esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the other hand, the assessment of the plantar fascia is mainly studied using sensors placed on specific points of the foot and ankle, which register the data through different analysis systems [9; 12; 15]. [PERSON] et al. did not use these tools, but the gait assessment was measured using Zebris with data such as unilateral step length (41.13 cm and 41.40 cm), load response (18.1 cm and 17.05 cm), pre-swing (18.09 and 17.65 cm), swing (31.92 cm and 33.21 cm), and step time (0.34 s and 0.46 s) being reported [11]. On the other hand, the Optogait system is frequently used in the current literature, and its validity has been shown when compared to other gait assessment tools [16; 17; 18]. Its use in foot and ankle joint studies is limited. However, [PERSON] et al. used the Optogait system to study the protonated foot instead of the plantar fascia [19]. Only a few studies have researched changes in the spatio-temporal gait parameters in subjects suffering from PF [11; 15]. Nowadays, the majority of the studies refer to healthy people when assessing gait [16; 17]. In addition, the study of the spatiotemporal parameters of human gait can be studied with different measurements and technologies. On the one hand, there are measurements that analyze human gait with pressures sensors, for example Gait Rite, an instrumental treadmill, or the Tekscan Walkway System [18]. On the other hand, there are Vicon or Gait Real-time Analysis Interactive Lab (GRAIL) [19], optical sensors in anatomical references that report data on human gait movement and trajectory. In addition, there are also photoelectric sensors such as Optogait [20], which is a single layer pressure sensitive walkway measuring temporal and spatial parameters, and providing the easy identification of gait anomalies [20]. To shed light on the differences in spatio-temporal gait parameters between subjects with and without PF would be of great clinical interest since it may be useful in the detection of potential or existing PF, it may be used as an improvement indicator after receiving a treatment program, and it may be used in clinical settings to lead the re-education of the gait of people with PF. Our hypothesis is that the spatio-temporal gait parameters in subjects with PF are altered when compared to control subjects. These differences can be quantitatively measured, and thus, they serve as both a preventive and a diagnostic measurement when the treatment program and follow-up are carried out. Furthermore, we hypothesize that subjects with PF present a different relationship in spatio-temporal parameters than those with PF. The aim of the present study was to determine the level of association of the spatio-temporal gait parameters present in both subjects suffering from PF, and subjects without PF. The secondary aim was to study differences in the spatio-temporal parameters between both groups.
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 2 Method
### Smagorinsky BGK Collision Model  
The Smagorinsky model [21] is a subgrid-scale model used in a large eddy simulation (LES) of turbulent flows. The model introduces a turbulent viscosity term to the governing equations of fluid flow, which is based on the strain rate tensor of the flow field. The model filters out the small unresolved vortices by replacing them with an artificial viscosity increase. The large eddies are preserved. The modified strain rate tensor describes the production of turbulent kinetic energy in the flow.  
The turbulent viscosity term is given by the following:  
\[\
u_{t}=(C_{S}\triangle x)^{2}|\mathbf{S}|, \tag{4}\]  
where \(\
u_{t}\) is the turbulent eddy viscosity, \(\triangle x\) is the grid spacing, \(C_{S}\) is the Smagorinsky constant, and \(|\mathbf{S}|\) is the magnitude of the strain rate tensor. The Smagorinsky constant is the filtering parameter that determines which eddies are neglected.  
The modified momentum equation with the addition of the turbulent viscosity term becomes the following:  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla \mathbf{u} =-\frac{1}{\rho}\
abla p+\
u_{eff}\
abla^{2}\mathbf{u}+\frac{ \mathbf{F}}{\rho}, \tag{5}\] \[\
u_{eff} =\
u_{m}+\
u_{t}. \tag{6}\]  
For the incompressible NSE with a Smagorinsky LES approach, the lattice Boltzmann equation using the BGK collision operator [22] can be rewritten as follows:  
\[f_{i}(\mathbf{x}+\mathbf{e}_{i}\Delta t,t+\Delta t)-f_{i}(\mathbf{x},t)=-\frac{\Delta t}{ \tau_{eff}(x,t)}(f_{i}-f_{i}^{eq})+\Omega^{F}, \tag{7}\]  
where \(\tau_{eff}(x,t)=\frac{\
u_{eff}(x,t)}{\zeta_{s}^{2}}\frac{\triangle t}{ \triangle x^{2}}+\frac{1}{2}\) is the effective relaxation time adapted to the Smagorinsky model. Here, \(c_{s}\) is the discrete speed of sound.  
Due to obstacles in the path of a fluid, flow instabilities can be induced even at low Reynolds numbers. Therefore, it is necessary to adjust the relaxation time accordingly. The Smagorinsky BGK model accomplishes this by automatically increasing the relaxation time at the cells with a high shear rate. In the case of a laminar flow, the turbulent viscosity \(\
u_{t}\approx 0\). Choosing a correct Smagorinsky constant secures a stable run of the simulation.
## 2 Metallic Nanoparticles in Conjugation with Chlorin Derivatives
### Gold Nanoparticles  
Due to the plasmon resonance and possible surface functionalization, gold nanoparticles constitute good nanocarriers for PSs, such as chlorins, and enhance the photodynamic effect of the obtained nanosystem. Several studies, as discussed below, indicate their interesting potential for PDT. For example, [PERSON] et al. presented the potentiation of singlet oxygen formation by Ce6 via plasmon resonance energy transfer (PRET) from gold nanostructures [27]. The PS was deposited on gold nanorods via electrostatic interactions. Under 660 nm light irradiation, the 10-fold increase in singlet oxygen production by PRET-Ce6 nanoparticles was noted compared to that with free Ce6. In addition, high singlet oxygen production was detected within MDA-MB-231 cancer cells. Moreover, over a 2-fold higher cell viability reduction for PRET-Ce6, compared to that with Ce6, was observed [27]. The simplest conjugation of gold nanoparticles and Ce6 based on the physical adsorption of Ce6 on the surface of the gold nanoparticles (AuNPs) was proposed by [PERSON] et al. [28]. The obtained material demonstrated low photoactivity in the isolated form, whereas significant fluorescence and PDT activity was observed for the aggregated form. The authors irradiated the in situ aggregated nanoparticles inside HeLa cancer cells with red and near-infrared light. They obtained excellent PDT and PTT performance, leading to the effective eradication of cancer cells with a killing efficacy of up to 76%, in comparison to 25% with aggregated bare AuNPs and 32% with the use of isolated Ce6 [28]. The enhancement of cancer cell treatment by a combination of photothermal and photodynamic therapy was reported by [PERSON] et al. [29]. These authors developed Au nano cup-based nanoparticles bearing Ce6 as a PS. Chlorin was bound with a nano cup via a polyethylene glycol (PEG) chain, anchored to gold by sulfur and chlorin by an amide bond. It was observed that nanoparticles activated with light at 660 nm (for PDT) and 808 nm (for PTT) reduced HeLa cell viability to 10.3%, compared with 67.5% for PDT and 48.2% for PTT when used separately. Simultaneously, the experiment with no light activation was performed. In the dark conditions, no impact on the viability of the cells was noticed. The photothermal effect in the solution, observed after the irradiation of nano cups with light at 808 nm and dose 150 J/cm\({}^{2}\), revealed an increase in the sample temperature up to 67 \({}^{\circ}\)C. Moreover, in vivo trials were performed using a nude mouse model with a subcutaneous HeLa tumor. After 24 h from intravenous nanoparticle injection, the authors observed excellent visualization of the tumor via photoacoustic and computed tomographic techniques. Moreover, they performed PDT and PTT and noted that the combined methods
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Surface Water Mapping
#### 3.2.3 Data Fusion  
The monthly composites were used as inputs to a logistic regression model trained using labels from [PERSON] et al. (2019). These labels were acquired using a random sampling approach across all months in 2019 and were sampled across five different strata defined using the RESOLVE ecoregions dataset (Figure 1) ([PERSON] et al., 2019). Stratification improved performance across the complex biogeography of China, from the arid Tibetan plateau to the tropical and subtropical regions of China.  
One logistic regression model was created for each of the optical-SAR sensor combinations and scored respectively: (1) Sentinel-1, (2) Landsat-8, (3) Landsat-8 and Sentinel-1, (4) Sentinel-2, and (5) Sentinel-2 and Sentinel-1. The scoring dictated the model priority when compositing the logistic regression outputs into a single water probability score. The scores were determined based upon the model accuracies during training and a priority in spatial resolution, i.e., a preference for Sentinel-2 over Landsat. This fusion approach enables all-weather capabilities.  
The resulting probability was returned in geographic projection (EPSG:4326), which was thresholded to be converted into a binary water/not-water classification. A bitmask of the sensor combinations was also returned. The algorithm has been prepared to ensure applicability over large areas and spanning many different eco-regions, but the inherent bitmask and probability outputs ensure flexibility and adaptability for local optimization through the selection of improved thresholds.  
Lastly, a 'post-processing' stage was completed whereby a maximum surface water extent mask was created from the Sentinel-2 imagery across the entire time-series. This mask is used to remove commission errors in the Landsat due to less accurate cloud and cloud shadow masking, and some residual noise in the Sentinel-1 predictions. Additionally, urban areas can cause confusion in both optical and SAR imagery, for example misclassification of roads, warehouses, and solar PV systems. These areas were removed using the Global Human Settlement Layer (GHSL) (Stein  
are reported as percentages of pixels correctly classified using the metrics overall accuracy (OA), user's accuracy (UA) and producers' accuracy (PA).
###### Abstract
When the smart radio environment capability of IRS is combined with the MTC system, it provides numerous applications in smart cities, industrial automation, healthcare, smart transportation, wireless sensor networks, the IoT, 5G, and B5G cellular networks. IRS-assisted MTC system is shown in Figure 1. To meet practical deployment difficulties, discrete phase shifters are considered at the IRS [7] in the proposed system model.
## 4 Application Example
### Influence of the Number of the Air Intake Wells  
In this section, four conditions are considered with the change in the number of air intake wells. Condition 1 is that all air intake wells are retained. Condition 2 is that the no. 1 and no. 2 air intake wells are retained, with the no. 3 air intake well removed. Condition 3 is that the no.1 and no. 3 air intake wells are retained, with the no. 2 air intake well removed. Condition 4 is that the no. 1 air intake well is retained, with the no. 2 and no. 3 air intake wells removed. The air velocity and air pressure in the different cases are shown in Figure 10. The corresponding total ventilation rates of the air supply system are 4537, 3876, 4056 and 2434 m\({}^{3}\)/s, respectively.  
The air intake well number can influence the ventilation rate directly. The airflow sucked into the spillway tunnel is mainly from the no. 1 air intake well. The no. 2 air intake well, located closer to the no. 1 air intake well, can better share the airflow that is sucked into the spillway tunnel than the no. 3 air intake well. Therefore, compared with the no. 3 air intake well removed, the air velocity and sub-pressure in the no. 1 air intake well increase notably with the no. 2 air intake well removed. When the no. 2 and no. 3 air intake wells are removed, the ventilation rate decreases greatly and is 0.5 times that of condition 1, with three air intake wells retained. The air velocity in the no. 1 air intake well sharply increases, even higher than 110 m/s. The sub-pressure at the end of the no. 1 air intake well sharply increases, even reaching \(-\)21 kPa, and the sub-pressure in the spillway tunnel also sharply increases. It is concluded that this condition is highly dangerous for the safety of air supply systems.  
The analysis indicates that the number of air intake wells has a large effect on the ventilation characteristics of the air supply system at the Jinping-I project. Using the multi-intake-well air supply system could effectively reduce the sub-pressure in the spillway tunnel and the air velocity in the air intake well.  
Figure 9: Influence of the cross-sectional height of the spillway tunnel on the ventilation characteristics.
# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
## 3 Methodology
### Resource Limitation  
1. Resource total quantity. Due to the limited amount of equipment resources, the number of resources used in repair work for each disaster point must be within the upper limit of the available amount of all types of resources. Equation (11) describes such constraints. \[R_{i}^{k}\,\leq\,\sum_{n\in SN}r_{n}^{k}\,,\,\forall\,i\,\in\,SI\,,\,\forall\, k\in SK\] (11) The total number of resources used for all repair activities in-progress must be within the resource limit of each resource type \(k\) at unit time \(t\). Equation (12) describes such constraints. \[\sum_{i\,\in\,ST_{t}}R_{i}^{k}\,\leq\,\sum_{n\,\in\,SN}r_{n}^{k}\,,\forall\,k \,\in\,SK\,,\,t\,=\,1,2,3,\ldots,\,T\] (12) 2. Resource dispatching consistency. Complying with the rule of meeting the maximum number of resource dispatching from the resource point (or the preceding repair activity) to the succeeding repair activity equipment resources, the number of dispatching resources for all transit activities must be within the maximum number of resources of each resource type \(k\), which is shown as Equations (13) and (14), respectively. \[r_{n}^{k}\,\geq\,\sum_{j\,\in\,SI}LR_{nj}^{k}\,,\forall\,n\,\in\,SN\,,\forall\, k\,\in\,SK\] (13) \[R_{i}^{k}\,\geq\,\sum_{j\,\in\,SJ}LR_{ij}^{k}\,,\forall\,i\,\in\,SI\,,\forall\, k\,\in\,SK\] (14) In order to further confirm the quantity of equipment resources dispatched for repair activities, the sum of the resource amount dispatched and remaining after the dispatch should be equal to the amount as before the dispatch, which is shown as Equation (15). \[R_{i}^{k}\!\!=\,\,RN_{i}^{k}+\,\sum_{j\,\in\,SI}LR_{ij}^{k}\,,\forall\,i\,\in \,SI\,,\forall\,k\,\in\,SK\] (15)3. Resource receiving consistency. Equipment resources are dispatched from preceding repair activities or resource points. Therefore, the quantity of resources received by any repair activity must be equal to the sum of resource quantity transited from the preceding disaster points or resource points, which is shown as Equation (16). \[R_{i}^{k}=\sum_{j\ \in\ SJ}LR_{ij}^{k}+\sum_{n\ \in\ N}LR_{nj}^{k},\forall\ j\ \in\ SJ\,\forall\ k\ \in\ SK\] (16) (4) Resource limitation due to specific restrictions of different disaster points. Each damaged road section has the maximum number of resources that can be accommodated at the same time during the execution of its own repair activity, due to site or terrain restrictions, so the number of resources at each disaster point must be constrained by the defined upper limit, shown as Equation (17). \[R_{i}^{k}\ \leq\ r_{i}^{k}\,\ \forall\ i\ \in\ SI\,\forall\ k\ \in\ SK\] (17)
## 3 Research Methods
### Optimal Parameter Geographic Detector (OPGD)  
The Geodetector model is a spatial statistical approach grounded in variance analysis, designed to quantify the individual impacts of factors as well as their interaction effects on the dependent variable [47]. In this model, continuous variables are typically discretized and transformed into categorical variables. The OPGD model is an improved model based on the basis of the Geodetector model by solving for the optimal parameters [35]. This model achieves more precise spatial analysis by determining the optimal method for discretizing spatial data and the optimal number of breakpoints for all explanatory variables. It selects the discretization method yielding the highest q value by comparing q values derived from various discretization techniques. The alternative discretization methods employed in this study include the equal interval breakpoint method, natural interval breakpoint method, quantile interval breakpoint method and geometric interval breakpoint method. In this study, the analysis based on the OPGD model encompasses two components: factor detector and interaction detector.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### Problem Motivation  
Despite ongoing advancements in logistics and information systems, several unresolved challenges still persist within the coffee supply chain [5]. As can be seen in Figure 1, the following problem areas illustrate the limitations of the existing model and the opportunities for synergy using blockchain and IoT technologies [6].  
Figure 1: Comparative illustration of coffee supply chain challenges and proposed blockchain-IoT enhancements, emphasizing improvements in traceability, data integrity, transparency, and automated enforcement.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification
### Experiment Results of Lane Line Positioning  
In this paper, the monocular camera was used to calculate the relative position of the left and right lane lines, and the lane line positioning was determined. The true value was obtained by the data acquisition vehicle. The monocular camera positioning results of the lane line itself are as follows.  
The error of distance \(D\) has no obvious change with the number of frames, and the overall error is relatively stable. According to a statistical analysis of all the error data, as shown in the right of Figure 8 above, the error distribution conforms to a skewed normal distribution, and most of the error distribution is within 0.3 m. The reliability of the vehicle position assumption used in this paper is generally stable.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & & \\ \hline
**Reference** & **Technology** & **Methods** & **Results** & **Limitation** & **Year** \\ \hline
[19] & \begin{tabular}{c} Mobility \\ Assistance Robotic \\ rollator \\ \end{tabular} & \
\begin{tabular}{c} Data was collected by \\ using a Laser range finder \\ \end{tabular} & Detected gait phases &
\begin{tabular}{c} Users needed to \\ wear fitted clothes \\ \end{tabular} & 2014 \\
[20] & \begin{tabular}{c} Assistive Robot \\ \end{tabular} & \begin{tabular}{c} A proximity Sensor was \\ used by a robotic walker \\ to measure the distance \\ between the user’s leg and \\ the robot. \\ \end{tabular} & \
\begin{tabular}{c} Controlled forward \\ walking speed \\ was detected. \\ \end{tabular} &
\begin{tabular}{c} Only distance and \\ walking speed \\ were detected. \\ \end{tabular} & 2018 \\
[21] & \begin{tabular}{c} Robotic Walker; \\ Walk-IT \\ \end{tabular} & \
\begin{tabular}{c} Multi-camera and \\ multimodal dataset was \\ used for \\ biomechanical analysis. \\ \end{tabular} &
\begin{tabular}{c} Analysis of posture and \\ gait, pose estimation, \\ and human gait \\ detection and \\ tracking algorithm. \\ \end{tabular} & Need to wear full \\
[22] &
\begin{tabular}{c} Need to wear full \\ body motion \\ tracking system. \\ \end{tabular} & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work based on robotic walkers, canes, wheeled walkers, and standard walkers.
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 3 Similarity-Based LSTM Operation
### Cell-Skipping Method Using Similarity Score  
For the input voice signal, in general, the MFCC-based speech recognition manages the excessive number of windows for taking account of various speakers having different speeds. Therefore, even a single character input is divided into several windows having similar MFCC coefficients, generating similar features handled by consecutive LSTM cells. As a result, in the baseline DeepSpeech system in Figure 2, the output sequence at the FC5 layer contains several identical characters for a single input character, and the final CTC step is inevitable to remove these redundant results. As described in our conference paper [14], we newly propose the cell-level deactivation and approximation schemes for the energy-efficient speech recognition by detecting these redundant positions at the run time, which is conceptually illustrated in Figure 4a. At the time stamp of \(t\), the LSTM cell compares the current input \(\mathbf{x}_{t}\) with the prior one \(\mathbf{x}_{t-1}\) to obtain the similarity score \(S_{t}\) before activating its gate operations. More precisely, we can compute the similarity score at the \(t\)-th window sample as follows.  
\[S_{t}=\frac{C(\mathbf{x}_{t}/2^{w}-M(\mathbf{x}_{t})\odot\mathbf{x}_{t-1}/2^{ w})-C(\mathbf{x}_{t})}{C(\mathbf{x}_{t})} \tag{6}\]  
For the input \(1\times k\) vector, the function \(C(\cdot)\) returns the number of nonzero elements, whereas the function \(M(\cdot)\) generates a \(1\times k\) masking vector at which its elements become one for the nonzero positions and otherwise zero. In order to ignore the small variations in two adjacent inputs, we define the clipping parameter \(w\) to adjust the meaningful ranges for computing the similarity score. As reported in [14], input vectors with high similarity scores successfully correspond to the redundant time positions that generate the characters to be deleted at the later CTC step.  
The similarity-based cell skipping method is then simply introduced by setting the skipping threshold of \(\theta_{\text{s}}\). When the current \(S_{t}\) is larger than the pre-defined threshold, then we simply copy the prior outputs without activating the LSTM cell operations, as exemplified in Figure 4b, which can significantly save the overall computational costs. As the consecutive skips may degrade the recognition accuracy severely, as described in [14], it is possible to limit the number of serial skips properly, leading to the energy-efficient and accurate LSTM processing scenario. In this work, to develop the cost-effective LSTM accelerator, we allow only two consecutive skips of LSTM operations for highly-similar inputs, which can reduce the overall complexity by 26.8% under the 1% accuracy drop compared to the baseline LSTM operations activating all the cells [14].  
Figure 4: Proposed LSTM cell processing for (**a**) normal mode, (**b**) skip mode, and (**c**) approximate mode.