esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of Federated Learning
FedNova [13] is a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence. FedNova is complementary to gradient compression or quantization. FedMA [14] takes an arduous approach. It continues to freeze layers one by one. It builds up a common ground for every node and slowly reduces divergence by freezing parameters. The development of FedAvg-based federated learning algorithms has been towards lowering divergence of clients in non-IID. However, all these methods either result in extremely slower convergence or require increased costs in computing and communication. Recently, the field of federated learning has branched out to two new frontiers. The first branch is personalizing. It embraces the divergence to a certain level instead of eliminating it. Recent methods such as FedBN [15] and FedBABU [16] use certain layers as guidelines. FedBN excludes batch normalization layers from aggregation. Each client keeps personalized statistics to normalize input data. FedBABU separates neural network architecture to the body and head. Only the body, the feature extractor, is updated whilst the head, classifier, is frozen. The body will update in accordance to the fixed head. It also uses fine-tuning after aggregation. Both methods achieve high performance in personalized settings. However, if a personalized local model is evaluated in another client's personalized setting, it would falter. Moreover, these methods operate in a specific non-IID setting, FedBN using different datasets for each client and one client per dataset, and FedBABU using the old non-IID proposed in FedAvg [1]. The second branch is replacing the averaging algorithm with knowledge distillation [17]. FedDF [18] and FedGen [19] both utilize knowledge distillation as an aggregation technique instead of averaging algorithm. The replacement allows each client to have a neural network that is specialized to its hardware properties. Although knowledge distillation requires a certain number of data collected in the server, ablation studies show that it can be replaced by randomly generated inputs. Still, the ablation studies also note that the performance is enhanced when the distribution of inputs for knowledge distillation resembles that of the local training datasets [18]. In summary, the development of federated learning is confronted on four fronts: how to aggregate knowledge; should generalize or personalize, how to reduce drift or divergence, and which non-IID to handle. As such, solving a federated learning problem is accompanied by the risk of having the solution being less effective in other federated learning situations.
# 3-D Numerical Study on Progressive Failure Characteristics of Marbles under Unloading Conditions
## 3 Numerical Results and Discussions
### Uniaxial Compressive Simulations Results  
Based on the calibrated microscopic material parameters in Section 3 and geometrical parameters in Table 2, eight different kinds of pre-fissured marble specimens were constructed using discrete element method. In order to study the mechanical properties and failure behaviors of pre-fissured marble specimens subjected to uniaxial compressive loads, numerical results were firstly compared with the previous experimental data [66], as shown in Table 3. It can be found from Table 3 that numerical results are in good agreement with the previous experimental data in laboratory [66]. The difference of mechanical properties of Type-A and Type-B pre-fissured marble specimens between numerical results between experimental data [65] was mainly caused by many uncertain factors in laboratory tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Numerical Results**} & \multicolumn{2}{c}{**Experimental Data [66]**} \\  & \(\sigma_{c}(\textbf{MPa})\) & \(E(\textbf{GPa})\) & \(\sigma_{c}(\textbf{MPa})\) & \(E(\textbf{GPa})\) \\ \hline \multirow{2}{*}{Intact (30 MPa)} & 85.9 & 35.9 & 83.5 & 36 \\  & 160.5 & 53.2 & 154.4 & 53.6 \\ \multirow{2}{*}{Type A} & 36.8 & 25.5 & 50.7 & 27.7 \\ \multirow{2}{*}{Type B} & 60.8 & 33.0 & 57.1 & 30.1 \\ \multirow{2}{*}{Type C} & 14.0 & 12.9 & 15.2 & 15.9 \\ \multirow{2}{*}{Type D} & 13.6 & 11.9 & & \\ \multirow{2}{*}{Type E} & 30.3 & 24.4 & 31.3 & 22.5 \\ \multirow{2}{*}{Type F} & 44.1 & 24.8 & 41.5 & 27.7 \\ \multirow{2}{*}{Type G} & 55.0 & 28.1 & 66.7 & 35.5 \\ \multirow{2}{*}{Type H} & 51.8 & 30.4 & 60.8 & 27.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of macro-mechanical properties of marbles.  
The 3-D final failure patterns of the uniaxial compression of the pre-fissured marble specimens are plotted in Figure 7. In Figure 7, the red portion indicates shear micro-cracks, and the green portion indicates tensile micro-cracks. It can be found from Figure 7 that the final failure modes in different types of pre-fissured marble specimens under uniaxial compressive loading conditions were mainly dominated by tensile cracks. Meanwhile, the shear cracks were mainly distributed around the pre-existing fissure tips. Furthermore, 3-D final failure patterns also demonstrates that the ultimate failure modes of different marble specimens belong to splitting tensile failure mode. Since, the present researches are mainly focused on the mechanical and failure characteristics of pre-fissured marble specimens under unloading conditions, numerical results of failure behaviors of marble specimens can be considered as the comparing references.
## 1 Introduction
This only becomes an issue if the casks or barrels being used to store the beer are damaged or have leaks that allow oxygen to come in contact with the beer. The volatile acidity in commercial lambic beers ranges over 4-fold [6], which likely is related to differences in oxygen contact during storage [6; 8]. The fourth and final stage is marked by an increase in the number of yeast cells. _Brettanomyces_ plays an essential role in the development of the aromatic and flavor profile of lambic beer. Lambics, similar to other beers, are composed of a number of chemical and metabolic reactions [9]. A critical step in identifying what flavor and aroma compounds are present within a product is selecting the correct extraction technique. When selecting the best extraction technique, that technique must be capable of isolating all of the characteristic compounds that play a vital role in the overall flavor and aroma of the product without creating any artificial products [10]. Solid-phase microextraction (SPME) is one of the most commonly used extraction techniques for beer and other alcoholic beverages [11]. SPME has been found to be a suitable technique for the extraction of the volatile and semi-volatile compounds found in lambic beer when compared to other extraction techniques [12]. The majority of the volatile and semi-volatile compounds identified in lambic beers are a result of yeast metabolism [13]. The volatile and semi-volatile compounds found within lambic beer come from a number of different chemical groups, such as esters, acids, higher alcohols (fuel alcohols) and phenols, just to name a few [6; 7]. Esters play an important role in the aromatic profile of this particular style of beer, which are by-products of a number of different microorganism [6; 14]. Esters are well known for their fruity-floral aromas [13]. The two most influential esters produced in lambics are ethyl lactate and ethyl acetate, which originate from lactic and acetic acids during stage three [6; 14]. Gueuze is result of the careful blending of different aged and different fasting gueuze beers [4; 15]. Gueuze beers are a combination of young (1 year old) and old (2 and 3 years old) beer. Gueuze is similar to champagne in that they both undergo a secondary fermentation period in the bottle. The purpose of this project was to characterize the volatile and semi-volatile compounds found in aging lambic beer samples through the use of headspace (HS-SPME) analysis using gas chromatography-olfactometry (GC-O) and gas chromatography-mass spectrometry (GC-MS).
# Characterization and Clinical Trial of an Innovative High-Speed Lancing Device
## 1 Introduction  
In 2015, the number of diabetes mellitus (DM) patients globally exceeded 400 million people. To control their physical condition, DM patients must use a blood glucose meter to perform self-monitoring of blood glucose (SMBG) many times daily. The first step in SMBG is to collect several microliters of blood from a blood capillary. This step requires the use of piercing device to perform an invasive act into a finger or another part of the body. Although only a very small amount of blood is required, the pain and psychological barrier of piercing are the main causes of the failure of general DM patients to fail to perform SMBG as frequently as required by their doctors [1; 2; 3; 4].  
According to the relevant literature, to reduce the pain that is caused by piercing, many SMBG methods have been developed, as follows. (1) Change the shape and level angle of the lancet tip to reduce the size of the wound that is produced by piercing [5; 6]. (2) Coat the lancet surface with a special material to reduce the friction between the skin and the lancet and thereby to reduce pain [7]. (3) For patients with various skin conditions, use a lancing device with an adjustable piercing depth or with a small lancet diameter. For example, when sampling blood from babies with thin skin, the depth of piercing depth of the lancet on can be adjusted to reduce the pain caused [8; 9; 10]. (4) Change the mechanical design of the lancing device to reduce the vibration of the lancet to cause a smallerwound [11; 12; 13; 14; 15]. Reducing both the period for which the lancet remains in the skin and the number of piercings of the skin will be very helpful in reducing the pain that is caused by the lancet during blood sampling [16; 17]. However, the above improvements cannot ensure that the lancet pierces the skin only once after the finger is picked. Additionally, the piercing velocity, piercing acceleration and duration of puncture have not been analyzed.  
The main purpose of this study was to significantly reduce pain in the blood sampling process and shorten the duration of the pain after blood sampling. Motivated by the inadequacy of the relevant literature and defects in the marketed lancing devices, this work develops an innovative high-speed lancing device, which has the characteristic that when it picks a finger, the lancet pierces the skin only once and remains in the skin for an extremely short period, enabling the sampling of the blood to be completed rapidly. The innovative high-speed lancing device is compared with a common, commercially available lancing device. Furthermore, clinical trials that involve a questionnaire survey to elucidate the pain index are performed to prove further that the innovative high-speed lancing device can reduce the pain level of diabetes patients during the blood sampling process [18; 19].
## 3 Experimental Results  
Figure 6 shows the number of generated combinations of weights for each profile and precision. For example, if we consider, h = 0.005, profile #0 and NQoS = 4, there are 70,058,751 possible combinations generated. SEFI filters the solutions that satisfy the restriction imposed by Equation (2), so there are less possible solutions to the problem than combinations generated, as we can see en Figure 7.  
Following with the last example, only 1,373,701 combinations are solutions. SEFI applies the fitness function to all these solutions in order to obtain the combination with the lowest fitness value; hence its corresponding network will be used for the VH decision.  
\begin{table}
\begin{tabular}{l l l} \hline  & **S3** & **V5** \\ \hline Prototyping board & Digilent Nexys2-500 & Xilinx XUPV505-LX110T \\ FPGA & Spartan 3E: xc3s500e-fg320-4 & Virtex5: xc5 vlx110t-ff1136-1 \\ Processor/memory & Microblaze with FPU and PLB/external & Microblaze with FPU and PLB/256 KB local \\ Operating system & Standalone & Standalone \\ Clock frequency & 50 MHz & 125 MHz \\ Occupied slices & 43\% & 10\% \\ Power consumption & 0.097 W & 1.34 W \\ \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the implemented architecture.  
Figure 6: Experimental results: Generated combinations for each profile and precision.  
We have also observed two interesting results, after examining detailed data from many other experiments. On the one hand, the best network could be any of the considered ones in any time, showing the importance of searching within a wide space of solutions. On the other hand, when the number of QoS considered parameters grows, other network different to the best one previously found can emerge now as the best.  
Obtaining the best solution becomes slower to compute when considering more QoS parameters or more precision, because of the increased number of generated combinations. Figure 8 shows the computing times for both FPGA devices in all the cases, with values from milliseconds to several minutes.  
Depending on the number of the discovered networks and their characteristics, the type and technology of the mobile sensor, the requirements of the application, and so on, SEFI can initially adjust the precision, profile and number of QoS parameters in order to obtain solutions in real time or in an acceptable time.  
Figure 8: Experimental results: Computing times, for each profile, precision and FPGA.  
Figure 7: Experimental results: Combinations as solutions, for each profile and precision.
## 5 Resolution Methods
### Genetic Algorithm
#### 5.1.2 Initial Population  
The initial population plays an important role in the performance of genetic algorithms. An initial population with great diversity between solutions can avoid falling into a premature convergence or a local minimum. Different assignment and sequencing rules were used to achieve the diversity of solutions:  
* Random assignment (RA): The operations are randomly assigned to machines.
* SPT assignment (SPTA): For each operation, the machine with a smaller processing time is selected to perform this operation.
* LPT assignment (LPTA): For each operation, the machine with a longer processing time is selected to perform this operation.
* Minimum machine workload assignment (MMWA): The operations are iteratively assigned to machines based on their processing times and machine workloads. The workload of a machine depends on its type. For the set of machines \(M_{1}\), the workload is the sum of processing times of operations assigned to the machine. For the set of machines \(M_{2}\) and \(M_{3}\), the workload is the total machine occupation time. The procedure consists in finding the machine with the minimum workload for each operation.
* Random sequence (RS): This heuristic randomly orders the operations on each machine.
* SPT sequence (SPTS): Operations with the shortest processing time will be processed first.
* Most number of operations Remaining (MNOR): This heuristic consists of processing the operations of the sub-lot of the job that has the most operations remaining as a priority.
* Most work remaining (MWR): The operations that have the most remaining processing time will be prioritized for processed.  
For the construction of the initial population using the heuristics described above, the procedure consists of randomly choosing an assignment and a sequencing heuristic and building solutions, such as the constraints of precedence between the operations of sub-lots of jobs, the due dates of jobs, the time windows of availability of material resources, and the respect for the production capacities of machines.  
Figure 3: Representation of the assignment of operations to machines and their sequencing.
## 3 Results
### Agronomic Parameters  
The data on aerial fresh weight, number of leaves, stem diameter, and leaf area across the different treatments are presented in Table 5. The Biochar treatment caused higher fresh weight, leaf dry weight, and stem diameter compared to the other treatments. Nonetheless, none of these results were significant when compared with the Control treatment. However, a significant difference was observed between the Biochar and Leachate treatments, with Biochar increasing fresh weight by 26.2% compared to Leachate. Similarly, the Leachate and Leachate + Biochar treatments slightly increased leaf dry weight relative to the Control, although no improvements were observed in the other measured variables. For some parameters, such as the number of leaves and leaf area, the combined Biochar + Leachate treatment had a significantly detrimental effect, resulting in decreases of 11.9% and 29.4%, respectively, compared to the Control. The agronomic quality and productivity were evaluated. Table 6 shows the corncob variables and popcorn yield. Overall, the evaluated treatments had a significant detrimental effect on all measured parameters compared to the Control treatment, with reductions observed in grain weight per corncob (\(\geq\)27.6%), corncob length (\(\geq\)8.8%), corncob diameter (\(\geq\)1.9%), and popcorn yield (\(\geq\)20.8%). However, among the treatments, the Biochar treatment showed better performance than the Leachate + Biochar treatment, and both outperformed the Leachate treatment. In general, the leachate treatment had a negative impact on all evaluated variables, showing the lowest values for grain weight (10.68 g), \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & **Mesophilic Aerobes** & **\(\mathbf{\mathrm{\textit{Bacillus spp.}}}\)** & **Actinomycetes** & **\(\mathbf{\mathrm{\textit{Molds and Yeasts}}}\)** & _Pseudomonas_ **\(\mathbf{\mathrm{\textit{ spp.}}}\)** \\ \cline{2-5}  & & **logio (CFU g\({}^{-1}\))** & & **logio (MPN g\({}^{-1}\))** \\ \hline Control & 6.4 \(\pm\) 0.13 b & 6.04 \(\pm\) 0.17 & 6.19 \(\pm\) 0.26 & 4.18 \(\pm\) 0.18 c & 1.99 \(\pm\) 0.77 \\ Leachate & 6.49 \(\pm\) 0.15 b & 6.08 \(\pm\) 0.13 & 6.25 \(\pm\) 0.14 & 4.38 \(\pm\) 0.24 bc & 1.44 \(\pm\) 0.75 \\ Biochar & 6.64 \(\pm\) 0.1 a & 6.25 \(\pm\) 0.2 & 6.3 \(\pm\) 0.26 & 4.63 \(\pm\) 0.18 a & 1.49 \(\pm\) 0.84 \\ Leachate + Biochar & 6.53 \(\pm\) 0.12 ab & 6.11 \(\pm\) 0.14 & 6.12 \(\pm\) 0.11 & 4.49 \(\pm\) 0.27 ab & 1.72 \(\pm\) 0.81 \\ \hline \(p\)-value & 0.0021 & 0.1585 & 0.2029 & 0.0012 & \textless{}0.4741 \\ Significance & ** & ns & ns & ** & ns \\ \hline \hline \end{tabular} Note: The data in the table express the mean and standard deviation (\(\mu\pm\sigma\)) of the evaluated parameters. Those values with different letters in the same column indicate significant differences between the treatments. \(p<\) 0.05 (**), Not significant (ns).  
\end{table} 
Table 4: Quantification and enumeration of microbial composition.
# Survey of Smart Parking Systems
## 7 Discussion and Open Problems
### Routing Algorithms  
Analysis of routing algorithms is scarce in the literature. No work was found that proposed a study of the different algorithms used in SPS.  
We identified studies that investigated and proposed a parking slot assignment game or competition for a place [302; 303; 304], or models that studied parking actions in different contexts [305; 306; 307]; however, they did not address in a global way which algorithms are present in the literature and which implementation characteristics they have.  
In Section 5 of this paper, the algorithms from the 274 selected papers were classified and three main groups of algorithm types were identified (static, dynamic, and real-time), as well as other typesof algorithms that were used but to a lesser extent. These three large groups were described and the relationships between the algorithms and the types of SPS classified in Section 3 were established. It is interesting to note that dynamic algorithms were the most widely implemented in the literature.  
It would be enriching to propose for the future a more in-depth analysis of all the algorithms, i.e., a study that goes beyond the present survey and establishes which data are used by each of the algorithms, and how they are used, weighing the results obtained, and which would be the most appropriate to implement in different contexts.  
As we identified a potential increase in SPS, we believe that, with cities becoming increasingly digitally connected, and where every digital user has real-time information in their hands, it would be highly favorable for parking systems if the underlying algorithms that are implemented address the sampling of data and indication of results in real time. This, in addition to promoting the use of these systems, will lead to a better reading of the information by users and, most importantly, would become a fundamental factor in making decisions in the hunt for a free parking space.
## 2 Materials and Methods
### Clinical Assessment  
OCD diagnosis in both pediatric and adult patients will be established according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Neuropsychological tests will be performed with the use of the MATRICS Consensus Cognitive Battery [25], as it has been employed in children, adolescents, and young adults.  
* Adult population: OCD symptom severity will be assessed with the Italian version of the Yale-Brown Obsessive-Compulsive Scale-Second Edition (Y-BOCS-II) [26]. The Brown Assessment of Belief Scale (BABS), a semi-structured interview assessing seven different items related to the level of insight on a particular belief, will be employed to assess the level of insight surrounding the eventual obsessive thoughts [27].
* Pediatric population: OCD symptoms severity will be assessed with the use of the Italian version of the Children Y-BOCS scale [28]. Behavioral and affective symptoms will be assessed through the use of the Italian version of the Child Behavior Checklist (CBCL 6-18) [29]. To assess the level of insight surrounding obsessional beliefs, the BABS-Adolescents will be employed [27]. The Yale Global Tic Severity Scale will be employed to assess the severity of motor and vocal tics, such as the number, frequency, intensity, complexity, and interference with daily functioning [30].
* Pittsburg Sleep Quality Index assessing (1) subjective sleep quality, (2) sleep latency, (3) sleep duration, (4) usual sleep efficiency, (5) sleep disorders, (6) use of hyproniducent, and (7) diurnal dysfunction.  
Whole sample: sociodemographic, personal, and family history comprising: (1) patient's ethnicity, (2) psychiatric comorbidities, (3) medical comorbidities, (4) father's age at birth, (5) mother's age at birth, (6) socio-economic status (self-assessed on three levels), (7) education level, (8) father ethnicity, (9) mother ethnicity, (10) family history of mental disorders, (11) family history of suicide behaviors, and (12) lifetime history of substance use disorders.