esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
## 2 Proposed Matryoshka Design
As a first approach, the matryoshka geometry's resonant frequencies (\(f_{res1}\) and \(f_{res2}\)) can be estimated by [20; 21]  
\[f_{res1}(GHz)=\frac{0.3}{L_{ef1}\sqrt{\varepsilon_{ref}}}, \tag{1}\]  
and  
\[f_{res2}(GHz)=\frac{0.3}{\left(\frac{L_{ef2}}{2}\right)\sqrt{\varepsilon_{ref}}}, \tag{2}\]  
with  
\[L_{ef1}=3(L_{1}-2w_{1})+2(L_{2}-2w_{2})+3(L_{3}-2w_{3}), \tag{3}\]  
\[L_{ef2}=3L_{1}+2L_{2}+3L_{3} \tag{4}\]  
and  
\[\varepsilon_{ref}=\frac{\varepsilon_{ref-CPW}+1}{2}. \tag{5}\]  
The variables \(L_{ef1}\), \(L_{ef2}\), and \(L_{ef3}\) are the effective length of each ring used to calculate the resonant frequencies and the variables \(\varepsilon_{ref}\) and \(\varepsilon_{ref-CPW}\) are the effective relative dielectric constant for the microstrip and for a coplanar waveguide, respectively. The latter is obtained by using numerical integration software like APPCad [42] with the values of the dimensions and materials used. The FSSs proposed in this paper were manufactured using sticker printing, in which an array of the unit cells with the designed geometry is printed into a metallic surface of the FR-4 substrate plate and then corroded by iron perchloride. The iron perchloride removes the metallic surface from the plate, except on the parts covered by the sticker printing. The FSSs are presented in Figure 5 and contain 8 \(\times\) 8 cells. For the cascaded configuration, the FSS layers are positioned on top of each other, as shown in Figure 6. The layers are spaced by acrylic supports that make it possible to adjust the \(g\) spacing between them, as shown in Figure 7. It is possible to see that the second FSS is the negative geometry of the first FSS; thus, the metallic surfaces of the two layers do not overlap when cascaded. The acrylic supports and FSSs are fixated by two iron screws of 1 cm diameter and 25 cm length and attached to the measurement window. The screws press the FSS and support so there is no additional gap between layers, except the spacing provided by the acrylic support. This fixation method also aligns the unit cells of both FSSs. Figure 5: Fabricated FSSs: (**a**) shows the FSSs with matryoshka geometry, and (**b**) shows the FSS with inverted matryoshka geometry.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN  
In this section, we review ECS introduced for WSN in the literature. We broadly classify ECS introduced for WSN in three areas: FEC, adaptive techniques and other techniques. First, we will review FEC only followed by adaptive techniques and other techniques.
## 5 Performance Analysis
### Performance Measurements
Therefore, the utilization percentage for both eMBB and mMTC requests can be defined as: \[U=\sum_{\forall S}\frac{i+j+k}{C}\pi_{i,j,k,m,n}\] (11)
5. The average delay (**D**) for both eMBB and mMTC services: each service type has its own queue, and we can consider D as the average delay time for each queued request until it is served. In order to calculate D, we need to know the queue length for both queues 1 QeMBB and 2 QmMTC. Using Little's laws, we divide the queue length of each service by its arrival rate. Let us assume that L\({}_{1}\) and L\({}_{2}\) are the queue lengths for eMBB and mMTC, respectively. Therefore, the average delay for eMBB (D\({}_{1}\)) can be calculated as follows: \[D_{1}=\frac{L_{1}}{\lambda_{2}}\] (12)where \(L_{1}\sum_{0\leq m\leq M}m\pi_{i,j,k,m,n}\). In the case of mMTC services, the average delay (D\({}_{2}\)) is calculated as follows: We will include the interrupted connection with its total arrival rate since there will be interrupted requests of type mMTC, shown as feedback, by giving the interrupted request another chance to be serviced in C-RAN. Hence, when we calculate D\({}_{2}\), we need to add the interrupted connection rate to the mMTC arrival rate. Here, RInterrupt represents the interrupted rate of mMTC. Therefore, D\({}_{2}\) can be written as:  
\[D_{2}=\frac{L_{2}}{\lambda_{3}+R_{Interup}} \tag{13}\]  
where \(L_{2}=\sum\limits_{\forall S}n\pi_{i,j,k,m,n}\)_and_\(R_{Interup}=\sum\limits_{ \begin{subarray}{c}i+j+k=C\\ >c<C\end{subarray} }\frac{\lambda_{1}k}{C-p}\pi_{i,j,k,m,n}\). \(0\leq n\leq N\)  
This equation represents the average delay of mMTC. Using the little theorem, this average can be calculated by dividing the average mMTC queue size (L\({}_{2}\)) by the total mMTC arrival rate. The total arrival rate includes the interrupted mMTC that is inserted back into its queue. Using the system states and the steady state probabilities, this term has the following form:  
\[R_{Interup}=\sum\limits_{\forall S}\frac{\lambda_{1}k}{C-p}\pi_{i,j,k,m,n} \tag{14}\]  
The term L\({}_{2}\) can be found by adding the average number in the queue for all the system states that have queued requests. Using the system states and the steady state probabilities, this term has the following form:  
\[L_{2}=\sum\limits_{\forall S}n\pi_{i,j,k,m,n} \tag{15}\]
## 2 Description of the Proposed System  
The proposed infrared positioning system is based on a set of four LEDs (at 3D positions \(b_{i}\)), located at known points in the environment, as well as a receiver and a synchronization module. The overall system is shown in Figure 1.  
The system consists of a synchronism system (a special beacon), which emits a 455 kHz IR carrier during 1 ms, periodically every 0.5 s. This signal is received by each one of the beacons, as well as by the reception module. When a synchronization pulse is captured by both systems, transmissions and acquisition start simultaneously. Note that, as the speed of the light is very high, the transmissions from the beacons and the corresponding acquisition at the receiver happen at the same time for practical purposes. After the signal is acquired, it is transmitted through an USB port to a computer, where it is processed to estimate the position of the receiver. This procedure allows the system to update the receiver position every 0.5 s.  
Figure 1: General diagram of the proposed positioning system.  
At the first stage, the received signal is processed to obtain the central points (\(x_{ri},y_{ri}\)) of incidence from the transmitters on the surface of the QADA, where \(I=\{1,2,3,4\}\) is the order of the transmitter. This procedure is based on the correlation of the received signal with the transmitted codes \(c_{i}\). The resulting correlation peaks are used to estimate the signal level from each transmitting beacon \(b_{i}\). In [20], the reader can find out how to obtain the central point of incidence for each transmitted code \(c_{i}\) on the surface of the QADA by means of the non-linear least squares (NLS) and Gauss-Newton minimization algorithms. Afterwards, the corresponding algorithm detects the rotation of the receiver around the \(Z\) axis, and continues with the estimation of the final coordinates (\(x,y,z\)) of the receiver position using a least squares estimator (LSE) and some trigonometric considerations, as can be also observed in [20,21].  
The following sections include a detailed description of each module used in this proposal, separately.
## 3 Results
### Transport of Cells
#### 3.2.2 Transport of Cells with an Obstacle  
We performed cell transportation with an obstacle in the workspace. To avoid colliding with the obstacle, the target cell was transported along a collision-free path as shown in Figure 12a-d. When the cell reached the waypoint in the path, the microtool rotated around the cell center to adjust its direction. After turning to the new direction, the microtool continued to move forward. Figure 12e illustrates absolute cell position errors, which are less than 2.5 \(\upmu\)m. Notably, although the microtool rotated around the cell center and no push forces were exerted on the cell from 8 to 17 s, dynamic position errors still occurred. This phenomenon is mainly induced by the Brownian motion of the cell and fluctuation of the fluid around it.
## 3 Results  
### Effect of Contact Time, pH, Adsorbent Dose and Lactic Acid Concentration on Lactic Acid Adsorption  
The contact time experiments showed that the adsorption reached its equilibrium between 2 and 6 h with lactic acid adsorbed onto GAC (29 mg lactic acid/g adsorbent), RLX425 (18 mg lactic acid/g adsorbent) and AMB400 (10 mg lactic acid/g adsorbent) from an initial concentration of 4.5 g lactic acid/L (Figure 1a). From this batch experiment, a contact time of 4 h was selected for the subsequent experiments. The experimental results confirmed that both GAC and RLX425 show selectivity at a pH (2.0) below \(pK_{a}\) (3.86) value of the lactic acid and AMB400 at a pH (5.0) above the \(pK_{a}\) value (Figure 1b). The adsorption of lactic acid depends on the degree of ionization of lactic acid in the bulk solution, which was calculated from the relation between the \(pK_{a}\) value and the pH of the solution. The degree of ionization was found to be 13.5%, 18.1%, 24.2% and 6.7% at pH 2.0, 2.35, 5.0 and 6.5, respectively. Figure 1c shows the lactic acid recovery efficiency with different adsorbent dose in the range of 5% to 30% (\(w/v\)). The increase in GAC dose from 5% to 30% (\(w/v\)) increased the lactic acid recovery efficiency from 79.0 (\(\pm\)0.5)% to 90.1 (\(\pm\)0.3)%. Similarly, for RLX425, the increase of adsorbent dose from 5% to 30% (\(w/v\)) increased the lactic acid recovery efficiency from 27.8 (\(\pm\)0.7)% to 62.9 (\(\pm\)3.9)%. Concerning AMB400, the increase of adsorbent doses from 5% to 30% (\(w/v\)) increased the lactic \begin{table}
\begin{tabular}{c|c|c|c|c} \hline
**Isotherms** & **Non-Linear Form** & **Linear Form** & **Plot** & **Equation** \\ \hline Langmuir & \(q_{e}=\frac{q_{e}\kappa_{\text{L}}C_{e}}{1+K_{\text{L}}C_{e}}\) & \(\frac{1}{q_{e}}=\frac{1}{q_{n}}+\frac{1}{K_{\text{L}}q_{n}C_{e}}\) & \(\frac{1}{q_{e}}\) vs. \(\frac{1}{C_{e}}\) & (I) \\ \hline Freundlich & \(q_{e}=K_{\text{F}}C^{\frac{1}{2}}_{e}\) & \(log\;q_{e}=log\;K_{F}+\frac{1}{n}log\;C_{e}\) & \(log\;q_{e}\) vs. \(log\;C_{e}\) & (II) \\ \hline Temkin & \(q_{e}=\frac{RT}{RT}ln\;A_{T}C_{e}\) & \(q_{e}=\frac{RT}{RT}ln\;A_{T}+\frac{RT}{RT}ln\;C_{e}\) & \(q_{e}\) vs. \(ln\;C_{e}\) & (III) \\ \hline \multicolumn{4}{l}{Notes: \(q_{e}=\text{amount of adsorbate in the adsorbent at equilibrium (mg/g), }C_{e}=\text{equilibrium concentration of lactic acid in the solution (mg/g), }d_{n}=\text{maximum monolayer coverage capacities (mg/g), }K_{\text{L}}=\text{ [PERSON] isotherm constant (L/mg), }n=\text{ adsorption intensity, }K_{\text{F}}=\text{ Freundlich isotherm constant (mg/g) (L/g), }A_{T}=\text{Temkin isotherm equilibrium binding constant (L/g), }K_{T}=\text{Temkin isotherm constant, }R=\text{ universal gas constant (8.314)/(mol K))}\) and \(T=\text{temperature (K); Source: Modified from [PERSON] et al. [28], [PERSON] [29] and [PERSON] [31].} \\ \end{tabular}
\end{table} 
Table 1: List of non-linear and linear forms of adsorption isotherm model equations.
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 3 Results
### Duration of Antimicrobial Effects  
Duration of the antimicrobial effects of the different chitosan:ECM scaffold formulations was first tested using the agar well diffusion assay, which showed notable differences in the response of the two different bacterial strains to the scaffold supernatants (Figure 3). Chitosan:ECM scaffolds without antibiotic showed no ability to inhibit bacterial growth (data not shown), except for a \(\sim\)11 mm diameter zone of inhibition seen in _Staph. Aureus_ bacteria exposed to 1 h supernatant samples of scaffolds 1% (_w/v_) ECM powder.
## 4 Materials and Methods  
### Experimental Design, Animals, Diets and Cheese Production  
The experiment took place at a buffalo dairy farm (Campania region, southern Italy) producing Mozzarella cheese. Thirty-two homogenous lactating buffaloes were assigned to either a control (Ctl) or an experimental (Exp) group, balanced for number (n =16), milk yield (13.3 \(\pm\) 1.5 kg/d), days in milk (110 \(\pm\) 13 d), and parity (3.0 \(\pm\) 1.3). The 2 groups were housed into two adjacent free-stall barns equipped with separate mangers and water troughs. The buffaloes were milked twice daily (h 0500-0017) in the same milking parlor furnished of pipeline milk system for the transport of milk to refrigerate (4 \({}^{\circ}\)C) tanks. The Ctl group was fed the standard TMR used by the farmer that was modified for the Exp group in order to contain 20 kg (about 26.5% of the diet on a dry matter (DM) basis) of fresh sorghum (_Sorghum bicolor_ (L.) [PERSON] x _Sorghum sudanense_ (_Piper_) Stapf.; commercial hybrid Nicol, Pioneer Hi-Bred International, Johnston, IA, USA). The chosen rate of fresh forage inclusion allows to little change the daily feeding routine, has a limited impact on workload, and is able to extend the period of fresh forage availability. Sorghum was at the early milk stage, i.e., growth stage 5-6 according to the scale of [PERSON] and [PERSON] [60]. In this stage, the panicles are forming and all leaves are still green. Fresh cut sorghum was mixed into the TMR with the other ingredients. The two diets were kept isointrogenous and isocaloric (Table 1) and were offered once daily (08:00 h) in an amount to occur 10% of orts. After 10 days of adaptation to diets, for 3 days, the milk from Ctl and Exp groups was separately collected at the evening and morning milking (approximately 200 kg of milk/group) and immediately sampled (n. 6 200 mL samples/group). Bulk milk from the two groups was transported to the dairy in separate stainless steel tanks under refrigeration (4 \({}^{\circ}\)C), and used to produce mozzarella cheese, at the same time in separate vats, according to the traditional procedure previously described [26]. Briefly, raw buffalo milk was gently heated (37 \({}^{\circ}\)C) and added with natural whey starter culture from the previous day manufacture and calf liquid rennet (75:25 chymosin/pepsin ratio, 235 IMCU/mL Clerici Linea Rossa ab/r Cadorago, Como, Italy). At curd formation, the coagulum was reduced to small particles (2-3 cm) and held under whey until the pH fell to 4.85, a value allowing manual stretching of curd into hot water (90-95 \({}^{\circ}\)C). Thereafter, the stretched curd was mechanically formed into 50 g of mozzarella cheese small balls that were cooled in water and placed in brine (10% NaCl). Three 500-g samples were taken for each group at each day of mozzarella production, packed in 100-g plastic bags containing diluted brine solution, and transferred to the laboratory where they were kept at \(-20\)\({}^{\circ}\)C until analysis. Further details about experimental design, sorghum crop management practices, animals, feeds, and diets are given by [PERSON] et al. [26; 61].
## 3 Materials and Methods
### Deep Learning Approach  
#### 3.2.1 Dataset Structure  
To train and evaluate the DL models, all images from the AgRobTomato Dataset and all images, except for the green tomato images, from the RipTomato Dataset were selected. Since the AgRobTomato Dataset already had many green tomato images, it was decided not to select images of tomatoes at this stage of ripeness from the RipTomato Dataset to balance the total set of selected images. A total of 632 images were selected.  
Since it involves supervised learning, the models need to be provided with an annotated dataset. Thus, the images were manually annotated using the open-source annotation tool CVAT (see [[https://cvat.org/](https://cvat.org/)]([https://cvat.org/](https://cvat.org/)), accessed on 22 October 2021), indicating by rectangular bounding boxes the position and class of each plant. The images were annotated with the 4 chosen ripeness classes. After annotating, the images of both datasets were exported under the Pascal VOC format [65] and the YOLO format [66] to train the SSD and YOLO frameworks, respectively.  
High-resolution DL models are time and computationally consuming and cannot process full-sized images, considering the input of square images, thus rescaling them before processing. For this reason, to avoid distortion, the original images of the AgRobTomato Dataset were split into images with a resolution of 720 \(\times\) 720 px. Due to their original resolution, the RipTomato Dataset images needed to be rescaled (961 \(\times\) 720) px and cropped to achieve the 720 \(\times\) 720 px resolution. The split operation doubled the images from the AgRobTomato Dataset, thus increasing the total number of images to 1081. However, some of them contained few annotations, and the splitting resulted in non-annotated images. These images were then removed from the dataset, being left with 1029 images.  
To train and validate the different models, the images were divided into 3 sets:  
* Training set--60% of the data;
* Validation set--20% of the data;
* Test set--20% of the data.  
Figure 2: Classes defined according to the colour of tomato during ripening: Green (**a**)—more than 90% of the surface is green; Turning (**b**)—10 to 30% of the surface is yellow; Light Red (**c**)—between 60 to 90% of the surface is red; Red (**d**)—90 to 100% surface is red.  
Data augmentation was used to artificially increase the dataset, improving the overall learning procedure and performance by inputting varied data into the model [67]. The transformations were carefully chosen, applying those that could happen in an actual robotic harvesting operation, just to the training and validation sets with a random factor, as displayed in Figure 3.  
The data augmentation led to 7598 annotated images. The training and validation sets contained 5543 and 1850 images, respectively, while the test set was composed of 205 images.
## 4 Discussion  
The measurement of the color represents a fundamental step in dentistry with the aim of guaranteeing the major similarity between natural teeth and prosthetic or restorative materials. This aspect is more and more expected by patients, especially in aesthetic areas of the mouth. In addition to a visual evaluation, color assessment in dentistry can be realized instrumentally with the aid of spectrophotometers and colorimeters [27]. Instrumental colorimetry has the advantage of avoiding errors in color assessment by the operator, and additionally, it is more precise than the naked eye in finding out slight differences in colored objects on flat surfaces [28]. Colorimeters measure the quantity of light reflected by selected colors (like red, green, and blue), and color measurement is generally expressed using the CIELAB color system, developed by the International Commission on Illumination Colorimetry [29]. According to this method, the color difference value, \(\Delta\)E, is expressed as the relative color change between repeated color measurements. The chemical structure of a resin composite and the properties of its particles have a direct influence on the surface texture and the susceptibility to extrinsic staining. Besides material composition, the finishing and polishing can also affect the composite surface quality and are consequently related to the early discoloration of resin composites [8]. As regards tooth bleaching, this is a common clinical procedure that is more and more required by patients for aesthetic purposes. Tooth whitening can be realized both by clinicians in a dental setting as well as by the patients themselves at home. The most common chemical substances used for teeth whitening are hydron peroxide (HP) and carbamide peroxide, whose action is exerted thanks to their oxidizing power on exogenous or endogenous pigments responsible for external and internal tooth discoloration, respectively. Professional bleaching, also called \"in-office\" bleaching, is realized using a 35 wt% HP solution in water for about 20-30 min. Considering its highly oxidizing power, particular attention should be focused on avoiding the contact of HP with soft tissues, thus requiring the protection of these latter with a liquid dam. Conversely, domiciliary bleaching consists of the application of a 10-20% carbamide peroxide-containing gel inside a mouthguard which is worn by the patient overnight [30]. A relevant point related to tooth bleaching which must be taken into account is the risks associated with this procedure. The risks commonly linked to tooth whitening are increased tooth sensitivity and gingival irritation. Both these side effects mostly depend on the concentration of the specific peroxide bleach component used. In particular, tooth sensitivity usually arises at the end of the treatment and can last for several days after. As regards gingival irritation, it generally begins within a day of treatment and can last for several days as well. Additional risks for the tooth exposed to bleaching agents are
## 2 General Considerations
### Voltage Balancing of Multilevel Converters  
Especially for three- or multilevel converters also voltage balancing functionality is required to keep both positive (\(\mathrm{C_{o,p}}\)) and negative capacitor (\(\mathrm{C_{o,n}}\)) banks on the same voltage level. The simplest way to guarantee a symmetrical split DC-link would be resistive balancing. However, losses of this methodology become highly unfavorable, especially during light and no-load operation due to their lossy nature regarding the small input power for these operating conditions. These high losses will eliminate the expected effect of the WBG circuit and should thus be avoided. Therefore, another option could be to extend the circuit with active balancing methods relying on operational amplifiers or bipolar transistors, as discussed in [14]. If such an additional balancing circuit is required, expenses of the total extension further increase. A rather attractive and low-cost option for successful voltage balancing could be reusing existing equipment, in this particular case those IGBTs of a t-type inverter which are connected to the respective midpoint of the power stage (\(\mathrm{S_{i,2}}\), \(\mathrm{S_{i,3}}\)). Voltage imbalance is usually caused according to effects such as capacitor leakage currents, the dead-time of switching circuits, etc. and thus a process that comes with rather a low dynamic. Therefore, these inner leg IGBTs shall only be operated for very short time frames during the light load condition. Furthermore, these inner switches are stressed with lower blocking voltage, which results in lower switching losses during operation compared to their outer leg counterparts.