esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The popularity of running has been increasing over the last decade due to its benefits for health, accessibility, and low cost, becoming one of the most common ways to exercise [1]. Despite health benefits, running-related injuries are inherent to the activity itself, with a high yearly incidence ranging from 19.4 to 79.3% [2]. Several factors were associated with running injuries, and the fatigue related to prolonged running is one of the most common factors, being pointed out as one of the possible causes of injury [2].  
Fatigue is an intrinsic process related to every physical activity, and it has been traditionally divided into peripheral and central fatigue, producing limitations at the spinal or supraspinal level and modifications at the muscular level, respectively [3, 4].  
Although there is little modest scientific evidence, unfatigued running allows the athletes to maintain the preferred movement path [5], but it becomes altered when running into fatigue by reducing the capacity of the muscles to control sagittal [6, 7, 8, 9, 10, 11] and non sagittal [12] plane joint movements, inducing kinetic and kinematics modifications during running [6; 7; 8; 9; 10; 11].  
While the effects of central fatigue have been widely investigated, few studies have analyzed the influence of peripheral fatigue on running biomechanics [9; 13; 14; 15].  
An increase of range of movements during stance phase, especially of knee joint, has been shown after central [6; 7] or peripheral fatigue [9; 15] in order to reduce vertical ground reaction forces (vGRF) during fatigued running. Moreover, some investigations suggested an increased stance time after central fatigue [8; 11; 16; 17] and also a sustained frequency and length stride if the running speed was constant [8; 11; 16; 18]. However, the evidence is limited in these spatiotemporal parameters after peripheral fatigue.  
A moderate increase in central fatigue and small increase in peripheral fatigue evaluated by maximal voluntary isometric contraction after a half-marathon race has been described [3]. It has also been shown that central mechanisms were mainly responsible for the reduction of maximum knee flexors torque after 24 h on a treadmill compared with peripheral fatigue [4]. Nevertheless, there are no studies that compare the effects of both types of fatigue evaluated in isolation using the same methodology in running kinematics.  
So, the aim of this study was to identify and compare the effects of central and peripheral fatigue on angular kinematics and spatiotemporal parameters during running using the same methodology. Based on previous research [6; 7; 8; 9; 10; 11], we hypothesized that changes associated with central and peripheral fatigue would increase the range of movements of the lower limb at stand phases (absorption and propulsion phases), and no modifications would be found in the swing phase. Additionally, spatiotemporal parameters also would be altered, including increased stance time, but not frequency and length stride, related to changes in running kinematics. We also hypothesized that changes would be higher after central fatigue protocol.
## 4 Document Visualization  
In the previous section, we demonstrated that our method significantly improved the projections of several models commonly used in DR publications. In information visualization, DR algorithms are used in a wide range of scenarios to produce 2D layouts that enable further data exploration. We analyze one such case: document exploration.  
Documents are complex data samples that are composed of words and can have a complex structure. We hereby use scientific articles as examples and have as a goal the creation of a 2D layout that shows the distribution of documents in a database.  
Figure 6: Dimensionality reduction of the USPS dataset utilizing different DR algorithms. The PaCMAP (**a**) exhibits good results with the original data. Applying our vector modification (**b**), although numerically inferior to the original, the clusters are not substantially changed. For the tSNE (**c**), the modified version may give poorer results due to the fragmentation of some of the clusters that can be seen in (**d**). trimap DR (**e**) is less effective than PaCMAP or UMAP at cluster separation. Our algorithm output (**f**) appears visually akin to the original version (**e**). Finally, UMAP (**g**) yields results similar to PaCMAP, with well-concentrated clusters and only minor instances of collision or overlap. In this case, the data modification in (**h**) causes slight proximity changes in a couple of clusters.  
Figure 7: The Coil20 dataset demonstrates enhanced clustering accuracy across all DR algorithms following our modification, except for the tSNE algorithm. This dataset contains 20 classes. Observe that the overall distribution of clusters improves after our modification (second and last column).  
To visualize documents, we need to transform those into simpler representations, such as vectors. There are many ways to make this transformation, one is to extract concepts from the text and represent those. Topic modeling has been previously used [50] to compare PhD theses, for example. Recent work has also used Latent Semantic Analysis-based representations followed by some DR algorithm [51; 52] to obtain 2D representations of the documents.  
Finally, Cartolabe is a tool for the exploration of massive document databases as point clouds with many interaction features. To achieve such layout, documents are transformed into vectors doc2 vec [17] and projected using UMAP.  
Following this idea, we build a dataset and explore how our vector transformation algorithm behaves when applied to doc2 vec embeddings.
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### BRDF Correction  
For the MSC, which has a field of view angle of 62.6\({}^{\circ}\), the effects of the Bidirectional Reflectance Distribution Function (BRDF) cannot be ignored. Wide-field satellite sensors provide limited observational angular information, making it challenging to fit BRDF model parameters directly from multi-angular datasets. To address this, MODIS BRDF products are used as prior knowledge to fit the parameters of a semi-empirical kernel-driven BRDF model.  
The kernel-driven model is widely used to describe BRDF characteristics of surface features, offering simplicity, computational efficiency, and physical interpretability. It approximates the surface BRDF as a linear combination of three kernels: the isotropic kernel for Lambertian scattering, the volumetric scattering kernel for turbid medium scattering, and the geometric optics kernel for surface scattering. The model is expressed as:  
\[R(\theta_{s},\theta_{v},\varphi,\lambda)=f_{iso}(\lambda)+f_{vol}(\lambda)K_{ vol}(\theta_{s},\theta_{v},\varphi)+f_{geo}(\lambda)K_{geo}(\theta_{s}, \theta_{v},\varphi) \tag{8}\]  
where \(f_{iso}\) represents the Lambertian reflectance coefficient; \(K_{vol}\) and \(f_{vol}\) denote the volumetric scattering kernel function and its corresponding kernel coefficient, respectively; and \(K_{geo}\) and \(f_{geo}\) represent the geometric optics kernel function and its associated kernel coefficient, respectively.  
This study employs the RossThick-LiSparseR (RTSLR) model, which consists of the RossThick volumetric scattering kernel combined with the LiSparseR geometric optical kernel. This model is widely used and is also the basis for the operational MODIS BRDF and albedo products [23].  
To account for the seasonal variation in the reflectance characteristics of vegetation and other surface features, the MODIS BRDF data product is first statistically analyzed for each season and then classified by land cover type. Using Equation (8), BRDF kernel coefficients are separately fitted for each land cover type based on the corresponding BRDF data, yielding BRDF model coefficients specific to different seasons and land cover types. These coefficients are then applied to MSC images, where land cover classification enables BRDF correction. By mitigating anisotropic reflectance effects, BRDF correction ensures more accurate surface reflectance retrieval, ultimately improving the quality of MSC images [24].
## 2 Materials and Methods
### Experimental Data Processing and Cross-Reactivity Calculation  
The concentration dependences of the recorded optical signals for all types of analyses were built based on the results of testing solutions of SAs. The obtained curves reflecting competitive interactions were approximated by four-parameter sigmoidal fittings: Y = (A - D)/(1 + (x/C)B) + D. C-values of fit represent the point of inflection of the curve (i.e., the point on the S-shaped curve halfway between A and D). Therefore, the C-values were used as the IC50 (i.e., the analyte concentrations causing 50% inhibition of the detected label's binding) to calculate cross-reactivities as described in Section 1.
## 1 Introduction
However, it is evident that there is a lack of reliable vibration-damping devices and proven-effective methods for vibration reduction in mine hoisting systems. The field still largely adheres to traditional design theories. Effectively mitigating vibration in mine hoisting systems remains a critical concern for ensuring both mine production safety and efficiency. This paper drew from the use of the MRF rheological principle of vibration damping in the mine hoisting system and designed a new type of roller cage shoe by applying the excellent characteristics of MRF dampers. MRDs and disc springs were used as the buffers of the roller cage shoe for vibration reduction, and the performance of vibration reduction was optimized and verified through simulations. This study can provide new ideas for the stability design of ultra-deep mine hoisting systems. This new roller cage shoe offers advantages such as low power consumption, a compact size, a controllable damping force, a rapid response (in milliseconds), a simple structure, excellent temperature stability, and resilience to impurities. The main structure of the content of the present paper is (1) Section 2: the structural design of the cushioning cage shoe based on MRDs; (2) Section 3: the influence of structural parameters on the performance of the damper; (3) Section 4: the simulation and analysis of the virtual prototype of the cage shoe cushion; and (4) Section 5: the conclusion.
## 2 Related Work  
Energy consumption is an essential factor as the nodes which are battery operated devices and not rechargeable energy ones fail easily due to the energy depletion. Therefore, energy consumption becomes a primary concern in most WSN applications. A WSN protocol should typically ensure that connectivity in a network is maintained, even in the presence of node failure (e.g., due to some security attack or other reason like energy depletion). In the existing literature, a number of issues relating to routing design and security issues have been reviewed.  
Energy-efficient routing algorithms for data gathering are a major concern in WSNs. Routing tree structures are adopted in many previous works [10, 12, 13, 14, 15] to collect data: a sensor node transmits its data and the data from its child nodes to its parent node. In [12], the authors considered the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, and proved that minimum-energy data gathering problem is NP-complete and declared that the optimal result is between Shortest Path Tree (SPT) and Traveling Salesman Problem (TSP). In [13], the authors proposed an optimal algorithm called MEGA for foreign-coding and an approximate algorithm called LEGA for self-coding. In MEGA, all nodes firstly send their gathered data to the sink node via the Minimum Spanning Tree (MST) and then each encoding node sends its respective encoded data to the sink node through the SPT rooted at the sink. In LEGA, the sink node broadcasts its packet to its neighbor nodes and each node sends its data to the sink node by the constructed Shallow Light Tree (SLT). By constructing the SLT, LEGA achieves a 2(1+\(\sqrt{2}\) )-approximation of the optimal data gathering route. [PERSON] _et al._[14] proposed a scheme, called NNT, which was a variant of using greedy algorithm to construct a minimum Steiner tree. NNT builds a slightly suboptimal tree with low energy complexity, and it is proved that NNT can be used to design a simple dynamic algorithm for maintaining a low-cost spanning tree.  
However, these above literatures only pay attention to transmission cost in building the routing tree, and neglect the cost of aggregating correlated data. In some practical applications, such as image aggregation, the aggregation cost may be greater than the transmission cost [16]. Therefore, in addition to transmission cost, aggregation costs can significantly affect routing decisions when data aggregation is involved. [PERSON] _et al._[10] put forward the MFST algorithm, which was applied to collect data with aggregation by an energy-efficient method in WSNs. MFST takes both transmission cost and aggregation cost into account, and chooses aggregation nodes based on the quantity of data generated by each node. [PERSON] _et al._[15] further proposed an improved MFST algorithm called AFST. AFST dynamically decides whether to proceed with data aggregation when each relay node transmits data, rather than merely optimizing the data transmission route.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 3 Results and Discussion
### Performance of Classification Models Based on Full Spectra  
The performance of developed PLS-DA, KNN, and RBF-SVM models with their optimal parameters are shown in Table 1. These models were established based on raw or preprocessed spectra (400-1000 nm) including SNV, SNV+detrending, normalization, 1 st, and 2 nd derivatives, respectively. Results showed that raw full spectra performed not worse than other preprocessed spectra, and the 2 nd derivative slightly decreased the performance. The developed optimal KNN model, which had the advantage of simple implementation, was capable in performing not bad identification with CCR of 79.00% in the prediction set. It could be also observed that PLS-DA and RBF-SVM models presented overall better results than KNN models with predicted CCRs above 89% in all sets. Among them, the PLS-DA model based on raw full spectra was found to perform best with the optimal LVs number of 13, calibration set CCR of 97.33%, cross-validation set CCR of 95.33%, and prediction set CCR of 92.00%.  
As for different preprocessing methods, they were tentatively used for each model to reduce spectral noise and scatter effects, and the preprocessing method with the best results can be the most suitable one. The overall results implied that raw spectra were informative enough and any extra spectral preprocessing steps done to the raw spectral data was not necessary. Raw full spectra constructed PLS-DA and RBF-SVM models gave both the best results compared to other preprocessed full spectra. Furthermore, since the absolute difference among the three sets was small, PLS-DA model developed by raw full spectra was a more robust one than the RBF-SVM model. Therefore, the PLS-DA modeling method developed by raw spectra was chosen for further evaluation.  
To further assess the capabilities of the optimal models, confusion matrices exhibiting the specific group characteristics and affinity are presented in Table 2. As for the selected PLS-DA model, Ningguo walnuts seemed to be more easily misclassified into Lin'an variety. There were 5, 8, and 6 Ningguo walnuts being misclassified as Lin'an in calibration, cross-validation, and prediction set, respectively, while the numbers that Lin'an misclassified as Ningguo were 3, 6, and 2. The RBF-SVM models also performed better for Lin'an variety identification than Ningguo variety in different sets. Especially in prediction set, RBF-SVM model gave the same classification results with the PLS-DA model.  
Figure 3: First two PC score images of Ningguo and Lin’an Chinese walnuts. PC: principal component; PC\({}_{1}\): first principal component; PC\({}_{2}\): second principal component.
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 5 Evaluation
### Ablative Studies  
We perform ablative studies by removing separately Loukas coarsening and GNN. If we remove both, we fall back to Graph2 Vec. The dimension of the embeddings is chosen equal to 1024.  
Graph2 Vec+GNN  
We remove Loukas coarsening. The only difference with Graph2 Vec is the replacement of **WL** iterations with forward pass through a GNN. All the attributes available are used.  
Graph2 Vec+Loukas  
We remove GNN. The resulting algorithm is Graph2 Vec applied on the sequence of coarsened graphs. On the coarsened graphs, new labels are generated by concatenating and hashing the labels of the nodes pooled (like a **WL** iteration would do). The sequence of (unconnected) graphs is fed into Graph2 Vec. Continuous attributes are ignored because **WL** can not handle them.  
Results  
On datasets with small graphs (less than 30 nodes in average) the use of coarsening is hurtful, resulting in loss compared to Graph2 Vec. As soon the graphs get big, coarsening leads to huge improvements in accuracy. We also notice that the usage of GNN and its ability to handle continuous attributes, and to be trained to extract co-occurring features, leads to significant improvements on all datasets.
## 2 Sample Preparation  
### Base Material and Nanofiler  
In this study, silicone rubber was used as the base material. The silicone rubber used was transparent Sylgard 184 silicone elastomer with a dielectric strength of 24 kV/mm, tensile strength of 6.2 MPa, tear strength of 2.7 kN/m, and low viscosity. The hardener used was dimethyl, methylhydrogen siloxane. The material was purchased from Dow Corning and was used as received. The nanofiller used was fumed silica (SiO\({}_{2}\)), with an average size of 12 nm and average pore of 4 nm, supplied by Sigma Aldrich.
# Using IoT for Cistern and Water Tank Level Monitoring
## 5 Discussion  
It should be noted that our current proposal uses open-source tools and low-cost sensors. This implementation was tested on a physical scale model that does not include testing of sensor accuracy, latency, response time, sensor update speed concerning water status, security, and Internet access speed, among others. Therefore, we can point out that it is a low-cost solution that is functional for home use and possibly scalable for neighborhoods.  
A significant difference to highlight in our proposal is that it stores data in a database to allow analysis; for the moment, it only stores them in a database.  
We have seen some proposals in related work. Unlike these works, our proposed system improves solutions in several key respects: while other proposals only monitor water levels, our system meters water flow and measures water quality, providing critical information on water potability.  
Regarding energy efficiency, MQTT improves power consumption by reducing communication overhead and facilitating efficient data transmission in low-power networks. The low-power components mentioned above, e.g., NodeMCU, make our system more efficient than other approaches that do not use it.  
With respect to cost, our proposal uses affordable components and open-source software, making it economical and viable for deployment in various environments, including homes and neighborhoods.  
Our system is potentially scalable. Due to standard IoT technologies and the MQTT protocol, it can quickly adapt to different needs, making expansion easy.  
IoT data security is a pending subject that poses many risks. We know that IoT devices generally have limited capabilities, making them easy targets for hacking and malware and challenging to implement robust security measures.  
Finally, regarding hardware and cloud infrastructure adjustments for scalability, it is necessary to upgrade the microcontrollers to ones with more processing power and memory in order to manage multiple sensors and improve connectivity. Regarding cloud infrastructure, scalable storage services such as Google Cloud IoT or other solutions that manage large volumes of data generated by distributed sensors are required.  
To summarize, this system provides more complete and accurate monitoring and does so efficiently and cost-effectively, with the ability to scale as needed.
## 3 Results and Discussion
### Influence of Germination and Fermentation on the Thermo-Mechanical Properties of Doughs
#### 3.2.2 Influence of Sourdough Addition  
Regardless of the investigated grain sample, the addition of sourdoughs prepared with raw grain flours (G_SD, T_SD, S_SD and TT_SD) resulted in doughs with lower DS and C2 values (Table 2). The sourdough fermentation caused the DS shortening from 8.82-9.93 min to 5.23-6.32 min, and the C2 diminishing from 0.42-0.47 Nm to 0.31-0.37 Nm. On the other hand, the addition of sourdough prepared with germinated grain flour resulted in higher DS and lower C2 values compared to the corresponding samples with raw grain flour. This trend registered for the dough samples including sourdoughs might be the result of the low pH value. In agreement with [PERSON] et al. [33], the optimal pH for gliadin hydrolysis is 4.25. Moreover, [PERSON] and [PERSON] [26] and [PERSON] et al. [34] showed that dough behaviour during mixing at 30 \({}^{\circ}\)C and during temperature increase from 30 to 55-60 \({}^{\circ}\)C is influenced by pH, affecting both DS and C2 values. Similar results for DS and C2 were obtained when preparing dough with raw and germinated soybean flour added to the wheat flour [32]. The authors  
Figure 4: Mixolab curves of the white wheat flour (WF) supplemented with 15% whoheemal flours obtained by milling (**a**) raw (G, T, S and TT) and (**b**) germinated grains (gG, gT, gS and gTT).  
explained the reduction of C2 values by protein weakening during dough mixing and heating by 4 \({}^{\circ}\)C/min, but also by the action of proteolytic enzymes that are activated during germination.  
The dough samples prepared with sourdoughs made of raw G, S and T flours displayed similar pasting properties, the torque values being significantly higher with respect to the TT-based sample (Figure 5). For instance, the values of C5 that defines starch retrogradation, ranged from 2.25 Nm, corresponding to the sample with TT-based sourdough, to 2.72 Nm in case of the sample with S, suggesting different optimal end-use for each sample. According to [PERSON] and [PERSON] [30] the flours suitable for bread making, bricoche and biscuits have C5 of 2.05, 2.43 and 2.75 Nm, respectively.  
Grains germination improved starch gelatinization, starch stability and starch retrogradation properties of the dough samples prepared with sourdough (Table 2). The decrease of the pH during sourdough fermentation limited the activity of \(\alpha\)-amylase, and consequently improved starch stability. The most significant improvement of the pasting properties was observed in case of the dough sample including sourdough prepared with germinated G flour.