esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Region on Sensory and Chemical Profiles of Pennsylvania Gruner Veltliner Wines
## 3 Results and Discussion
### Chemical Analysis
#### 3.2.2 Volatile Aroma Analysis  
In Year 1 (wines made in 2018), a total of 33 compounds were detected, and 12 were found to differ significantly among all wine samples. A number of these compounds were fermentation-derived esters including ethyl decanoate, ethyl hexanoate, ethyl octanoate, as well as hexyl acetate and 2-hexen-1-ol acetate (Table S6). These compounds contribute to a general fruity aroma in wine and include odors of apple and peach [43]. Significant regional effects were found in six of these volatile compounds (Table S7). The NC region was highest in hexyl acetate, while ethyl hexanoate was higher in SE wines than in NC and NW wines, and 1-hexanol was found in higher concentrations in the NE region compared to the NC region.  
In Year 2 wines, made in 2019, 51 compounds were detected and 20 volatile compounds differed significantly in concentration (Table S8). Fifteen of these 20 compounds also differed significantly by region in the second year (Table S9). SE wines had significantly higher concentration of the following compounds compared to the NW and NC regions: ethyl acetate, 1-propanol, butyl acetate, isobutanol, amyl acetate, 1-hexanol, acetic acid, and 2-hexen-1-ol. While some of these compounds are associated with apple aroma, namely butyl acetate and amyl acetate, SE1 and SE3 were the two wines with the lowest green apple aroma ratings, suggesting these compounds are not contributing to perceived apple aroma. Although chemical/solvent aroma was not significantly different between the wines, the three SE samples had the highest chemical/solvent aroma ratings among the samples, which could be caused by their higher concentrations of ethyl acetate. In contrast, SE wines were significantly lower in ethyl butyrate than NW wines. Wines from the NW region were lowest in 3-hexen-1-ol, which gives a grassy aroma in wines. NC wines were highest in methyl hexanoate and methyl octanoate, while NC wines were lowest in these compounds. Methyl octanoate can provide a range of odors including orange, herbal, and vegetable. Wines from the NW region were higher in canned vegetable aroma and citrus flavor, and it is possible methyl octanoate could be contributing to panelists rating these attributes higher in NW wines.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## Materials and Methods  
### Patient Sample  
To validate the use of the lumbar drilling template, a clinical trial was performed at San Giovanni di Dio e Ruggi D'Aragona public hospital (Salerno, Campania, Italy). Twenty patients (8 women and 12 men) were enrolled randomly. The patient inclusion criterion was people who were waiting for vertebral stabilisation surgery for degenerative disease of the lumbar spine, including _de novo scoliosis_, with a maximum age of 70 years and a minimum of 50 years, without home pharmacological therapy.  
During the trial, thirty vertebrae were surgically treated by the traditional procedure, and thirty vertebrae were treated using the custom-made template. All vertebrae were in the lumbar spine except for a single patient whose vertebrae T10 and T11 were operated on, respectively, without and with the template.  
The segments on which to intervene using the template and on which to proceed in a traditional way were chosen randomly. The comparison between the two approaches took place in the same operative conditions (health status of the patient, presence of staff in the operating room, side effects, etc.).
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Disruptive Health Innovations
#### 3.5.4 Open Source EHR/EMR Systems  
The open source EHR or Electronic Medical record (EMR) is very crucial to improve the efficiency of present resource-constrained healthcare systems. Open source software is gaining attention for is adoption in the healthcare industry, which is helping to overcome several barriers (i.e., excessive cost, lack of interoperability and the transience of vendors). The fully-integrated and secure EMRs have various interesting features like practice management, electronic billing, scheduling, internationalization, patient demographics, medical reports and patient access portal. These features enable people to adopt EMRs that results in cost reduction and capacity and quality enhancement, and they especially lessen the disparities between wealthy and poor. Therefore, various open source task forces, scientists, organizations and research groups are offering open source platformsfor the compilation of EMRs. Examples include OpenEMR, OpenMRS, FreeMED, OSCAREMR, OSCARMcMasterand VistaA.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 2 Clustering Techniques
### Fuzzy Clustering  
Partition-based clustering, hierarchical clustering, and density-based clustering have one similarity, which is ensuring one data point belongs to only one cluster. These algorithms cannot handle overlapping clusters. Fuzzy clustering overcomes this problem by allowing each data point to belong to more than one cluster. Similar to partition-based clustering, the performance of fuzzy clustering is also sensitive to the centroids initialization. _Fuzzy c-means_ (FCM) clustering is discussed here as it is widely applied in web usage mining.  
FCM works by assigning each data point corresponding to each cluster centroid according to the distance between them. The shorter distance between the cluster centroid and data point will create a higher membership value of the data point to the centroid. The membership value refers to the degree of which the data point belongs to the cluster, ranging from 0 to 1. In the traditional fuzzy clustering, the centroid of a cluster is determined from the average of all the points weighted by their membership value. In _FCM_, the parameter, \(m\) determines the degree of fuzziness. The \(m\) value should be larger than 1. If \(m=1\), the resulting cluster is crisp, in which each data point belongs to only one cluster. With larger values of \(m\), the data point tends to belong to many clusters. Thus, a suitable \(m\) value should be selected to get optimal clusters [50; 51]. The basic steps are illustrated in Figure 8.  
Figure 8: Basic steps of _FCM_.  
Step 3. Calculate the value of centroid and update the membership value.  
Step 4. Repeat step 3 until the objective function is less than a threshold value.  
In [52; 53; 54; 55], the traditional _FCM_ is used to perform clustering in web usage mining. Over the years, the researchers improved fuzzy clustering through the initialization method and membership value assignment. The existing research attempted to improve the centroid initialization by using information entropy and genetic algorithm [56; 57]. To improve the membership value assignment, researchers also introduced an iterative version and penalty factor [58; 59]. Besides that, _FCM_ can also be used to improve other algorithms, such as the Kohonen clustering [60] and PageRank algorithm [61]. To improve the existing PageRank algorithm, both _FCM_ and information entropy are used to calculate the weight of the pages. The improved PageRank algorithm overcomes some of the weaknesses in the traditional algorithm, such as an excessive focus on historic pages, topic drifting, and indifferent link weights. [PERSON] et al. [60] improved the existing Kohonen clustering by integrating a modified learning rate to speed up the convergence. Not only that, the improved Kohonen clustering incorporates the merits of fuzzy theory and neural network to provide a way to handle an extensive amount of weblogs with high complexity and uncertainty, resulting in high-quality clusters.
## 5 Outer and Inner Loop Optimization Algorithm with Online Learning
### Inner Loop Optimization: A TD3-Based Algorithm for RIS Phase Shifting and Power Allocation
The update is performed as follows:  
\[\theta^{qi^{\prime}} =\tau\theta^{qi}+(1-\tau)\theta^{qi^{\prime}}\quad(i=1,2) \tag{28}\] \[a^{(t)}=\{\mathbf{\Theta},\{\mathbf{W}_{i}\}_{i\in N}\}\]0  
The analysis of the time complexity of the proposed algorithm is described in the following. **Initialization:** Initialization involves setting up neural networks and related parameters. The time complexity for initialization is typically constant, denoted as O(1). **Data Collection:** The first loop runs for N D2D pairs. Inside this loop, we collect data for T time steps. The time complexity for data collection is O(N \({}^{\star}\) T). **Training (Nested Loops):** There are nested loops within the data collection loop for the selection of actions, execution of actions, storage of experiences, and updating of networks. This loop involves operations that depend on the size of the mini-batch \(\mathcal{B}\). If the size of the mini-batch is B, the time complexity for the inner loops would be O(B). **Update Critic and Actor Networks:** The critic and actor networks are updated periodically based on the time step and the replay buffer size. The time complexity for updating networks depends on the specific operations in the update equations and the size of the neural networks. **Time Complexity Analysis:** The overall time complexity is influenced by the number of D2D pairs (N), the time steps (T), and the mini-batch size (B). The total time complexity is approximately O(N * T * B *...), where...represents the complexity of operations inside the loops and network updates. **Conditional Updates:** The conditional updates involving the actor policy and target networks depend on the time step and are performed periodically. The time complexity of these updates is \(O(T/T_{d})\). Executing the procedures detailed in Algorithm 2 leads to the maximization of the achievable energy efficiency within the communication scenario.
# Analysing and Computing the Impact of Geometric Asymmetric Coils on Transformer Stray Losses
## 2 Modelling and Considerations
### Stray Losses Modelling with FEM  
Analysing the stray magnetic flux \(\phi d\) and its density \(B_{d}\) along with the extra losses in the tank and structural components using the Finite Element Method (FEM) requires the computation of induced losses attributed to eddy current (\(P_{e}\)), excluding hysteresis. To achieve this, a model founded on the solution of the magnetic vector potential \(\mathbf{A}\) in the frequency domain [24,25] was used. The expressions for this model are as follows:  
\[\mathrm{j}\omega\sigma\mathbf{A}+\
abla\times\left(\frac{1}{\mu}\
abla\times A \right)-J_{e}=0, \tag{4}\]  
\[P_{e}=\frac{1}{2}Re\left[\sum_{i=1}^{n}(\rho_{i}J_{ei}{}^{*}J_{ei})V_{i}) \right], \tag{5}\]  
where variable \(n\) represents the number of elements. Each component contributing to the tank, including the tank walls and magnetic shunts, is characterised by a volume denoted as \(V_{i}\). Furthermore, \(\rho_{i}\) stands for the diagonal matrix incorporating the resistivity values specific to the materials constituting each element. Additionally, \(J_{ei}\) represents the induced current density vector resulting from the stray or stray magnetic field.
## 5 Communication Protocol Design
### PHY Layer Description
#### 5.1.3 Transmitter Design  
The DS-CDMA transmitter is associated with a low-complexity architecture that naturally lends itself to the flash FPGA-based SoPC found on the Marmote SDR platform. The MAC-level operations, such as generating the PHY payload and scheduling the transmissions, are handled by the microcontroller, and the assembled PHY payload is transferred to the FPGA fabric through the AMBA bus interface. The PHY-level operations are implemented entirely in the FPGA fabric, as shown in Figure 6. Transmission of a packet starts with prepending the PHY header to the PHY payload and passing the serialized binary data through a differential encoder. The encoded binary data is then spread by the locally synthesized PN sequence. As both the encoded data and the generated PN sequence consist of binary symbols, the multiplication reduces to the inexpensive binary exclusive or (XOR) operation, which is favorable, as the current flash FPGA fabric lacks hardware multipliers. The PN generator is a linear feedback shift register that produces a \(2^{11}-1\) long m-sequence at a fixed 2 MHz chip rate and features an adjustable generator polynomial and spreading factor. The spread binary symbols are mapped to symbols \(\{-1,1\}\) and filtered by a root-raised-cosine (RRC) pulse shaping filter before phase modulating the carrier.  
The corresponding FPGA resource utilization is coarsely summarized in Table 3. The entire DSSS transmitter path (not including the chip filter) takes approximately 14% of the available resources. The PN generator is responsible for 2% and the differential encoder, XOR multiplier and symbol mapper for  
Figure 6: Simplified block diagram of the DSSS-based code division multiple access (DS-CDMA) transmitter implemented on the Marmote software-defined radio (SDR) platform.  
less than 2% in total. Thus, similarly as in Section 4.2, the APB bus interface and configuration registers contribute most to the 14.1%. The chip filter, on the other hand, is a 31-tap (finite impulse response) FIR filter synthesized from a high-level design description with 16-bit coefficients. While it consumes 28.4% of the available total FPGA logic resources, the filter is a non-critical element, and its complexity could be greatly reduced without significant performance degradation.
# Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys
## 3 Results and Discussion  
### Chip Morphology  
Figure 5 shows the chip macro-geometrical evolution for each feed, for both tested alloys. It can be observed that a continuous chip was obtained in the lowest feed range (0.05 mm/r and 0.1 mm/r), whereas a segmented and shorter chip was obtained in the highest feed range (0.2 mm/r and 0.3 mm/r). Besides, a tendency to form chip nests was observed for \(f\) = 0.05 mm/r. It can be noticed that this evolution was similar in both UNS A92024 and UNS A97075 alloys. All of these observations can be explained by taking into account the increase in cutting force and compression stress, due to the increase of feed. The shear limit is easily reached when the highest feed values are applied and, as a result, a segmented chip is obtained. In addition, a lower value of chip thickness is obtained when a lower value of feed is applied. This makes a chip less rigid and chip nest formation is more likely. It should be pointed out that the observed behavior in both alloys is in good agreement with the results obtained in previous studies [(23; 31)].  
Figure 6 shows SOM images of the chip's longitudinal and transverse section, as a function of feed.  
Apparently, higher segmentation in a chip's longitudinal section is observed for higher \(f\) values. The chip longitudinal section images show a saw-teeth form of chip for \(f\) = 0.2 mm/r and \(f\) = 0.3 mm/r  
Figure 4: (**a**) Longitudinal and (**b**) transverse chip geometric parameters.  
Figure 5: Chip macro-geometrical evolution as a function of feed.  
The shear sliding effect is stronger for higher feed-rates, due to the fact that the shear limit is more easily reached.  
Furthermore, this effect is more noticeable when the feed increases from 0.1 up to 0.2 mm/r. These changes are less apparent once the shear strength limit is reached (from \(f\) = 0.2 mm/r up to \(f\) = 0.3 mm/r).  
In addition, this is more intense in the UNS A97075 alloy, than in the UNS A92024 alloy. This behavior can be explained by taking into consideration the high Zn content of the UNS A97075 alloy, which provides it with a higher plasticity. Thus, a chip of the UNS A97075 alloy is able to support a larger deformation before breaking. This is in good agreement with the data observed in Figure 5, where a slightly shorter chip is obtained in the UNS A92024 alloy for 0.2 mm/r and 0.3 mm/r.  
Finally, no substantial changes were observed in chip morphology as a function of axial machining length (\(L\)), as can be observed in Figure 7. In this figure, SOM images of the chip's longitudinal section of the UNS A92024 alloy as a function of \(L\) are shown, for some of the \(f\) values tested.  
In this regard, both alloys exhibited the same behavior. These observations are in good agreement with the evolution along \(L\), shown by each of the geometric parameters analyzed (which will be shown later).
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### FSO Communication--Spatial Profile Challenges
#### 2.3.2 Beam Centroid Positioning PAT System  
In long-range operations, such an interplanetary investigator with realism, e.g., the realistic interstellar explorer (RISE), FSOC optical systems are favored even for applications with a low data-rate downlink [55]. In applications such as up/downlink, and long-range terrestrial/inter-satellite/space FSO communications, to improve the consistency of a light ray centroid on the sensor plane, an accurate beam centroid PAT system being on board is mandatory. The typical accuracy of beam steering is \(\leq\)1 prad with a peak-to-peak range of \(\geq\)\(\pm\)10 mrad [56]. Since the installation of a PAT system is necessary, it has to be achievable according to size compactness, optimum operation, low weight, etc. It is important that the PAT system must be very accurate (with an accuracy \(\approx\)1 prad in both the axes). This can be attained by making use of micro-scale tip/tilt platforms which are available commercially, such as piezoelectric actuator (or micro electromechanical systems  
Figure 5: Aperture-averaged FSO communication receiver.  
(MEMS) actuator)-based fast-steering mirrors (FSM). Figure 6 shows the schematic of a PAT system's main electro-optic subsystems. An opto-electronic position detector (OPD) is a photodiode, which has a 4-Quadrant (4Q) arrangement and is used as the principal sensor for tracking in a PAT system. Bi-directional A/D converters are used to convert the error values of a beam centroid measured by the 4Q-OPD into digital data. A mono-pulse arithmetic circuit is used to compute the position error signals: x-axis tip-tilt (\(E_{x}\)), \(y\) axis tip-tilt (\(E_{y}\)) and sum (\(\Sigma\)) as [56]  
\[E_{x}=E_{tilt}=\frac{(A+D)-(B+C)}{\Sigma}, \tag{12}\]  
\[E_{y}=E_{tip}=\frac{(A+B)-(C+D)}{\Sigma}, \tag{13}\]  
where \(\sum\) denotes the summation of the receiving signals from all the four quadrants as \((A+B+C+D)\), which normalizes \(E_{x}\) and \(E_{y}\) as in Equations (12) and (13). The angle of arrival (AOA) and the position of the laser spot are computed using the total energy and the error position values [57].  
A digital signal processing (DSP) controller is built in the system which uses the information of the centroid position of the beam (\(E_{x}\) and \(E_{y}\)) to generate control data that steers and steadies the arriving beam to center it on the detector plane. The FSM obtains the control data via a digital-to-analog converter (DAC) and a high-power amplifier. To attain high accuracy (in terms of pad) in beam steering, a tri-piezoelectric-actuated platform (or equivalent MEMS platform) is used and a steering mirror is mounted on it [58]. The displacement of the beam's center is evaluated using a 4Q-OPD and adjusted with a confined controller, resulting in beam steadiness at the detector's center being refined [59].
## 3 Public Datasets for Scene Understanding Methods
### Solutions to Handle Dataset Imbalance  
With the knowledge that class imbalance affects learning performance, application-oriented solutions can be discussed. Different techniques for classical machine learning have been published to compensate for the immense hindering effects that class imbalance has on standard learning algorithms. These techniques generally approach the problem from one of two sides. They either try to change the condition of the class composition on the dataset side (active) or keep the dataset unchanged and adjust training or inference on the algorithm side (passive). Techniques that combine data-based and algorithm-based adjustments can be summarized as hybrid approaches [90]. Active techniques (basic concept depicted in Figure 8), which change the class distribution, include oversampling the minority classes, undersampling the majority classes, and a combination of the two methods. A collection of frequently used sampling techniques and their approach to the imbalance problem is given in Table 2. These techniques alter the dataset to make standard training algorithms work. Active sampling techniques are well developed. A detailed review of various oversampling and undersampling techniques is given by [91; 94]. [PERSON] et al. [95] published an open-source Python toolbox including a wide range of state-of-the-art methods to cope with the problem of imbalanced datasets. Figure 7: Quantitative evaluation of the [PERSON] dataset configuration [20]. (**a**) Test and training data split partitioned by semantic classes. Numbers show the ratio between the testing and training split. (**b**) Class distribution available in the dataset comparing instances vs. raw per-point segments. (**c**) Exemplary confusion matrix to represent the average precision of semantic labeling per segment from a support vector machine (SVM) model. Axis flipped for the unified convention. Published by [PERSON] et al. [20]. **Undersampling** the majority class is a common first choice as this can help reduce the overall storage size and training time, which can be beneficial for large datasets and slow classifiers. However, undersampling carries the inherent risk of losing potentially useful information from the majority class and the risk of creating biased data selection. The sample might not accurately represent the real world, causing the model to output
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 5 Behavior Simulation
### Performance Comparison with Other Structures
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Routing Set (N)**} & \multirow{2}{*}{\(M_{b}\) (KB)} & \multicolumn{2}{c}{**BF-Chaining**} & \multicolumn{2}{c}{**PBF**} & \multicolumn{2}{c}{**VBF**} \\ \cline{3-10}  & & \(A_{h}\) & \(W_{h}\) & \(A_{h}\) & \(W_{h}\) & \(A_{h}\) & \(W_{h}\) \\ \hline \multirow{4}{*}{1000} & 1 & 2.385 & 9 & 1.170 & 5 & 0.203 & 5 \\  & 2 & 1.633 & 9 & 1.004 & 2 & 0.004 & 3 \\  & 4 & 1.153 & 4 & 1.000 & 1 & 0.000 & 0 \\ \hline \multirow{4}{*}{5000} & 8 & 1.565 & 10 & 1.052 & 3 & 0.024 & 4 \\  & 16 & 1.140 & 4 & 1.001 & 2 & 0.000 & 0 \\  & 32 & 1.015 & 3 & 1.000 & 1 & 0.000 & 0 \\ \hline \multirow{4}{*}{14,553} & 16 & 1.784 & 12 & 1.491 & 6 & 0.110 & 5 \\  & 32 & 1.335 & 6 & 1.054 & 4 & 0.003 & 5 \\  & 64 & 1.046 & 4 & 1.001 & 2 & 0.000 & 0 \\ \hline \multirow{4}{*}{30,000} & 32 & 1.619 & 13 & 1.322 & 6 & 0.124 & 6 \\  & 64 & 1.160 & 6 & 1.037 & 3 & 0.004 & 6 \\ \cline{1-1}  & 128 & 1.019 & 3 & 1.001 & 2 & 0.000 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison on the number of off-chip hash table accesses.