esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Data Recording Details
### Data Collection  
We collected a dataset containing fEPSPs recorded from hippocampal slices on the C57\(BL/6\) mouse line of 2-3 months of age.  
Slices of the hippocampus of 400 \(\upmu\)m thickness were prepared using a vibratome (MicromHM 650 V; Thermo Fischer Scientific, Waltham, MA, USA). They were permanently placed in artificial cerebrospinal fluid (ACSF) with the following composition (mM): NaCl 127, KCl 1, KH\({}_{2}\)PO\({}_{4}\) 1.2, NaHCO\({}_{3}\) 26, D-Glucose 10, CaCl\({}_{2}\) 2.4, MgCl\({}_{2}\) 1.3 (pH 7.4-7.6; Osmolarity \(295\pm 5\) mOsm). The solution was constantly saturated with a gas mixture containing 95% O\({}_{2}\) and 5% CO\({}_{2}\). In our experiments, we used only surviving slices. For morphological analysis, a Scientifica SliceScope (Scientifica Ltd., London, UK) microscope with aerial 4\(\times\) lens was used.  
The fEPSPs were recorded using glass microelectrodes (Sutter Instruments Model P-97) from the Harvard Apparatus, Holliston, MA, USA (Capillares 30-0057 GC150F-10, 1.5 \(OD\times 0.86\)\(IK\times 100\) Lmm, Quantity: 225). Stimulation and registration electrodes were placed in the DG region and CA3 and CA1 regions, respectively. The electrodes were installed in the holder of a HEK EPC 10 USB PROBE 1 preamplifier head connected to a HEK EPC 10 USB biosignal amplifier. A stainless-steel bipolar electrode was used for electrical stimulation of the DG area in the hippocampus. The electrodes were filled with the ACSF solution. The pipette tip resistance was 4-8 M\(\Omega\). We applied a 50 \(\upmu\)s stimulation signal whose amplitude was increased from 100 to 1000 \(\upmu\)A with a 100 \(\upmu\)A step.  
A stimulating electrode was placed in the DG area and sent electrical square current pulses to activate the cells in the DG area of the hippocampus. Recording electrodes were installed in the pyramidal neuron dendrites of CA3 and CA1, the hippocampus areas. This made it possible to record the activation of the perforant pathway in the hippocampus due to electrical stimulation. The following shows the original trace of the fEPSP recorded in the dendrites of the CA3 and CA1 hippocampus areas. A similar protocol is presented in our previous works [37; 39; 40; 48; 49]. Data visualization and recording were performed using Patchmaster software.  
When collecting biological data from real experimental animals, the individual differences of each animal should be taken into account. However, averaging data from at least three experimental animals, particularly from the brain sections of experimental animals (with n not less than 5), is relevant for further analysis, including the application of machine learning. Furthermore, when collecting biological data, the minimum number of animals should be used, following bioethical considerations. In our study, a dataset of 76 traces with nine hippocampal sections obtained from five experimental animals was used.  
Figure 2: Pipeline for data processing which includes four main steps.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 3 Results
### Bandgaps  
A bandgap study of the mirror- and glide-symmetric periodic structures when varying the size of the hole (the triangle side \(e\)) and the period \(d\) of the unit cell is shown in Figure 11. The glide-symmetric configuration offers a wider bandwidth compared to the mirror-symmetric one, as also pointed out by previous studies on holey structures with circular [9] and elliptical [31] holes. Furthermore, the relative bandgap increases as the period of the unit cell is larger. Additionally, the relative bandgap is an increasing function of \(e/d\); that is, the larger the triangular hole, the larger the relative bandgap. This fact can be explained taking a look at the dispersion diagrams in Figure 4. At the \(\Gamma\) point of the mirror-symmetric configuration, the second mode approximately stays at the same frequency but the first mode decreases in frequency as the size of the hole increases, opening a wider bandgap. A similar behavior is also observed for the glide-symmetric configuration but considering now the second and third modes. Unlike what found for triangular holes, in the case of circular and elliptical holes [9; 31], there is an optimum ratio between the size of the hole and the period at which the bandgap is maximum. Beyond that ratio, the relative bandgap starts to decay.  
Figure 10: Normalized magnitude of the coefficients \(C_{i}\) in the glide-symmetric configuration for wave propagation in \(x\) and \(y\) directions at different frequencies: (**a**) \(10\,\mathrm{GHz}\), (**b**) \(20\,\mathrm{GHz}\), (**c**) \(30\,\mathrm{GHz}\), (**d**) \(40\,\mathrm{GHz}\). The normalized magnitude of the tangential electric field, calculated according to (1), is also displayed for both propagating directions. The geometrical parameters of the considered unit cells are: \(h_{hole}=1.5\,\mathrm{mm}\), \(g=0.2\,\mathrm{mm}\), \(h_{t}=3\,\mathrm{mm}\), and \(d_{x}=d_{y}=4.5\,\mathrm{mm}\). The dispersion diagram of the structure is displayed in the green curve of Figure 5b.  
Figure 12 presents a bandgap study when varying the gap height \(g\) and the hole depth \(h_{\text{hole}}\) for both the mirror- and glide-symmetric unit cells. In all cases under study, the glide-symmetric configuration offers a wider bandgap than the mirror-symmetric one. In both cases, the relative bandgap is inversely proportional to the gap height and directly proportional to the hole depth. Similarly to what was depicted in Figure 6, there is a threshold after which increasing the hole depth has no longer influence on the relative bandgap, since the modes exponentially attenuate inside the hole.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 4 Simulation Results and Discussions
### Near-Far Effect Resistance Performance Comparison  
In this simulation, these tested multiuser detection algorithm is under the imperfect power control assumption, i.e., the power is not equal at the receiver for each user. Assume a 50 user condition; \(E_{b}/N_{0}\) of the first user is 6 dB, while \(E_{b}/N_{0}\) of the remaining 49 users changes from 0 dB to 10 dB simultaneously. For the proposed multiuser detection algorithm, 5 iterations are employed. The result is shown in Figure 7.  
As shown in Figure 7, for both channels we get similar near-far effect resistance results. In this 50 user system when the SNR of interference user is larger than 4 dB, the BER performance is better than MF, DEC and MMSE; when the SNR of interference user increases up to 10 dB, the BER performance even coincides with OMUD. Besides, the performance of OMUD and DEC remain unchanged when the power of interference users change. While MF and MMSE suffers performance degradation when the power of interference users get stronger. For the proposed multiuser detector, the weak users actually benefit from the strong interference whereas the strong users suffer performance loss from the  
Figure 7: The near-far effect resistance performance (50 users). (**a**) AWGN; (**b**) IEEE 802.15.4a CM1.  
weak interference. This phenomenon was also observed in other multiuser detection algorithms based on interference cancellation [28; 29].
## 2 Materials and Methods
### Performance Metrics  
Intersection over Union (IoU) is a commonly used metric for evaluating segmentation algorithms. It measures the overlap between predicted and ground truth segmentation masks. Additionally, accuracy, precision and recall were also used as performance metrics. Accuracy represents the proportion of correctly predicted pixels (both foreground and background). Precision indicates the percentage of predicted foreground pixels that are correctly classified, highlighting the model's conservativeness by minimizing false positives (\(FP\)). Recall represents the percentage of ground truth pixels correctly identified by the model, reflecting its ability to capture relevant information by minimizing false negatives (\(FN\)). While maximizing both precision and recall is ideal, precision is prioritized in this application due to the high cost of \(FP\), whereas \(FN\) poses minimal risk. Note that balancing these metrics is challenging, as they directly impact the demanding IoU score. The instance segmentation performance of the model cannot be adequately measured using the AP score [48], as the network is designed for semantic segmentation. Hence, we developed a custom metric, the Centroid Correlation Cofficient (CCC), tailored to estimate how accurately individual masks are obtained for each ground truth instance, crucial for approximating instance segmentation through semantic segmentation. The CCC metric ideally achieves a value of 1.0, typically ranges between 0.95 and 0.3, but can be negative in highly fragmented segmentations. The CCC metric measures the average deviation between the centroids of inferred blobs and ground truth blobs, assuming each  
Figure 4: ResNet architecture. Colors used: illac—input; light blue—strided convolution; light orange—convolutions; dark orange—BatchNorm and ReLU activation; red—MaxPooling; green—dropout; yellow—sum; dark blue—upsampling; purple—sigmoid activation. blob represents an instance. It penalizes deviations from the ideal scenario, where each instance corresponds to a single blob, and accounts for mismatches in the number of blobs and cases where blobs lack a direct counterpart. The CCC pseudocode implementation is presented in Algorithm 1. If the number of blobs is the same in both segmentations, they are paired such that the total distance within the pairs is minimized. If the number of blobs does not match, each blob in the segmentation with more blobs is associated with the nearest blob in the other segmentation. The average distance is calculated as the sum of these distances divided by the number of blobs in the segmentation with fewer blobs. Hence, this approach penalizes a blob less when it is closer to any blob in the other segmentation and penalizes based on the disparity of blob count. The average distance is normalized against the maximum possible distance (the image diagonal), yielding a result between 0 and 1 when the same number of masks matches, and greater than 0 otherwise. Finally, the square root of this measure is computed to expand the most common range, and is then subtracted from 1 to obtain a value less than 1.
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results
### Changes in IR Thermal Spectral Responses to Herbicides  
The temperature difference estimated based on the plant leaf temperature determined by the IR thermal image analysis also showed distinctive differences among the herbicides tested in this study (Figure 5). All tested herbicides exhibited distinct temperature variations compared to the untreated control. An initial increase in temperature difference was observed for paraquat, tiafenacil, glufosinate, and glyphosate, and then a decrease was observed at a certain point, with the plants being dehydrated. As observed in the other spectral responses, paraquat (PSI) showed the greatest change in temperature difference, showing an increase until 6 HAT and then a sudden decrease from 24 HAT. Glufosinate (GS) showed a continuous increase in temperature difference until 24 HAT, while tiafenacil (PPO) and glyphosate (EPSPS) showed an increase until 48 HAT and then a decrease afterwards. Isoxaflutole (HPPD) displayed a significant increase as late as 120 HAT, while pencosulam (ALS) did not show any noticeable change even at 120 HAT.
# Bacteriotherapy in Breast Cancer
## 4 Bacteria as a Carrier for Cancer Therapeutic Agents
### Listeria Monocytogenes  
_Listeria monocytogenes_ is an intracellular pathogen, which can have direct access to the cytoplasm of APC by the hemolytic activity of listeriolysin O (LLO). The infection caused by this bacterium is a classic model to induce the response of protective cellular immunity. Listeriolysin O (529 acid amine) has a hemolytic activity, can perforate the phagosomal membrane, and then the bacterium can escape from the vacuole to the cytoplasm [107]. These bacteria are widely used as a vaccine vector and this vaccine is used for generating the immune responses against the breast cancer (4T1, MDA-MB-231) and other human tumors in phase I and II of clinical trial. Lm-LLO-E7 is a recombinant _Listeria monocytogenes_ (rLm) that secretes E7 as a fusion protein in conjugation with the non-hemolytic listeriolysin O (LLO). E7 is a protein produced by human papillomavirus-16 (HPV-16), associated with cervical cancer cells [100]. The other form of rLm is called ADXS31-142, which expresses HER2/neu oncogene as a fusion protein to a non-hemolytic fragment of listeriolysin O (LLO) by the highly attenuated _Listeria_ vector LmddA. This vector can spread on a cell-to-cell basis, but it does not have any selection markers for antibiotics [101; 102]. The results of the in vitro and in vivo studies indicate the attenuated _Listeria monocytogenes_ (LM) based vaccine that expressed listeriolysin O (LLO) and amino acid fragments 311 to 660 of TAA Mage-b, called LM-LLO-Mage-b \({}_{311-660}\). This _Listeria_-based vaccine has a dual mode of action; first, it can kill the tumor cells via activation of NADP\({}^{+}\) oxidase and raise the intracellular level of Ca\({}^{2+}\), both resulting in the production of high ROS levels, in vitro. The second action is the ability to induce strong CTL responses and depletion of TCD8\({}^{+}\), which has resulted in killing the breast tumor cells in mouse breast tumor model 4T1 [108] (Table 4) (Figure 2).
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 2 System Modeling
### UWOC Link Modeling  
This section deals with UWOC link modeling. It has been assumed that the optical signal transmission underwater is remarkably influenced by absorption and scattering, rather than the turbulence caused by the temperature gradient and air bubbles under the water. The combined effect of the fading can be appropriately characterized by the Exponential Generalized Gamma distribution with different water salinity. The CDF of the instantaneous SNR, \(F_{\gamma UW}(\gamma)\), of the UWOC link is given by Equation (21) in [33] as  
\[F_{\gamma UW}(\gamma)=wG_{1,2}^{1,1}\Bigg{(}\frac{1}{\lambda}\Big{(}\frac{ \gamma}{\mu_{r}}\Big{)}^{\frac{1}{2}}\Big{)}\Big{|}\begin{array}{c}1\\ 1,0\end{array}\Bigg{)}+\frac{1-w}{\Gamma(a)}G_{1,2}^{1,1}\Bigg{(}\frac{1}{b^{ c}}(\frac{\gamma}{\mu_{r}})^{\frac{1}{2}}\Big{|}\begin{array}{c}1\\ a,0\end{array}\Bigg{)}, \tag{4}\]  
where \(w,\lambda,a,b,c\) are the parameters associated with the EGG distribution, \(r\) is set to 2, which specifies the intensity modulation and direct detection (IM/DD) scheme, and \(\mu_{r}\) is the average SNR of the UWOC link. The parameters employed for different water salinity and the temperature gradient for varying bubble levels are taken from [33].
## 4 Results and Discussions
### Comparison of Different Correlation Threshold  
Besides the structure of the stacked LSTM layer, it is also important to optimize the largest time lag \(r\) and correlation threshold \(\phi_{\text{th}}\). These two parameters are the key parameters in the newly designed STF layer. Time lag \(r\) decided the feature pool of the model. Correlation threshold \(\rho_{\text{th}}\) decided the quality of the feature pool. Since the air quality time series pattern in one place usually follows a daily period, and one day may not enough to extract the useful pattern, we therefore set the test range of \(r\) as 48 h  
It can be seen from Figures 5-7 that both two parameters can affect the model performance a lot. For example, the R\({}^{2}\) value can change from the lowest 0.909 to 0.958. It is almost 5% performance. The setting of \(\rho_{\text{th}}\) shows a clear separation for model performance. When \(\rho_{\text{th}}\leq 0.4\), the R\({}^{2}\) value never gets lower than 0.920, and when \(\rho_{\text{th}}>0.4\), most of the R\({}^{2}\) values are lower than 0.920. This is because when \(\rho_{\text{th}}\) is too large, many useful inputs will be filtered out, and result in limited knowledge to learn. But when \(\rho_{\text{th}}\) goes too low, the inputs will keep much noise, and therefore the model performance drops. Overall, it can be seen from Figures 5-7 that when \(r=12\), and \(\rho_{\text{th}}=0.4\), the model has the lowest RMSE, MAE and highest R\({}^{2}\) value. This pair is then the optimal pair for these two parameters. The goodness of fit plot is shown in Figure 8.  
Figure 6: Mean absolute error (MAE) values of the model using different pairs of \(r\) and \(\rho_{\text{th}}\).  
Figure 7: R\({}^{2}\) values of the model using different pairs of \(r\) and \(\rho_{\text{th}}\).
## 3 Results and Discussion
### Minor Volatile Compounds
& 9 \\ Ethyl hexadecanoate & 628-97-7 & \(145\pm 16\)\({}^{\rm a}\) & 182 \(\pm\) 13\({}^{\rm b}\) & 204 \(\pm\) 6\({}^{\rm b}\) & 473 \(\pm\) 6\({}^{\rm c}\) & 3 & *** & *** & *** & 2000\({}^{\rm I}\) & 8 \\ \hline Aldehydes (4) & & & & & & & & & & & & \\ \hline Hexanal & 66-25-1 & \(7.6\pm 0.1\)\({}^{\rm b}\) & 6.8 \(\pm\) 0.8\({}^{\rm b}\) & 3.9 \(\pm\) 0.3\({}^{\rm a}\) & 10.1 \(\pm\) 0.3\({}^{\rm c}\) & 3 & ns & *** & *** & 5\({}^{\rm X}\) & 3 \\ Nonanal & 124-19-6 & \(5.7\pm 0.5\)\({}^{\rm c}\) & 5.4 \(\pm\) 0.2\({}^{\rm c}\) & 1.7 \(\pm\) 0.3\({}^{\rm a}\) & 4.2 \(\pm\) 0.3\({}^{\rm b}\) & 3 & *** & *** & 2.5\({}^{\rm II}\) & 5 \\ Decanal & 112-31-2 & \(5.9\pm 0.6\)\({}^{\rm c}\) & 5.3 \(\pm\) 0.4\({}^{\rm bc}\) & 4.7 \(\pm\) 0.4\({}^{\rm ab}\) & 4.2 \(\pm\) 0.4\({}^{\rm a}\) & 3 & **ns** & ns & 1.25\({}^{\rm II}\) & 5,8 \\
2-Phenyl-acetaldehyde & 122-78-1 & \(129\pm 9\)\({}^{\rm c}\) & 81 \(\pm\) 3\({}^{\rm ab}\) & 69 \(\pm\) 9\({}^{\rm a}\) & 86 \(\pm\) 9\({}^{\rm b}\) & 3 & *** & *** & 4\({}^{\rm XI}\) & 3,7 \\ \hline Ketones (1) & & & & & & & & & & & & & \\ \hline Acetophenone & 98-86-2 & n.d. \({}^{\rm a}  
The loadings of each volatile compound in the two PCs plotted (Figure 3) allows us to understand the separation of the wine samples. For instance, compounds such as ethyl hexanoate, acetophenone, ethyl hexadecanoate, farnesol, and nerolidol have strong positive contributions to PC2 (Table S2. Supplementary Materials), influencing the separation of AGGLO + Act. Conversely, compounds like phenethyl hexanoate and ethyl decanoate show negative loadings on PC1, which contribute to the separation of AGGLO wines from the other wines. The grouping of WY and WY + Act, both with positive PC1 scores, suggests that the presence of esters such as the acetates of higher alcohols and ethyl esters of 2 and 3-methylbutyrate, E-methyldihydrojasmonate, and ethyl hexanoate significantly influence these samples. As a summary, the tested additive has a different effect on the volatile profile on wines from AGGLO yeast and those obtained by spontaneous fermentation (WY), probably due to the enrichment of the must in the ONC that it provides and its influence on the production of certain esters and higher alcohols [45]. These results underscore the complex interplay between yeast strain, fermentation dynamics, and nutrient supplementation on the volatilome of wines and, consequently, on their aroma. Floculant yeast has a higher nitrogen demand for its development [58], which could explain the different effects of ONCs in spontaneous and flocculant yeast fermentations. A possible underlying mechanism is the increased nitrogen flux through the Ehrlich pathway, in which amino acids are degraded to form keto acids, which are then converted into aldehydes and higher alcohols. These compounds can combine with cellular or wine acids, contributing to ester formation. However, while this mechanism is consistent with yeast physiology, additional studies would be needed to confirm its specific role in the interaction between the ONC and flocculant yeast under these conditions.
## 3 Results Analysis
### NS03 Cavity Test Results  
The NS03 cavity underwent an RF test after 72 h of cooling. The cavity was cooled to about 2.58 K, with a temperature difference of less than 0.1 K. The ambient magnetic field before cooldown had the following conditions: \(B_{x}=14\) mG, \(B_{y}=-1.1\) mG, and \(B_{z}=-1.56\) mG. During the cooldown, \(B_{max}\) reached a maximum of 120 mG due to thermal currents. We performed RF tests in CW mode under three cooling conditions: the first was direct cooling without controlling the cooling rate, the second was two cycles of cooling by switching the cryocoolers on and off between 30 K and 12 K, and the third was the slow control of the cooling rate at 0.06 K/min using the heaters, after the cavity was warmed up to 25 K, with the heaters turned off after cooling down to 15 K. The cavity temperatures and ambient magnetic fields for these three cooling processes are given in Figure 13, and the corresponding cavity surface temperature gradients and ambient magnetic fields at 18 K are given in Table 3. The cavity was cooled to a base temperature of 2.58 K before each RF test, and the measured \(Q_{0}-E_{acc}\) curves under the three cooling conditions are given in Figure 14, with the points on the graphs being the thermal stability points.  
The overall lower measured \(Q_{0}\) and \(E_{acc}\) may be due to the fact that the film was partially damaged or contaminated during transportation and processing, which will be discussed in Part 5. Nevertheless, a relatively good \(Q_{0}-E_{acc}\) curve was measured under the third cooling condition. The \(Q_{0}\) was over 3 \(\times\) 10\({}^{9}\) at low \(E_{acc}\). The \(E_{acc}\) reached a maximum value of 3.42 MV/m when the forward power was loaded to 3.04 W. At this time,  
Figure 12: The \(Q_{0}-E_{acc}\) curves of the Nb\({}_{3}\)Sn cavity for RF testing under different conditions. The color of the data points shows the \(T_{avg}\) of the cavity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cooldown** & **Cooling Rate/ K/min** & **Temperature Gradient/K/m** & \(B_{x}\)/mG** & \(B_{y}\)/mG & \(B_{z}\)/mG \\ \hline
1 & 2.55 & 8.72 & 4.04 & 6.71 & \(-\)2.47 \\
2 & 12.39 & 0.86 & 9.79 & \(-\)1.60 & \(-\)1.35 \\
3 & 0.06 & 0.17 & 10.15 & \(-\)1.08 & \(-\)0.81 \\ \hline \hlinethe \(Q_{0}\) was \(1.1\times 10^{9}\), the \(P_{l}\) in the cavity was 1.372 W, and the \(T_{avg}\) stabilized at 3.86 K. Continuing to increase forward power, both \(Q_{0}\) and \(E_{acc}\) showed a decrease.  
Figure 14: \(Q_{0}-E_{acc}\) curves under the three cooling conditions. The color of the points indicates the average temperature of the cavity, and each point was at steady state during the measurement.  
Figure 13: Cavity surface temperature and ambient magnetic field variations under three cooling conditions: (**a**) direct cooling without controlling the cooling rate, (**b**) two cycles of cooling by switching the cryocoolers on and off between 30 K and 12 K, and (**c**) using the heaters to control the cooling rate. The location of the thermometer and heater is shown in Figure 8.