esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of a Weighted ICP Algorithm to Precisely Determine USV Movement Parameters
## 2 Evaluation of the Accuracy of Determining the Coordinates of a Position Using LIDAR
### The Determination of USV Movement Parameters Using the Scan-Matching Method
It is required to determine two weighted average coordinates from the cloud m(\(k\)) and m(\(k+1\)), dependent on point clouds (\(k\)), (\(k+1\)) and assigned weights in the previous stage:  
\[\text{m}(k)=\frac{\sum_{i=1}^{n}w_{i}\text{P}_{i}(k)}{\sum_{i=1}^{n}w_{i}}, \tag{15}\]  
Figure 2:0  
Subsequently, we can move on to determine the weighted covariance matrix, which will be used to calculate the innovation of rotation matrix and translation (in a given iteration of the algorithm)  
Figure 2:1  
Using the SVD decomposition method on the C matrix  
Figure 2:2  
where matrices U, \(\Sigma\), V are characteristic matrices for the SVD method. By means of decomposition carried out on designated matrices, we obtain the innovations in given iteration--of the R\({}_{it}\) rotation matrix and the T\({}_{it}\) translation:  
Figure 2:3  
Figure 2:4  
The obtained values in the **R\({}_{iter}\)** and **T\({}_{iter}\)** matrices are partial values developed in a given iteration of the algorithm. Based on this, the set m(\(k+1\)) is translated at last iteration (see Figure 6) and as a result the translation and rotation is obtained:  
Figure 2:5  
Figure 2:6  
where \(\textbf{R}_{it=1}\) and T\({}_{it=1}\) are initialized values. The translation vector \(\textbf{T}(k+1)=\left[ \begin{array}{c}\Delta x\\ \Delta y\end{array} \right]\) thus obtained will be used to determine  
\[\text{SOG}(k+1)=\frac{\sqrt{\Delta x^{2}+\Delta y^{2}}}{\Delta t}, \tag{23}\]  
Figure 6: Left side—2-point clouds; right side—2-point clouds aligned. where \(\Delta t\) is the distance in time between the moment \((k)\) of performing the measurements of \(\mathbb{Z}(k)\), and the moment \((k+1)\) of performing subsequent measurements of \(\mathbb{Z}(k+1)\). On the other hand, the final form of the rotation matrix \(\mathbf{R}(k+1)=\left[ \begin{array}{cc}\cos\Delta\theta&\sin\Delta\theta\\ -\sin\Delta\theta&\cos\Delta\theta\end{array} \right]\) will be used to determine:  
\[\mathrm{ROT}(k+1)=\frac{\mathrm{arc}\,\sin(\Delta\theta)}{\Delta t}, \tag{24}\]  
and  
\[\mathrm{HDG}(k+1)=\mathrm{HDG}_{0}+\mathrm{arc}\,\sin(\Delta\theta), \tag{25}\]  
where \(\mathrm{HDG}_{0}\) is the initial HDG value determined using other methods (devices) prior to the commencement of measurement.
## 3 Control Method and Control Characteristics of the Switching Process of the DFVSPS Unit
### Generating Mode
#### 3.1.1 Start-Up in Generating Mode  
There are two ways to start a DFVSPS unit in generating mode. The following is illustrated by combining the motion equation of DFIM shown in Formula (9).  
Method 1: Start-up using mechanical torque. At this time, the rotor side of DFIM in not controlled, and the stator side of DFIM is short-circuited by three phases, so \(T_{e}=0\). Open the guide vane opening and start using the mechanical torque of pump turbine. Regarding the setting of the guide vane opening at the time of start-up, one method is to set the guide vane opening to the no-load guide vane opening (about 5% or so) until the unit is accelerated to the rated speed and then connected to the grid. Another method is to first open the guide vane opening to a larger guide vane opening to shorten the start-up time of the unit, and then reduce the guide vane opening to the no-load guide vane opening after the speed reaches a certain set value. In addition, connect to the grid until the speed reaches the rated speed.  
Method 2: Start-up using electromagnetic torque. At this time, the stator of DFIM is short-circuited in three phases. Electromagnetic torque is generated by the excitation control system on the rotor side of DFIM. After the unit accelerated to the rated speed, the control strategy of the excitation control system is changed for grid connection.  
Method one is used to start the unit in this paper. The rotor side of DFIM is not controlled, the stator side of DFIM is short circuited in three phases, and the guide vane opening is opened to the no-load guide vane opening, which is 6% until the speed rises to the rated speed. The simulation model was established in MATLAB/Simulink (version, Manufacturer, City, US State abbrev, USA) (The values of the parameters of DFIM in this paper are shown in Table 1 and other value of parameters used in this paper are \(T_{w}=0.5\), \(T_{y1}=0.07\), \(T_{y}=1\), \(h_{s}=1\), \(q_{n1}=0\), \(A_{t}=1\), \(G_{max}=1\)), start-up in generating mode was simulated, and the following simulation waveform could be obtained (The positive direction of each power is subject to the power in generating mode, as shown in Figure 1. The speed direction in the generating mode is specified as positive direction of speed).  
According to Figure 8, the guide vane opening and mechanical power of pump turbine remain constant during the whole start-up stage. In addition, the speed of unit slowly increased to the synchronous speed.
## 3 Experimental Data and Estimation Algorithms
### Sliding Innovation Filter  
The sliding innovation filter (SIF) is a recently proposed estimation strategy. It was designed using a predictor-corrector estimation architecture [25]. Much like the KF, the SIF can be described as a two-stage process, where predictions of the state estimates and error covariances are made in the first stage by using the previous time step's information. The prediction estimates are then updated using the measurement information and a correction factor referred to as the SIF gain [25].  
The SIF algorithm uses the same equations (8)-(11) as in the KF algorithm but significantly differs in the update stage. The SIF gain is formulated using the measurement matrix, the innovation, and a sliding boundary layer term [25]. In the update stage, the SIF uses the gain to drive the state estimates within the defined boundary layer, which forces the state estimates to within a region of the true trajectory [25]. The SIF algorithm can be summarized by the following set of equations:  
\((i)\)_Update Stage_ :  
\[K_{k+1}=C^{+}\overline{sat}\Big{(}\Big{|}\widetilde{z}_{k+1|k}\Big{|}/\delta \Big{)}\hat{x}_{k+1|k+1} \tag{17}\]  
\[\hat{x}_{k+1|k+1}=\hat{x}_{k+1|k}+K_{k+1}\Big{(}\hat{x}_{k+1}-C\hat{x}_{k+1|k} \Big{)} \tag{18}\]  
\[P_{k+1|k+1}=(I-K_{k+1}C_{k+1})P_{k+1|k}(I-K_{k+1}C_{k+1})^{T}+K_{k+1}\ R\ K_{k+1}^{T} \tag{19}\]  
Note that \(C^{+}\) refers to the pseudoinverse of \(C\), \(\overline{sat}\) refers to the diagonal of the saturation term (value between \(-1\) and \(+1\)), and \(\delta\) is the sliding boundary layer width.
## 3 Results and Discussion
### Workability  
GPC has sticky characteristics due to the high viscosity of the SH and SS solutions [11]. Therefore, the workability of the GPC mixes is generally lower than the conventional OPC mixes. The GPC mixes can be compacted well by using vibrators, even for the concrete with relatively low slump values. Therefore, keeping in view the compaction conditions, GPC can be classified into three categories depending upon the slump values achieved, i.e., highly workable, medium workable, and low workable [21]. The GPC mixture is considered highly workable when it achieves a workability of 90 mm and over. When the workability of the GPC mixture is between 50 mm and 89 mm, it is considered as medium workable, while slump values below 50 mm are classified as low workable. In the present study, a medium workable mix (of slump value between 50 and 89 mm) was considered as the target slump. It can be observed from Figure 3 that the slump values of the control mix samples have an average value of 85 mm. The average slump values of the GPC-A, GPC-B, GPC-C, GPC-D, and GPC-E mixes were observed as 75 mm, 75 mm, 80 mm, 73 mm, and 70 mm, respectively. It was observed during the test that the workability of QFS-GPC decreased gradually by increasing the replacement levels of QRD from 5% to 20%. Therefore, the target slump values (medium workability [21]) of the mix groups were achieved by gradually increasing dosages of SP from GPC-A to GPC-E as mentioned in Table 5 while keeping the amount of water added the same in all the QFS-GPC mixes. It was also confirmed from the previous studies that the use of QRD up to 50% as replacement of sand in both OPC and GPC mixes reduced the workability [24]. In QFS-GPC mixes, the incorporation of QRD has negative effect on the workability. It results in decreasing the workability of the mix considerably, and handling become difficult during the placement of fresh QFS-GPC by incorporation of QRD. Hence, to attain the medium workability standard [21], extra water and superplasticizer were used.
## 2 Spaceflight Environment and Ocular Risks
### Ocular Surface and Tear Film Changes  
The tear film, essential for maintaining corneal hydration and protecting the ocular surface, is particularly susceptible to the effects of microgravity. In space, unpredictable pooling of tear fluid destabilizes the tear film, leading to symptoms of dry eye disease, a common complaint among astronauts [20; 22]. Compounding these challenges are environmental factors such as low humidity aboard spacecraft, lower atmospheric pressure in space suits, and elevated CO\({}_{2}\) levels, which accelerate tear evaporation and exacerbate irritation. Additionally, space radiation is a significant risk factor for dry eye disease in spaceflight. Several terrestrial studies corroborate the increased odds or incidence following radiotherapy for head and neck cancers [27]. Studies report a tolerance dose of 35-50 Gray for ocular surface changes (dry eye disease, tear break-up time) following radio therapy [28]. Space radiation is of lower magnitude with more unpredictable absorption rates. It is hypothesized that a greater risk for ocular surface disorder may occur in the spaceflight environment [29]. Furthermore, meibum can be modeled as a non-Newtonian  
Figure 1: Environmental stressors in spaceflight, including microgravity, radiation, and celestial dust, lead to biological responses such as oxidative stress and immune suppression, resulting in outcomes like corneal abrasions, infections, and vision impairment.  
fluid, and microgravity is known to decrease shear stress, theoretically leading to decreased meibum outflow [30; 31]. Altered tear film and dry eye disease increase the risk of corneal abrasions, frequently reported during missions [20; 32].  
Tear film instability not only compromises the ocular surface but also heightens the risk of epithelial damage, abrasions, and ulcers. The cumulative effects of long-duration missions make effective hydration strategies a priority for preserving ocular health [33]. While artificial tears provide immediate relief, their transient effects highlight the need for more advanced interventions [13]. Recent advancements in bioengineered tear substitutes, including sustained-release formulations of anti-inflammatory peptides and biopolymers, have shown enhanced stability and efficacy in simulated microgravity environments. These innovations represent a promising approach to mitigating tear film instability during extended space missions [34].
## 4 Evaluation Metrics
### Robustness  
The robustness of the CCES scheme refers to the capability against an attacker or hostile network environment, which negatively affects the quality of covert communication. We utilize the bit error rate (BER) to measure the robustness, as defined below:  
\[BER=\frac{S_{error}}{S_{all}}, \tag{13}\]where \(S_{error}\) and \(S_{all}\) represent the number of error bits and the total number of transmitted bits, respectively.  
Here, we propose another definition of BER with the end spreading degree \(e\) and the loss tolerance degree \(\tau\). In the CCES scheme, \(P_{s}\) denotes \(m_{k}^{\prime}\) by \(e\)\(DstPort\) sequences (i.e., \(DstPort_{k}\)) and \(P_{r}\) regains it during the demodulation the end spreading sequences, namely \(DstPort_{k}\). However, whether \(DstPort_{k}^{\prime}\) could be demodulated correctly relies on the loss tolerance degree of \(DstPort_{k}^{\prime}\). Suppose the number of same elements in \(DstPort_{k}\) and \(DstPort_{k}\) is denoted as \(e\) and the function \(\Gamma\) means whether \(DstPort_{k}^{\prime}\) can be demodulated correctly, as indicated by 0 or 1. Thus,  
\[\Gamma(DstPort_{k},DstPort_{k}^{\prime})=\left\{\begin{aligned} & 0,\ \ \ e-\tau\leq e<e\\ &\\ &\\ &\\ \end{aligned}\right. \tag{14}\]  
When \(e\in[0,e-\tau)\), the \(DstPort_{k}^{\prime}\) can not be demodulated correctly; thus, the BER could also be defined as:  
\[BER=\frac{\sum_{i=1}^{t}\Gamma(DstPort_{i},DstPort_{i}^{\prime})}{t}, \tag{15}\]  
where \(t\) means the number of \(m_{k}^{\prime}\) in \(M^{\prime}\).
# Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
## 3 Results
### Interoposterior Translation
#### 3.3.1.3 The 2D Measurements Quantifying Tibofibrular Rotation  
Assessment of fibular rotation plays a crucial role in evaluating the integrity of the syndesmotic joint. Injuries are generally characterized by diastasis and external rotation of the fibula [3; 37]. The most cited method was initially described by [PERSON] et al., measuring fibular rotation relative to the tibial incisura, 1 cm proximal to the tibial plafond [5; 13; 20; 22; 26; 28; 29; 31; 33; 36]. Alternative measurements are performed at the level of the talar dome or slightly distal therefrom, determining rotation along the medial and lateral malleolus [28; 37]. However, some studies do not quantify rotation in degrees but rather use ratios, whereby an increase correlates with the external rotation of the fibula, e.g., the ratio of ATFW and PTFW [5; 20; 28]. An additional measurement ratio, defined by [PERSON], analyzes fibular rotation around the tibial centroid [23; 26; 29; 40]. Measurements are depicted in Figure 3A-C and described in Table 1.  
The orientation of the fibula within an uninjured syndesmosis has been described in relative detail in the currently available literature [5; 13; 20; 22; 28; 29; 31; 33; 36]. When imaged via CT, a mean internal rotation of 13.6\({}^{\circ}\) is observed in non-weight-bearing conditions, as opposed to 12.3\({}^{\circ}\) under weight-bearing conditions. Therefore, the fibula is exposed to an average 1.3\({}^{\circ}\) of external rotation when loaded. Fewer studies are available in the field of syndesmotic lesions. A mean external rotation of 7.61\({}^{\circ}\) was observed when comparing CT and WBCT. Furthermore, interobserver reliability was good in both non-weight-bearing (ICC = 0.689) and weight-bearing (ICC = 0.783) conditions. The reviewed studies reported no significant differences for age or sex, except for [PERSON] et al., who noticed a naturally significant increase in internal rotation of 0.2\({}^{\circ}\) per year [31].  
The bimalleolar angle was ascribed by two studies at different heights on axial CT images. [PERSON] et al. measured the level of the talar dome, whereas [PERSON] et al. suggest that the ideal plane is located 4-6 mm more distal [28; 37]. An average external rotation of 7.67\({}^{\circ}\) was achieved despite varying measurement heights. Good reliability was outlined by both authors (ICC = 0.68).  
Just a handful of studies examined fibular rotation ratios, all within healthy syndesmosis populations [5; 20; 23; 26; 28; 29]. A mean Tang ratio of 0.85 was found for both CT and WBCT. The corresponding ICC values are poor (ICC = 0.47) and good (ICC = 0.79), respectively. As for ratio A/B, a mean of 0.55 was found for CT and 0.62 for WBCT, thus resulting in a 0.07 increase of the external rotation when loaded. A good ICC was found in both CT (ICC = 0.72) and WBCT (ICC = 0.79).
## 2 Materials and Methods
### INPR Theory  
#### 2.3.1 Traditional INPR Theory  
The convex set model characterizes the uncertainty of parameters by a bounded convex set. NPR models, based on the convex set model, primarily include the interval and ellipsoid models, as shown in Figure 2. Among them, the interval model based on interval mathematics is a general method [23].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Frequency of floods & 20\% & 50\% & 75\% & 90\% & 95\% \\ \hline Annual flow (10 thousand m\({}^{3}\)/s) & 46,961 & 28,484 & 18,599 & 12,921 & 10,677 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of annual runoff at the dam site.  
Figure 1: The construction site of Qianping Reservoir.  
Currently, the area method is the primary approach for INPR [24; 25; 26]. This method represents the interference relationship between stress (upstream flood level \(Z\)) and strength (elevation of the top of the water-retaining structure \(H\)) on a two-dimensional variable space by using a failure surface to divide the interval variables into a safe domain and a failure domain [27; 28], as shown in Figure 3. The limit state function of the overtopping event during the construction period of dams is \(G=Z-H\). When \(G\) is less than 0, the system is safe, when \(G\) is greater than 0, the system fails, and when \(G\) equals 0, the system is in a critical state. Therefore, for the overtopping event during the construction period of dams, when \(G\) equals 0, it is the limit state of the system, and at this time \(Z=H\) is the failure surface. The area above the failure surface where \(G\) is greater than 0 is called the failure area, and the area below the failure surface where \(G\) is less than 0 is called the safe area.  
In Figure 3, \([Z_{1},Z_{2}]\) is the range of upstream flood level \(Z\), and \([H_{1},H_{2}]\) is the range of top elevation \(H\) of the water-retaining structure. The ratio of the area \(S_{F}\) of the failure domain to the total area \(S_{T}\) of the basic variable domain is used as the probability of overtopping during the construction period of dams [27; 28], as shown in Equation (3).  
\[P_{f}=\frac{S_{F}}{S_{T}}, \tag{3}\]
## 3 Model Uncertainty Parameters
### Parameter Uncertainty Analysis Interval  
In the case of random variable \(x\) following uniform and normal distributions, the determination of specified quantiles is relatively straightforward. When the random variable follows a log-normal distribution with a mean of \(\mu_{x}\) and a coefficient of variation of \(\text{cov}_{x}\), then \(\text{ln}x\) conforms to a normal distribution. The mean and standard deviation of \(\text{ln}x\) can be derived using Equation (1).  
\[\begin{cases}\mu_{\text{ln}\,x}=\ln\frac{\mu_{x}}{\sqrt{1+\text{cov}_{x}^{2}} }\\ \sigma_{\text{ln}\,x}=\sqrt{\ln(1+\text{cov}_{x}^{2})}\end{cases} \tag{1}\]  
To summarize the uncertainty factors and their distribution characteristics, we use the 5 th percentile as the lower bound, the 50 th percentile as the median, and the 95 th percentile as the upper bound of the uncertainty analysis interval. This provides the range for the uncertainty parameters associated with the numerical model of continuous beam bridges, as detailed in Table 2.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Real-Time Single Image Super-Resolution Methods  
Enhancing the spatial resolution of images to improve their clarity and detail is the primary aim of real-time SR. Several strategies are used to achieve this. Distillation makes real-time processing more feasible by training a smaller, more efficient model to replicate the performance of a larger, more complex model. Re-parameterization improves effectiveness and performance by modifying the model's parameters, often by transforming or simplifying the network architecture. Model-based techniques leverage prior knowledge or assumptions about the data, sometimes incorporating mathematical models or constraints, to guide the resolution enhancement process. Quantization reduces the precision of the model's parameters, which accelerates processing times by using less memory and computational power. Lastly, optimization techniques utilize various algorithms to fine-tune the model's performance, balancing resource usage, accuracy, and speed. Figure 5 illustrates the taxonomy of real-time SR methods.
## 4 Design Methodology of Composite-Anchor Uplift Piles
### Ultimate Bearing Capacity with Surrounding Soil Failure  
When the uplift pile's bearing capacity is controlled by the failure of the surrounding soil, the calculation of bearing capacity should meet the following requirements:  
Figure 7: Load–displacement curves with different diameters of steel strands.  
For the pile group with block failure:  
\[N_{k}\leq T_{gk}/2+G_{gp} \tag{1}\]  
For the pile group with non-block failure:  
\[N_{k}\leq T_{uk}/2+G_{p} \tag{2}\]  
\[T=T_{uk}/2 \tag{3}\]  
where \(N_{k}\) is standard value of tension force on single pile (kN); \(T\) is characteristic value of uplift bearing capacity of single pile (kN); \(T_{gk}\) is calculated value of the ultimate bearing capacity of single pile when the non-block failure occurs, which can be determined by Formula (5); \(T_{uk}\) is calculated value of the ultimate bearing capacity of single pile when block failure occurs, which can be determined by Formula (4); \(G_{gp}\) is equivalent pile-soil weight of single pile when the pile group undergoes block failure; and \(G_{p}\) is weight of single pile when the pile group undergoes non-block failure.  
The ultimate uplift bearing capacity of a single pile in the preliminary design phase can be estimated as follows.  
(1) For pile groups with non-block failure, the single pile's ultimate bearing capacity can be calculated by  
\[T_{uk}=\sum\lambda_{ip}q_{sik}u_{i}l_{i} \tag{4}\]  
where \(u_{i}\) is perimeter of pile (m); \(\lambda_{ip}\) is uplift coefficient of the \(i\)-th layer of rock (or soil), which can be taken from Table 5 and should be determined in combination with the results of on-site static load tests; and \(q_{sik}\) is standard value of the ultimate lateral resistance of the \(i\)-th layer of rock (or soil) on the pile-side surface (kPa), which should be determined through on-site tests or local experience. During preliminary design, this can be determined using the empirical parameter method outlined in the current standard \"Technical Code for Building Pile Foundations\" (JGJ 94-2008), where \(l_{i}\) is pile length in the \(i\)-th layer of rock (or soil) (m).  
(2) When the pile group experiences block failure [24], the single pile's ultimate bearing capacity can be calculated by  
\[T_{gk}=\frac{1}{n}u_{i}{\sum\lambda_{ip}q_{sik}l_{i}} \tag{5}\]  
where \(u_{i}\) is the perimeter of outer boundary of the pile group (m).  
(3) The tensile bearing capacity of the pile's normal section shall comply with the following formula [29],  
\[N\leq f_{y}A_{\mathrm{s}}+f_{py}A_{py} \tag{6}\]  
where \(N\) is design value of axial tension at the pile top under basic load effect combinations; \(f_{y}\), \(f_{py}\) are design values of tensile strength for steel bars and steel strands, respectively; and \(A_{\mathrm{s}}\), \(A_{py}\) are cross-sectional areas of ordinary steel bars and steel strands, respectively.