esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Supervisor Block
When \(df_{a}\) and \(w\) are calculated, both values are compared and \(B\) is calculated. \(B\) is a Boolean value that indicates when \(w\) is lower than \(df_{a}\). \(B\) indicates if the distance between \(f_{m}\) and the estimation is within the allowable distance. \(B\) is used to calculate \(C_{t}\) according to Equation (18). \(C_{t}\) determines how many times the allowable distance between \(f_{m}\) and the estimation has been exceeded in the last \(L\) iterations. Next, \(D\) is calculated. \(D\) indicates if \(D\) indicates if \(C_{t}\) is larger than the maximum allowable number of times the distance can be exceeded, \(M_{d}\). With the values \(B\) and \(D\), the _reset_ output is calculated, and it is _Ture_ when both \(B\) and \(D\) are _True_. Also, when _reset_ is _True_, the output \(f_{0}\) is updated with the \(f_{me}\) value. If _reset_ is _False_, the value of the previous iteration is kept. The output \(ex\_dfm\) is calculated with the values \(B\) and \(M_{i}\). The output \(ex\_dfm\) is _True_ when one or both values are _True_. \(M_{i}\) is _True_ when the total of the counter \(C_{i}\) is bigger than the maximum, \(IM\). The counter \(C_{i}\) is reset to zero when \(ex\_dfm\) is _True_, and its count is increased when it is _False_. The output \(ex\_dfm\) is _True_ in two situations: (i) when the distance between \(f_{m}\) and the estimation \(f_{me}\) is greater than the maximum allowable distance, \(w\); and (ii) when \(ex\_dfm\) has not been _True_ for the last \(IM\) iterations.
## 3 Methodology
### Overall Visual Comfort System Architecture  
To apply the suggested methodology in real time, the indoor illuminance values must be retrieved by an indoor sensor that reports \(lux\) values. Furthermore, as the earth rotates on its axis once every 24 h, every location on Earth experiences one sunrise and one sunset every day. However, because the earth is a sphere, different locations receive different amounts of sunlight at different times of the day, which leads to differences in sunrise and sunset times. Additionally, the Earth's rotation is not perfectly uniform, and its orbit around the sun is not a perfect circle, but rather an ellipse. These factors can also contribute to differences in sunrise and sunset times in different locations. As a result, sunrise and sunset times vary depending on the latitude and longitude of a particular location.  
Figure 4: Lux scale recommended by European Lighting Standard ‘EN12464-1’.  
Figure 5: Flow chart of process for adapting the mathematical model to a regression model.  
A specific service is used to return the time interval (as analyzed in Section 3.1 and presented in Figure 1) based on the actual location of the building (i.e., the latitude and longitude) and the current timestamp and return the value of \(w\) needed to estimate the visual comfort. Finally, the real-time \(lux\) values and \(w\) are provided as the inputs for the ML model (i.e., regression algorithm), which estimates the visual comfort value. Figure 6 depicts the overall conceptual architecture of the system.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 6 Conclusions  
In this paper, we carry out a systematic study on traffic crash prediction (TCP) at or near road intersections. To achieve the goal, we propose a three-phase data-driven deep learning model, called _Road Intersection Traffic Crash Prediction (RoadInTCP)_, to predict traffic crashes at intersections by exploiting heterogeneous big data publically available. Our contributions lie in the various ideas for data collection and preparation including not only topological-relational features (_tr-features_) and additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections but also the sequential features, as well as an effective data sampling approach in data preparation to facilitate model training to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data. Empirically, we validate our ideas and show that the proposed _RoadInTCP_ model robustly outperforms all the compared models and demonstrates the robustness and generality of our models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **2014** & **2014** & **2015** & **2015** & **2016** \\  & **(First Half)** & **(Latter Half)** & **(First Half)** & **(Latter Half)** & **(First Half)** \\ \hline RoadInTCP\({}_{tr}\) & 0.8236 & 0.8135 & 0.8132 & 0.8328 & 0.8101 \\ RoadInTCP\({}_{ATI}\) & 0.8126 & 0.8093 & 0.8195 & 0.8411 & 0.8069 \\ RoadInTCP\({}_{iter}\) & 0.8235 & 0.8097 & 0.8149 & 0.8332 & 0.8208 \\ RoadInTCP\({}_{temporal}\) & 0.8291 & 0.8205 & 0.8042 & 0.8414 & 0.8286 \\ RoadInTCP & 0.8499 & 0.8568 & 0.8366 & 0.8694 & 0.8493 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impacts of Different Features of Model Structures on Experiment Performance.  
**Author Contributions:** Conceptualization, M.W., W.-C.L. and G.Y.; methodology, M.W. and W.-C.L.; software, M.W.; validation, G.Y. and Q.F.; formal analysis, M.W. and W.-C.L.; investigation, M.W. and N.L.; resources, F.W. and N.L.; data curation, M.W. and Q.F.; writing--original draft preparation, M.W. and N.L.; writing--review and editing, W.-C.L. and G.Y.; funding acquisition, M.W. and Q.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by Special Fundamental Research Fund for the Central Public Scientific Research Institutes (NO. 602024Y-11792).  
**Data Availability Statement:** All datasets used in this study are publicly available and are discussed in Sections 3 and 5. They are also available from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
## 4 Practical Applications
### Osteogenic Involvement  
Another recent study by [PERSON] et al., presented the role of mechanical stimulation in MSC osteogenic differentiation [40]. This work explains about the ability of cyclic tensile strain (CTS) to regulate the initiation of MSC differentiation and, more specifically, their involvement in the endochondral pathway. MSCs embedded in fibrin hydrogels experienced uniaxial tensile deformation in a novel bioreactor system. The authors found out that CTS, in the absence of differentiation factors, can enhance the expression of tenogenic and osteogenic markers. The lack of evident chondrogenesis suggests that CTS could take part in directly initiating intramembranous ossification. When in the presence of chondrogenic growth factor (TGF-\(\beta\)3), instead, CTS induced increased proteoglycan and collagen production and enhanced upregulation of the markers of endochondral ossification (Bone morphogenetic protein 2 (BMP2), Runt-related transcription factor 2 (RUNX2), (Alkaline phosphatase) ALP, Osteopontin (OPN), Collagen Type X Alpha 1 Chain (COL10A1)). The authors concluded that CTS is an inducer of both endochondral and intramembranous ossification of stem cells, depending on the environment.  
Endochondral ossification-based engineering techniques are promising strategies to provide regeneration of large defects. Hybrids of bone and cartilage tissue from induced pluripotent stem cells (iPSCs) have been achieved by the combined use of osteogenic and chondrogenic media in addition to mechanical stimulation, by means of shaking forces [41]. iPSCs offer the advantage of self-organizing in culture medium into cell aggregates, known as embryoid bodies (EB), without the support of scaffolds, providing a model to examine mechanisms of tissue differentiation and organ development. In this  
Figure 3: Different distributions of mesenchymal stem cells (MSCs) in fibrin–polyurethane composite scaffolds. [PERSON] et al., imposed a protocol of mechanical loads on scaffolds seeded with MSCs. The study comprised three types of distribution: cells evenly dispersed throughout the scaffold (Uniform), cells asymmetrically disseminated, forming a thin layer on the surface (Asymmetric), and cells allowed to adhere only to the upper face of the scaffold (Surface Only).  
experiment, the aggregates, when maintained in osteogenic medium culture, underwent osteogenic induction, reaching mineralization; when, instead, the aggregates were set first in osteogenic and later in chondrogenic medium, they were able to originate both tissues by expressing osteogenic and chondrogenic marker genes. The percentages of bone and cartilage composition seem to be subject to variations in culture periods, medium components, and shaking frequency, allowing the generation of easily manipulated osteochondral organoids.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion
### Effect of Solution pH on Cu(II) Adsorption  
The pH value is considered one of the most important parameters during metal removal processes. The dependence of the adsorption of Cu(II) on surface-modified PAN fibers on the initial pH of the solution is presented in Figure 6. At low solution pH levels, the modified amine groups are protonated and the surface of the adsorbent is more positively charged, which is not favorable for the adsorption of metal cations [19; 25]. The amount of Cu(II) ions adsorbed on the surface-modified PAN fibers increased significantly as the number of protonated amine groups decreased, owing to the increase in the solution pH [2]. The optimum solution pH was determined to be 4, and the corresponding adsorption amount was 87.767 mg/g for the initial Cu(II) solution concentration of 100 mg/L.
## 3 Controller Design Approach
### Genetic Algorithm  
The GA is an optimization method that based on the mechanism of natural selection and natural genetics. The search process is very similar to the natural evolution of biological creatures in which successive generations of organisms are born and raised until they are able to breed. Just like in the animal kingdom, only the fittest will survive to produce, while the weakest will be omitted [48]. The GA can be applied to solve a variety of optimization problems that are not well suited for standard optimization algorithms such as problems in which the objective function is discontinuous, non-differentiable, stochastic, or highly nonlinear [49]. The algorithm begins by creating a random initial population. Then, it uses the current population to create the children to make up the next generation. The algorithm selects a group of individuals in the current population, called parents, who contribute their genes to their children. It usually selects individuals that have better fitness values as parents. The GA creates three types of children for the next generation [50]:  
1. Elite children are individuals in the current generation with the best fitness values. These individuals automatically survive to the next generation.
2. Crossover children are created by combining the vectors of a pair of parents.
3. Mutated children are created by introducing random changes to a single parent. Both processes of crossover and mutation are essential to the GA. Crossover enables the algorithm to extract the best genes from different individuals and recombine them to get potentially superior children. While, mutation improves the diversity of the population to increase the probability that the algorithm will generate individuals with better fitness values. The algorithm is repeated for many generations till stops if one of the stopping criteria is met.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 3 Communication Optimization
### Dynamic Synchronous Parallel Strategy  
#### 3.2.1 Problems of SSP  
In Section 2.3, we have briefly introduced the two problems of SSP, here we take a typical distributed machine learning model training as an example to illustrate the problems.  
The first problem is that SSP is not optimized for the cluster that is composed of similar performance worker nodes. As shown in Figure 5, there are five worker nodes to train machine learning, the coordinate axis refers to the number of iterations performed by each worker node after the last synchronization barrier. We can clearly see that the performance of each worker node is similar. If the stale threshold is set to 3 or more, each worker node will perform a number of iterations without updating the global model parameters since SSP does not set a single worker node threshold. The fault tolerance of the iterative-convergent algorithm is abused, and the finite of the fault tolerant is neglected. Accuracy of the model is seriously decreased and the convergence of the model is not guaranteed.  
The second problem is that SSP cannot cope with external interferences in the training process. As shown in Figure 6, the difference between iterations of the worker node 1 and iterations of the worker node 3 reaches the stale threshold \(s\), the worker node 1 waits for the other worker nodes to complete their iterations, then PS performs a global model parameters synchronization, and worker nodes enter the next iteration with the new global model parameters. However, in a new iteration, worker nodes 3 due to some reasons (e.g., the completion of other unrelated computing tasks) improved its computing performance, at this time the number of iterations performed by each worker node is not much different, worker nodes will perform a considerable number of iterations because the stale threshold \(s\) cannot be reached, which ignores the dynamics of the fault tolerance, and ultimately leads to a serious decrease in the accuracy of model training or even no convergence.  
Figure 5: Stale Synchronous Parallel (SSP) is not optimized for the cluster with similar performance worker nodes.  
Figure 6: SSP cannot cope with external interference in the process of training model.
# Sensors for Sustainable Smart Cities: A Review
## 4 Challenges and Opportunities
Solid waste management is crucial in any town or city, but take a new role in the smart city scheme, and it is focused on a more clean, tidy, and healthy environment for living, using sensors and IoT technologies to improve waste management [42; 43]. Currently there are only sensors that can identify wet, dry, or metal garbage; however, it would be optimal to develop sensors that allow identifying waste in greater detail. For this reason, new sensors oriented to waste segregation need to be developed and implemented. Segregation is a key component in the waste management system, as it allows much of what is discarded to be recycled or reused, resulting in a reduction in the amount of garbage that reaches landfills [128; 172]. Innovations in energy consumption monitoring in buildings, public lighting systems, and urban spaces using ICTs are an excellent option due to their adaptability. The literature proposes the implementation of virtual sensors by building information modeling (BIM), integrated with IoT devices including qualification tools to develop ecological buildings [134; 187]. Intelligent lighting systems with sensors adaptable to weather conditions, hours of use and presence of people or vehicles [20] where the street lamps serve as Wi-Fi connection points, allowing interconnected networks over the entire urban area monitoring the quality of the environment, noise levels, and surveillance, among others. Battery or energy consumption, high volume of data storage and security, life span and replacement, size, cost, installation, and maintenance are the main deficiencies when designing a WSN. Studies have proposed the implementations of energy efficiency surveillance using multimodal sensors [188] and low power hardware systems [189]. The implementation of low-cost sensors and using energy harvesting are the most attractive technologies for buildings' sensors in the future. Artificial intelligence, big data, and machine learning [190; 191] become essential due to the vast amount of data gathered and analyzed in the presented applications.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 3 Background  
In this section, the relevant aspects of the IEEE 802.11p and IEEE 1609.4 protocols are illustrated.
# Microarchitectural Characterization on a Mobile Workload
## 4 Mobile Device Workloads  
This paper describes a detailed analysis of a mobile performance benchmark. Benchmarking is a method of measuring performance against a standard or given set of standards. Processor performance depends on the types of workload used, and designers typically use benchmark suites with the initial assumption that the workloads they provide are representative of user programs [5]. In the field of benchmarking, there are three types of benchmark to in general [6; 7]:  
* Synthetic benchmarks (e.g., Whetstone Benchmark, Dhrystone Benchmark, etc.) developed to measure system-specific parameters (by CPU, compiler, and so on).
* Application-based benchmarks (\"real-world\" benchmarks) developed to compare different system architectures in the same real application fields.
* Algorithm-based benchmarks (a compromise between the first and the second type) developed to compare different system architectures in special (synthetic) fields of application.  
In mobile worlds, there are many performance benchmarks but only a few are accepted as industrial standard benchmarks. Among them, Geekbench is a commonly referenced workload. Geekbench is mainly focused on CPU. It also provides GPU workloads but it is located in a separate space. It is called compute benchmark.  
Geekbench is a CPU-centric Primate Labs benchmark. It is widely used in mobile contexts for evaluating CPU performance. Cross-platform compatibility is a key reason for  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Hardware** & **Specification** \\ \hline OS & Android 9.0 (Pie) \\  & Octa-core (2 xM4 (2.73 GHz) \\ CPU & +2 xCortex-A75 (2.31 GHz) \\  & +4 xCortex-A55 (1.95 GHz)) \\ GPU & Mali-G76 MP12 \\ Memory & RAM—6 GB \\  & L1 /D cache1—64 KB/64 KB (private) \\ M4 Cache & L2 cache—1 MB (private) \\  & L3 cache—3 MB (shared) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exynos9820 hardware specification.  
its wide usability. The covered platforms are Linux, Windows, iOS, macOS, and Android [8]. Geekbench's binary size is relatively small since most workloads are synthetically designed with small size code. Its runtime is about 90 s on premium devices (e.g., Galaxy S10+). As the most recent version, Geekbench ver. 5 is divided into two categories: CPU benchmark and compute benchmark for GPU. In this paper, we will focus only on the CPU benchmark of Geekbench ver. 5. Geekbench's CPU benchmark contains a single-core test and multi-core test and each consists of 21 workloads sorted by crypto, integer, and floating-point workload. The details are in Table 2.  
The overall performance score consists of three sections. Each section has its weight which is decided by Primate Labs, but the weight allocation logic is not clear. The scoring weights are crypto 5%, integer 65%, and floating point 30%. The weights have been changed from the previously released official versions. This might be an issue since the competitive scores are directly affected by the designed weights.
# Brewing with Unmulated Cereal Adjuncts: Sensory and Analytical Impacts on Beer Quality
## 3 Results and Discussion  
### Sensory Evaluation of Adjunct and Control Beers  
#### 3.1.1 Attribute Generation  
In the initial sessions, panellists generated over 200 attributes describing the adjunct and control peers, which were then consolidated over several sessions by means of Check-All-That-Apply (CATA) tests and group training sessions. The final 18 attributes with their description and reference training materials are presented in Table 1. The lexicon comprised of three basic tastes, seven aroma, six flavour, one trigeminal sensation (astringent) and one temporal character of bitterness (lingering bitterness). Four terms (green apple, banana, malaty and grainy) were assessed both for aroma (orthonasal assessment) and flavour (retronasal assessment). Natural and chemical references were identified by the panel to be used in the evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modality** & **Attribute** & **Definition** & **Reference Material** \\ \hline \multirow{8}{*}{**Aroma**} & Green Apple/Emulsion Paint & The skin of a green apple and the aroma of emulsion paint & \(15\) mg/L of acetaldehyde in water (Aroxa) \\ \cline{2-4}  & Banana/Pear Drops & Artificial banana and pear drop sweets & 3.5 mg/L of isoamyl acetate (Aroxa) in water \\ \cline{2-4}  & Sweetcorn/Cooked Vegetable & Tinned sweetcorn and cooked vegetables & \(150\) mg/L of dimethyl sulphide (Aroxa) in water \\ \cline{2-4}  & Smoky & Stale acid smoke such as stale cigarette smoke or faint burnt rubber & \\ \cline{2-4}  & Malty & Sweet processed or cooked malt grains & \(20\) g of malt loaf, \(20\) g of barley malt extract \\ \cline{2-4}  & Grainy & Dry light barley malt grains & \(50\) g of dry light barley malt \\ \cline{2-4}  & Plum Fruit & Cooked plum fruit (plums, damsons and prunes) & \\ \hline \multirow{8}{*}{**Flavour**} & Green Apple/Emulsion Paint & The skin of a green apple and emulsion paint & \(15\) mg/L of acetaldehyde in water (Aroxa) \\ \cline{2-4}  & Banana/Pear Drops & Artificial banana and pear drop sweets & \(3.5\) mg/L of isoamyl acetate (Aroxa) in water \\ \cline{2-4}  & Apple & Bruised sweet red apple & \\ \cline{2-4}  & Malty & Sweet processed or cooked malt grains & \(20\) g of malt loaf, \(20\) g of barley malt extract \\ \cline{2-4}  & Grainy & Dry light barley malt grains & \(50\) g of dry light barley malt \\ \cline{2-4}  & Plum Fruit & Cooked plum fruit (plums, damsons and prunes) & \\ \hline \multirow{8}{*}{**Taste**} & Sweet & Intensity of sweetness after swallowing & \\ \cline{2-4}  & Sweet & Intensity of sourness after swallowing & \\ \cline{2-4}  & Bitter & Peak intensity of bitterness after swallowing & \\ \cline{2-4}  & Lingering Bitterness & Intensity of bitterness \(20\) s after swallowing & \\ \hline \multirow{2}{*}{**Mouthfeel**} & \multirow{2}{*}{Astringent} & Drying sensation in the mouth after swallowing & \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The adjunct beer attribute lexicon, description and reference standards developed in the study.