esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Temperature on Catalytic Activity  
Once we identified **N-HT-S** and **HT-S** as the best catalysts in terms of reaction times and isolated yields, the next objective was to study the influence of the temperature on these transformations, which were performed under the same conditions, with varying temperatures. Table 8 shows the results of this study.  
As expected, the reaction slows down as the temperature decreases. Moreover, diol **9** appears to be more reactive than diol **10**. Remarkably, in all experiments, excellent yields were obtained without an additional purification step.  
Catalyst **HT-S** significantly reduced reaction times for all the temperatures studied, and even at 25 \({}^{\circ}\)C, the reaction was complete after one day for both diols. As shown in Table 8, reaction times increased up to one week when catalyst **N-HT-S** was employed instead. Therefore, the sulfonated hydrothermal carbon derived from saccharose, **HT-S**, is clearly the most efficient among all the catalysts included in this work, in agreement with the fact that such a catalyst showed the highest acidity, with a PZC value of 1.9.  
Figure 8: NMR monitoring of the formation of **11** after filtration of the catalysts **HT-S**.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Entry** & **Reaction** & **Catalyst** & **Time** & **Temperature** & **Yield** \\ \hline
1 & **9\(\rightarrow\)11** & **N-HT-S** & 8.0 h & 60 \({}^{\circ}\)C & 93\% \\
2 & **10\(\rightarrow\)12** & **N-HT-S** & 1 d & 60 \({}^{\circ}\)C & 91\% \\
3 & **9\(\rightarrow\)11** & **HT-S** & 1.0 h & 60 \({}^{\circ}\)C & 93\% \\
4 & **10\(\rightarrow\)12** & **HT-S** & 1.5 h & 60 \({}^{\circ}\)C & 92\% \\
5 & **9\(\rightarrow\)11** & **N-HT-S** & 3 d & 40 \({}^{\circ}\)C & 91\% \\
6 & **10\(\rightarrow\)12** & **N-HT-S** & 8 d & 40 \({}^{\circ}\)C & 88\% \\
7 & **9\(\rightarrow\)11** & **HT-S** & 4.0 h & 40 \({}^{\circ}\)C & 94\% \\
8 & **10\(\rightarrow\)12** & **HT-S** & 3.5 h & 40 \({}^{\circ}\)C & 93\% \\
9 & **9\(\rightarrow\)11** & **N-HT-S** & 7 d & 25 \({}^{\circ}\)C & 98\% \\
10 & **10\(\rightarrow\)12** & **N-HT-S** & 8 d & 25 \({}^{\circ}\)C & 91\% \\
11 & **9\(\rightarrow\)11** & **HT-S** & 1 d & 25 \({}^{\circ}\)C & 92\% \\
12 & **10\(\rightarrow\)12** & **HT-S** & 1 d & 25 \({}^{\circ}\)C & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Influence of the temperature on the formation of ketals **11** and **12**.  
We also performed a kinetic NMR study for the reaction of diol **10** catalyzed by **HT-S** at 40 \({}^{\circ}\)C (Scheme 2, Figure 9). The reaction progresses rapidly as diol **10** and intermediate **I\({}_{\textbf{10-12}}\)** are consumed, thereby forming acetonide **12** quantitatively in less than 4 h. First-order rate constants of 1.004 h\({}^{-1}\) and 0.709 h\({}^{-1}\) were determined for the first and second step, respectively, with half-life times of 0.7 and 1.0 h for diol and intermediate (Figure S17).
###### Abstract  
The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open-source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc.  
agro-plastics; digital Atlas; agricultural plastic surface; remote sensing indices; RPGI; plastic footprint +
Footnote †: journal: Applied Sciences
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods
### Modelling Methodology
#### 2.2.2 DL Algorithms  
DL algorithms were employed to model the risk of decompensation as a sequence-to-sequence task. We chose two DL architectures, both designed for handling time series: many-to-many LSTM and CNN-LSTM.  
The **Many-to-Many LSTM architecture** is a type of recurrent neural network designed for sequence-to-sequence tasks [13]. It takes a sequence of input data and generates a corresponding sequence of output predictions, allowing for variable-length input and output sequences.  
The **CNN-LSTM architecture** has two identifiable stages: a channel-wise CNN and an LSTM stage [14]. The rationale behind this architecture is that the CNN stage processes sequential data with multiple channels, generating sequential outputs for each channel, whilst the LSTM stage integrates them to predict the sequence output.
## 6 Conclusions  
A comprehensive survey regarding recent advances and technical solutions in the design and development of breakthrough space-air-ground-integrated networks for supporting seamless and ubiquitous wireless connectivity for future 6G wireless communications has been carried out. The paper opens with an extensive overview about the space segment by focusing on satellites classification, constellations, and current and future trends on antenna technologies. Then, a detailed investigation regarding the air layer is provided, and its prominent role in the delivery of next-generation services is described and discussed. Moreover, particular attention is also paid to the ground segment focusing on both user terminal and gateway antennas. Finally, relevant application scenarios regarding the paradigm of SAGIN in present and future wireless communications are discussed, covering 5G, B5G and 6G use cases.  
Conceptualization, F.A.D. and S.G.; writing--original draft preparation, F.A.D., S.G., N.J.G.F., M.B. and S.M.; writing--review and editing, F.A.D., S.G., N.J.G.F., M.B. and S.M.; supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
Work partially supported by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).  
Not applicable.  
The authors declare no conflict of interest.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 2 Thermal Characterization of Amorphous Metals
### Improvements to the Temperature Control--Temperature Modulation  
Furthermore, one of the key aspects of FSC that has proven to be essential for distinguishing and studying the metastable phases, which appear during the vitrification, process [67] is the ability to determine the heat capacity of each of these phases in comparison to conventional DSC, where the measurement is much slower and a lot of the data on the short-lived phase changes cannot be appropriately collected. Temperature-modulated DSC (TMDSC) with the stochastic modulation of temperature (TOPEM) is being used for the highest accuracy of results [68]. It is a technique of DSC where stochastic temperature modulation is superimposed on a conventional DSC program, in comparison, to TMDSC, which superimposes a sinusoidal heating/cooling rate. Additionally, it does not need a reference sample and shows better repeatability and more representative results [69]. Sample mass is usually determined by conventional DSC through measuring the heat capacity or enthalpy of a known substance. [PERSON] et al. investigated two differently thermally modified Pd\({}_{40}\)Ni\({}_{40}\)P\({}_{20}\) metallic glasses obtained by different thermal cycling treatments for the purposes of measuring and identifying \(\beta\)- and \(\alpha\)-relaxation modes, which correspond to the shadow glass transition and enthalpy recovery, i.e., rejuvenation, respectively. Using TOPEM DSC coupled with a structural analysis of medium-range order (MRO), they found that MRO in glasses can be influenced by sub-\(T_{g}\) quenching and annealing, which could lead to new properties of bulk metallic glasses [70].
## 2 Materials and Methods
### Real-Time PCR  
RNA from 5 \(\times\) 10\({}^{6}\) cells was isolated using the RNeasy Mini Kit (Qiagen, Venlo, The Netherlands), and complementary DNA (cDNA) was generated by reverse transcription on CFX96 real-time system (BioRad) using a SensiFAST cDNA Synthesis Kit (Bioline, London, UK). Template RNA and resulting cDNA quality and concentration were assessed using the ND-1000 Nanodrop system (Thermo Fisher Scientific, Waltham, MA, USA). The relative amount of survivin was measured by real-time PCR (qPCR) using SensiFAST SYBR No-ROX Kit (Bioline) and calculated using Bio-Rad CFX Manager Software. The sequences of primer pairs designed to target survivin (transcript variant 1, NCBI RefSeq NM_001168.3) were as follows: forward CCACTGAGAACCAGCCAGAC, reverse TGTTCCTCTATGGGGTCA. For relative quantification by the 2\({}^{-\Delta\Delta\text{Ct}}\) method, GAPDH expression was measured as a reference, using GAAACTGTGGCCGTGATGGC and CCGTTCAGCTCAGGATGAC as the forward and reverse primers, respectively. The primer efficiency was checked according to the Pfaffl method, and it was found to be almost 100% for both survivin and GAPDH primer pairs.
## 4 SOC Estimation in the Entire SOC Area  
### EKF-Based SOC Estimator  
The EKF algorithm is used to estimate the SOC in this paper. The EKF considers the noise characteristics of the current and voltage sensors, and effectively overcomes the problem of random errors [28]. The schematic of EKF is illustrated in Fig. 6. The EKF is based on dynamic equations. Assuming that \(k\) is the discrete-time index, \(\mathbf{x_{k}}\) is the state vector to be estimated, \(\mathbf{z_{k}}\) is the output vector, and the system input vector is \(\mathbf{u_{k}}\). The battery model can be expressed by the following state equations:  
\[\mathbf{x_{k+1}}=\mathbf{f}(\mathbf{x_{k}},\mathbf{u_{k}})+w_{k} \tag{12}\]  
\[\mathbf{z_{k}}=\mathbf{h}(\mathbf{x_{k}},\mathbf{u_{k}})+v_{k} \tag{13}\]  
where \(w_{k}\) denotes random process noise, \(v_{k}\) denotes measurement error,\(\mathbf{f}(\mathbf{x_{k}},\mathbf{u_{k}})\) is a nonlinear state transition function, and \(\mathbf{h}(\mathbf{x_{k}},\mathbf{u_{k}})\) is a nonlinear measurement function.  
The state equations of the SOC estimator can be expressed as:  
\[\mathbf{x_{k}}=(\text{SOC}_{k},u_{1,k},u_{2,k},\text{ASOC}) \tag{14}\]  
\[\mathbf{h_{k}}=OCV(\text{SOC}_{surf,k})-u_{1,k}-u_{2,k}-R_{0}I_{k}+v_{k} \tag{15}\]  
Figure 5: Parameter identification results under dynamic stress test (DST) working condition. (**a**) RMSE comparison; (**b**) Terminal voltage profiles for 2 RC and E2 RC.  
\[\mathbf{u_{k}}=I_{k} \tag{16}\]  
In Figure 6, the superscript \"\(-\)\" and \"\(+\)\" indicate a priori estimate and a posteriori estimate at time step \(k\), respectively. \(\mathbf{P}\) is the covariance matrix of uncertainty in state estimation; \(\mathbf{Q}\) is the covariance matrix of process noise; \(\mathbf{R}\) is covariance matrix for measuring uncertainty; \(\mathbf{\overline{K}_{k}}\) is kalman gain matrix. The detailed EKF algorithm can be found in Reference [29].
# Coupling Denoising to Detection for SAR Imagery
## 4 Discussion  
Our proposed detection framework obviously achieves a better performance through combining a denoising network with an existing detection network; however, more parameters and the complex structure demand larger memory for model storage and higher computing cost. We report average inference times (measured in seconds/(patch image) on a Titan Xp GPU) for the purpose of time complexity analysis, as presented in Table 4. Compared with the existing two-stage object detection network like Faster RCNN [45] in the first row of Table 4, our detection framework further requires denoising time and time for fusing region proposals during inference. The denoising time makes up a large portion of the added running times, so the most promising way for reducing the average inference time would be adopting a relatively light denoising network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **+Despeckling** & **AP** & **Airplane (A)** & **Etcetera (E)** & **Ship (S)** \\ \hline \multirow{4}{*}{ResNet-50} & - & 52.05 & 53.90 & 54.54 & 47.72 \\  & preprocessing & & & & \\  & (Lee filter [22]) & 53.52 & 54.63 & 56.96 & 48.98 \\  & preprocessing & & & & \\  & (PPB filter [25]) & 51.16 & 54.35 & 53.68 & 45.44 \\ \cline{2-6}  & \begin{tabular}{c} within network \\ (ours) \\ \end{tabular} & 55.90 & 58.82 & 54.04 & 54.84 \\ \hline \multirow{4}{*}{ResNet-101} & - & 54.29 & 54.65 & 59.80 & 48.43 \\  & preprocessing & & & & \\  & ([PERSON] filter [22]) & 56.19 & 58.04 & 60.59 & 49.95 \\ \cline{1-1}  & preprocessing & & & & \\  & (PPB filter [25]) & 52.96 & 53.16 & 58.17 & 47.54 \\ \cline{1-1} \cline{2-6}  & \
\begin{tabular}{c} within network \\ (ours) \\ \end{tabular} & 52.96 & 53.16 & 58.17 & 47.54 \\ \cline{1-1} \cline{2-6}  &
\begin{tabular}{c} within network \\ (ours) \\ \end{tabular} & 60.81 & 65.03 & 61.67 & 55.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of detection performance on our constructed dataset with TerraSAR-X and COSMO-SkyMed images. By incorporating region proposals from denoised SAR images within detection network, our model shows significant improvement in AP. The entries with the best APs for each object category are highlight in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input of DetNet.** & **Feature Map** & **AP** & **Airplane (A)** & **Etcetera (E)** & **Ship (S)** \\ \hline Denoised only & - & 52.96 & 56.71 & 53.59 & 48.57 \\ Real + Denoised & Denoised & 53.96 & 57.16 & 51.17 & 53.54 \\ Real + Denoised & Real (ours) & 55.90 & 58.82 & 54.04 & 54.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study across the input type of detection network and feature map forwarded to subsequent sub-network for classification and bounding box regression for each RoIs. The entries with the best APs for each object category are highlight in bold. The backbone is ResNet-50-FPN.
## 3 Proposed Solution
### Adaptive MMAC  
In this section, the authors present a novel approach to utilize the performance capabilities of the simulated MMAC protocols in different situations. In Section 5.2, the analysis shows that E-SA-MMAC and its variant Max-E-SA-MMAC perform the best in terms of throughput and fairness in three or fewer channels due to the utilization of the control channel for data transmission. However, when presented in environments with a greater number of wireless channels, say twelve, the control channel becomes the bottleneck as more data transmission channels are available for negotiation. If the negotiation is interrupted due to the data transmission on the control channel, data transmission dedicated  
Figure 1: Timing diagram for proposed MMAC protocol.  
Figure 2: Flow diagram of the proposed E-SA-MMAC protocol.  
channels are under-utilized as the \"handshake\" process is delayed. Therefore, the authors propose a dynamic MMAC, which we refer to as adaptive MMAC, which adapts to the number of wireless channels available and uses the control channel for data transmission, only when there are three or fewer channels. Otherwise, it performs the BiMMAC routine of using the control channel for contention mechanisms only.  
Figure 3 describes the flow diagram of the Adaptive MMAC protocol. The portion in red is the difference between this protocol and the E-SA-MMAC protocol, enabling the protocol to behave differently based on the resources available.
## 4 Results of Experiments
### Rdata Dataset  
This experiment takes the 43 instances of the Rdata dataset, with a flexibility rate \(\beta\) ranging from 0.13 to 0.4. The results generated by the GA-RRHC are compared in Table 7 with those obtained by the GLNSA, HA, and IJA, which are the methods that report results for this dataset. Figure 16 depicts the number of best results obtained for each algorithm in this dataset, where the GA-RRHC calculated the 23 best values, behind the HA and IJA.  
Table 8 sorts each algorithm with its average \(RPD\) and the comparative Friedman test. From the results, it can be noted that the GA-RRHC ranked third overall, as might be expected in instances with low flexibility. The statistical analysis shows no significant difference with the IJA.  
Figure 16: Number of best results per algorithm for the Rdata dataset.  
Figure 15: Comparison of the RPD difference per algorithm for the BRdata dataset.  
This experiment verifies that the GA-RRHC performs comparably to the IJA for problems with low flexibility and outperforms the GLNSA.
## 3 Results  
### Planimetric Evaluation  
Removal of both the skin substitutes and the epidermal coverage was completed for all wounds on the designated days (see Materials and Methods) to investigate wound closure. For the control wounds, complete epithelialization and closure of the wound were observed after 13.50 days (\(\pm\)1.19 days) on average postoperatively. For the collagen-elastin matrix-treated wounds, these occurred after 10.67 days (\(\pm\)0.94 days, \(p=0.0002\)). In comparison, wounds treated with the collagen-gelatin scaffold containing rhGDF-5 were completely closed after 12.50 days (\(p=0.0271\)) on average for the single-application groups and after 10.92 days (\(p<0.0001\)) for the multiple-application groups. More specifically, the time required for complete wound closure, for the single-application dressings, was, on average, 12.33 days (\(\pm\)1.37 days, \(p=0.0987\)) for the 30 g/m\({}^{2}\) scaffold with 100 ng rhGDF-5 condition, 12.67 days (\(\pm\)0.94 days, \(p=0.1775\)) for the 75 g/m\({}^{2}\) scaffold with 500 ng rhGDF-5 condition, 12.33 days (\(\pm\)1.37 days, \(p=0.0987\)) for the 150 g/m\({}^{2}\) scaffold with 1000 ngrhGDF-5 condition, and 12.67 days (\(\pm\)0.94 days, \(p\) = 0.1775) for the 90 g/m\({}^{2}\) scaffold with 5000 ng rhGDF-5 condition (Figure 1). For the groups with multiple applications of the dressing, the wounds were closed after 11.33 days (\(\pm\)0.94 days, \(p\) = 0.0015) for the 30 g/m\({}^{2}\) scaffold with 100 ng rhGDF-5, 10.33 days (\(\pm\)0.75 days, \(p\) = 0.0002) for the 75 g/m\({}^{2}\) scaffold with 500 ng rhGDF-5, 10.67 days (\(\pm\)0.94 days, \(p\) = 0.0002) for the 150 g/m\({}^{2}\) scaffold with 1000 ng rhGDF-5, and 11.33 days (\(\pm\)0.94 days, \(p\) = 0.0021) for the 90 g/m\({}^{2}\) scaffold with 5000 ng rhGDF-5 (Figure 1). These results show that the required time for complete wound closure decreased when treated with multiple applications of the wound dressing. For the multiple-application treatment of 75 g/m\({}^{2}\) scaffold with 500 ng rhGDF-5, this was especially evident as it showed an acceleration of 3.17 days in wound healing compared to the untreated controls (Figure 2).
## 3 The Proposed Method and Scenarios
### Initial Authentication Phase
#### 3.4.2 Vehicle and TGS Communication Stage  
The decryption of \(AT_{ASIV}\) by the key \(K_{Vs}\) is necessary for the vehicle, followed by the acquisition of \(K_{TGSse}\). Subsequently, two messages will be generated by the vehicle, with the initial message comprising \(ID_{S}\) and \(LT_{TGT}\). The second message, the \(AU_{VITGS}\), includes \(ID_{v}\) and \(TS_{VA}\). Encryption of the \(AU_{VITGS}\) is carried out using \(K_{TGSse}\). This key uses the AES encryption that utilizes the 128-bit key length. The 128-bit AES is  
Figure 6: Initial authentication phase.  
lightweight and suitable to be implemented in the VANET environment rather than AES 192-bit and AES 256-bit. The VANET has several resource constraints, including low power consumption and memory efficiency. Vehicles in a VANET environment may rely on embedded systems with limited power. AES-128 strike a balance between security and power consumption. The AES-128 also requires less memory and processing than AES with longer key lengths, which is advantageous in devices with limited memory, such as those in vehicular communication systems. Moreover, our system also utilizes blockchain, which has a limited block size. This particular phase is illustrated in step (c) within Figure 6. After generating the \(AU_{VITGS}\), the \(TGS\) will sent it to the blockchain network to store it in step (d). Then, the \(AU_{VITGS}\) is sent to the RSU in step (e). The details of \(AU_{VITGS}\) storage in the blockchain is explained in Section 3.5.  
In addition to the aforementioned generated messages, a vehicle is set to transmit the \(TGT\) acquired from the \(AS\). Subsequently, the vehicle will dispatch the trio of messages illustrated in Equations (6)-(8) to the \(TGS\). Upon receipt, the \(TGS\) will verify the presence of \(ID_{5}\) in plaintext within its own registry. The \(ID_{5}\) should be found in the \(TGS\) server's records; the \(TGS\) will duplicate the \(K_{S_{5}}\). Encoded within the \(TGT\) lies a \(K_{TGSse}\), which enables the \(TGS\) to decipher the \(AU_{VITGS}\). Those messages will be sent to the vehicles in step (f).  
\[TGT= K_{TGSs}[(ID_{p}||ID_{TGS}||TS_{TGT}||\quad IP_{p}||LT_{TGT}||K_{TGSse})]. \tag{6}\] \[AT_{VITGS}=(ID_{s}||Req_{LT}). \tag{7}\]  
\[AU_{VITGS}=K_{TGSse}[ID_{p}||TS_{VA}]. \tag{8}\]
## 4 Results and Discussion
### Sensitivity Analysis and Model Calibration and Validation

Table 15: Performance of the SWAT model for water yield estimation in calibration and validation periods under the wet year condition. Figure 8: Monthly observed and estimated streamflow at Khlong Bang Yai watershed under the dry year condition: (**a**) calibration period; (**b**) validation period. Figure 9: Scatter plot between observed and estimated streamflow at Khlong Bang Yai watershed under the dry year condition: (**a**) calibration period; (**b**) validation period.