf{F}^{H}\Big{(}\mathbf{H}_{\text{TR}}+\sqrt{\zeta_{\text{F}}} \mathbf{H}_{\text{RR}}^{H}\mathbf{\Phi}_{1}\mathbf{H}_{\text{TB}}\Big{)}^{H}+ \sigma_{\text{U}}^{2}\mathbf{I}\Big{)}\Big{)}\\ &\quad-2\operatorname{Re}\Big{(}\text{Tr}\Big{(}\sqrt{P_{\text{U} }\zeta_{\text{F}}}\mathbf{U}_{\text{U}}\mathbf{A}_{\text{U}}\mathbf{H}_{\text {BR}}^{H}\mathbf{\Phi}_{1}\mathbf{G}^{H}\mathbf{\Phi}_{2}\mathbf{H}_{\text{U} }\Big{)}\Big{)}+\text{Tr}(\mathbf{A}_{\text{U}})-\log_{2}\lvert\mathbf{A}_{ \text{U}}\rvert,\end{split}  \tag{21}\]  
\[
## 3 System Model
### Communication Channels
#### 4.3.1 Connection Directly
Figure 12: Flowchart of the direct data transmission channel of the unreachable nodes \(u_{A}\) and \(u_{B}\). * After receiving this message, node \(u_{A}\) sends a UDP packet to the IP address and port number of node \(u_{B}\) mapped to the public network. * At this point the \(u_{B}\) node can accept the packet as it has already established a mapping in the previous NAT device or firewall. Next, a UDP packet is sent to the IP address and port number mapped to the public network by node \(u_{A}\). At this point the direct connection data channel has been successfully established. It should be noted that the unreachable node \(u_{A}\) and the reachable node, the unreachable node \(u_{B}\) and the reachable node and, finally, the unreachable nodes \(u_{A}\) and \(u_{B}\) are authenticated and encrypted using the previous method based on \(NodeID\), which ensures transmission security and is not described in detail in Figure 12 and in the detailed steps.
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 3 Satellite Observing System Wettzell
### Measurements and Configurations
During the experiments, the telescope is rotated in equidistant steps for a homogeneous coverage of the working range of the SOS-W from \(0^{\circ}\) to \(360^{\circ}\) for the azimuth angle and \(0^{\circ}\) to \(180^{\circ}\) for the elevation angle. Figure 5 depicts the swivel-mounted laser telescope SOS-W in different elevation positions \(\omega_{k}\). The orange square symbolizes one of the photogrammetric markers \(\mathbf{p}_{i}\), which is observed in several telescope orientations. Table 1 summarizes the total number of analyzable images for the seven different experiments and the numbers of chosen azimuth and elevation positions. The number of images varies not only depending on the number of telescope positions, but also with the accessibility of favorable camera positions to achieve a wide range of different view angles. Each configuration is performed at least twice, so almost identical results can indicate their reliability. The horizontal distribution of the three different configurations is shown in Figure 6. The data presented in this study are available in the Supplementary Material. Figure 5: Symbolic illustration of the swivel-mounted laser telescope SOS-W. The azimuth axis is represented by a green dotted line. The elevation axis is depicted by a blue dot. A mounted marker \(\mathbf{p}_{i}\), symbolized by an orange square, is shown in several telescope orientations denoted by the elevation angle \(\omega\). A single experiment takes between five and eight hours and is comparable to the common procedures for reference point determination using polar measurement instruments and only a few mounted markers [49; 50]. In contrast to polar measurement systems, which sequentially observe the positions of mounted markers at the telescope, a single image of the photogrammetric measurement system captures several mounted markers simultaneously, and only a small number of images is needed to derive spatial coordinates. Thus, an increasing number of mounted markers does not significantly increase the number of images and the measurement effort.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 5 Security Analysis
### Security Proof  
5.1.1 Security Properties and Proofs  
**Definition 1** (Immutability Game).: _Let \(\mathcal{A}\) be a probabilistic polynomial-time (PPT) adversary against the main chain's immutability. The game proceeds as follows:_  
1. _The challenger_ \(\mathcal{C}\) _initializes the main chain relay platform._
2. \(\mathcal{A}\) _submits a transaction_ \(T_{x}\) _to_ \(\mathcal{C}\)_._
3. \(\mathcal{C}\) _generates block_ \(B_{i}=(H_{i-1},\,\Upsilon\!I_{s},\sigma_{\text{consensus}})\)_._
4. \(\mathcal{A}\) _outputs an altered block_ \(B^{\prime}_{i}\
eq B_{i}\) _such that_ \(H(B^{\prime}_{i})=H(B_{i})\)_._\(\mathcal{A}\) _wins if \(B_{i}^{\prime}\) is accepted by honest nodes._  
**Theorem 1** (Data Immutability).: _The main chain provides immutable storage for DNS resolution data under cryptographic hardness assumptions._  
Proof.: The adversary's advantage is bounded by the following:  
\[\begin{array}{c}\operatorname*{\mathrm{immut}}_{\mathcal{A}}(\lambda)= \Pr[\mathcal{A}\;\mathrm{wins}]\leq\mathsf{negl}(\lambda).\end{array} \tag{1}\]  
This holds because \(\mathcal{A}\) must either  
* Forge the consensus signature \(\sigma_{\mathrm{consensus}}\), violating the EU-CMA security of the signature scheme; or
* Find a hash collision \(H(B_{i}^{\prime})=H(B_{i})\), violating the collision resistance of the hash function.
# Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
## 1 Introduction  
The number of Artificial Neural Network (ANN) applications is growing steadily. Their introduction in the area of lossless image coding is restricted by the high cost of their training process. The domain is dominated by simple and very fast algorithms: JPEG-LS [1], JPEG 2000 [2], WebP [3], and even CALIC [4] belong to this group today. On the other hand, there is a set of computationally complex \"classical\" methods, and these, at the same time, set very high performance standards: TMW\({}^{\text{LEGO}}\) (2001) [5], WAVE-WLS (2002) [6], MRP 0.5 (2005) [7], its newest version being MRP-SSP [8] (2014), and finally, PMO [9, 10, 11] (2018, 2019, 2022) and EM-WLS [12] (2020) algorithms. First, codecs using ANNs were trained on samples from the coded image, and worked as backward predictors [13, 14, 15]. ANN-based coders had relatively high computational complexity, and despite this their performance was average; even for the best ones, their performance was worse than for classical techniques [16]. Codecs based on deep learning are more efficient [17, 18, 19, 20], but their relatively fast operation is possible only when powerful GPU units are used [21, 22, 23]. Codecs are usually trained on sets of training images. An analysis of deep-learning methods' advantages and drawbacks can be found in [17, 23, 24]; see also Section 2.2 of this paper. Its final conclusion remains the same: their computational complexity is very high, while performance is still not as good as that of the best \"classic\" approaches. The modern overview of ANN-based codecs for image compression can be found in [25].  
In this paper, two concepts leading to an important reduction of ANNs' computational complexity while improving their data estimation property are presented. The idea consists of preprocessing ANN input data, resulting in the reduction of ANN training time, and inthe second case in the simplification of ANN structure; see Section 5. These are Adaptive Neural Networks (AdNNs), hence, they are not initially trained; adaptation is done using local pixels surrounding the coded one; see Section 3. The use of AdNNs instead of a deep-learning approach is commented on in Section 2.3. In Section 4, the outline of the whole coder is provided. The data compaction properties of new algorithms are better than those for other ANN-based lossless image coders; see Section 6, Tables 2 and 3. In Section 2.1, the general types of lossless image coding techniques are outlined. Section 2.3 presents the index notation used in the paper.
## 3 Background and Methods
### Transfer Learning  
Transfer learning can be defined as a technique that enables the improvement of learning a specific task by applying pre-learned knowledge from a similar other task. When using neural networks, training usually begins on a neural network model in which weights are randomly initialized. In the learning phase, we want to adjust the weights in the model so that the model will map the given input data to the known outputs as accurate as possible. Because the initial, randomly initialized weight values are generally far from optimal, we need a lot of time and a large number of training instances to successfully train the model. Using transfer learning, training of the selected specific task starts on a pre-trained model, where the weight values are not random, but are already adapted to the previous learning situation. Therefore, if we use an appropriate pre-learned model, we can generally consume much less time to adapt such a model to the selected specific task and often achieve even better predictive performance even on a smaller training set.  
The idea of transferring the learned knowledge is not new [29] and appears in the literature under various names--as inductive transfer [30], incremental or cumulative learning [31], and multitask learning [32], which is the most closely related to modern transfer learning techniques. One of the most common ways to use transfer learning when training CNNs is fine-tuning. In fine-tuning, we transfer weight values from the convolutional base of the pre-trained CNN model, since the bottom convolutional layers of the neural network are intended for the extraction of general, more abstract features. In classifying images and recognizing objects on them, these general features are usually quite similar regardless of the domain from which the images come. Specific domain features, on the other hand, are extracted mainly in the top layers of the CNN. By utilizing fine-tuning, we primarily want to adjust these specific top layers of the neural network, while leaving the rest more or less unchanged.  
The success of implementing the transfer learning with fine-tuning depends largely on the appropriate setting of training parameters. The use of an approach that ensures the appropriate setting of hyper-parameters is thus crucial to achieve a high learning ability of the CNN model.
## 2 Materials and Methods
### Staining and Fluorescence Microscopy  
The fluorescent probes, carboxyfluorescein diacetate (cFDA), and propidium iodide (PI) (Sigma Aldrich, Steinheim, Germany) were applied to assess the viability of _F. graminearum_. The procedure of staining was based on the method described by [26; 27] with some modifications. A stock solution of cFDA (1 mg/mL) was prepared in dimethyl sulfoxide (DMSO), and a stock solution of PI (1 mg/mL) was prepared in distilled water and stored in the refrigerator. Centrifuged cultures of _F. graminearum_, prepared as described in Section 2.8, were suspended in 5 mL of 50 mM phosphate buffer with 25 \(\upmu\)L cFDA and 25 \(\upmu\)L PI at 30 \({}^{\circ}\)C for 15 min. Next, the staining solution was removed by centrifugation (8000 rpm, 1 min). The pellet was examined under the fluorescence microscope Olympus BX53 equipped with specific wavelength filters filter set (CFDA excitation/emission: 485/530; PI excitation/emission: 538/617).
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 5 Simulation Setup and Results
### Results in Fiber Nonlinear Regime  
In our Monte-Carlo simulations, we use the typical standard single mode fiber (SSMF) parameters, listed in Table 1. Nine WDM, with spacing of 50 GHz, channels are co-propagated with the middle channel chosen as probe to study the impact. With SSMF modelled as part of the ROADM in Figure 4, the launch power swept is from \(-1\) dBm to \(+1\) dBm by 0.5 dBm step.  
With \(x\) is time-domain signal measured at DAC output and \(M\) the number of samples, PAPR is defined as:  
\[PAPR=\frac{\max\bigl{\lvert}x_{m}(n)\bigr{\rvert}^{2}}{E\bigl{\lvert}x_{m}(n) \bigr{\rvert}_{2}},m=1,\dots,M. \tag{19}\]  
It is observed that by optimizing the split of amplitude compensation between the Tx and Rx, the peak to average power ratio (PAPR) was reduced, at the Tx, by 0.78 dB in the case of 18 xWSSs and 0.57 dB in the case of 12 xWSSs compared to the PAPR when full pre-compensation of channel response is used. Our results show a reduction of 0.8 dB and 1.1 dB in R-OSNR, versus pre-compensation only, which is shown for 12 xWSSs and 18 xWSSs cases respectively. The gain is a combination of improvement in linear and nonlinear performance. Therefore, the reduction of PAPR is acting as fiber nonlinearity mitigation. Since nonlinear effects in long-haul fiber propagation are proportional to the instantaneous power of the transmitted signal, as shown in [35], therefore high PAPR would decrease system performance. Lessening the PAPR, as discussed thoroughly in literature especially in the context of OFDM and multi-carrier transmissions [36; 37; 38], is one of the main methods to mitigate the nonlinear effects in long-haul propagations in fiber. From the nonlinear perspective, our results are consistent with [39], where authors showed that a reduction of 1 dB, from 7 to 6 dB, improved Q-factor by more 0.8 dB in 1040 Km propagation for coherent-optical orthogonal-frequency division multiplexing (CO-OFDM) simulation.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### Determining Criteria Weights  
In this sub-section, the IVFFN approach ranks the importance of the primary and secondary parameters that influence where ATMs are located. The IVFFN steps are applied to determine the weights of the criteria. Figure 5 illustrates this hierarchical structure involving the main criteria, sub-criteria, and alternatives. Banks prefer to locate ATMs in hotels to meet their guests' needs, increase their satisfaction, and acquire their loyalty. Health centers and hospitals are places where people urgently need cash. Therefore, ATMs in such areas can be helpful in health crises. Having ATMs near the town hall is important to meet the cash needs of visitors and employees and to increase the variety of services. Shopping centers, markets, and other commercial areas are ideal locations for ATMs because people often go to these areas to withdraw money or make other financial transactions. In principle, when determining alternative locations, the aim is to increase user access and transaction volume by selecting areas with heavy foot traffic. The three experts evaluate these alternatives and criteria by constructing pairwise comparison matrices through linguistic terms in Table 2.  
Figure 5: Hierarchical structure of the ATM location selection problem.  
First, the weights of the main criteria are calculated. For this purpose, the following calculation procedure is applied. After converting the pairwise comparison matrices into IVFFNs with the corresponding scale, the expert assessments are combined with the IVFFWG operator using Equation (11). Equations (12) and (13) calculate the difference matrix \(D=\left(d_{ij}\right)_{mxm}\) between the lower and upper values of the membership and non-membership degrees. Then the interval product matrix \(S=\left(s_{ij}\right)_{mxm}\) is found based on Equations (14) and (15). The unnormalized weight matrix is found by using the indeterminacy value calculated using Equation (17) and the interval product matrix. In the last step, each criterion's priority weight value is obtained using Equation (18), and the criteria weights are presented in Table 6. Final criteria weights are obtained by multiplying the relevant main and sub-criteria weights.
## 5 Conclusions  
This study proposed investigating urban layout optimization method and validated the results in an urban area in Shenyang City, located in a cold region. Instead of using the regular cube previously utilized as the carrier of morphological research, this study extracted the urban form prototype within the selected area. Urban block building type and street width were selected as the optimization input indices. Through establishment of an evaluation system suitable for urban thermal and wind environment in cold regions, urban microclimate performance can be converted into UTCI as an outdoor comfort index. Microclimatological simulation is no longer just an evaluation tool but is transforming into part of a performance-driven design process. Morphological and microclimate indices were included as building density, plot ratio, SVF, building enclosure, and variation of building heights, as well as mean radiation temperature, UTCI, average wind speed, and unacceptable wind speed ratio. All these indices were tested at the urban and street levels.  
Instead of using UTCI, this study applied the percentage of acceptable UTCI in a selected area as optimization performance index to cover the whole block. As a result, optimization can achieve 87.7% of acceptable UTCI in the urban block level and 90.3% in the urban street level. In optimization results as high than 85% of acceptable UTCI range, multistory blocks were more suitable than high-rise blocks and open space. Multistory blocks with high building enclosure are usually located on the north side, while high-rise blocks are on south sides. Open space is not a good choice. Future work should incorporate more urban design indices into the urban block optimization to validate the performance-driven urban layout optimization. Further, the optimization framework for urban design with achieving max acceptable UTCI can be easily extended in other cities of different climate zones.  
Conceptualization, methodology, formal analysis and writing--original draft: [PERSON], [PERSON]; Simulation, data analysis, and visualization: [PERSON], [PERSON], W.W., K.L., N.X.; Writing--discussion of the original draft: [PERSON], [PERSON], [PERSON]; Writing--revision & editing: [PERSON], [PERSON], and [PERSON]; Supervision: [PERSON], [PERSON].  
The work described in this study was sponsored by the projects of the National Natural Science Foundation of China (NSFC#51978144, NSFC#51678127), the National Scientific and Technological Support during the 12 th Five-Year Plan Period (No.: 2013 BAJ10B13), and China Scholarship Council (CSC#201706095035). Any opinions, findings, conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the National Scientific and Technological Support committee and NSFC.  
The authors declare no conflict of interest.
# Traceability of Surgical Instruments: A Systematic Review
## 4 Results
### Short-Term Benefits: Patient Safety and Logistical Ergonomics
#### 4.1.2 Logistical Ergonomics  
Enhanced traceability systems improve the ergonomic flow of surgical procedures by streamlining stock management and preparation processes.  
* At Davis Ambulatory Surgical Center (2021) [31], RFID technology reduced setup times from 23 min to 17 min, demonstrating the efficiency of real-time monitoring and automatic counting.
* Shimane University Hospital (2013) [27; 30] achieved better workflow by rationalizing instrument storage, reducing containers to 70 instruments, which simplified tool retrieval and minimized surgeon fatigue.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 6 Conclusions  
In this study, the enhanced P model was utilized to GPP of tropical vegetation, employing remote sensing data and meteorological reanalysis. We analyzed the spatial and temporal trends of GPP and investigated the impacts of temperature and land cover changes using Pearson correlation analysis and contribution assessments.  
1. Trend Analysis: The annual average value of GPP in the tropics exhibited a fluctuating pattern, ranging from 2603.9 to 2757.1 cm\({}^{-2}\) a\({}^{-1}\). GPP showed a significant increasing trend over the past 20 years.
2. Climate Influence: In inland areas, precipitation significantly affected GPP, while temperature was a more decisive factor in coastal regions.
3. Land Cover Impact: There were notable differences in annual GPP among various land cover types. Specifically, converting bare land to cropland resulted in the most substantial increase in GPP, whereas the transformation of broadleaf evergreen forests into cropland led to the most pronounced decline.
4. Combined Effects of Climate and Land Cover Change: While land cover change was the primary driver of GPP alterations in mixed forests, deciduousiferous forests, and urban areas, climate change factors predominantly influenced GPP in other regions.  
Conceptualization, Y.C. and S.Z.; methodology, Y.C. and S.Z.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, Y.C., S.Z. and J.G.; data curation, Y.C. and S.Z.; writing--original draft preparation, Y.C. and Y.S.; writing--review and editing, Y.C., S.Z., J.G. and Y.S.; visualization, Y.C.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the National Natural Science Foundation of China, grant number 42361053, the Hainan Provincial Natural Science Foundation of China, grant number 422 RC598, and start-up fund of Hainan University, grant number KYQD(ZR)22081.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.