esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Study Limitations and Strengths  
As a retrospective study, the present study does have some limitations. Treatment was not randomized, patients and providers were not blinded to the treatment provided and no placebo or saline group was used. The number of cases included in each treatment group was different. Initially, a relatively large number of patients were present in each group; however, a significant number of patients were lost to follow-up between 3 and 12 months post treatment. There were baseline differences between the groups in the grade of OA, VAS and WOMAC. This is largely due to the retrospective nature of the study as in these patients presenting with milder degenerative changes in the clinical setting, it is more likely to start with the least invasive and also the least expensive treatment [15; 47]. Lastly, the efficacy of the treatment could be affected by other factors such as at the frequency/number of treatment visits. The BMAC group had only one single visit, whereas three visits were needed in the HA group, and six were needed in the ACS group. The number of visits and time spent with the treating physician might have an additional placebo effect on the perceived efficacy of treatment, although this has not been confirmed by previous research [48].  
The strengths of the study are that this is the first study to report comparison of three different orthobiologic treatments. All patients were treated by the same surgeon with the same technique and underwent the same rehabilitation protocol, therefore minimizing variability of treatment procedure among involved individuals.  
This study highlights the potential of orthobiologics in the treatment of knee OA. In the future, prospective trials with large patient numbers and longer follow-ups are needed to further elucidate the duration of their efficacy. For this, patients should be additionally followed with MRI imaging tests to get objective data as well as patient subjective assessments of treatment outcomes.
## 3 Results
### Calculation of the Values of the Euclidean Distance between the Risks of IS  
Analyzing the cluster assignments and exploring the characteristics of each cluster is an important step in understanding the results of a k-means clustering algorithm. Cluster centroids represent the \"average\" or \"center\" of each cluster in the feature space. These centroids are vectors with the same dimensions as original data. We accessed the cluster centroids in scikit-learn after fitting the k-means model. The centroids can provide insights into the central tendencies of each cluster. To identify the most representative documents within each cluster, we calculated the distance between each document and the cluster centroid. Documents that are closest to the centroid are typically considered the most representative of that cluster. We can then access the documents from the original data using these indices to examine what makes each cluster unique. We used the Euclidean distance on data. The Euclidean distance \(d\) of two data cases \(\left(x_{1},x_{2}\right)\) is defined as the square root of the sum of squared differences \(d(x,y)=\sqrt{\sum\left|x_{i}-y_{i}\right|^{2}}\). The Euclidean metric has a great advantage in that it remains unchanged even when a common value is added to each variable of the data. This means that any translations or shifts in the data will not affect the metric. However, it is worth noting that the Euclidean distance is not scale invariant. This means that if the data were to be multiplied by a common factor, it would change the distance calculations. When it comes to class or cluster problems, using the squaring function in Euclidean distance has a significant implication. It effectively sets the limit for inner class distances at 1 and between (inter) class distances. This implicit definition helps in determining the proximity and relationships within and between different classes, making it a valuable tool in problem solving. Although it makes sense to use this approach for straightforward distributions like normal (Gaussian) distributions, it may not be as suitable for more intricate variables with bimodal or multimodal distributions. Other methods may be more appropriate in such cases to ensure accurate analysis and interpretation of the data. When embarking on the exciting journey of data mining and knowledge discovery in multivariate data, it is crucial to start by thoroughly analyzing the distribution of individual variables. This foundational step provides valuable insights that form the building blocks for uncovering meaningful patterns and relationships within data.
## 2 Materials and Methods
### Geometry
The physical values for the simulations are further detailed in Table 1. Subfigure **(E)** shows the geometry of the installed baffle with its dimensions shown in meters. simulations were performed to simulate the flow conditions. A final 16 th simulation was performed using the actual implemented baffle geometry. A visual representation of the configurations is shown in Figure 3(A1-A8), which depicts the symmetric configuration with baffles at the bottom (\(SCB\)); Figure 3(B1-B4), which depicts the symmetric configuration with the baffles at the bottom and wall (\(SCBW\)); and Figure 3(C1-C3), which depicts the asymmetric configuration with baffles at the bottom (\(ACB\)). Figure 3: The figure shows different variants of (**A1–A8**) symmetric with baffles at the bottom (SCB); (**B1–B4**) symmetric with baffles at the bottom and side walls (SCBW); and (**C1–C3**) asymmetric baffle at the bottom (ACB) inlay (beige) and baffle (green) positions. In (**B1–B4**), side wall baffles are depicted on one side for illustration purposes; however, in the simulations, they were implemented on both sides.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 2 System Design
### Hardware Components
#### 2.1.2 TDS Sensor  
The TDS sensor measures the total dissolved solids (TDSs) in water [16] by assessing its conductivity, reflecting the concentration of dissolved substances such as salts, minerals, and metal ions, and it is used to evaluate water purity in various applications, with a measuring range of 0-1000 ppm and a resolution of 1 ppm [17]. The measurement error of this sensor is typically \(\pm\)10 ppm or 5% of the reading, whichever is greater. Factors such as water temperature, calibration status, and environmental conditions may affect accuracy.  
The formula for calculating the TDSs is provided in [18].  
The following cubic polynomial describes the relationship between TDSs and the sensor output voltage Figure 5:  
\[\mathrm{TDS}=66.71\cdot V^{3}-127.93\cdot V^{2}+428.7\cdot V \tag{2}\]  
where  
* \(V\): Sensor output voltage (in volts, V);
* TDS: Total dissolved solid concentration (in ppm).  
This equation is derived from experimental calibration and provides a precise estimation of TDSs based on the sensor's voltage output.  
Figure 4: PH voltage vs. PH concentration.
###### Abstract  
Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol's performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%.  
WSNs; quality of service; energy-aware; multi-hop routing Article
## 3 Results and Discussion
### Recast Layer  
The recast layer was analyzed by SEM images, and the results are shown in Figure 11. SEM of a machined surface by diamond wire sawing (Figure 11a) shows that no recast layer is presented on the machined surface, but the spalling of the Al\({}_{2}\)O\({}_{3}\) ceramic was found on the profile of the cutting slit. Figure 11b shows the SEM of machined surface by diamond wire sawing combined with oil film-assisted ECDM. There was no evidence supporting the existence of recast layer on the machined surface, and the spalling of Al\({}_{2}\)O\({}_{3}\) ceramic was found on the profile of the cutting slit. As the voltage increased, the spalling of Al\({}_{2}\)O\({}_{3}\) ceramic became severe, but there was still no recast layer on the machined surface (Figure 11c,d). The absence of recast layer is due to the low energy of spark discharges by oil film-assisted ECDM.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 6 Simulation Results
The impact of increasing SNR on the total probability of detection attained by different fusion scheme is depicted in Figure 9. The figure shows that adaptive combined algorithm outperforms the four other schemes over the SNR range of \(\gamma\in[-4,2]\), since the eligibility threshold of this algorithm adaptively changes to improve the total probability of detection. Furthermore, the figure shows that the adaptive combined algorithm and the weighted fusion algorithm have almost the same total probability of detection for SNR higher than 2 dB, since probability of detection significantly increases by increasing the SNR. Moreover, the impact of SNR on the total detection error rate for different schemes is investigated in Figure 10. The figure shows that adaptive combined algorithm incurs the least total probability of error, \(Q_{ER}\) over the SNR range of \(\gamma\in[-4,2]\). This reduction in \(Q_{ER}\) comes as a result of increasing the total probability of detection of the algorithm. The figure also shows that both adaptive combined algorithm and weighted fusion algorithm have almost the same detection error rate in higher SNR range, because the attained total probability of detection of both schemes are almost the same over high SNR range as shown in Figure 9.  
Figure 8: Probability of detection of different schemes vs. different SNR. Figure 9: Total probability of detection attained by different schemes vs. SNR.
## 3 Metabolic Profiling of Urine: Recent Trends
### MEPS and SPME Advantages for the Metabolic Profile of Urine
#### 3.2.3 Neurodegenerative Diseases (NDDs)  
The accumulation of oxidative damage has been implicated in aging and various neurological disorders, including Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) [120, 121]. Therefore, the search for urinary biomarkers of NDDs was primarily focused on oxidative damage biomarkers. To this point, [PERSON] _et al._[122] showed that 8-OHdG alone, as well as the ratio 8-OHdG/2-dG (the corresponding non-hydroxylated base, 2'-deoxyguanosine) were significantly different in healthy controls and Parkinson disease (PD) patients. Similarly, [PERSON] _et al._[123] reported increased levels of free 8-OHdG in urine, plasma and cerebrospinal fluid (CSF) of ALS patients. These is, therefore, important evidence that support the importance of 8-OHdG as a biomarker to complement NDDs diagnosis, and a faster methodology using MEPS/UHPLC-UV has been already described [90, 110]. Regarding F2-isoprostanes as biomarkers of oxidative stress, unlike the CVDs ([105] and references within, [106, 107]) and ODs [119], there are conflicting results about their role as biomarkers in different NDDs, such as AD, PD, ALS or [PERSON]'s diseases [124, 125, 126, 127, 128, 129, 130]. This inconsistency is probably due to the inability of some of the methodologies developed to differentiate between the several isoprostane isoforms, originating misleading results that need to be clarified with further studies. However, as there is a clear increase in the lipoperoxidation products, malondialdehydedehyde (MDA) and 4-hydroxy-2-nonenal (4-HNE) in fibroblastsand lymphoblasts of familial AD [131], it is very plausible that isoprostanes, at least some isoforms, also by-products of lipid peroxidation, are increased in most NDDs. As shown by [PERSON] _et al._[131], MDA quantification in urine is a promising opportunity to improve NDDs diagnosis, and a fast and reliable MEPS/UHPLC-UV methodology for the simultaneous quantification of MDA and 8-OHdG has been already described [90, 110]. Finally, [PERSON] _et al._[132] showed that PD patients under L-dopa treatment present significantly higher values for urinary dopamine, homovanillic acid, free normed adrenaline and free metadrenaline. L-dopa is a precursor of dopamine and other catecholamines and an important antiparkinsonian drug. Therefore, this is an interesting TDM application that could be improved by the utilization of fast MEPS or SPME strategies, such as the ones reported for dopamine quantification [98, 99, 100].
# Social Recommendation Based on Quantified Trust and User's Primary Preference Space
## 5 Social Recommendation Model Based on User Primary Preference Space (UPPS)
### The SoRec Model  
SoRec, proposed by [PERSON] et al. [23], is one of classic social recommendation methods and was used as the baseline in our experiment. SoRec assumed that the rating system shares the same preference space with the social network and uses the probabilistic matrix factorization method to model. SoRec used the matrices \(U\), \(V\) and \(Z\) to represent the user feature matrix, the item feature matrix and the trust feature matrix, respectively. Column vectors \(U_{i}\) and \(Z_{k}\) represent user-specific and factor-specific latent feature vectors, respectively. Figure 5 shows the probabilistic graphical model of SoRec.  
Figure 4: Quantification of trust network by TQ_RWR. (**a**) initial trust network; (**b**) reconstructed trust network.  
SoRec defines the conditional distribution over the observed rating matrix and social network relationships as:  
\[P(R|U,V,\sigma_{R}^{2})=\prod_{i=1}^{N}\prod_{j=1}^{M}[N(R_{ij}\Big{|}g(U_{i}^{T} V_{j}),\sigma_{R}^{2})]^{I_{ij}^{R}}. \tag{4}\]  
\[P(T|U,Z,\sigma_{T}^{2})=\prod_{i=1}^{N}\prod_{k=1}^{N}[N(T_{ik}\Big{|}g(U_{i}^{ T}Z_{k}),\sigma_{T}^{2})]^{I_{ik}^{T}}. \tag{5}\]  
where \(N(x|\mu,\sigma^{2})\) is the probability density function of the Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\). \(I_{ij}^{R}\) and \(I_{ik}^{T}\) are the indicator functions. \(g(x)=1/(1+exp(-x))\) is the logistic function, making it possible to bound the range of \(U_{i}^{T}V_{j}\) and \(U_{i}^{T}Z_{k}\) within the range \([0,1]\). The vectors \(U_{i}\), \(V_{j}\) and \(Z_{k}\) are the preference feature vectors of user \(u_{i}\), item \(v_{j}\) and trustee user \(u_{k}\), respectively.  
They also place zero-mean spherical Gaussian priors on user, item and factor feature vectors:  
\[P(U|\sigma_{U}^{2})=\prod_{i=1}^{N}N(U_{i}|0,\sigma_{U}^{2}I), \tag{6}\]  
\[P(V|\sigma_{V}^{2})=\prod_{j=1}^{M}N(V_{j}|0,\sigma_{V}^{2}I), \tag{7}\]  
\[P(Z|\sigma_{Z}^{2})=\prod_{k=1}^{N}N(Z_{k}|0,\sigma_{Z}^{2}I). \tag{8}\]  
According to the Bayesian inference, the posterior distribution of the parameters is proportional to the product of the prior distribution of the parameters and the likelihood function of the data. Hence, the posterior probability of the feature matrix is:  
\[\begin{array}{l}P(U,V,Z|R,T,\sigma_{R}^{2},\sigma_{T}^{2},\sigma_{U}^{2}, \sigma_{V}^{2},\sigma_{Z}^{2})\\ =P(R|U,V,\sigma_{R}^{2})P(T|L,Z,\sigma_{T}^{2})P(U|\sigma_{U}^{2})P(V|\sigma_{ V}^{2})P(Z|\sigma_{Z}^{2}).\end{array} \tag{9}\]  
Using the stochastic gradient descent method, they solve the matrices \(U\), \(V\) and \(Z\) to maximize the posterior probability of Equation (9), and finally predict the user's score using the formula \(\hat{R}_{ij}=U_{i}^{T}V_{j}\).
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Data Processing
#### 2.3.2 PPP Combined Daily Solutions (RTKLIB)
Figure 5 depicts the combined solution at Aboa on 1 January 2017. Finally, we chose the median coordinates of the daily combined solution as the daily representative to form the coordinate time series. Figure 4: Convergence time for the PPP forward (FWD) and backward (BWD) solutions at Aboa on 1 January 2017. Figure 5: Daily PPP combined (CMB) solution at Aboa on 1 January 2017. Forward (FWD) and backward (BWD) solutions were combined using the inverse variance weighting approach. The solutions are expressed in terms of North, East, Up (N,E,U) offsets with respect to the mean position. The time is expressed in the seconds of day (SOD).
## 1 Introduction  
### Mine Safety Management System for Underground Mines  
Fatal worker accidents frequently occur in underground mines owing to their dark and enclosed workspaces. According to the mining statistics analyzed by the Centers for Disease Control and Prevention (CDC), the fatal worker accidents that occurred in the underground mines in the United States have accounted for 390 deaths over 10 years (2008-2017) [1]. To prevent fatal worker accidents in underground mines, information communication technology (ICT)-based mine safety management systems are being developed in the mining industry, and these systems are being implemented in underground mining sites worldwide [2; 3; 4; 5; 6; 7; 8; 9]. In a mine safety management system, a wireless network environment is built within an underground mine [10; 11; 12; 13; 14; 15; 16; 17; 18]. The system tracks equipment and worker locations in real time and monitors the mine operational environment [19; 20; 21; 22; 23; 24; 25; 26]. Commercial mine safety management systems include Mine Site Technologies' IMPACT [27], Strata's STRATACONNECTTM[28], Becker Mining Systems' MineView(r)[29], and ORBCOMM's Real-time Location System [30]. The benefit of mine safety management system is that it can predict the occurrence of worker accidents and makes it possible to promptly respond to accidents by identifying the location of all workers in underground mines.
## 2 Materials and Methods  
### Data Acquisition and Presentation  
The oblique photogrammetry 3D model uses the multi-view aerial images (usually one orthophoto and four oblique) of oblique photogrammetry as the data source, using mature 3D reconstruction software (such as ContextCapture) to reconstruct the continuous triangulation model with real texture mapping through image correction, aerial triangulation encryption, dense matching, triangulation, and texture mapping [27]. In this study, the oblique photography models covering the Northeastern University campus were collected by UAV. Consistent with the content of this study, the area covered by these data includes complex building models. In the data acquisition process, the UAV had five lenses, and the sensor size was 35.9 mm \(\times\) 24 mm. The 3D model format was generated by ContextCapture, and the final model was proven to have good quality.  
With the development of oblique photography, the range and accuracy of 3D reconstruction scenes are increasing. Reconstruction scenes contain a large number of terrain and ground objects, so the amount of model data grows rapidly. However, it is very difficult to render massive model data in real time only by relying on the processing power of computer hardware. Therefore, the oblique photography 3D model usually adopts a combination of LOD (levels of detail) and spatial division to organize and manage the model data. The common storage formats for 3D models include OBJ and OSGB. Among these, the oblique photogrammetry 3D models in OBJ format usually do not support LOD. As a common plain code data format, they are usually used as an intermediate format for data exchange. Therefore, the visualization efficiency of model files in OBJ format is not high, which is not conducive to the real-time rendering of models with a large amount of data, such as oblique photography 3D models. In order to solve the problem of model restoration, the more efficient OSGB format was selected for the experimental data [28]. The OSGB format is a binary 3D model format embedded with texture images formulated by the open-source visualization engine OSG. It has a higher reading speed and better rendering efficiency because it contains an LOD, quad-tree structure and binary storage. However, due to the existence of LOD, the same ground object is stored in multiple levels of model files according to the level of detail, and the quad-tree structure also leads to the ground objects in each model file not being exactly the same. Therefore, to restore the oblique photogrammetry 3D model, it is necessary to process the model in memory and modify the existing model file that is not loaded locally to ensure the effect of model restoration [29; 30].
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 2 Related Work
A second study by [PERSON] _et al._ proposed an optimization model taking the issue of voice-based transmissions into account in order to better ensure that cellular system resources are allocated in an energy-efficient manner [24]. On the other hand, a hybrid model aimed at providing energy efficiency evaluations for converged wireless/optical access networks was proposed by [PERSON] _et al._[25], while in a study by [PERSON] and [PERSON], the authors proposed an algorithm dedicated to reducing the total amount of power consumed in distributed models [26]. More recently, [PERSON] proposed a rate-and-power control scheme for the BS transmission to address the problem of energy minimization at BS transceivers while also ensuring required service quality and fairness for all users [27]. After that, [PERSON] further constructed a practical framework for addressing energy-efficient BS transmissions based on the involvement of both radio resource allocation and CC activation/deactivation [28]. Nonetheless, while the aforementioned studies authored by [PERSON] _et al._[21], [PERSON] and [PERSON] [23], and [PERSON] [27; 28] did explicitly investigate the issue of energy savings as it relates to BSs with transceivers, the designs utilized in those papers would still not be sufficient to fully address the real-world network environments of the near future. More specifically, [PERSON] _et al._[21] did not consider the emerging context of 4G BSs, whereas [PERSON] and [PERSON] [23] derived their sub-optimal energy-saving transmission algorithm on the assumption of constantly backlogged flows, making the algorithm somewhat less than fully realistic in terms of reflecting system performance given that in real-world situations, traffic is usually dynamic and fluctuating, as opposed to always being backlogged. In addition, the transmission schemes designed by [PERSON] [27; 28] were not applicable to the cases of BSs with more than two CCs being utilized. Accordingly, to the best of this author's knowledge, previous studies have yet to fully investigate how to minimize power consumption in relation to multi-CC BS transceivers, especially in the context of the activation/deactivation of multiple CCs. Furthermore, given the aforementioned goal of green communications, the capacity of BSs to avoid wasting power in the operation of their transceivers is only likely to grow in importance.