esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Thirty patients were observed, but eight were excluded from the analysis because their clinical course worsened resulting in pneumonia and therefore they underwent NIV treatment, along with other complications which made them unsuitable for physiotherapy tests. Then, twenty-two patients (10 males, 12 females) were eligible for the study and treated with HFNC, mean age 64.5 \(\pm\) 5.9 years (Table 1).  
Only one patient presented with no comorbidities. At baseline, patients showed acute hypoxemic respiratory failure (AHRF) and mean P/F of 235 \(\pm\) 7.35. At T1, the mean of FiO2 during treatment with HFNC was 34.46 \(\pm\) 7.22 to obtain a SpO2 mean of 95% \(\pm\) 0.59%. At T2, P/F ratio with HFNC was 331 \(\pm\) 10.91; only one patient used HFNC with enrichment of oxygen (FiO2 28%) (Table 2).  
Statistically significant results were observed in the evaluation of the functional profile with respect to the evaluation scales used as reported in Table 3 for \(p\)\(<\) 0.05. At the end of rehabilitation, after 20 days of hospitalization 21/22 patients no longer needed oxygen therapy support.  
\begin{table}
\begin{tabular}{l c} \hline Age (Years) (Mean \(\pm\) SD) & 64.5 \(\pm\) 5.9 \\ \hline Hypertension & 9 (47\%) \\ \hline COPD & 2 (10\%) \\ \hline OSAS & 2 (10\%) \\ \hline Diabetes & 6 (31\%) \\ \hline Smokers/Ex Smokers & 3 (15\%) \\ \hline COPD = chronic obstructive pulmonary disease; OSAS = obstructive sleep apnea syndrome. \\ \end{tabular}
\end{table}
Table 1: Patient characteristics (comorbidities).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **T0** & **T1** & **T2** & \(p\)**Value** \\ \hline
**P/F r.** & 235 \(\pm\) 7.35 & 237 \(\pm\) 8.15 & 331 \(\pm\) 10.91 & \textless{}0.05 \\ \hline
**FiO2\%** & 21 \(\pm\) 9.34 & 34.46 \(\pm\) 7.22 & 22.95 \(\pm\) 3.45 & \textless{}0.05 \\ \hline
**SpO2\%** & 86 \(\pm\) 4,54 & 95 \(\pm\) 0.59 & 97 \(\pm\) 1.01 & \textless{}0.05 \\ \hline
**HR b/min** & 88 \(\pm\) 7.68 & 76 \(\pm\) 8.78 & 68 \(\pm\) 5.32 & \textless{}0.05 \\ \hline
**RR r/min** & 20 \(\pm\) 4 & 16 \(\pm\) 2.05 & 12 \(\pm\) 1.39 & \textless{}0.05 \\ \hline \end{tabular}  
\(\dagger\)To (at admission in the COVID-19 Emergency Division); T1 (at five days from the admission in the hospital); T2 after 20 days of hospitalization; P/F r = PaO2/FiO2; FiO2% = fraction of inspired O2; SpO2% = oxygen saturation; HR b/min = heart rate; RR r/min = respiratory rate per minute.  
\end{table}
Table 2: Respiratory function profile.
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 4 Experiment
### Data Preprocessing
#### 4.3.2 SMOTE Oversampling  
The training set data were augmented using the synthetic minority over-sampling technique (SMOTE) method. SMOTE increases the number of minority samples by generating new synthetic samples around the minority class samples for data balancing. From Table 4, taking time step 6 (w = 6) as an example, the number of samples of ST firms in the original training data was 756, and the number of samples of non-ST firms was 4228. Using the SMOTE method, new synthetic samples were generated to increase the number of samples of ST firms from 756 to 4228, which was equal to the number of samples of non-ST firms. SMOTE effectively increased the number of samples of the minority class (ST firms) and made the data more balanced and reduced the bias due to sample imbalance.
## 3 Results and Discussion  
### Analysis of Chemical Structure Using ASA and FT-IR Methods  
Figure 2 reveals the main idea behind the paper.  
The chemical analysis, performed using atomic absorption spectrometry (ASA), is summarized in Table 3 for the samples Mg_10, Mg_15, Mg_16, and Mg_17. The results reveal consistent findings across all the tested samples. A notable feature observed in the analyses was the presence of functional groups, including hydroxyl (-OH) groups, which exhibited a characteristic stretching vibration band at 3352 cm\({}^{-1}\). Additionally, carbon-oxygen (C-O) bonds were identified with a band at 1019 cm\({}^{-1}\), which is typical for the formation of oxide layers and suggests the presence of oxygenated species on the surface. Carbon-chlorine  
Figure 1: A schematic diagram of the electrochemical setup for biocorrosion studies on implant samples prepared using the PEO method, and a graphical representation of Tafel parameter determination.  
(C-CI) bonds were also detected at 787 cm\({}^{-1}\), indicating potential interactions between the magnesium surface and chlorine species from the electrolyte during the plasma electrolytic oxidation (PEO) process. These findings confirm that electrochemical modification led to the creation of new surface coatings on the magnesium samples.  
Further confirmation of the surface modifications was obtained through Fourier-transform infrared (FT-IR) spectroscopy, as presented in Figure 3. The FT-IR spectra provided additional insights into the chemical composition and bonding structures of the modified magnesium surfaces, confirming the successful formation of protective coatings. The detected functional groups and their respective vibration bands suggest that the electrochemical treatment facilitated the formation of a robust oxide layer, enhancing the surface properties of the magnesium alloys. These modifications are critical for improving the corrosion resistance and biocompatibility of the implants, making them more suitable for use in biomedical applications such as bone and dental implants.  
\begin{table}
\begin{tabular}{c c} \hline
**Element** & **Content (\%)** \\ \hline Zn & 2.4 \\ Mg & 75.4 \\ Al & 22.1 \\ Other & 0.10 \\ \hline \end{tabular}
\end{table}
Table 3: Analysis results showing the metal content (%) in the alloy sample subjected to the PEO process, which was used as implant material.  
Figure 2: A diagram of the process of implant surface preparation using the PEO method, along with the compounds that constitute the electrolyte components.
## 3 Results  
### Prototype  
An off-the-shelf drone was used for the prototype to benchmark the system requirements with the specification data sheet, as shown in Table 5, to make sure it satisfied the pertinent requirements.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Item** & **Recommendation** & **Justification** \\ \hline \multirow{3}{*}{Surveillance camera} & HD & For better awareness and investigation outcomes, navigation, and night operation [24] \\  & Night vision & For later retrieval, decumulations, investigation, training, etc. [25] \\ \hline \multirow{2}{*}{Surveillance camera} & HD and high capacity & To sustain longer missions [23] \\ \cline{2-3}  & Hony & To sustain long operations and avoid losing the drone vehicle [26] \\ \hline \multirow{2}{*}{Light} & LED & Navigation, searching, investigation, etc. [30] \\ \cline{2-3}  & Regional or national level & Navigation, tracking, investigation, and tagging [31] \\ \hline \multirow{2}{*}{Access to specialized database (e.g., offenders database)} & Based on customizable operational needs & To support better decision making [32] \\ \cline{2-3}  & AI prediction algorithms (e.g., crowd detection, abnormal behavior, facial recognition) & Suitable for operation within crowded and urban environments & To support better decision making [33; 34] \\ \hline \multirow{2}{*}{Sensor array (e.g., fire, nose, and thermal detectors and anti-collision sensors)} & \multirow{2}{*}{Based on application requirements} & Environmental sensing, risk assessment data gathering, and safe operations [30] \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Item** & **Requirement** & **Data Sheet Justification** & **Matching** \\ \hline \multirow{6}{*}{Drone vehicle} & Portable & 595 g & \\ \cline{2-3}  & Easy to operate and deploy & Yes & \\ \cline{2-3}  & Can handle mild weather conditions & Stabilization; 3-axis (tilt, roll, pan) & \\ \cline{2-3}  & Good life cycle and easy to maintain & & \\ \hline \end{tabular}
\end{table}
Table 5: Benchmarking the system prototype requirements [authors]. _\(\blacktriangledown\)_ mean it portable (tick) as the rest.  
The B-D4S prototype (Figure 10) was established and successfully deployed and tested with Qatar Traffic Police. The prototype included an off-the-shelf drone, DJI Air 2S [35], with GPS and surveillance cameras of 5.4 K resolution, a take-off/landing pad on the roof of a traffic police patrol car, and a control joystick with the video stream displayed on a monitor fixed on the car's dashboard.
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
###### Abstract  
As part of the CAMaRSEC research project, long-term indoor environmental measurements with accompanying occupant surveys were conducted over one year in 49 households in 15 high-rise residential apartment buildings in Hanoi, Vietnam. A comprehensive analysis of the collected data revealed differences in the indoor environment and energy consumption patterns during the operational phase of the buildings, as well as their correlation with diverse occupant behaviors. In addition, by comparing subjective thermal evaluations based on occupant surveys with predictions based on comfort models, the limitations of existing models in predicting the thermal sensations of local people were identified. Furthermore, the findings indicated that the apartment building design standard in Vietnam underestimates the thermal adaptation of occupants, which may lead to significant building performance gaps. Larger scale surveys and measurements are required to provide sufficient databases to refine local building design standards, especially for mixed-mode buildings.  
post-occupancy evaluation; residential buildings; air conditioning; indoor environmental quality; household energy consumption; occupant behavior; hot and humid climate
## 2 Multilayered MM-Based Effective Medium Approach
### Effective Parallel Permittivity  
Herein, an analytical formula expressing the parallel permittivity component characterizing the structure under consideration is derived. It is worthwhile mentioning that:  
\[\overset{\rightarrow}{D}=\epsilon_{eff}\overset{\rightarrow}{E} \tag{2}\]  
where \(\epsilon_{eff}\) is the effective permittivity. It is a well-known fact that the tangential component of the electric field should be continuous across a boundary of two different media. Thus, it follows:  
\[E_{m}^{||}=E_{d}^{||}=E^{||} \tag{3}\]  
where \(E_{m}^{||}\) is assigned as the electric field in the metallic layers, \(E_{d}^{||}\) is the electric field in the dielectric sheets, and \(E^{||}\) is the electric field of the subwavelength MM. The overall displacement might be found by averaging the displacement field components arising from the metallic and dielectric building blocks:  
\[D^{||}=\rho D_{m}^{||}+(1-\rho)D_{d}^{||} \tag{4}\]  
Rearranging the above equation, the following equation is obtained:  
\[\varepsilon_{eff}^{||}E^{||}=\rho\epsilon_{m}E^{||}+(1-\rho)\epsilon_{d}E^{||} \tag{5}\]  
One can conclude with the final equation, as follows:  
\[\varepsilon_{||}=\rho\epsilon_{m}+(1-\rho)\epsilon_{d} \tag{6}\]
## 2 Data Containers for an Optical Encryption
### MCDDC's Features and Parameters
Due to the availability of high error correction levels, it is possible to use MCDDC with multiple gray levels. User can choose a number of gray levels in accordance with their experimental setup's signal-to-noise ratio (SNR). The increasing of a number of gray levels correspondingly increases a density of data encoding. As an example, in Figure 2 a file (format txt, size 3 kB) is presented, encoded in a sequence of five binary QR codes or two CDDC grayscale messages with four gray levels; useful data area for QR code and CDDC were chosen as the same, 97 \(\times\) 97 modules. However, as was mentioned above, grayscale containers are more fragile. If each module of MCDDC contains several bits of data, one error leads not to one lost bit but to several of them. It is important to find a balance between the higher density of data encoding and the increased requirement for the system's signal-to-noise ratio.
## 5 Conclusions  
(1) According to the principle of the RSR method, an FBG-type compressive stress sensor was designed and developed. The sensor consists of six pressure sensing units and one temperature compensation unit, which can achieve the measurement of stress state at a point.  
(2) The stress sensor adopts fiber WDM and fusion technology. Each sensing unit occupies a 5 nm wavelength, and the maximum range is designed to be 30 MPa. A single stress sensor only occupies one FBG demodulation channel.  
(3) The calibration results show the maximum calibration range of the sensor is 30 MPa, the maximum output wavelength of the full range is 2900 pm, and the maximum sensitivity is 96.656 pm/MPa. The stress sensor designed here shows a strong test performance and a high long-term stable performance, with a maximum error of 1.51%. Test results also indicate that all of the zero drift, linearity, repeatability, and hysteresis indexes are lower than 1.5%.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, B.L. and S.W.; data curation, Z.Y.; writing-original draft preparation, Y.Z. and S.W.; writing-review and editing, Y.Z., S.W. and Z.Y.; funding acquisition, Y.Z., B.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (Grant No. 51874275, 51974289).  
Not applicable.  
All data used to support the findings of this study are included within the article, and there are no restrictions on data access.  
The authors declare no conflict of interest.
# Greening the NetFPGA Reference Router
## 5 Experiments
### Combined with Other Green Techniques
For Ethernet port shut-down, the control policy combines a network level power management technique with a local level approach. Ethernet port shut-down should be controlled at a higher level by a network-wide global power management policy with energy-aware traffic engineering capability, so that traffic destined for a port can be diverted to other active ports to enable the safe shut down of a port. Otherwise, a sudden surge of traffic to a disabled port can result in significant packet loss, as the disabled port will be unable to respond quickly enough when the new packets arrive. With energy-efficient traffic engineering, the disabled port is able to be woken up in advance, allowing traffic to traverse the port again. The energy-aware traffic engineering is performed at a scheduler to decide when to safely enable or disable every Ethernet port. Figure 17 presents a simple example of energy-aware traffic engineering. Router A sends 300 Mb/s of aggregate traffic to Router E. To prevent network congestion from traffic bursts, traditional routing with load balancing may split the 300-Mb/s traffic into 100 Mp/s for each of the three links. Whereas, energy efficient traffic engineering reroutes and aggregates the 300-Mb/s traffic from three separate links (each link 100 Mb/s) to one single link (300 Mb/s), so that the Ethernet ports on the links without traffic can be disabled for a period of time to achieve more power savings. If the router is in the idle state and no traffic is being processed, the FSR can disable all four Ethernet ports and switch to the lowest frequency 7.8 MHz with a power consumption of 5.118 W, instead of the RR, keeping all four Ethernet ports at 125 MHz with a power consumption of 10.725 W. Thus, in the idle state, the FSR can save up to 52.28% of the power consumption compared to the RR. Table 6 summarizes the average power consumption for the experiments with real traces. The RR has the highest power consumption of 11.750 W. The FSR with STP consumes the least power of 10.565 W and saves 10.09% of power. The differences of power consumption among the three different control policies in the FSR are small because the differences only come from the different numbers of transitions. However, if dynamic frequency scaling (DFS), traffic re-routing through network-wide green traffic engineering (GTE) and Ethernet port shut-down (EPS) are all implemented, 46.04% of power can be saved in this specific experiment.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model
#### 3.3.2 Ground Coverage  
The ground coverage of the drone-mounted camera is a trapezoid \(\mathcal{A}\) shown in Figure 4. The ground coverage of this trapezoid is defined as  
\[\left\{\begin{array}{l}a=\dfrac{2h\cos\dfrac{\phi_{v}}{2}\tan \dfrac{\phi_{h}}{2}}{\cos\left(\theta-\dfrac{\phi_{v}}{2}\right)}\\ b=\dfrac{4h\tan\dfrac{\phi_{h}}{2}}{\cos\theta}-a\\ c=h\dfrac{\tan\left(\theta+\dfrac{\phi_{v}}{2}\right)}{\tan\left(\theta- \dfrac{\phi_{v}}{2}\right)}-\tan\left(\theta-\dfrac{\phi_{v}}{2}\right)\end{array}\right\} \tag{5}\]  
where \(a\) is the top length, \(b\) is the bottom length, \(c\) is the height of the trapezoid \(\mathcal{A}\), and \(h\) is the altitude of the receiver camera. The receiver can simultaneously receive the optical signals from the sensor nodes within this trapezoid at the same time. These sensor nodes are grouped as a single virtual sensor node. The sensors within a trapezoid are grouped to improve the efficiency of data collection. Since the size of the trapezoid increases in accordance with the altitude \(h\), it is efficient to maximize \(h\).  
Figure 3: Perspective transformation to image plane.
###### Abstract
We developed a multiple-swarm PSO (MSPSO) scheme, which is designated as MSPSO-IPTBK, to analyse the performance improvement of IP traceability in the attack path reconstruction process for different dynamic network topologies. The validity of the proposed algorithm was demonstrated by performing a series of experiments using ns-3 simulator with the BRITE (the Boston University Representative Internet Topology Generator) topology generator. The ns-3 tool is free, licensed under the GNU GPLv2 license and publicly available for research, development and use in academic network simulations. The BRITE is a topology generation framework constructed with ns-3 to create large-scale internet topologies efficiently. It takes advantage of ns-3's simulation capabilities. In summary, the primary contributions of this study are as follows:  
* The multimodal optimisation problem was solved using the MSPSO method. Moreover, different botnet attack sources were analysed with high accuracy for the reconstructed attack path to identify the most probable attack paths. This identification was performed for assisting security managers to identify the sources of DDoS attacks. * The optimal route-searching process of the MSPSO algorithm was improved to prevent the PSO algorithm from converging prematurely to a local suboptimal solution in a large search space. * The accuracy of the MSPSO algorithm was obtained as 95.89% for the effects of dynamic traffic in the experimental network (number of nodes = 24) and as 94.64% for the network topology (number of nodes = 64). * Compared to traceback accuracy with other route search algorithms such as the A* algorithm [19] and the PSO [11; 12; 13; 14], the performance of the MSPSO-IPTBK algorithm in reconstructing attack paths provides superior performance for analysing the attack origins from multiple different data sources using ns-3 with the BRITE framework. The remainder of this paper is organised as follows. Section 2 presents previously published studies in the IPTBK field and describes the use of existing methods in solving the IPTBK problem. Section 3 describes the MSPSO scheme for solving the IPTBK problem. Section 4 presents the experimental results obtained using the MSPSO algorithm and describes the global heuristic performance of the algorithm. Finally, Section 5 provides some concluding remarks.
## 3 Results and Discussion
### Analysis of the Impact of the Number of Evaluations  
The importance of choosing the right number of evaluations of the optimization function lies in the need for faster processing without significant loss of total coverage \(\mathcal{C}\). In this subsection, we study the impact of the evaluations for the problem of placing a single isotropic sensor in three selected environments. To account for the stochastic variability inherent in optimization algorithms, each test case was repeated 30 times. For the sake of brevity, the results are presented for one environment only. The analysis of the coverage in the other environments leads to the same results. For all tests, a rasterization value is set to \(r_{\mathrm{v}}=0.1\,\mathrm{m}\). Table 3 shows the descriptive statistics for using different numbers of evaluations \(E_{\mathrm{v}}\) of the optimization for the sensor placement in the two-dimensional representation of the Environment 1. This table shows the average and standard deviation of the coverage \(\mathcal{C}\) of all runs for each algorithm. In order to statistically analyze the effects of the optimization algorithm used and the number of evaluations of the optimization function on the average coverage, we performed a two-way repeated measures (RM) ANOVA. Namely, a \(3\times 14\) ANOVA was utilized, with _Algorithms_ (three instances) and _Number of evaluations_ (14 instances) being the within-subjects factors. The test yielded the following results:  
* The mean coverage differed significantly between the optimization _Algorithms_: \[F(2,1218)=173.998,\,p<0.001.\] A post hoc analysis with a Bonferroni adjustment showed that PSO achieved statistically higher coverage compared to ABC and FWA. ABC also outperformed FWA \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Environment 1**} & \multicolumn{2}{c}{**Environment 2**} & \multicolumn{2}{c}{**Environment 3**} \\  & & **2D** & **3D** & **2D** & **3D** & **2D** & **3D** \\ \hline \multirow{2}{*}{TPE [s]} & \multirow{2}{*}{0.53} & \multirow{2}{*}{30.72} & \multirow{2}{*}{0.99} & \multirow{2}{*}{74.49} & \multirow{2}{*}{0.85} & \multirow{2}{*}{56.25} \\ \cline{2-2} \cline{6-7}  & & & & & & \\ \hline \multirow{2}{*}{ES} & \(\mathcal{C}\) & 0.1446 & - & 0.1191 & - & 0.1359 & - \\  & \(\mathcal{E}\) & 221,301 & \(1.107\times 10^{9}\) & 210,197 & \(1.133\times 10^{9}\) & 205,530 & \(1.028\times 10^{9}\) \\ \hline \multirow{2}{*}{RES} & \(\mathcal{C}\) & 0.1446 & 0.0795 & 0.1191 & 0.0814 & 0.1359 & 0.0742 \\  & \(\mathcal{E}\) & 1567 & 21,230 & 1533 & 17,051 & 1534 & 16,874 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time per evaluation \(TPE\), coverage values \(\mathcal{C}\) and number of evaluations \(\mathcal{E}\) of the optimization function for ES and RES for single sensor placement problem in all three environments.