esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although there are many studies on the control problem of a Markov jump system, very few studies have investigated the problem of the model reference tracking control of a Markov jump system. Because different reference models correspond to completely different algorithms, it is difficult to guarantee the tracking effects of different models. The model reference tracking problem is much more complicated than the analysis or stability of Markov jump systems only. Partly unknown transition rates and state incompleteness are still open issues. Therefore, this motivated us to address this problem subject to partially unknown TRs based on the observer. In this paper, we studied the observer-based model reference tracking control along with partly unknown transition rates. Firstly, an observer was designed for a given system by a descriptor augmentation transformation. Then the controller was designed by using the properties of the observer and variational methods, which consist of two parts: a feedback controller and feedforward compensator. We propose sufficient conditions for the stochastic stability of the system under the condition where the self-transition rate can be completely unknown; moreover, the parametric solution of the feedback controller is given using the above condition. Finally, the parametric solution of the feedforward compensator is given using the generalized Sylvester equation. The feasibility of the above theorem and algorithm is proved via several numerical examples in the last part. The notations adopted in this paper are standard. \(\mathbb{R}^{n}\) is the n-dimensional real Euclidean space. \(\mathbb{R}^{m\times n}\) is the normed linear space of all m by n real matrices. \(\|x\|\) represent the Euclidean norm of \(\mathbb{R}^{n}\) or the operator norm of \(\mathbb{R}^{m\times n}\). \(A^{T}\) is the transpose of a matrix (or vector) \(A\). \(A<(\leq)0\) mean \(A\) is a negative(semi-) definite symmetric matrix. In symmetric block matrices, we use \"\(*\)\" as an ellipsis for the terms induced by symmetry, \(diag\{\cdots\}\) for a block-diagonal matrix, and \(sym(X)=X+X^{T}\).
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 1 Introduction
### Contributions  
This paper contributes the design, implementation and evaluation of a Q8S prototype with the following features:  
* Automatic deployment of a heterogeneous Kubernetes cluster on a homogeneous OpenStack cloud including nodes with emulated hardware via QEMU;  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature** & **EMUSIM [16]** & **iContinuum [18]** & **LSC Emu. [20]** & **Q8S** \\ \hline Real Workloads & sampling & yes & yes & yes \\ Kubernetes Support & no & yes & yes & yes \\ Advanced Networking & no & yes & no & no \\ Dense Packing & - & no & yes & no \\ Hardware Emulation & no & no & no & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature comparison for frameworks that employ emulation. “Real Workloads” refers to the ability of the framework to test against actual workloads instead of simulated ones. “Kubernetes Support” refers to whether the framework is capable of running Kubernetes on its nodes. “Dense Packing” refers to the ability of a framework to deploy multiple emulated nodes on the same physical node or VM. “Advanced Networking” refers to the possibility of setting up complex network topologies, for example, mimicking edge devices connected from outside the data center. “Hardware Emulation” refers to the emulation of heterogeneous hardware such as different CPU types including different CPU architectures such as ARM on x86 hosts.  
* Configurable settings to specify the hardware to emulate as well as how many nodes should be deployed for a given specification;
* Open-source access to the Q8S Python code and Bash scripts, enabling extensibility to include additional features of QEMU and libvirt.  
By utilizing Q8S, researchers can deploy emulated heterogeneous Kubernetes clusters to test, evaluate, and train AI schedulers for heterogeneous environments without actual access to such hardware. The content of this paper is based on the master's thesis of one of the authors [33].
## 5 Simulation Results and Discussions
### Simulation Results and Analysis
#### 5.2.2 Convergence Analysis  
It can be seen from the training process that the loss function of the DC-CAAE method proposed in this paper is a strict convergence process. As shown in Figure 6, the model loss decreases from about 1000 to 1 and tends to be flat after epoch 15. That is to say, the model reaches a stable state after that epoch.
## 2 Mechanism Analysis
### Calculation of Degree of Freedom
#### 2.3.2 2T1R Motion Mode
When the mechanism is in the 2T1R motion mode, the prismatic pairs of branched chains 1, 2 and 3 and the cylindrical pair of branched chain 4 are used as the driving pairs. In both motion modes, the degree of freedom of the moving platform of the mechanism is 3, and the number of driving pairs is 4, which represent a redundantly actuated parallel mechanism.
# Design and Impedance Control of a Traveling Wave Ultrasonic Motor with a Spherical Rotor
## 6 Position-Based Impedance Control  
Impedance control is an effective control method for providing interaction stability between a robot and its environment. It does not directly control the desired position and force, but the interaction between a robot and its environment is equivalent to an impedance relationship. A general impedance is composed of inertia, a spring, and damping, and the expected interaction force is:  
\[F=K\Delta X+B\Delta\dot{X}+M\Delta\ddot{X}, \tag{6}\]  
where \(K\), \(B\), and \(M\) are the expected stiffness, damping, and inertia coefficients, respectively; and \(\Delta X\) is the difference between the reference position and the actual position that produces the desired force. Figure 9 shows the proposed force feedback control scheme, where position-based impedance control was transformed into admittance control. The interaction torque detected by the torque transducer was calculated through the target admittance, and the modified desired position was input into the inner position control loop to achieve the position tracking, such that the TWUM showed the target dynamic characteristics.  
Suppose the motor behaves as a linear spring, then the relationship between the actual torque and the reference position deviation is:  
\[\Delta\theta_{r}=\tau_{r}/K_{d}. \tag{7}\]  
Let \(e_{\tau}\) be the torque tracking error, and is expressed as:  
\[e_{\tau}=K_{d}(\theta_{0}-\theta_{r})-\tau_{r}=K_{d}(\theta_{d}-\theta_{r}), \tag{8}\]  
where \(\theta_{d}\) denotes the modified desired angular position. This equation means that \(e_{\tau}\) will be close to 0 if accurate position tracking can be achieved. In order to understand the performance of the proposed method, the virtual linear spring and virtual wall experiments based on the impedance control method were conducted.
# Crack Location and Degree Detection Method Based on YOLOX Model
## 3 Dataset Construction
### Image Acquisition System  
To obtain the required dataset and verify the application of the proposed method, an image acquisition system is built as shown in Figure 6. It is composed of a high-speed industrial camera, fill lights, a tripod, a slide rail, a laser rangefinder, and a computer.  
The selection of industrial cameras is mainly based on resolution. If the resolution is too low, it will be hard to acquire high-resolution images, which is easy to cause thin cracks to be difficult to identify or misjudge. The image acquisition system in this paper adopts the high-speed industrial camera MARS4112S-23 UM, equipped with a LEM2520 CD-H2 industrial lens, and the main parameters are shown in Table 1. To avoid ghosting and darkness, this study tries to acquire crack images in the daytime with sufficient sunshine. Dual LED ring fill lights are used to ensure uniform illumination. The working distance is measured by a laser rangefinder. Fixing the acquisition devices on the slide rail can help to keep the images collected at the same working distance.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset
### LLVIP Dataset  
The LLVIP dataset [88] is a low-light vision-infrared pairing dataset. The collection contains 30,976 photos and 15,488 pairs of photographs, the majority of which were captured in very dark situations, and all images are strictly time- and space-aligned. Additionally, a substantial variety of pedestrian photos taken in poor light are included in the dataset. Low-light visible photos are given rich supplemental information by infrared photographs. Furthermore, this dataset has unusually good image quality; the original visible light image's resolution is 1920 \(\times\) 1080, while the infrared image's resolution is 1280 \(\times\) 720. These benefits all point to the LLVIP dataset's high value in the area of detecting pedestrians in low light. The LLVIP dataset will help to advance the field of computer vision by enabling the use of image fusion, pedestrian recognition, and image-to-image conversion in low-light vision. Figure 14 depicts a selection of photos from the LLVIP dataset.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 4 Traffic State Analysis and Assessment Using ETC Data
### Regional Traffic State Analysis
A comprehensive analysis of the development status of ETC data-based urban traffic state analysis and assessment technologies reveals that this technical system has formed relatively complete analytical frameworks across three levels: cross-sections, road segments, and regions. Based on technological development trends, future research priorities will concentrate on intelligent analytical methods, precise analytical results, and real-time analytical applications. The applications of new technologies, including artificial intelligence, big data, and cloud computing, will further promote technological innovation and application expansion in this field.
# Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System
## 4 Simulation and Discussion
### Simulation Results  
#### 4.2.1 Case 1: No Load Condition  
In this case, there is no load nor there is any variation in the parameters. The desired trajectory is selected as a sinusoidal signal whose amplitude is 50 mm and its frequency is 0.1 Hz. The reference signal is \(x_{d}=50\mathrm{sin}(0.2\pi t)\) [mm].  
Figure 2 shows the comparison of tracking performances obtained using the PID controller, the DRC, and the proposed nonlinear adaptive robust controller. It is evident that the PID controller presents some lag in tracking the reference signal while DRC and NARC can track the reference signal exactly. The errors in tracking using the three different controllers are shown in Figure 3. It can be observed that, compared with the PID controller, DRC and NARC controllers have smaller tracking error and better dynamic performance. However, there is a small jitter when the DRC and NARC are used.  
In order to evaluate the effectiveness of the proposed controller under a load disturbance, a sudden load of \(d=250\) is added to system at \(t=10\) s. The corresponding tracking response with three different controllers are shown in Figure 4. As shown in Figure 4, when the sudden load is applied, some tracking fluctuation can be seen with the PID controller. However, the tracking performance of DRC and NARC is largely unaffected. The strong robustness of DRC and NARC is attributed to the exact estimate of disturbance with a discrete disturbance estimator. The tracking errors under a sudden load disturbance with different controllers are shown in Figure 5. It can also be seen that the tracking error with PID is larger than that obtained with the DRC and NARC, which further confirms that the DRC and NARC are more robust than the PID controller because of the discrete disturbance estimator.  
Figure 3: Tracking errors with no load condition, (**a**) tracking error of PID; (**b**) tracking error of DRC; (**c**) tracking error of NARC; (**d**) comparison of tracking errors with the above controllers.  
Figure 2: Comparison tracking performance with no load conditon.
# Weather Radar in Complex Orography
## 3 Challenges  
A large amount of literature is available on the challenges of radar measurement and sources of error in general, see [112] for an overview. For the most part, these also apply to mountainous regions. However, there are three categories of challenges that are specific to the usage of radars in a mountainous region.
# The BCL Method for DSSC: Basis and Applications +
## 7 The Role of Lithium on the Photo Conversion Efficiency of Ruthenium Dyes
### Adduct Generation  
At first, a study was carried out to know the interaction between lithium and the Donor-Linker-Acceptor (DoLAc) system composed by [Ru(dmb)\({}_{2}\)dcb] -Ti(OH)\({}_{3}\) in the ground state, according to Equation (36):  
\[[\text{Ru(dmb)}_{2}\text{dcb}]\ -\text{Ti(OH)}_{3}+\text{Li}^{+}\rightarrow[\text{ Ru(dmb)}_{2}\text{dcb}]\ -\text{Ti(Li)(OH)}_{3} \tag{37}\]  
After performing a series of geometrical optimizations with the lithium cation in different places of the molecule, the final results always agreed that the most stable structure was with the lithium captured by the Ti(OH)\({}_{3}\), which concords with experimental observations.  
This result is shown in Figure 31, where it can be seen that through a thermal pathway (path b) an interaction between the complex and the lithium ion does not occur because it is attracted by the oxygen atoms of the Ti(OH)\({}_{3}\) group.  
The procedure for obtaining the adduct through a photochemical pathway is displayed on Figure 31 through path (a). The optimized structure of the Lithium adduct is obtained through a four steps procedure: first the ground state 1(S0) is optimized. Second, employing this structure as starting geometry, its triplet state is generated and optimized, 1(T1). Through the third step, the Lithium atom is added and the structure with the cation is reoptimized and the new geometry for 1(T1)Li is obtained. In the last step, the multiplicity is changed to singlet and the a new optimization gives the ground state of the metastable adduct 1(S0)Li.  
Figure 30: Scheme showing the photochemical generation of the meta-stable lithium adduct. (**a**) Ground state, the ruthenium dye is bound to the surface of TiO\({}_{2}\) (**b**) the excited dye acts as photo-base and captures a Li\({}^{+}\) ion from its neighborhood. (**c**) After deactivation, a Li–dye adduct is formed. Atom color legend: White (Ti), red (O), yellow (Li), black (C), blue (N), and pink (Ru).  
An analysis of the results in Table 6 shows that the interaction of lithium with the complex is evidenced in a loss of planarity of the dcbp ligand, which merges from the decrease in the alpha angle between the two pyridine rings, from 178\({}^{\circ}\) in the free complex to 161\({}^{\circ}\) in the adduct. This distortion in planarity is originated in the triplet state and continues to be present when the system decays to the basal state. A second change that must be considered is the decrease in the distance (d4) between the oxygen of the carbonyls of the dicarboxylate substituents passing from 6.11 A in the complex to 4.03 A in the adduct.  
The above data are consistent and indicate that the ligand undergoes a type of folding where its molecular structure adopts a shape similar to a vessel, which consequently allows the capture and stabilization of the cation within it.
## 3 Estimation of Node Position Using Indirect Observations  
In this section, we review the basic theory that is exploited in the proposed algorithm described in Section 4. In the first subsection, we summarize the multilateration problem, and in the second subsection, we describe the Weighted Least Squares (WLS) applied to the method of indirect observations.
## 1 Introduction  
Artificial intelligence (AI) has become an indispensable tool, with abilities that include generating human-sounding text and convincing images and videos, as well as assisting individuals and groups in decision making by applying critical thinking [1]. In higher education, AI tools and platforms provide instant and personalized feedback, including relevant arguments and logic, and they have the potential to provide emotional support and motivation to students [2, 3]. AI systems are also beneficial for instructors, supporting them in preparing learning materials, formulating convincing arguments and teaching strategies, and easing their burden by assisting in assessments and providing support in grading different forms of assignments.  
Custom-trained tutors that utilize GPT architecture, such as large language models (LLMs), are based on \"vanilla\" AI models, i.e., generic LLMs, and can be easily developed without specific programming or advanced technical competencies. Educators can fine-tune commercially available LLMs by using plain-language prompt engineering techniques and training them with their teaching materials, including slides, case studies, tests, and assignments. Such technology opens the door for non-technical educators to engage with the latest \"frontier\" of AI tools and models and apply them in the specific context of their academic teaching.  
In this study, we aimed to evaluate a custom GPT tutor, developed by the authors and applied to a short academic course on the topic of blockchains in an undergraduate business school setting. Our goal was to provide a preliminary evaluation of the tutor's pedagogical competencies and their contribution to students' competencies, both from the students' viewpoint.  
Therefore, we set the following research questions (RQs):  
* RQ1: How do students rate the perceived pedagogical quality of the custom GPT tutor?
* RQ2: To what extent does the perceived pedagogical quality predict students' self-reported generic competencies?  
Answers to these questions would inform instructors seeking to integrate AI-based tutors into business curricula without increasing their contact hours.
## 5 Conclusions  
### Discussion  
We propose a novel RHRM recommendation framework that filters only helpful reviews and reflects them in the personalized recommendation service. To achieve our study objective, we built CNN-BiLSTM hybrid models that demonstrate excellent classification performance in NLP studies to filter helpful reviews. We have also evaluated the performance of the proposed recommendation framework in this study by utilizing UBCF, SVD, and NCF techniques that are widespread in the use of recommender systems studies. To evaluate the recommendation performance, we used large numbers of Amazon publicly accessible datasets [62; 63]. The experimental results show that the RHRM framework outperforms the prediction performance of the existing recommendation framework without regard to review helpfulness. Experimental results also suggest that the review's helpfulness information can significantly impact user preference ratings. In other words, users' high quality of reviews can provide higher reliability than preference rating information given by users [14]. Furthermore, we have identified that the CNN-BiLSTM hybrid model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Metrics Factor** & **Mean** & **Standard Deviation** & \(t\)**-Statistics** & \(p\)**-Value** \\ \hline \multirow{3}{*}{UBCF} & MAE & Existing & 0.716 & 0.435 & 32.007 & 0.000 \\  & RMSE & Proposal & 0.617 & 0.355 & 32.007 & 0.000 \\  & RMSE & Existing & 0.870 & 0.498 & 39.388 & 0.000 \\  & proposal & 0.732 & 0.402 & 39.388 & 0.000 \\ \hline \multirow{3}{*}{SVD} & MAE & Existing & 0.601 & 0.374 & 33.589 & 0.000 \\  & RMSE & Proposal & 0.511 & 0.311 & 33.589 & 0.000 \\  & RMSE & Existing & 0.710 & 0.426 & & \\  & Proposal & 0.592 & 0.351 & 38.775 & 0.000 \\ \hline \multirow{3}{*}{NCF} & MAE & Existing & 0.600 & 0.422 & & \\  & Proposal & 0.468 & 0.383 & 41.192 & 0.000 \\ \cline{1-1}  & RMSE & Existing & 0.747 & 0.506 & & \\ \cline{1-1} \cline{2-6}  & Proposal & 0.605 & 0.444 & 37.542 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Two samples t-tests between existing and proposed framework in several recommendation method types.  
Figure 8: Prediction performance of MAE (**a**) and RMSE (**b**) of NCF model.  
used in this study outperforms other deep learning models such as CNN and Bi-LSTM single model. This demonstrates the advantages of the CNN-BiLSTM hybrid model in semantic feature extraction. We have also identified that the classification performance of the CNN-BiLSTM hybrid model depends on the vocabulary size and the review length used in model training. We found that when the word size was 80,000, and the review length was maximum, and the model indicates excellent classification performance through various experiments. Because using all vocabulary as training data includes noise features that are insignificant to the analysis, this result in increased computational costs, time, and reduced classification performance [17].
## 3 Methodology
### Post-Processing
#### 3.4.1 Merging and Cleaning Data  
To tackle these issues, we first consolidate all PS points, removing any with negative height values that were not reliable. We then align each point by its SAR coordinates, enabling us to track the height values consistently across each stack. This alignment is crucial for monitoring changes over time and correcting any discrepancies in the data.
## 3 Results and Discussion
### Zinc  
The average Zn contents for both species were 44.3 mg/kg for red deer and 29.1 mg/kg for wild boar. The Zn content in deer closely matched the findings of other authors, with [PERSON] et al. [58] reporting 43.4 mg/kg and [PERSON] et al. [18] reporting 39.2 mg/kg. Additionally, the average Zn content in deer was consistent with the Zn level found in farmed deer muscle (46 mg/kg) [59]. A recent study has suggested that Zn levels in deer meat can fluctuate seasonally, with higher levels in stags hunted during autumn, potentially attributed to seasonal changes in plant composition and availability [11]. Moreover, [PERSON] and [PERSON] [60] indicated that the consumption of deer muscle could represent an important contribution to Zn intake. Another study indicated that the Zn content in deer can vary depending on the muscle type of the animal [29]. On the other hand, Zn content in wild boar was similar to the contents shown by other authors as such [PERSON] et al. (27 mg/kg) [25]; [PERSON] et al. (28.7 mg/kg) [27]; [PERSON] and [PERSON] (31.5 mg/kg) [31]; and [PERSON] et al. (32.3 mg/kg) [32].  
As occurs for the rest of the trace elements studied, these Zn contents are higher than those found in similar areas from slaughtered animals such as beef (27-45 mg/kg) and pork (20-27 mg/kg) [34]; (13.1-27.2 mg/kg) [40]. Just like Fe, one of the main dietary sources of Zn is meat and meat products. Several studies have shown lower serum/plasma Zn concentrations in vegetarians or vegans than omnivores [61; 62; 63].  
In this study, we found that an intake corresponding to a red deer serving covers 15-20% of the Zn DRI from the 50 th percentile (male and female) and fulfills the Zn DRI from the 90 th percentile. In the case of wild boar, the intake for consuming this meat would be 6.8 mg for the 95 th percentile, covering up to 61-75% of the DRI. The bioavailability of this Zn present in game meat would also be increased by the presence of animal proteins which, when hydrolyzed by digestive enzymes, form low molecular mass peptides. These peptides join to Zn, forming soluble compounds with this element until Zn is absorbed by the enterocytes [64].  
Finally, the 95 th percentile Zn intake accounts for 13.4 and 6.8 mg/day for red deer and wild boar, respectively. These values are approximately two and four times lower than the tolerable upper intake level (25 mg/day) established by the European Food Safety Authority [43].