esa-sceva/satcom-chunk-collection huggingface dataset:
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 3 3 GPP's V2X Architecture  
3 GPP initially introduced the V2X architecture in Release 14 [21]. Apart from the main 4G network components, 3 GPP has defined two additional functional entities, namely the V2X Control Function and the V2X Application Server (V2X AS). The former is the logical function that is used to control and provide the User Equipment (UE) with parameters that are required to access V2X communications. The primary service of the V2X AS is to receive UL data from the UE over unicast and deliver the data to the target UEs over unicast, multicast, or broadcast. In addition to the functional entities, ref. [21] defines the basic UE reference points for V2X communications, which are the PC5 a.k.a. sidelink for Modes 3 and 4 and the LTE-Uu. The PC5 reference point is used for Proximity Services (ProSe) direct communication and the LTE-Uu is the air interface between UE and the Evolved Universal Mobile Telecommunications System Terrestrial Radio Access Network (E-UTRAN). 5 GAA has analyzed the 4G architectural options and proposed architectural recommendations for the V2X communications [22]. One of them was the introduction of the Common Cloud Entity (CCE). CCE can distribute the PC5 parameters to the V2X Control Functions and the Cloud Entities (i.e., Proprietary Cloud Entity (PCE)) of the various stakeholders, which, upon their turn, will provide the PC5 configuration to the UE. The interface between the CCE and the PCE has to be standardized according to the recommendation of the 5 GAA; however, this action has not yet been performed. The 5G cellular system has improved the Radio Access Network (RAN)--the so-called 5G-NR--in terms of an increased throughput, reduced delay in the control and user planes, and more sophisticated support of QoS. It is definitely more suitable for supporting the most demanding V2X UCs. However, the overall system is not complete as parts of the picture are still missing (e.g., multicasting and broadcasting in 5G networks over the Uu interface). Figure 1 presents the 5G Architecture for V2X communications comprising information from [23; 24]. In [23], the following network functions further support the V2X communication:  
* The Policy Control Function (PCF) is responsible for providing authorization and policy parameters and retrieving V2X information from the Unified Data Repository (UDR);
## 4 Multi-Scale High-Dimensional Mapping Entropy (MHDME)
### Simulation Comparative Analysis  
To investigate the law of variation and measure its robustness, white noise is used as an example. The time-domain waveform diagram and spectrum diagram of white noise with data length \(N=3000\) and noise intensity of 0.3 are shown in Figure 7. What can be concluded from Figure 7 is that the white noise remains constant in energy and complexity in each frequency band.  
The multi-scale high-dimensional mapping entropy (MHDME) and fine composite multi-scale high-dimensional mapping entropy (RCMHDME) with multi-scale sample entropy (MSE) and refined composite multi-scale sample entropy (RCMSE) methods were used for complexity feature extraction analysis of Gaussian white noise signals, respectively. The parameters were set as follows: dimension \(m=2\), similarity tolerance \(r=0.15\) SD, time delay \(\lambda=1\), and data length \(N=3000\). Entropy was computed for under 50 datasets, followed by plotting the respective error bar graphs. The outcomes are presented in Figure 8.  
Compared with MSE and RCMSE, the MHDME and RCMHDME methods proposed in this paper have smaller error bars. Consequently, the accuracies of MHDME and RCMHDME are superior, yielding measurements that more closely approximate true values with reduced deviation. Results derived from the MHDME and RCMHDME methods demonstrate high consistency, exhibit resistance to random error, and present improved stability. Notably, the RCMHDME method displays minimal entropy fluctuation at larger scales, signifying that the algorithm's performance remains more uniform and consistent across various time scales. The entropy of the original multi-scale algorithm produces  
Figure 7: Time-domain waveform and normalized spectral diagram of the white noise signal. (**a**) Time-domain diagram of white noise signal. (**b**) Spectrum diagram of white noise signal.  
slight fluctuations at some scales, and the refined composite multi-scale entropy has smaller errors and better smoothness at a wide range of scales. Therefore, it has been proven that the high-dimensional mapping entropy algorithm proposed in this paper is more accurate and smoother.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods
### Synthesis
#### 2.1.1 3-(2-Oxobenzo[d]oxazol-3(2\(H\))-yl)propanehydrazizide (**3**)  
To a solution of ester **2** (9 g, 6.5 mmol) in propan-2-ol (30 mL) hydrazine hydrate (3.77 g, 75.3 mmol) was added dropwise, and the mixture was heated under reflux for 5 h. The crystallization of the product took place during the reaction. After the completion of the reaction, the reaction mixture was left in the refrigerator overnight, and then the formed precipitate was filtered off, washed with cold propan-2-ol, and recrystallized from propan-2-ol.  
Yield 8.25 g (91%), mp 185-190 \({}^{\circ}\)C;  
\({}^{1}\)H-NMR (400 MHz): \(\delta\) 2.50 (t, 2H, \(J\) = 6.8 Hz, CH\({}_{2}\)CO), 4.02 (t, 2H, \(J\) = 6.8 Hz, CH\({}_{2}\)N), 4.16 (br s, 2H, NH\({}_{2}\)), 7.12 (t, 1H, \(J\) = 7.7 Hz, H\({}_{\rm{Ar}}\)), 7.21 (t, 1H, \(J\) = 7.7 Hz, H\({}_{\rm{Ar}}\)), 7.26 (d, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 7.32 (d, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 9.11 (s, 1H, NH);  
\({}^{13}\)C-NMR (101 MHz): \(\delta\) 31.64, 38.58 (CH\({}_{2}\)CO, CH\({}_{2}\)N), 109.49, 109.55, 122.12, 123.85, 130.95, 141.93, 153.57, 168.83 (Car, CO);  
IR (KBr): \(\
u_{\rm{max}}\) = 3332, 3315 (NH\({}_{2}\), NH), 1746 (C=O) cm\({}^{-1}\).  
Anal. Calcd for C\({}_{10}\)H\({}_{11}\)N\({}_{3}\)O\({}_{3}\) (221.22), %: C, 54.30; H, 5.01; N, 19.00. Found: C, 53.10; H, 4.77; N, 18.80.
## 4 Mathematical Formulations
### Simultaneous Pickup and Delivery Vehicle Routing Problem: MILP 1  
This section models the Simultaneous Pickup and Delivery Vehicle Routing Problem variant (SPDVRP). In SPDVRP, the operations of pickup and delivery are performed simultaneously at each customer node. Each customer is visited once, their order (in terms of goods) is all delivered, and all boxes are picked up. The variables and parameters of the proposed model are as follows:  
* **Indices and parameters**:
* \(n\): number of customers (\(i=1,\dots,n\)). * \(m\): number of vehicles (\(i=1,\dots,m\)). * \(d_{i}^{(w)}\): customer \(i\) demand in \(Kg\). * \(d_{i}^{(c)}\): volume demand of customer \(i\) in terms of number of boxes. This parameter is deduced from the following relation: \(d_{i}^{(c)}=\frac{d_{i}^{(w)}}{a_{i}}\). * \(\alpha_{i}\): average weight of a full loaded box dedicated for a customer \(i\). * \(Sc_{i}^{ini}\): initial balance of boxes that exists at the store of a customer \(i\). \(\beta\): weight of an empty box. \(t_{ij}\): travel time between two customers \(i\) and \(j\). \(TS_{u}\): unitary time service expressed in minute per box. \(TS_{f}\): fixed service time. \(TS_{j}\): total service time at a customer \(i\). \(TC_{j}\): time necessary to load each vehicle at the depot. \(TPC_{u}\): unitary time needed to prepare the boxes expressed in terms of minute per box. \(T_{max}\): maximum duration of a tour. \(Q_{k}^{(w)}\): weight capacity of a vehicle \(k\) expressed in \(numberofboxes\). \(Q_{k}^{(c)}\): volume capacity of a vehicle \(k\) expressed in \(Kg\). \(C_{ij}\): travel cost from a customer \(i\) to another \(j\). **- Decision variables**:  
\(W_{j}\): weight of the vehicle load before leaving a customer \(j\). \(W_{(0,k)}\): weight of goods at the depot for each vehicle \(k\). \(C_{j}\): number of boxes before leaving a customer \(j\). \(C_{(0,k)}\): number of boxes loaded at the depot for a vehicle \(k\). \(U_{j}\): variable used for sub-tours elimination. \(x_{ij}^{k}=\left\{ \begin{array}{l}1\;if\;the\;vehicle\;travels\;from\;i\; to\;j.\\ i,j=1,...,n(i\
eq j);\forall k=1,...,m\\ 0\;otherwise\end{array}
## 5 Conclusions  
To sum up, we compared the different configurations of a smart grid pilot in the Netherlands in order to identify the demand-supply balance of the configurations. Energy consumption and energy production is classified in three groups: low rate, normal rate, and energy self-consumption. The weekly purchase, PV output, and \(\upmu\)-CHP power generation is shown to highlight the seasonal complexity. The impact on the energy bill is discussed in the previous section, as well as the flexibility provided by smart appliances and heat pumps, which together corresponds to 14.4% of the electricity consumption for an average household.  
In conclusion, \(\upmu\)-CHP might be a good solution for northern countries such as the Netherlands to provide heat and electricity when PV infeed is weak. The installation costs and the complexity to integrate this kind of equipment in existing buildings, as well as the insulation class of the household might be the barriers in those configurations. Regarding flexibility contributions, we support the findings of the social scientists that cleaning practices are potentially highly flexible for residential consumptions, which we demonstrated in this work to be as flexible as their usage time across the whole year.  
Writing--original draft preparation, formal analysis, investigation and algorithms, C.G.; conceptualization and validation, C.G. and A.R.; resources, review, editing, A.R. and C.G.; supervision, project administration, data and funding acquisition, A.R.  
**Funding:** This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ERA-Net Smart Grids plus, grant number 646039, from the Netherlands Organisation for Scientific Research (NWO).  
**Acknowledgments:** Our project has received funding in the framework of the joint programming initiative ERA-Net Smart Grids Plus, with support from the European Union's Horizon 2020 research and innovation programme. Furthermore, we would like to acknowledge all participants in the smart grid pilot (PowerMatching City) involved in this study for their willingness to share their data, experiences, and knowledge with the researchers.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Disclaimer:** The content and views expressed in this material are those of the authors and do not necessarily reflect the views or opinion of the ERA-Net SG+ initiative. Any reference given does not necessarily imply the endorsement by ERA-Net SG+.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 3 Methodology
### Baseline Models
#### 3.3.2 Mask R-CNN (R50-FPN) Architecture  
Mask R-CNN [23] is an extension of Faster R-CNN, with the main difference being the inclusion of an additional output branch for generating object masks. While Faster R-CNN outputs class labels and bounding-box offsets, Mask R-CNN also generates binary masks for each region of interest (ROI). The procedure for Mask R-CNN (R50-FPN) [28] is similar  
Figure 4: The architecture of Faster R-CNN (R50-FPN) consists of 3 stages: (1) Backbone Network, (2) Region Proposal Network (RPN), (3) Box Head, and Fast R-CNN for object identification. Mask R-CNN (R50-FPN) employs the same 3-step procedure as Faster R-CNN (R50-FPN), but also includes a binary mask produced for each ROI in addition to class and box offset predictions in the third stage.  
to that of Faster R-CNN (R50-FPN), with the first two stages being the backbone network and RPN. In the third stage, in addition to class and box offset predictions, a binary mask is generated for each ROI. This allows for more precise spatial arrangement of objects, as it involves pixel-to-pixel alignment.  
Mask Representation: Mask R-CNN [23] predicts binary masks for objects in an image by using a fully convolutional network (FCN). The FCN generates an m x m mask for each region of interest (ROI), preserving the pixel-to-pixel correspondence through convolutions. This allows for precise extraction of the object's spatial structure. The RoIAlign layer in Mask R-CNN helps maintain the accuracy of the small RoI features by aligning them with the input, leading to better mask prediction performance. The RoIAlign layer is crucial for accurate mask prediction and ensures per-pixel spatial correspondence.  
RoIAlign: The RoIAlign layer in Mask R-CNN improves the accuracy of the features extracted from regions of interest (RoIs) compared to the standard RoIPool operation. RoIPool quantizes the RoI to the granularity of the feature map and divides it into spatial bins, which can result in inaccuracies. RoIAlign, on the other hand, aligns the retrieved features with the input, removing the harsh quantization introduced by RoIPool and providing improved alignment. This improved alignment allows for more accurate bounding box regression, resulting in better object detection performance compared to models like Fast R-CNN.
## 3 Results
### Characteristics of the Study Sample  
A total of 48 patients (12 male and 36 female) with a mean age of 19.79 \(\pm\) 11.78 years were included. The total group was divided into growing and non-growing subgroups. The growing group included 30 patients (19 male and 11 female) with a mean age of 13.5 \(\pm\) 2.08 years. The non-growing group included 18 patients (1 male and 17 female) with a mean age of 30.28 \(\pm\) 13.79 years. All included patients were treated using Invisalign with ClinCheck software where treatment mechanics did not include surgery or extractions. Throughout the Invisalign treatment, the overall mean number of refinements was 7.44 \(\pm\) 2.31; the growing group had 6.87 \(\pm\) 1.74, and the non-growing group had 8.39 \(\pm\) 2.85 refinements (Table 1).  
The ClinCheck-predicted tooth movement characteristics included showed an overall mean lower incisor intrusion of 2.32 \(\pm\) 1.82 mm; the growing group was 2.27 \(\pm\) 1.16 mm, and the non-growing group was 2.4 \(\pm\) 1.24 mm. The overall lower incisor change in inclination was 4.6 \(\pm\) 6.3 degrees; the growing group was 4.27 \(\pm\) 6.25 degrees, and the non-growing group was 5.13 \(\pm\) 6.25 degrees (Table 2).  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Growing + Non-Growing**} & \multicolumn{3}{c}{**Growing**} & \multicolumn{3}{c}{**Non-Growing**} \\ \cline{2-11}  & **N** & **\%** & **N** & **\%** & **N** & **\%** & **N** & **\%** \\ \hline Gender & & & & & & & & & \\ \hline Male & 12 & 25\% & 11 & 36.67\% & 1 & 5.9\% & \\ \hline Female & 36 & & 75\% & 19 & 63.33\% & 17 & 94.4\% & \\ \hline \multirow{2}{*}{Age (years)} & Mean & SD & Median & Range & Mean & SD & Median & Range & Mean & SD & Median & Range \\ \cline{2-11}  & 19.79 & \(\pm\)11.78 & 15 & 64 & 13.5 & 2.08 & 14 & 8 & 30.28 & \(\pm\)13.79 & 24 & 54 \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Refinements \\ (number of \\ ClinChecks per patient) \\ \end{tabular} } & \multirow{2}{*}{7.44} & \multirow{2}{*}{\(\pm\)2.31} & \multirow{2}{*}{6.87} & \multirow{2}{*}{\(\pm\)1.74} & \multirow{2}{*}{8.39} & \multirow{2}{*}{\(\pm\)2.85} & \multirow{2}{*}{} \\  & & & & & & & & & \\ \end{tabular}
\end{table}
Table 1: Baseline demographic characteristics among study groups.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ClinCheck-Predicted Movements**} \\ \hline \multicolumn{3}{c}{**Growing + Non-Growing**} & \multicolumn{3}{c}{**Growing**} & \multicolumn{3}{c}{**Non-Growing**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Intrusion & & & & & & & \\ (centroid-MP) & 2.32 mm & \(\pm\)1.82 & 2.27 & \(\pm\)1.16 & 2.4 & \(\pm\)1.24 \\ \hline L1 \(\Delta\)\({}^{\circ}\)\({}^{\star}\) & 4.6\({}^{\circ}\) & \(\pm\)6.3 & 4.27\({}^{\circ}\) & \(\pm\)6.25 & 5.13\({}^{\circ}\) & \(\pm\)6.52 \\ \hline \hline \end{tabular}  
\({}^{\star}\)Lower incisor inclination change. MP: mandibular plane, L1 \(\Delta\)\({}^{\circ}\): lower incisor inclination change.  
\end{table}
Table 2: Initial ClinCheck prediction descriptives for sample study groups.
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 5 Analysis
### Testing of Different PAMs
The accuracy is dependent on the competing effects of attempting to reduce the angle-of-view by distancing the camera from the PAM, while also keeping the distance at a minimum to maximize measurement resolution. For the 22.2 mm diameter PAMs, the camera was setup at the distance of 152 cm (60 in) for the shorter 236 mm length PAM, and it was placed 7.6 cm (3.0 in) farther away for the longer 508 mm PAM to capture its entire length. Those two camera placements resulted in angle-of-views of 8.84 and 18.04 degreesl, respectively, and measurement resolutions of 8.52 pixels/mm (216.35 pixels/in) and 8.92 pixels/mm (226.63 pixels/in), respectively. Both tests achieved a high degree of accuracy despite the slight changes in resolution and angle-of-view. Comparing the contraction measurements of the MTS machine and photogrammetric data, as performed in Figure 12b for the 236 mm PAM, resulted in a maximum error of 0.61 mm (0.042 in) (0.21%) for the 508 mm long PAM. This is in comparison to the 0.60 mm (0.024 in) (0.25%) error previously cited for the 236 mm PAM. Similarly, measurement of the 57.2 mm diameter PAM achieved results with a maximum error in the length contraction measurement of 1.3 mm (0.05 in) (0.36%). It is assumed that even with the larger diameter of this 57.2 mm PAM, its diameter is still significantly smaller than the length measurement that is used here to cap the maximum expected error in the measurement. The accurate characterization of the shape profiles of the four PAMs demonstrates that the photogrammetric methods presented in this research can be readily adapted to PAMs of different length and diameter scales. A new method of experimentally characterizing the volume of PAMs has been presented that can be performed without the need for specialized test equipment, and can be easily adapted for the measurement of PAMs of different sizes.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Proteins
#### 2.2.2 Silk Fibroin  
Silk fibroin is composed of a collection of proteins which originate from silkworms and spiders. Silk consists of a crystalline structure (fibroin) and an amorphous protein (sericin) [96]. Silk is a naturally occurring protein that is thought to be a beneficial biomaterial for creating DDSs [97]. Silk has multiple advantages (e.g., biocompatibility, biodegradability, non-toxic degradation products, a versatility of options for sterilization, soft aqueous processing that keeps the medication's bioactive qualities, mechanical stability, and self-assembly properties), which are applicable for developing sustainable DDSs [98]. Also, multiple preparation methods are used to obtain silk delivery system applications, (microspheres, lyophilized sponges, silk-coated polymeric particles, nanoparticles, hydrogels, films, and nanofibers) [99].  
To reduce the medication's dosage and adverse effects on healthy tissues, studies are developing DDSs in which silk is combined with other polymers or nanoparticles. For example, the drugs _cis_-dichloro diamino platinum and Paclitaxel, inserted in silk nanoparticles, reduced tumor growth and efficiency. More than that, due to the silk's pH sensitivity, DDSs based on silk are a suitable alternative because drug release kinetics appear regularly at low pH values [100]. A silk fibroin-polymethacrylate copolymer coating was recently developed for oral dosage forms. In vitro and in vivo research showed that the capsules coated with the newly developed silk fibroin formulation facilitate pancreatitis-dependent drug release. This novel formulation and its extensions demonstrated the ability to produce more effective and tailored DDSs for sensitive patients that have affected and variable intestinal physiology [101]. Other research presents a silk fibroin hydrogel used as a carrier for vincristine in [PERSON] tumors and the ultrasound as a method to accelerate the release of the drug. The study demonstrated that the ultrasound started increasing cell death rates, but the [PERSON] tumor cells were resistant to higher concentrations of released drugs [102].
# Design and Development of Hugging Mechanism for Capturing Chest and Back Auscultatory Sounds
## 2 Materials and Methods
### Articulated Arm with Multi-Layer Gear Coupling  
The role of the articulated arm is to wrap around the chest and back to accommodate individual differences in body shape. Therefore, it is necessary to be able to flexibly change the arm's posture to fit various body shapes. The articulated arm consists of multiple layers of connected gears to provide high precision, smooth motion, and enhanced torque transmission. In each layer, the gears are connected by an odd number of gears. The adjacent gears are connected, so the rotation angle of the gear at the root is synchronized with that of the gear at the tip. Figure 3 shows a conceptual illustration of the articulated arm with multi-layer gear coupling. In Figure 3, the power transmission for each color gear corresponds to the rotation of each color link. For example, when rotating the yellow gear (rightmost) at the root, only the yellow link at the tip rotates, while when rotating the blue gear (second from left) at the root, only the blue link at the tip rotates. Thus, the angle of rotation of each link can be controlled by the angle of rotation of the gear at the root of each layer. Note that the motion of the articulated arm can be observed in a Video S1 in the Supplementary Material. The developed articulated arm has a five-layer gear coupling, as shown in Figure 4. The position coordinates of each joint (\(x_{i}\), \(y_{i}\)) are obtained by the following equations:  
Figure 2: Components of (a) hugging mechanism: (b) articulated arm with multi-layer gear coupling and (c) cam mechanism for power transmission.  
\[x_{1}=L_{1}cos(\theta_{1}) \tag{1}\] \[y_{1}=L_{1}sin(\theta_{1})\] (2) \[x_{2}=x_{1}+L_{2}cos(\theta_{1}+\theta_{2})\] (3) \[y_{2}=y_{1}+L_{2}sin(\theta_{1}+\theta_{2})\] (4) \[x_{3}=x_{2}+L_{3}cos(\theta_{1}+\theta_{2}+\theta_{3})\] (5) \[y_{3}=y_{2}+L_{3}sin(\theta_{1}+\theta_{2}+\theta_{3})\] (6) \[x_{4}=x_{3}+L_{4}cos(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4})\] (7) \[y_{4}=y_{3}+L_{4}sin(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4})\] (8) \[x_{5}=x_{4}+L_{5}cos(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4} +\theta_{5})\] (9) \[y_{5}=y+L_{5}sin(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4}+ \theta_{5}) \tag{10}\]  
\(L_{1}\)-\(L_{5}\) and \(\theta_{1}\)-\(\theta_{5}\) represent each link length and the rotational angle of the gear at the root of each layer. In this study, all the link lengths are fixed at 120 mm.  
Resin gears are used to reduce the weight of the arm. The reason for this is that the rotation speed is unnecessary, and the load capacity of the resin gears is sufficient for this case. Meanwhile, the gears fixed to the links are made of stainless steel (SUS304) because of the keyway required. Resin spacers are also inserted between each gear to reduce resistance between the gears in the layer direction.  
Figure 3: Multi-layer gear motion of articulated arm. The rotation of the root gear can deliver the rotation of each joint angle independently. Each color gear corresponds to each color rotating joint.