esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Optimization of Explicit Runge-Kutta Methods with Higher-Order Derivatives
## 3 Stability Polynomials of RKMs with Higher-Order Derivatives  
Stability analysis is based on the application of analyzed method to the solution of the initial problem for the standard scalar Dahlquist equation \(y^{\prime}=\lambda y\), where \(\lambda\) is a complex constant. For the considered one-step explicit methods, stability function \(\mathcal{R}(z)\), \(z=\lambda\Delta t\) is represented by a polynomial, which is defined as  
\[y_{n+1}=\mathcal{R}(z)y_{n}.\]  
The stability domain is defined as [2]  
\[\mathcal{D}=\{z\in\mathbb{C}:|\mathcal{R}(z)|\leq 1\}.\]  
Let us represent the stability polynomials obtained for the methods considered above. In the case of GJRKMs at \(s=3\), \(\mathcal{R}(z)\) is written as  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+b_{3}a_{3,4 }a_{2,2}z^{5}, \tag{7}\]  
where \(b_{3}\), \(a_{3,4}\), and \(a_{2,2}\) are obtained from the order conditions as functions of \(c_{2,1}\). These expressions are not presented here according to their cumbersome nature.  
Stability polynomials for TDRKMs (3) and (4) for the cases of \(s=3\) and \(s=4\) are written as  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{z^{5 }}{120}+\frac{5c_{3}-3}{1200(2c_{3}-1)}z^{6}, \tag{8}\]  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{z^{5 }}{120}+\frac{z^{6}}{720}-\frac{(5c_{3}c_{4}-4c_{3}+3-4c_{4})(3c_{4}-2)}{2160(5 c_{4}^{2}-5c_{4}+1)}z^{7}-\]  
\[-\frac{(5c_{3}c_{4}-4c_{3}+3-4c_{4})(3c_{4}-2)(2-3c_{4}-3c_{3}+5c_{3}c_{4})}{4 320(5c_{4}^{2}-5c_{4}+1)(10c_{3}c_{4}-5c_{3}+3-5c_{4})}z^{8}. \tag{9}\]  
For ThDRKMs (5) and (6) in the cases of \(s=2\) and \(s=3\), the stability polynomials are presented as\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{c_{2}}{4 8}z^{5}+\frac{c_{2}^{2}}{144}z^{6}, \tag{10}\]  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{z^{5} }{120}+\frac{z^{6}}{720}+\frac{(5c_{2}-2)^{3}c_{2}\widehat{a}_{3,2}}{(2c_{2}-1)( 5c_{2}^{2}-4c_{2}+1)}z^{7}+\]  
\[+\frac{(5c_{2}-2)^{3}\widehat{a}_{3,2}c_{2}^{2}}{240(2c_{2}-1)(5c_{2}^{2}-4c_{2 }+1)}z^{8}+\frac{(5c_{2}-2)^{3}\widehat{a}_{3,2}c_{2}^{3}}{720(2c_{2}-1)(5c_{2}^ {2}-4c_{2}+1)}z^{9}. \tag{11}\]  
For the comparison, let us represent stability polynomials for the classical explicit RKMs of fourth, fifth, and sixth orders, respectively, as follows:  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}, \tag{12}\]  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{z^{5 }}{120}+\frac{z^{6}}{720}, \tag{13}\]  
\[\mathcal{R}(z)=1+z+\frac{z^{2}}{2}+\frac{z^{3}}{6}+\frac{z^{4}}{24}+\frac{z^{5 }}{120}+\frac{z^{6}}{720}+\frac{z^{7}}{5040}. \tag{14}\]
## 3 Results and Discussions
### Artificial Neural Network (ANN)
#### 3.4.3 ANN Architecture and Training Parameters  
The ANN model is structured with an input layer consisting of a single neuron representing dose values. This is followed by two hidden layers containing 8 and 16 neurons, respectively, designed to capture the complex nonlinear relationships between input and output data. The output layer consists of 221 neurons corresponding to the TL glow curve values (Figure 5).  
The model was trained using three different optimization algorithms, LM, BR, and SCG, each offering different advantages in terms of convergence speed, regularization, and computational efficiency. Training was performed for a maximum of 1000 epochs to ensure sufficient learning with a learning rate of 0.001 to control step size during weight updates. A minimum gradient threshold of \(10^{-7}\) was set as a stopping criterion to avoid over computation. Furthermore, the training process includes an early stopping mechanism  
Figure 5: Architecture of the proposed ANN model.  
that allows a maximum of 15 validation errors before stopping to avoid overfitting and improve the generalization ability of the model. The critical training parameters of the ANN model configuration are presented in Table 3.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
###### Abstract
The second aims to adjust the compressor operating mode at different times to achieve full or partial load shifting according to the time-of-use (TOU) tariff. However, the load level setting at different times is largely arbitrary, which can lead to insufficient or over-cooling and consequent energy wastage. As stressed by [PERSON] et al. [30], developing a practical peak shifting strategy for real-time optimization to achieve further cost reduction or performance improvement is of great significance. To combine the advantages of these two types of control strategies, this research innovatively proposes a peak shifting control strategy dedicated to cold stores which combines temperature setpoint control and compressor operating mode control to realize energy-saving operation. The cooling load of cold stores is strongly related to the weather conditions and thus has a remarkable periodicity in consecutive working days, which allows the LSTM algorithm to be used for its prediction. On the basis of the prediction results, two modes, A and B, are proposed to meet various control requirements in different cold stores. Actual operation data of a large-scale cold store are used to perform thermodynamic and thermo-economic analyses of the strategy to illustrate its validity. As an attempt to develop a peak shifting strategy in cold stores, this paper has achieved good peak shifting and cost-saving effects and thus can provide a reference for future research.
## 3 Results and Discussion
### Microbial Interaction and Methane-Producing Pathways of the Dominant Species in the PSD and AnMBR
The acetonitrile pathway is known to be the major pathway for more than 70% of methane production in most engineering anaerobic digestion processes [71]. In the acetonitrile methanogenesis pathway (Figure S4), genes encoding acetyl-CoA synthetase (K01895) and acetyl-CoA decarboxylase/synthae (K00193, K00194, K00197) are present in all archaeal MAGs and are involved in acetate oxidation [72]. However, genes encoding phosphate acetyltransferaseas (K00625 and K13788) and acetate kinase (K00925), which transform CoA into acetyl-CoA and acetate into acetyl phosphate, respectively, were absent. Furthermore, no archaeal MAGs harbored all the genes encoding enzymes in the complete pathway of acetotrophic methanogenesis, suggesting the predominant pathway for methane generation was not through acetate conversion in the PSD and AnMBR bioreactors. In the methylotrophic pathway (Figure S5), almost all the archaeal MAGs (except MAG11, classified as _Methanomicrobiales_ archaea) had genes encoding the three subunits of methyl-coenzyme M reductase, which is the key enzyme of biological methane formation [73]. However, all the subunits of heteroisulfide reductase have not been identified in all of these archaea MAGs, posing a question regarding the ability of the affiliated archaea to convert methanol to methane. However, it should be noted that the methanogenesis pathway was predicted based on the abundance of genes in MAGs, rather than metatranscriptomics or metaproteomics, which are required to further explore the active functions involved in the methanogenesis pathway in future studies. Figure 5: Numbers of genes involved in the relevant hydrogenotrophic methanogenesis pathway in archaeal MAGs. Various microorganisms are involved in and cooperate with each other to achieve methane formation in anaerobic digestion. According to the genome binning presented in this work, members of _Bacteroidetes_ and _Prolixibacteraceae_ were predicted to hydrolyze polysaccharide to acetate. Subsequently, _Synergistaceae_ may oxidize acetate to hydrogen and carbon dioxide. Finally, _Methanobacterium_ sp. was possible to reduced carbon dioxide to methane with hydrogen as the primary electron donor. Furthermore, sulfate was likely to be removed through desulfurization with the help of _Desulfovibrio desulfuricans_ (Figure 6).
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 5 Experimental Results
### Concurrent Wake-up Signals  
Figure 19 shows exemplary concurrent wake-up signals taken at a receiver node. The figures show normalized signal amplitudes over time for the cases of (a) two, (b) four and (c) six concurrent senders. As the signal amplitude is strongly connected to the distance between sender and receiver, the signal amplitudes are normalized to the range from zero to on, in all cases. The single wake-up  
Figure 17: (a) Photo of the prototype sensor node used in this work. (b) Schematic of the sensor node.  
Figure 18: Distribution of time offsets between two concurrent wake-up packets.  
packet is transmitted in the time period from 0 to 6 ms. After approximately 10 ms, the synchronization packet is transmitted, and the concurrent wake-up packets are transmitted in the time period from 14 to 20 ms. Nodes-1 to -6 sent at an output power of 0 dBm and were placed approximately at a distance of 1 m around the receiver. Nodes-1 and -2 sent with 868 and 868.125 MHz, Nodes-3 and -4 with 866 and 866.125 MHz and Nodes-5 and -6 with 865 and 865.125 MHz.  
It can be seen that in the case of two senders, the signal shape of the concurrent wake-up packet follows the shape as expected in Section 3.1. The beat frequency with amplitude \(A_{2}\) is on top of a DC-offset generated by the amplitude \(A_{1}-A_{2}\). In the cases of four and six concurrent senders, the signal follows the same shape, but also includes additional low frequencies that are probably caused by offsets of the 125 kHz, as discussed in Section 3.2. However, although the signal amplitudes are not constant over the duration of the wake-up packet, the receiver is able to detect them as valid wake-up messages. To further analyze the impact of additional nodes and to investigate the concept of frequency randomization, further tests are required that will be the subject to our future research.
## 3 Results
### Case Study 1: GSE179385 Dataset  
The GSE179385 dataset investigates how adipocyte hypoxia-inducible factor 2\(\alpha\) (_HIF2\(\alpha\)_) responds to cold [25]. It presents RNA-seq data for thermoneutral and cold exposures with _HIF2\(\alpha\)_ knockout and wild-type mice, each replicated three times. In order to investigate the role of _HIF2\(\alpha\)_ upon cold stress, we utilized cold wild-type and cold knockout raw count gene expression matrices as the input. We focused on endothelial PAS domain-containing protein 1 (_Epas1_), also known as _HIF2\(\alpha\)_, as the knockout TF. Then, we used a log2 FC threshold of 0.2 and a _p_-value threshold of 0.15.  
Figure 3 shows the results of the gene-set-level analysis of our system, where the DEGs between the KO and WT samples showed a remarkable enrichment in aspects related to thermogenesis, mitochondrial function, and fatty acid metabolism when exposed to cold conditions. This suggests that HIF2 may be involved in the regulation of thermogenesis.  
To explore the genes associated with the thermogenesis pathway, we selected the term \"thermogenesis\" within the table results, to examine the DEG mapping plot on the KEGG pathway, as shown in Figure 3, accessible via the web interface. Among the DEGs involved in thermogenesis, the uncoupling protein 1 (_Uclp1_) was upregulated in the KO samples. A previous study showed that modulation of the gut microbiota in _HIF2\(\alpha\)_ KO mice primarily stimulated thermogenesis, mainly through the upregulation of _Uclp1_ in inguinal adipose tissue [26].  
To investigate the interaction between _HIF2\(\alpha\)_ and DEGs, we visualized the interaction network and Figure 4 shows the network-level analysis. In the network-level analysis plot, certain upregulated candidates had been implicated in thermogenesis in previous investigations. For instance, the mechanistic target of rapamycin kinase (mTOR) gene holds pivotal roles in governing adipogenesis, lipid metabolism, and thermogenesis in adipose tissue [27; 28]. The peroxisome proliferator-activated receptor alpha (_Ppara_) gene regulates _Uclp1_ gene expression and is known to be a direct activator of peroxisome proliferatorator-activated receptor gamma coactivator 1-alpha (_Ppargc1a_) [29; 30]. _Ppargc1a_, which was directly connected with _Ppara_ gene in the network, is known to be a cold-responsive transcriptional coactivator that drives adaptive thermogenesis, which dissipates energy as heat in response to cold stress and overfeeding [31]. A previous study suggested that _HIF2\(\alpha\)_ suppresses PKA activity by inducing the expression of miR-3085-3p, resulting in the downregulation of protein kinase cAMP-activated catalytic subunit alpha (_Prkaca_) [25]. It is also noteworthy that the _Prkaca_ gene is of significant importance at the network level.  
Figure 3: Functional enrichment analysis of genes upregulated in KO in the GSE179385 dataset. Thermogenesis-related terms were highly enriched, and the plot below shows the mapping of KO DEGs to the Thermogenesis KEGG pathway in red.
# A Human-Inspired Control Strategy for Improving Seamless Robot-To-Human Handovers
## 1 Introduction
This facilitated the robot to autonomously select the best handover location based on context (i.e., accessibility, posture, a field of view and human's safety etc.). This offered safe, legible and socially acceptable paths for HRH tasks. Some researchers [36; 37; 38] examined a robotic mechanism guideline to allow the robot to grasp an object based on human-like reaching gestures before handing it over to a human partner. It involved the object's shape and function and the minimization of the risk of an injury occurring during the HRI execution. [PERSON] et al. [39] developed a general-purpose software framework for a Human support robot (HSR) service robot. It directly communicated with a human using complex speech commands. This allowed the robot to move smoothly in a confined space and also manipulate various household objects and autonomously pass them to a human. As extensively reviewed, the existing research on the development of HRH has almost underlined the investigation on behavioural strategies and the establishment of a framework for a robotic behaviour-based approach. This allowed the robot to approach a person, reach over an object and hand over it with human-like configurations and motions. However, there has been little discussion on the crucial challenges of the development of seamless HRI, since a robotic control system is further complicated by the dynamic nature of the human environment. Thus, it necessitates a very careful design and implementation of optimized behavioural control strategies to significantly improve the success rate of effective object handover tasks. This also protects a human partner from the risk of harm or injury by a robot in the cooperative tasks. To solve the limitations as mentioned, therefore, this paper presents the design of a human-inspired control strategy for improving seamless robot-human handovers using the hybrid PID position control and impedance control algorithm. This system will enhance the accuracy of positioning and force regulating abilities in the HRH system and also increase the smoothness of human-robot physical interaction. This paper details an optimized robotic control implementation for HRH by first understanding haptic dynamic interaction in HHH tasks in terms of interactive force, suitable transfer locations between the givers and receivers and the handover sequences. This knowledge is then used to design a robotic behaviour-based approach, which is the crucial aspect of the development of effective HRH. It is followed by how to develop optimized hybrid position/force control involving PID position control and force impedance control. The robotic control incorporates feedback signals from the HSR-3D-depth sensor, encoders (joint estimators) and multi-axis force/torque sensor attached to the robot wrist joint, respectively. The dynamic mechanical model of the physical HRH was additionally explained. Afterwards, the final sections express the control system evaluations, experimental results of the robotic HRH tasks and finally, conclusion.
## 4 Underwater Image-Enhancement Experiments and Result Analysis
### Quantitative Evaluation
In addition to visual and quantitative quality, runtime performance was evaluated to assess the practical feasibility of the WWSF algorithm. On images with a resolution of 946 \(\times\) 512, the average processing time was 0.1973 s per image, corresponding to approximately 5.07 FPS. These results suggest that the proposed method is computationally efficient and suitable for offline enhancement in practical underwater inspection tasks.
## 3 Experiments and Results
### MZI Sensor
The results show that the wavelength shifts for both heating and cooling overlapped well with the sensitivities of 0.114 nm/\({}^{\circ}\)C and 0.116 nm/\({}^{\circ}\)C, respectively, indicating that the sensor has stable temperature measurements. After two days, we also performed a repetitive experiment, and the result overlapped well with the previous results sensitivity of 0.111 nm/\({}^{\circ}\)C, as show in Figure 5c. Figure 3: **(a)** Schematic diagram of the micro taper structure fabrication process; **(b)** the photo under microscope; **(c–e)** the spectral responses with a sandwiched single mode fiber (SMF) length of 1, 2, and 3 cm, respectively; **(f)** the fast Fourier transform (FFT) spatial frequency spectra with arm lengths of 1, 2, and 3 cm, respectively. **(g)** The FFT spatial frequency spectra with waist diameters of 50, 60, and 80 \(\upmu\)m, respectively. Since the most common cross-sensitivity is caused by surrounding RI and strain applied to the sensor, the influence of RI and strain on the sensor's spectral response was investigated, as shown in Figure 6. Figure 6a shows that the sensor has an RI sensitivity of 5.128 nm/refractive index unit (RIU), and Figure 6b shows that the sensor has a strain sensitivity of 1.33 pm/\(\mu\epsilon\). Since the surrounding air of the sensor had a very low RI variation due to temperature, for example, at normal pressure, the sensor has a strain sensitivity of 1.  
the refractive indexes of air were 1.00027 and 1.00029 at 0 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, respectively; when the surrounding air has only \(2\times 10^{-5}\) RI variations, the RI-induced wavelength shift was negligible. Hence, the air-induced RI variations have limited influence on the measurement accuracy for the temperature sensor. However, the temperature is sensitive to strain, which is a disadvantage of this type of sensor.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling
#### 4.1.4 Loss Function  
The loss function is used to guide model optimization, resulting in higher quality point clouds.  
**Reconstruction loss.** The reconstruction loss constrains the geometry of the generated points so that they are underlying on the target surface. Commonly used reconstruction loss functions are Chamfer distance, earth mover's distance, and Hausdorff distance. They can measure the similarity between two point clouds. Their definitions are given in Equations (1)-(3).  
**Uniform loss.** To make the point cloud distribution more uniform, PU-GAN [13] proposes a uniform loss. The uniform loss assumes that the neighboring points are hexagonal, and the specific definition is given in Equation (6).  
**Repulsion loss.** The point cloud generated by feature expansion is often located near the original point. In order to solve this problem, a repulsion loss is proposed in PU-Net [5], which is defined as follows:  
\[L_{rep}=\sum_{i=0}^{S_{2}}\sum_{i=0}^{S_{2}}\eta(||x_{i^{\prime}}-x_{i}||)w(|| x_{i^{\prime}}-x_{i}||) \tag{7}\]  
where \(S_{2}\) is the number of output points, \(K(i)\) is the index set of the k-nearest neighbors of point \(x_{i}\), and \(||\cdot||\) is the L2-norm. \(\eta(r)=r\) is called the repulsion term, which is a decreasing function to penalize \(x_{i}\) if \(x_{i}\) is located too close to other points in \(K(i)\). To penalize \(x_{i}\) only when it is too close to its neighboring points, PU-Net adds two restrictions: (i) only consider points \(x_{i^{\prime}}\) in the k-nearest neighborhood of \(x_{i}\), and (ii) use the fast-decaying weight function \(w(r)\) in the repulsion loss, \(w(r)=e^{-r^{2}/\hbar^{2}}\).  
**Adversarial loss.** In recent years, GANs [42] have received extensive attention due to their powerful learning ability. A GAN consists of a generator and a discriminator. The discriminator takes the generator output and ground truth as input, and distinguishes whether each input is the generator output. The generator and discriminator are optimized alternatively during GAN training. There are currently many models that use adversarial learning to assist in training upsampling models, such as PU-GAN [13], AR-GCN [20], PUSA-GAN [43], and CM-Net [44]. Usually, a least-squares loss [45] is used as an adversarial loss:  
\[L_{G}=\frac{1}{2}[D(S_{1})-1]^{2} \tag{8}\]\[L_{D}=\frac{1}{2}\big{[}D(S_{1})^{2}+(D(S_{2})-1)^{2}\big{]} \tag{9}\]  
where \(D(S_{1})\) is the confidence value predicted by the discriminator from generator output \(S_{1}\). During the network training, the generator aims to generate \(S_{1}\) to fool the discriminator by minimizing \(L_{G}\), while the discriminator aims to minimize \(L_{D}\) to learn to distinguish \(S_{1}\) from \(S_{2}\).  
Researchers usually combine multiple loss functions using weights to form a compound loss to train the model.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 2 Theory and Prior Studies
### Speed Limit Estimation Method
#### 2.2.1 Engineering Study Methods  
Operating speed method: Most engineering study methods belong to this category. The speed limit is determined based on the 85 th percentile speed first and then adjusted in accordance with the traffic conditions and geometric structures. This method is applied to many regional roads.  
[PERSON] et al. [10] stated that the 85 th percentile speed reflected the collective judgment that most drivers choose a reasonable speed for the given traffic and road conditions.  
Road risk method: This is another engineering study method that sets the speed limit based on the risks associated with the road geometric design and traffic conditions. This method is mainly used in Canada and New Zealand [10].