esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stability of Freeze-Dried African Catfish Mucus in Milli-Q Water Using Zeta Potential  
Zeta potential is a vital tool that could be used to evaluate if an emulsifier can stabilize dispersions or emulsions. It depicts the potential charge in the boundary double layer at the droplet boundary of the continuous and dispersed phases [77; 78]. The absolute zeta potential of freeze-dried ACM in Milli Q Water was found to be \(-\)36.2 mV at a pH of 7.83. This data showed that sufficient repulsion exists between the droplets of the African Catfish mucins and Milli Q Water to keep particles away from each other. Published works propose that a dispersion becomes stable if its zeta potential is more than \(\pm\)30 mV [78; 79; 80]. Stability is a result of static electrical repulsive forces between the particles hence the dispersion does not form aggregates because the absolute zeta potential is greater than 30 mV [60; 78].
## 3 Results
### Biomarker Concentrations
#### 3.3.1 sST2  
In both the group of severe AS patients with sPAP \(<\) 40 mmHg and the group with sPAP \(\geq\) 40 mmHg, the plasma levels of sST2 increased after successful TAVR compared with the baseline expression in the first 24 h (sPAP \(<\) 40 mmHg: baseline--10,750. 80 \(\pm\) 5894.78 pg/mL, 24 h--14,220.25 \(\pm\) 11,628.33 pg/mL; \(p\) = 0.039. sPAP \(\geq\) 40 mmHg: baseline--16,134.67 \(\pm\) 13,351. 82 pg/mL, 24 h--17,372.76 \(\pm\) 13,826.10 pg/mL; not significant), decreased further after 96 h (sPAP \(<\) 40 mmHg: 96 h--9919.84 \(\pm\) 4239.27 pg/mL. sPAP \(\geq\) 40 mmHg: 96 h--13,217. 80 \(\pm\) 8591.19 pg/mL), and again after 3 months (sPAP \(<\) 40 mmHg: 3 months--10,120.52 \(\pm\) 4258.88 pg/mL. sPAP \(\geq\) 40 mmHg: 3 months--10,671.73 \(\pm\) 7354.71 pg/mL). Finally, after 12 months, there was a slight increase in sST2 in both groups (sPAP \(<\) 40 mmHg: 12 months--11,179.72 \(\pm\) 9233.51 pg/mL. sPAP \(\geq\) 40 mmHg: 12 months--12,149.09 \(\pm\) 10,029.74 pg/mL).  
A direct comparison of the two patient groups showed significantly higher plasma sST2 concentrations in patients with an sPAP \(\geq\) 40 mmHg, with exception of the analyses at 12 months (baseline: \(p\) = 0.007; 24 h: \(p\) = 0.006; 96 h: \(p\) = 0.014; 3 months: \(p\) \(\leq\) 0.001; 12 months: \(p\) = 0.092).
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 2 System Model and Problem Formulation
### Problem Formulation  
The aim of network slicing design is maximizing the overall utility achieved by the devices in the system, Firstly, inside each network slice, to achieve fairness among the inner-slice devices, the proportional fairness is utilized as the utility function of each device [28]. That is,  
\[U_{n}=\log(R_{n}). \tag{7}\]  
More importantly, considering the disparity of throughput in three network slices, weight preference is utilized to balance their contribution to the overall utility [29]. In this work, we use \(w_{\text{eM}}\), \(w_{\text{uR}}\) and \(w_{\text{mM}}\) to denote the weight for the devices in eMBB, URLLC and mMTC slices, respectively.  
Hence, we have the following optimization problem, via the BS-device association and bandwidth allocation to maximize the overall system utility:\[\max_{b_{nm^{\prime},x_{nm}}}w_{\text{eM}}\sum_{n\in\mathcal{N}_{ \text{eM}}}U_{n}+w_{\text{uR}}\sum_{n\in\mathcal{N}_{\text{uR}}}U_{n}+w_{\text{ mM}}\sum_{n\in\mathcal{N}_{\text{mM}}}U_{n}. \tag{8}\] \[\text{s.t.}\ \ \sum_{n\in\mathcal{N}}x_{nm}\cdot b_{nm}\leq B_{m},\ \ \forall m,\] (9) \[\sum_{m\in\mathcal{M}}x_{nm}=1,x_{nm}=\{0,1\},\ \ \forall n\in \mathcal{N},\] (10) \[R_{n}\geq R_{0},\ \ \forall n\in\mathcal{N}_{\text{eM}},\] (11) \[\sum_{m\in\mathcal{M}}b_{nm}\geq B_{0},\ \ \forall n\in\mathcal{N}_{\text{mM}},\] (12) \[\frac{L}{R_{n}}\leq T_{0},\ \ \forall n\in\mathcal{N}_{\text{uR}}, \tag{13}\]  
In addition, (9) represents the bandwidth allocated to the associated users that cannot exceed the overall bandwidth in this BS; (10) indicates that a device only can be associated with one BS at one-time instance; and (11), (12) and (13) represent that the network requirements in each slice introduced above.  
In essence, the above problem is a constrained mixed integer problem. In the following, we propose an augmented Lagrangian-based reinforcement learning (RL) with a soft actor-critic (SAC) framework to solve this problem. Then, with the optimal results on the device-BS association and the bandwidth allocated to each device, an extra but simple procedure is needed to complete the slicing details: /(1) Calculate the number of radio resource blocks (RRB) needed for each slice in one BS, and determine the corresponding collection of RRBs. (2) Each BS allocates a subset of the RRBs belonging to the slice to each serving device.
# Adaptive Versioning in Transactional Memory Systems +
## 6 Experimental Evaluation
### Experimental Setup  
We developed an STM-based implementation using TinySTM [26; 27]. TinySTM has implemented separately both lazy and eager versioning (called _Lazy_ and _Eager_) through _Write_Back_ and _Write_Through_ designs, respectively. With _Write_Through_ design, transactions directly write to original memory locations and revert their updates in case the transactions abort. However, with _Write_Back_ design, transactions work on a copy of data and delay their updates to the original memory locations until commit [26; 27]. Furthermore, _Write_Back_ design has two different implementations: _Write_Back_ETL_ and _Write_Back_CTL. _Encounter-time locking_ (ETL) detects conflicts early at the time of write and acquires the lock on the memory address before it is written. _Commit-time locking_ (CTL) defers conflict detection on memory address until commit, i.e., the lock is acquired on the memory address at the commit time. Therefore, there are two different implementations of _Lazy_ in TinySTM: one based on \(ETL\) called _Lazy_ETL_ and another based on \(CTL\) called _Lazy_CTL_. We obtain adaptive design _Adaptive_ETL_ using _Lazy_ETL_ and _Eager_ versioning. Similarly, we obtain adaptive design _Adaptive_CTL_ using _Lazy_CTL_ and _Eager_ versioning. We run experiments with five different designs _Lazy_ETL_, _Lazy_CTL_, _Eager_, _Adaptive_ETL_, and _Adaptive_CTL_.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 9 Joint Routing, Power and Bandwidth Allocation in FDMA WSNs
### Optimization Problem Formulation  
The following are the details of the optimization problem formulated in [4]:  
* Decision variables: All the decision variables are continuous non-negative variables. There are three sets, one set of variables is for the power values on the links \(p_{l}\), the second is for the flow capacities on the links \(f_{l}\) and the third is for the amounts bandwidth spectrum allocated to the links in the network \(w_{l}\).
* Objective function: is a linear function in the aggregate link powers i.e., \(\sum_{l\in\mathcal{L}}p_{l}\) (where \(\mathcal{L}\) is the set of links).
* Constraint sets: There are four functional constraint sets, these are, 1. a linear equality constraint set in the flow variables \(f_{l}\) for the conservation of data rate flows. These guarantee that for every node the difference between the out-going flows and the sum of the in-going flows is strictly equal to the rate of data generated by each node \(t_{i}\).  
Figure 13: Convergence to the Optimal Solution in [3].  
2. a convex non-linear constraint set that guarantees that the flows on each link are upper bounded by the Shannon capacity of the link. These constraints are function in both the powers on the links \(p_{l}\) and the bandwidths allocated to the links \(w_{l}\) and are given as, \(f_{l}\leq c_{l}\left(p_{l},w_{l}\right)\ \ \forall l\in\mathcal{L}\).
3. a linear constraint set in the power variables of the links that guarantees that for each node the aggregate transmission power on all its links does not exceed sensor's battery power, i.e., \(\sum_{l\in O\left(i\right)}p_{l}\leq P,\forall i\in S\).
4. A linear constraint set in the bandwidth variables that guarantee that the sum of bandwidths allocated on all the links of every node does not exceed the nodes' pre-allocated bandwidth \(W\), i.e., \(\sum_{l\in O\left(i\right)}w_{l}\leq W,\forall i\in S\).
## 3 Methods
### Geometric Calibration Model
#### 3.1.2 Uncertainty Evaluation of Geometric Calibration Model  
In addition to systematic errors, the geolocation of acquired images is also affected by random errors. The satellite imaging process is affected by various complex conditionssuch as attitude adjustment, attitude measurement accuracy, and imaging environment. In addition, as a microsatellite, BNU-1's low cost and the imaging environment of polar regions limit, to a certain extent, the overall accuracy and stability of the measurement equipment of attitude and position. Moreover, due to the wide swath of BNU-1's camera, the imaging time of its single-scene image is long. As the satellite attitude is adjusted along the imaging, random errors in attitude measurement cause random geolocation errors in single-scene images and multiple-scene images. Since the measurement error of GPS can be regarded as translation error (Equation (1)), which is equivalent to the satellite rotating at a small angle, we only designed and carried out an experiment to simulate the influence of the satellite's attitude angle change on the geolocation change through Equation 1. The satellite's attitude is determined by the roll angle, pitch angle, and yaw angle. We randomly selected an image and simulated the angles of roll, pitch, and yaw, which changed from 0\({}^{\circ}\) to 0.5\({}^{\circ}\) with a step size of 0.1\({}^{\circ}\), to obtain 216 (6 * 6 * 6) groups of geolocations. The quintic polynomial method was used to fit the scatter plot.
## 2 Analysis of Ranging Consistency for ISL Payload
### Overview of ISL Payload Composition and Delay Distribution  
The ISL of a navigation constellation adopts a space- and time-division multiplexing access system. The navigation satellite based on the planning table of the entire network link is associated with different visible satellites in different slot divisions. The antenna beam should perform constant fast switching; thus, the beam agile active phased-array antenna (APAA) has become the first choice for a global ISL network. The ISL transceiver is specific payload equipment for inter-satellite ranging and communication. It consists of a digital baseband (DBD) signal-processing module and an radio frequency (RF) transceiver channel. The baseband module performs spread-spectrum signal processing and algorithm protocol implementation. The RF transceiver channel realizes the signal up-down conversion and power amplification. The ISL payload principle, architecture, and related delay distribution are shown in Figure 2.  
In Figure 2, IF represents the intermediate-frequency interconnection, and RF represents the radio-frequency interconnection. In DBD, IOB represents the input and output block, DAC represents the digital-to-analog converter, and ADC represents the analog-to-digital converter. In the RF channel, BPF denotes the band-pass filter, LO denotes the local oscillator, HPA represents the high-power amplifier, and LNA represents the low-noise amplifier. In APAA, SPDT represents the single-pole double-throw switch, TR denotes the transmitter/receiver, and HPA and LNA have the same meaning as those in the RF channel.  
Figure 1: One-way pseudo-range measurement process.  
Figure 2: Inter-satellite link (ISL) payload principle, architecture, and related delay distribution.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 4 Experiments
### Sequential Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Mean Translation Errors**} & \multicolumn{4}{c}{**Mean Rotation Errors**} & \multirow{2}{*}{**IoU**} \\ \cline{3-3} \cline{5-8}  & & **x (m)** & **y (m)** & **z (m)** & **Pitch (rad)** & **Yaw (rad)** & **Roll (rad)** & \\ \hline \multirow{3}{*}{Sequence 1} & EOR & 0.21 & 0.19 & 0.94 & 0.02 & 0.01 & 0.03 & 0.32 \\ \cline{2-8}  & MOR & 0.29 & 0.49 & 1.46 & 0.02 & 0.01 & 0.04 & 0.33 \\ \cline{2-8}  & VisionHD & 0.22 & 0.19 & 1.45 & 0.02 & 0.01 & 0.02 & 0.29 \\ \hline \multirow{3}{*}{Sequence 2} & EOR & 0.10 & 0.07 & 0.37 & 0.00 & 0.01 & 0.01 & 0.37 \\ \cline{2-8}  & MOR & 0.50 & 0.65 & 2.10 & 0.02 & 0.02 & 0.04 & 0.27 \\ \cline{2-8}  & VisionHD & 0.15 & 0.08 & 2.16 & 0.01 & 0.01 & 0.01 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sequence Experiment Errors and IoU. Figure 13: SDF Values and Quantitative IoU Results. (**a**) Sequence 1; (**b**) Sequence 2. For Sequence 2 in Table 5, EOR achieves the highest mean IoU (0.37), surpassing MOR (0.27) and VisionHD (0.33), indicating superior overall registration performance. Notably, EOR achieves the lowest errors across all six dimensions, with mean errors of 0.10 m (x), 0.07 m (y), and 0.37 m (z) in translation, as well as 0.01 radians in rotation. Figure 14 visualizes the 6-DoF errors for both sequences. Figure 14a confirms EOR's superior accuracy, particularly in the y and z directions for Sequence 1. For Sequence 2, Figure 14b corroborates the results from Table 5, showing that EOR consistently outperforms the other methods across all dimensions, despite occasionally exhibiting high yaw error values.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using IBM SPSS Statistics 26. Changes in postural control variables were evaluated with the paired-sample \(t\)-test or the Wilcoxon signed-rank test, depending on consistency of the variables' distribution with normal distribution, which was examined using Shapiro-Wilk test. Level of statistical significance was set at <0.05. The effect size (ES) for the paired \(t\)-test samples was calculated via dividing the mean difference by the standard deviation of the difference, while for the Wilcoxon signed-rank test, it was calculated through dividing the z-value by the square root of the observation number [33]. ES was interpreted as small (<0.3), medium (0.3-0.5), or large (>0.5) [33].
## 2 Description of Methodology
### Validation of DNN
As shown in Figure 5, the DNN prediction results agree well with the simulation results, and the MRE of the vibrationally excited CO\({}_{2}\) is only 0.036%. From Figure 5, it is obvious that higher vibrational levels of CO\({}_{2}\) possess a larger density. In RF CO\({}_{2}\) discharges under Martian pressure, a large number of CO\({}_{2}\) molecules at lower vibrational levels can gain vibrational energy through vibration-vibration (VV) relaxation and then be pumped to higher vibrational levels through the step-climbing mechanism along asymmetric stretching mode, leading to an increase in the density of higher vibrational levels. Neutral particles are the primary concerns of ISRU on Mars, so it is important to accurately predict the spatial distribution of neutral particles, such as CO, O, and O\({}_{2}\). Figure 3: Spatial distributions of time-averaged positive charge density, negative charge density, and electric field predicted by the DNN with a comparison of the results obtained from the fluid simulation. More importantly, the high computational efficiency of the DNN needs to be emphasized. The conventional fluid simulations using the improved Scharfetter-Gummel (iSG) method [55] to solve multiple governing equations require at least 20,000 RF cycles of operation to ensure that the various properties of CO\({}_{2}\) discharges achieve the dynamic steady state, which takes about 6 h (\(\sim\)21,600 s). However, if the time to call the library functions is not considered, after about two hours of training, the DNN can yield the corresponding essential characteristics of RF CO\({}_{2}\) discharge in about 0.01 s with very high accuracy. It can be said that the well-trained DNN improves the computational efficiency by a factor of about 10\({}^{6}\) times with respect to the conventional fluid simulations of similar accuracy. In other words, the application of the DNN enables almost real-time prediction of various characteristics of RF CO\({}_{2}\) discharge. Hence, the introduction of the DNN enables  
Figure 4: Spatial distributions of time-average charged particle density predicted by the DNN with a comparison of the results obtained from the fluid simulation. Figure 5: Spatial distributions of time-average vibrationally excited CO\({}_{2}\) density predicted by the DNN with a comparison of the results obtained from the fluid simulation. revealing the discharge characteristics and plasma chemistry in real time, although tens of species and hundreds of reactions are considered in CO\({}_{2}\) discharges; simultaneously, enough data can be predicted very efficiently by the DNN to more accurately describe the discharge evolution in RF CO\({}_{2}\) discharges.
in the middle of the session also leaves a considerable impact on the perceived video quality [17; 18]. Theoretically, such impacts can be represented by an exponential deterioration of memory retention in time (defined by _forgetting curve_) [19; 20; 21] for infrequent events or by _repetition_[21; 18] for the repeated impairments. However, the influence of forgetting behavior and repetition has not been carefully investigated in existing QoE models. Therefore, to fully express human memory effects on QoE assessment, in addition to the primacy and recency effects, the forgetting curve and repetition should be involved in the discussion. Apart from that, the factors that relate to video content also have a noticeable effect on perceived QoE. Those factors might be type of video, video complexity [22], etc. Additionally, some studies (e.g., [23; 24; 25]) have found that the user's interest in video content possibly generates the bias in his/her QoE evaluation. More concretely, the user tends to provide higher QoE scores for more attractive video contents. Such a behavior is influenced by the so-called degree of interest (DoI), which clarifies the interestingness of different video content, or the ability of the video content to attract the user and keep the user's interest [26]. However, existing studies often neglect this factor due to the fact that these numerical values might vary upon different users based on their personal interests.  
In this paper, we propose a novel cumulative QoE model that extremely well quantifies multiple effects of human-related factors, that is to say, perceptual influence factors, memory effect and degree of interest (DoI). We mainly consider our model on video-on-demand streaming services since the video's DoI have to be collected offline before video streaming via performing a subjective test. To assess the accuracy in predicting cumulative QoE, our proposed model is evaluated over LFOVIA database [12] and through the subjective evaluation. Evaluation results show that the cumulative QoE at different moments within a streaming session is precisely predicted by our proposed model. Our study is distinguished with existing works by the following contributions:  
* A cumulative QoE model is proposed that concurrently takes into account the human-related influence factors for predicting the time-varying cumulative QoE of on-demand streaming services.
* The novel memory weight, representing the effect of primacy, recency, forgetting and repetition is introduced in the proposed cumulative QoE model.
* The correlation between DoI and subjective QoE is investigated and confirmed in this study. Thereby, DoI becomes a potential QoE influence factor that is involved in the proposed model.  
The rest of this paper is organized as follows. In Section 2, we discuss the existing works related to our approach in terms of their drawbacks. Our proposed model and our investigations on the influence factors are presented in Section 3. Section 4 evaluates the performance of the proposal and discusses the advantages and disadvantages. Section 5 concludes this paper.
## 2 Modelling of the Scheduling Optimization Problem of MRO Processes  
First and foremost, the MRO process is required to be properly defined in order to model the scheduling optimization problem. Figure 1 describes an example of the MRO processes that have been defined in this paper. All components are initially inspected at an inspection station. Based on the identified damages on each component, a particular process flow will be assigned for that component. The process flow defines a series of operations to process the component. The operations of each process flow follow a defined sequence. After all operations of a process flow have been executed, the component is released from the MRO. Each operation is executed via various types of resources such as personnel, machines, materials, and tools. As a simplification, a machine represents all resourcesrequired to execute an operation. The different colored arrows in Figure 1 indicate the process flows of different components. The numbers of operations in process flows can be different from component to component. Depending on damages on the components, the processing times of the operations are also different from each other. The scheduling optimization problem in this paper covers the operations to process the components after they have been inspected at the inspection station.  
The MRO process must be modelled in order to implement the ACO algorithm for scheduling optimization. The MRO process flow of a single component is represented as a job. There are \(n\) jobs assigned for the corresponding \(n\) components to be processed. Let \(J\ =\ J_{i}\) is the set of n jobs, where \(1\leq i\leq n\). There are \(m\) machines to process the jobs. Each job \(J_{i}\) consists of a predetermined sequence of \(j\) operations. An operation is represented as \(O_{i,j}\), which means it is the \(j\)-th operation of job \(i\). Each of the operations can only be processed on a specific machine in the set of \(m\) machines. The operation \(O_{i,j}\) takes the processing time \(p_{i,j}\) to be completed. The objective of the scheduling optimization is to minimize the makespan or scheduling time, \(C_{max}\), which is the time required to finish all the operations of all the jobs. The constraints to the objective function are:  
* The machine's set-up time and transportation time required between operations are not considered.
* There is no dependency between machines.
* A machine can only execute other operations if the current operation is completed.
* The precedence constraints which indicate the sequence to execute the operations are only applicable for the operations in the same job.
* Only one operation of the same job can be executed at a time.