esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Compared with other types of bridges, the CFST (Concrete Filled Steel Tube) arch bridge has a higher bearing capacity, a better plasticity and strength, and is costly and convenient during construction. It has also solved the application and construction problems of high strength materials for arch bridge with long span [1, 2, 3, 4, 5, 6]. With Chinese characteristics, it has certain advantages and broad application prospects in the future medium and long span bridges [7]. Due to its wide range of application, especially for long span bridges, cable lifting construction has become the main construction method of arch bridges [8, 9, 10, 11]. In order to simplify and speed up the construction, and save costs, the tower bottom consolidation, tower and buckle frame integration structure was adopted [12]. When using cables to lift arch ribs, the working procedure of each span influences the deformation of the tower, causing deviation, and has a linkage effect on the arch rib hanging on it. When the linkage effect accumulates gradually, the assembly precision and arch rib linear will be affected if not adjusted [13, 14, 15], with the stress increased and the stability of tower reduced. The traditional passive control on a tower's horizontal deviation is usually achieved by rigidity.  
which often increases the section of wind cables and wastes materials. Therefore, relevant experts and scholars put forward the automatic dynamic adjustment technology of cable fastening [16]. In the late 1980s, some researchers began to study digital tensioning [17], and now, intelligent prestressed tensioning technology can be realized [18], and its accuracy is higher than that of traditional tensioning equipment [19]. There is much equipment in the traditional hoisting system, which is difficult to adjust and construct [20], thus the prestressed intelligent tensioning system is applied to the bridge [21]. In the construction of Guangxi Yongjiang River Bridge, the method of jacking and cable-stay suspension is studied and applied for the first time [22]. However, it is not used for bridge monitoring. Table 1 below lists the latest research results on intelligent tower deviation control systems and analysis and measurement methods.  
This article mainly has the following novelties:  
1. In virtue of the Beidou GNSS measurement system and hydraulic jack active control system that have been developed rapidly in recent years, the active control technology of the concrete-filled steel tubular arch bridge tower deviation was thoroughly studied. Besides, a perfect active control theory was established.
2. The paper designs and puts forward the smart active tower deviation control system to active control the horizontal displacement of the tower.
3. Through the measuring instruments and smart equipment with high precision, the paper solves the tower deviation in the process of traditional cable lifting system, and reduces the impact on the precision of arch truss cantilever. Thus, it is a scientific, feasible, economic, and reasonable optimization construction scheme.
## 2 Main Results
### Controller Design and Stability Analysis
The estimated errors can be expressed as \(\ddot{\theta}=\theta-\dot{\theta}\) and \(\ddot{\delta}=\delta-\dot{\delta}\). The initial values of \(\dot{\theta}\) and \(\dot{\delta}\) are assumed to be satisfied: \(\dot{\theta}(0)\leq 0\) and \(\dot{\delta}(0)\leq 0\) according to [44], which will realize these conditions for \(t>0\). To reduce the problem of control input saturation, the anti-saturation compensator is designed as follows:  
\[\dot{\zeta}=-(k_{3}+\frac{k_{4}}{k_{a2}^{2}-z_{2}^{2}})\zeta+\Delta u \tag{33}\]  
where \(\zeta\) denotes the state of the compensator with \(k_{4}>k_{3}>0\). Step 3: The barrier Lyapunov function candidate \(V\) is chosen as  
\[V=V_{2}-\frac{\tilde{G}\tilde{\theta}^{2}}{2\beta}-\frac{\tilde{G}\tilde{ \theta}^{2}}{2\gamma}+\frac{1}{2}\zeta^{2} \tag{34}\]  
The time derivative of \(V\) is  
\[ \begin{split}\dot{V}&=\dot{V}_{2}+\frac{\tilde{G}}{ \tilde{\beta}}\dot{\delta}\dot{\delta}+\frac{\tilde{G}}{\gamma}\dot{\theta} \dot{\theta}+\zeta\dot{\zeta}\\ &\leq\frac{-k_{1}z_{1}^{2}}{k_{a1}^{2}-z_{1}^{2}}+\frac{z_{2}}{k_ {a2}^{2}-z_{2}^{2}}g\rho u+\frac{{z_{2}}^{2}}{{2a^{2}(k_{a2}^{2}-z_{2}^{2})}^{ 2}}\tilde{G}\theta\Psi^{T}\Psi+\frac{|z_{2}|}{k_{a2}^{2}-z_{2}^{2}}\tilde{G} \delta+\frac{a^{2}}{2}\\ &+\frac{\tilde{G}}{\tilde{\delta}}\dot{\delta}+\frac{\tilde{G}}{ \gamma}\dot{\theta}\dot{\theta}+\zeta\dot{\zeta}\\ &=\frac{-k_{1}z_{1}^{2}}{k_{a1}^{2}-z_{1}^{2}}+\frac{z_{2}}{k_{a2 }^{2}-z_{2}^{2}}g\rho u+\frac{{z_{2}}^{2}}{{2a^{2}(k_{a2}^{2}-z_{2}^{2})}^{2}} \tilde{G}\theta\Psi^{T}\Psi+\frac{|z_{2}|}{k_{a2}^{2}-z_{2}^{2}}\tilde{G} \delta+\frac{a^{2}}{2}\\ &+\tilde{G}\tilde{\delta}\sigma_{2}\dot{\delta}-\tilde{G}\tilde{ \delta}\frac{z_{2}}{k_{a2}^{2}-z_{2}^{2}}tanh(\frac{z_{2}}{\lambda(k_{a2}^{2}- z_{2}^{2})})+\tilde{G}\tilde{\theta}\sigma_{1}\dot{\theta}-\tilde{G}\tilde{ \theta}\frac{{z_{2}}^{2}}{{2a^{2}(k_{a2}^{2}-z_{2}^{2})}^{2}}\Psi^{T}\Psi\\ &-k_{3}\zeta^{2}-\frac{k_{4}}{k_{a2}^{2}-z_{2}^{2}}\zeta^{2}+ \Delta u\zeta\end{split}  \tag{35}\]  
Now, the actual control input \(u\) is designed as  
\[u=k_{2}z_{2}-k_{2}\zeta-\frac{\dot{\theta}z_{2}}{{2a^{2}(k_{a2}^{2}-z_{2}^{2}) }}\Psi^{T}\Psi-\delta tanh(\frac{z_{2}}{\lambda(k_{a2}^{2}-z_{2}^{2})}) \tag{36}\]  
where \(k_{2}\) is a positive design parameter. Considering \(u\) into the second term of Equation (35), we get  
\[ \begin{split}\frac{z_{2}}{k_{a2}^{2}-z_{2}^{2}}g\rho u& =\frac{z_{2}}{k_{a2}^{2}-z_{2}^{2}}g\rho\left[k_{2}z_{2}-k_{2}\zeta -\frac{\dot{\theta}z_{2}}{{2a^{2}(k_{a2}^{2}-z_{2}^{2})}}\Psi^{T}\Psi-\delta tanh (\frac{z_{2}}{\lambda(k_{a2}^{2}-z_{2}^{2})})\right]\\ &\leq\frac{k_{2}{z_{2}}^{2}}{{k_{a2}^{2}-z_{2}^{2}}}\tilde{G}- \frac{k_{2}{z_{2}}z_{2}}{k_{a2}^{2}-z_{2}^{2}}g\rho-\frac{\dot{\theta}z_{2}^{2}}{ {2a^{2}(k_{a2}^{2}-z_{2}^{2})}^{2}}\Psi^{T}\Psi\tilde{G}-\frac{z_{2}}{k_{a2}^{2} -z_{2}^{2}}\dot{\delta}tanh(\frac{z_{2}}{\lambda(k_{a2}^{2}-z_{2}^{2})})\tilde{G} \end{split}
## 2 Related Works
The article [11] presents a study on the prediction of digital terrestrial television (DTT) coverage using ML regression techniques, through electric field intensity measurements in eight DTT channels operating in the city of Quito in Ecuador, showing the efficiency of using ML in this application. Furthermore, the article [30] conducted measurements in Cyprus considering six transceiver BSs, at frequencies of 433 MHz and 2.4 GHz. The mobile devices used in these field measurements were Sony Z2 phones equipped with TEMS for measuring received power and other key performance indicators (KPIs) of the investigated network. The mobile device's height was 1.5 m, with an average propagation distance of around 3.0 km and a specified measurement time of about 60 s on average. The aim was to investigate the impact of the plants' height and antenna height, at various stages of plants growth, on signal propagation through RSSI and PLR analysis. From these data, two ML models are proposed: the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN), compared with the following empirical models: free space, COST231 Hata, ECC-33, Ericsson, and ITU-R, through the metrics RMSE, MAPE, MAE, and \(R^{2}\), showing that RBFNN performed better than MLPNN and predicts the loss propagation closest to the measured data with minimal error. As can be seen in the works cited above, none of them addressed the analysis of the transmission and reception antennas matching polarizations. Moreover, the works do not analyze the different heights of the transmitter in environments with dense vegetation, relating the transmitter height to different parts of the trees. Furthermore, although some already employ machine learning techniques, none of them use a general regression neural network (GRNN). Therefore, from this overview of related works, it is noted that this research uses an trending approach, as it presents two neural network models, GRNN and MLPNN, using LoRa technology at the frequency of 915 MHz and considering the co-polarization of antennas, both transmitter and receiver, in Amazonian forest environments, to predict RSSI and SNR with greater accuracy.
## 4 Discussion
### Apple-Dandelion Honeys  
Melissopalynological analysis showed the presence of different classes of pollens in the apple-dandelion honey samples. The Rosaceae family, to which _Malus/Pyrus_ belongs, was significantly more common in these samples in comparison to chestnut, honeydew, multifocal, and rhododendron honey, while the values were comparable to the acacia ones. In apple-dandelion honey, together with Rosaceae and Asteraceae, Vitaceae pollens were also present in an important amount. A wide presence of different pollen families was observed in the samples of this honey due to the botanical characteristic of the area and agricultural production. The apple-dandelion honey was produced between the Val di Non and Val d'Adige valleys (Trentino Alto-Adige) where there are simultaneous crops of apple trees (Rosaceae) and grapes (Vitaceae), along with spontaneous dandelion meadows (Asteraceae). Literature data, in agreement with our study, generally reports that in dandelion honey, the percentage of Asteraceae pollens is below 50%, and it is normally contaminated by different pollen species, such as Salicaceae [9; 32].  
As already highlighted [19], the levels of glucose were particularly high in apple-dandelion honey in comparison to the other samples (Figure 2a). In general, in many honey types, fructose is the dominant monosaccharide, but only in very few honey types, such as dandelion (_Taraxecum officinal_) and rape (_Brassica napus_) honey, the glucose component could be greater than the fructose one [21]. Thus, due to the high glucose content, this honey presents a high crystallization tendency if compared to other honey groups [32]. The fructose/glucose ratio in the apple-dandelion samples was significantly lower (\(p\)-value \(<0.05\)) than in multiforal, acacia, rhododendron, and chestnut honeys due to the predominance of glucose (Figure 2a), indicating this parameter is effective to characterize this monofloral honey. Although the apple-dandelion honeys contained most of the investigated VOCs, unlike other floral honeys, benzeneacetaldehyde, thymol, geranic acid, octane, terpineol, \(\gamma\)-terpinene, and 2-nonanone were under the limit of detection; decanal and tetradecanoic acid were present only in one apple-dandelion sample. To the best of our knowledge, no studies have been published reporting the volatile organic composition of these honeys. Benzaldehyde, which was the most abundant compound in all the honey varieties, appeared to be particularly intense in the apple-dandelion samples, being statistically more common in this group (together with the acacia samples) with respect to the others (Figure 2b). Benzaldehyde has been reported as an important constituent of honeys of different floral origin, responsible for a pleasant aroma [22; 24].  
Figure 2: Box and whisker plot of glucose (**a**) and benzaldehyde (**b**) in the honey samples. Same letters indicate statistically significant differences.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 6 Characterizing the Key Recovery from ReRAM PUFs
### Software and Security Considerations  
The development of the protocols presented in this section was mainly based on generic software, that can eventually be implemented in hardware with secure components, as follows:  
* The XOR functions concatenated the input parameters, such as random numbers, with passwords for multi-factor access control.
* The hash function SHA-3 (512 bit), with its one-wayness, is at the core of several layers of protection, including:
* To convert the XORed input parameters into the message digest MD that feeds the XOF and selects the addresses of the PUF used for response generation. MD is also used to protect IDAccess after XORing operations.
* As part of the RBC, the hashing of the responses is used to uncover the original responses.
* The XOF SHAKE converted the MD into the set of addresses pointing to the PUF.
* The session keys were encrypted using AES-256, and the keys were generated from the original responses of the PUF. They were decrypted with the keys retrieved from the RBC-light, and with the fresh responses from the same PUF after RBC correction.
* The session keys generated from the pre-formed ReRAM PUFs were tested successfully as seed data to generate private and public key pairs for elliptic curve cryptography, Dilithium learning with error cryptography [37], and Saber learning with rounding cryptography [39].  
The objective of this research was not to develop a final product, which has to mitigate a potential list of attacks. For example, all custom software, including the RBC, was not designed to prevent side-channel analysis. The generic software modules, i.e., XOR, SHA-3, SHAKE, and AES was written in C and downloaded in the 200 MHz RISC microcontroller, rather than being executed through a secure crypto-processor. The aim was to focus on the development of an efficient key recovery scheme with acceptable performance, not to tackle and solve all security issues.  
Figure 16: Plotting the latencies for key recovery in seconds from the ReRAM PUFs. When the number of erratic bits is zero or one, the RBC-light can find the matching key in one cycle, within 3 s. Multiple cycles of key generation from the PUF are needed at higher error rates.
# Image De-Quantization Using Plate Bending Model
## 6 Discussion
### Advantages and Limitations  
As far as we know, other approaches have not tried to recover signals in non-continuous regions. Our masking approach has the potential to handle such situations, but it requires a mask. Identifying which sub-regions should be handled together is a segmentation problem, and it is outside of the scope of this paper.  
Does a single-pixel LMM region carry real information, or is it noise? The Laplace model effectively removes LMM regions (see the examples in [7]), and higher-order approximations (spline, biharmonic) try to take these points into account. If the pixel contains noise, these higher-order methods, including the proposed method, reconstruct a false local peak. On the other hand, if the data do not originate from noise, these methods better reconstruct the signal in the LMM region. In this case, the proposed model handles saddle points well, and yields good results for very low bit depths (2-3 bits).  
The biharmonic equation is based on a simple linear elasticity model [9], where the bending energy should be minimal. This model assumes constant stiffness. It is easy to generalize this model to more general elastic models. However, the biharmonic model is high enough order for achieving smooth results but simple enough in terms of parameters, while applying statistical models [8] requires assumption about the signal distribution, and using neural networks [14] requires representative training data.  
Handling non-equidistant discretization levels is trivial in the proposed model--only the upper and lower envelopes are needed to set appropriately.  
In term of computational time, the proposed method is slower than many competing methods, because large flat areas need significant time to converge, and the convergence is also affected by the cross-terms. The Laplace method converges faster, and the Laplace operator can be more efficiently calculated than the biharmonic. However, elastic problems have been studied for a long time, and optimized solvers were developed to handle them [15], which means that even more generalized models would be tractable.  
While the proposed method requires both masking and long computation until it achieves convergence, it processes the whole image. Many algorithms use block-based processing [8], which requires overlapping blocks in order to avoid artifacts, or try to detect the false contours, and operate around the contours [2; 14]. Using blocks and/or the neighborhood of the false contours becomes a limitation at extremely low bit depths (2-3 bits), where processing blocks are smaller than the distance between the contours.
## 4 Performance Evaluation
### Average Data Delivery Ratio  
Figure 9 depicts the average data success ratio, as the number of sinks and source nodes increases from 0 to 30 in increments of 5. From Figure 9(a), we assume that the sink is static and the source generates data packets only once with an initial energy of 0.1J. As shown in the figure, SLS has the lowest data success ratio. Here we can see that SLS has difficulty obtaining location information regardless of mobile sinks. Except for SLS, as shown in the figure, the average data success ratio decreases when the number of sinks and source nodes is over 15. However, SDLS still has a high success ratio, even when the number of sinks and source nodes is 30. From Figure 9(b), we assume that the sink speed is 10 m/s and the source node is generated 30 times with an initial energy of 1J. ALS has the worst data success ratio, as ALS has to continuously relay data, while SDLS provides the shortest path through the LSR for data transmission.
## 3 Results
### Frameworks for Robust and Decentralized Energy Management  
#### 3.5.1 Main Trends  
The increasing complexity of DES has driven the development of robust and decentralized energy management frameworks. These frameworks address uncertainties, enhance resilience, and maintain stability in multi-layered energy networks [78; 79]. A prominent approach involves hierarchical structures, where tasks are divided into distinct layers to optimize the performance and scalability. For example, a hierarchical energy management system for P2P trading incorporates three levels: individual household optimization, P2P energy sharing, and joint optimization among selected prosumers [20; 80]. This system demonstrated up to 8.96% annual cost savings for households participating in energy communities, emphasizing its potential to enhance economic efficiency while decentralizing decision making [81].  
Decentralized frameworks have also leveraged the flexibility of aggregated energy resources, such as EVs and TCLs. A multi-layered strategy integrates these resources into battery energy storage systems and virtual energy storage systems to balance supply and demand [82; 83; 84]. The coordination spans the transmission, distribution, and behind-the-meter layers, using CNN-LSTM algorithms to predict local loads and distributed optimization techniques to safeguard privacy. This structure provides economic and environmental benefits [85; 86]. Robust energy management frameworks have been instrumental in autonomous microgrids and nanogrids. By adopting secondary control mechanisms, such as power-flow-based algorithms in droop-controlled AC nanogrids, these systems achieve reliable P2P trading while maintaining voltage and frequency stability. Experimentally validated models show that such frameworks can ensure energy-sharing efficiency and operational consistency in complex distributed networks [20]. Furthermore, frameworks combining robust optimization and cooperative game theory have been developed to manage interconnected multi-microgrid systems. These approaches address the operational risks associated with renewable energy intermittency and market fluctuations. For instance, a stochastic hierarchical optimization model utilizes the Nash bargaining game to allocate revenues fairly among regional energy systems, improving renewable energy utilization and reducing carbon emissions in interconnected grids [87].
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion  
This study presents an investigation into the explainability of LSTM-based RNNs for GNSS multipath detection through LRP. The research findings offer several significant insights and implications for both theoretical understanding and practical applications.
###### Abstract
A forward model maps geometric parameter of a device to its optical response. It surrogates EM solvers to accelerate device performance evaluation in optimizations. On the contrary, an inverse model maps desired optical response to geometric parameters. It takes almost no time to generate a device for a specific demand after the model is well-trained, eliminating the need for optimizations. There are already some excellent review papers about optimization algorithms and DNNs for nanophotonics inverse design. For example, [PERSON] et al. focused on the adjoint method and its applications [7] and [PERSON] et al. concentrated on topology optimization of nanophotonics [15]. More recently, [PERSON] et al. and [PERSON] et al. presented very detailed reviews about DNNs-assisted metasurface and nanophotonics design [6; 8]. Different from these previous reviews, we review design methodologies for silicon photonics from the perspective of demands. According to different design goals for a single device or multiple similar devices, we separate the design methodologies into iterative optimization algorithms and DNNs-assisted methods. The iterative optimization algorithms are illustrated in the sequence of increased DOF: empirical structure, QR-code like structure and topology structure. We also separate DNNs into discriminative neural networks and generative neural networks which are trained as forward models and inverse models, respectively. Finally, we point out the challenges of existing design methodologies and highlight a future research direction: inverse designed optical neural networks (ONN).
# Modulation Characteristics of High-Speed Transistor Lasers
## 2 Inherent Advantages of TL  
In 2004, [PERSON] et al. introduced TL as a new type of heterojunction bipolar transistor laser [19]. The energy band structure of the device is shown in Figure 1. By introducing a QW active region and a Fabry-Perot resonant cavity into the base region of an electrical transistor, electrical and optical gains can be obtained [20,21]. For an input base current, the optical signal of the active region and the electrical signal of the collector can be obtained simultaneously, which provides a new direction for photoelectronic integration.