esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Quality of Life Assessment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Scale**} & \multirow{2}{*}{**Before HTR**} & \multirow{2}{*}{**After HTR**} & **Delta \({}^{*}\)** & **Relative Delta [\%] \({}^{**}\)** & \multirow{2}{*}{\(p\)} \\
**[Points]** & & & & **Mean [95\% CI]** & & \\ \hline PF & 47.8 \(\pm\) 7.2 & 53.9 \(\pm\) 8.3 & 6.1 [3.1; 9.2] & 13.9 [6.6; 21.2] & \textless{}0.001 \\ \hline RP & 46.0 \(\pm\) 7.5 & 52.9 \(\pm\) 8.4 & 6.9 [2.6; 11.1] & 17.1 [7.2; 27.0] & 0.003 \\ \hline BP & 45.7 \(\pm\) 9.2 & 54.7 \(\pm\) 9.8 & 9.0 [5.2; 12.8] & 21.6 [12.4; 30.8] & \textless{}0.001 \\ \hline GH & 45.8 \(\pm\) 7.8 & 47.5 \(\pm\) 9.7 & 1.7 [1.3; 4.8] & 4.2 [\(-\)2.7; 11.2] & 0.249 \\ \hline VT & 49.6 \(\pm\) 16.4 & 61.2 \(\pm\) 14.4 & 11.7 [5.5; 17.7] & 32.3 [12.5; 52.1] & \textless{}0.001 \\ \hline SF & 58.0 \(\pm\) 19.3 & 66.2 \(\pm\) 17.4 & 8.2 [\(-\)1.8; 18.1] & 29.7 [\(-\)1.5; 60.9] & 0.105 \\ \hline RE & 50.9 \(\pm\) 8.2 & 55.6 \(\pm\) 7.3 & 4.7 [0.7; 8.7] & 11.5 [2.4; 20.6] & 0.022 \\ \hline MH & 57.8 \(\pm\) 14.1 & 66.7 \(\pm\) 13.3 & 9.0 [4.4; 13.4] & 18.7 [6.7; 30.4] & \textless{}0.001 \\ \hline PCS & 40.8 \(\pm\) 7.2 & 45.5 \(\pm\) 7.4 & 5.7 [2.6; 8.8] & 16.6 [5.1; 28.2] & \textless{}0.001 \\ \hline MCS & 55.6 \(\pm\) 14.8 & 63.8 \(\pm\) 12.6 & 8.1 [2.6; 13.7] & 20.2 [5.0; 35.5] & \textless{}0.001 \\ \hline Summary & 60.2 \(\pm\) 17.5 & 70.7 \(\pm\) 17.1 & 13.8 [8.0; 19.6] & 15.4 [8.3; 22.5] & \textless{}0.001 \\ \hline \hline \end{tabular} \({}^{*}\) delta = after–before; \({}^{**}\) relative delta = 100% (delta/before): Changes in QoL in the Medical Outcomes Study Short Form 36 (SF-36). SF-36: PF–physical functioning, RP–de limitations because of physical problems, BP—body pain, GH—general health perception, VT—utility, SF–social functioning, RE–moile limitations because of emotional problems, MH—mental health, PCS—physical component summary, MCS—mental component summary.  
\end{table} 
Table 3: Comparison of Quality of Life before and after home-based telerehabilitation. MCS (r = \(-\)0.55, \(p\) = 0.014). Patients with ischemic etiology (1.1 \(\pm\) 18.4 vs. 14.7 \(\pm\) 14.3, \(p\) = 0.049), hypertension (\(-\)4.3 \(\pm\) 16.8 vs. 10.4 \(\pm\) 17.0, \(p\) = 0.059) and diabetes (\(-\)2.4 \(\pm\) 20.6 vs. 11.9 \(\pm\) 14.3, \(p\) = 0.042) achieved smaller increases and heart rate maximum in CPET. Similarly, less favorable changes in mental health were observed in patients with diabetes (1.5 \(\pm\) 12.7 vs. 14.1 \(\pm\) 13.4, \(p\) = 0.049) and hyperlipidemia (4.0 \(\pm\) 12.8 vs. 15.9 \(\pm\) 14.2, \(p\) = 0.073). By multivariate analysis, only baseline measurement (\(\beta\)\(\pm\) SE: \(-\)0.52 \(\pm\) 0.17, \(p\) = 0.009) and diabetes (\(\beta\)\(\pm\) SE: \(-\)11.0 \(\pm\) 5.1, \(p\) = 0.046) remained independently associated with HTR-induced change in MPC (coefficient of determination, R\({}^{2}\) = 0.49). The details are presented in Supplemental Materials Tables S3 and S4.
## 2 Microfabrication of Suspensions of Polymeric Fibers by Photo-Polymerization
### Control of Fiber Properties
#### 2.3.3 Fiber Elasticity  
We mechanically characterized the fiber through an in situ measurement of its Young's modulus with an adapted \"bending beam\" test [13]. The advantages of this in situ test are two-fold. First of all, the test reproduces the exact fabrication conditions used for the micro-fabrication (e.g., UV light intensity, exposure time, etc.), and second, the mechanical properties are tested in the fluid environment used to produce the fiber suspensions. This might be particularly important when working with diluted polymer solutions and, thus, hydrogels. The adapted bending test is the first in situ Young's modulus measurement method for particles fabricated with the photo-polymerization method.  
A fiber is fabricated across a confined microchannel, sitting inside slots, allowing it to deform under the drag force created by the flow. Images of the bending fiber are captured at steps of increasing flow rates, as illustrated in Figure 4. The measurement of the deformation at controlled flow forcing provides direct access to the fiber material Young's modulus.  
The experiment [12; 13] was conducted for five formulations in order to explore a range of Young's moduli. All contain PEGDA at Mw = 575 with Darocur 1173 photo-initiator at concentrations varying from 2%-10%. Young's moduli between 1 and 12 MPa were obtained depending on the photo-initiator content and the exposure time, as shown in Figure 4. Note that the nature of the gel, initiator, microscopic optics and projected UV light lamp all lead to a strongly setup-dependent measurement technique requiring an in situ measurement. The modulus can be varied further when working with polymer solutions at different dilutions in water, creating hydrogels with even lower Young's moduli [15].
## 2 Materials and Methods
### Uranium Transport and Adsorption Experiments  
#### 2.3.1 Simulation of Ideal Adsorption by Fine Sediments  
A batch experiment was performed to evaluate the ideal adsorption of uranyl compounds by fine sediments. The Langmuir model is valid for modeling monolayer adsorption onto a homogenous surface with constant adsorption energy; the Freundlich equation posits a heterogeneous surface and considers that molecules attached to a surface site will have an effect on the neighboring sites. Batch adsorption on FSC was evaluated using the Langmuir and Freundlich isotherm models to determine the adsorption mechanism that occurs between the sediment and different concentrations of UO\({}_{2}\)(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O in aqueous solution. Both mathematical models relate the uranium concentration in the solution and the adsorption capacity in the sediment when dynamic equilibrium is reached. U(VI) solutions (30, 60, 90, 120 mg L\({}^{-1}\)) were prepared from a stock solution of 253.2 mg L\({}^{-1}\) in deionized water. FSC samples (0.3 g and 0.5 g) were placed in contact with 150 mL of the specified initial concentration of U(VI) solutions at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) and a stirring speed of 90 rpm. The residual concentration of U(VI) was determined at different time intervals up to 48 h. To do this, the stirring was stopped for 15 min, 2 mL of aliquot was taken, passed through a 0.45 um Whatman(tm) regenerated cellulose filter, 5.4 mL of Ultima Gold AB scintillating liquid was added, and U(VI) was read on the Triathler Hidex 425-034 detector, Turku, Finland. The experiments were performed at two different pH (5.0 and 7.0). pH = 5 is obtained by dissolving the uranyl nitrate salt with distilled water. On the other hand, to obtain pH = 7, 0.1 M NH\({}_{4}\)OH, (28-30%, [PERSON]) was added to the UO\({}_{2}\)(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O solution.  
The adsorption isotherm was fitted using the Langmuir [42]  
\[\mathrm{Q_{e}=\frac{Q_{max}K_{L}C_{e}}{(1+K_{L}C_{e})}}, \tag{1}\]  
and Freundlich models [42]  
\[\mathrm{Q_{e}=K_{F}C_{e}^{\frac{1}{2}}} \tag{2}\]  
where \(\mathrm{Q_{e}}\) (mg kg\({}^{-1}\)) is the equilibrium adsorption capacity, \(\mathrm{C_{e}}\) (mg L\({}^{-1}\)) is the equilibrium residual concentration of (UO\({}_{2}\))\({}^{2+}\) in solution, and \(\mathrm{Q_{max}}\) (mg kg\({}^{-1}\)) represents the maximum adsorption capacity; \(\mathrm{K_{L}}\) (L mg\({}^{-1}\)) is the Langmuir equilibrium adsorption constant showing the adsorption affinity; \(\mathrm{K_{F}}\) (L kg\({}^{-1}\)) is the Freundlich distribution coefficient and n is the correction factor indicating the degree of nonlinearity between solution concentration and adsorption.  
SEM-EDAX (morphology and elemental composition) and XRD (mineral phases) were used to determine the chemical properties of the sediment surface before and after U(VI) adsorption.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 2 Adversarial Samples and Adversarial Attack Strategies
### Adversarial Capabilities and Goals
#### Training Stage Capabilities  
Attacks in the training stage attempt to influence or corrupt a target model directly by changing the dataset used for training. In this case, the most straightforward and weakest attack is to access part or all of the training data. The attack strategies in the training stage based on the adversarial capabilities can be divided into three categories:  
* **Data Injection.** The adversary does not have any access to the training data and learning algorithms but has the ability to add new data to the training dataset. The adversary can corrupt the target model by inserting adversarial samples into the training dataset.
* **Data Modification.** The adversary does not have access to the learning algorithms but has access to full training data. The adversary can poison the training data by modifying the data before it is used for training the target model.
* **Logic Corruption.** The adversary has access to meddle with the learning algorithms of the target model.
###### Abstract  
Incidents of lumbar degenerative diseases, such as spinal stenosis and degenerative spondylolisthesis, are increasing due to the aging population, and as a result, posterior lumbar interbody fusion (PLIF) is widely used. However, the interbody fusion cage used in the fusion surgery has been reported to cause subsidence in the fusion cage of the titanium material and bone nonunion in the case of the polyetheretherketone (PEEK) material cage. Therefore, we aim to reduce the possibility of subsidence of the spinal fusion cage through its elastic modulus difference with the cortical bone of the vertebral body. For the vertebral end plate, which is related to the fusion rate, we also aim to design a new composite vertebral cage, which integrates a cover of porous structure using the additive manufacturing method of titanium alloy to fabricate a prototype, and to biomechanically verify the prototype. The method was as follows. In order to find a similar pore size of human cancellous bone, the pore size was adjusted and the results were measured with SEM. The pore size of each surface was measured individually and the mean value was calculated. Next, an animal experiment was conducted to confirm the degree of fusion of each structural type, and prototypes of various structures were fabricated. The degree of fusion was confirmed by a push down test. A prototype of the fusion cage composed of titanium and PEEK material was fabricated, and the possibility of subsidence by existence of porous structure was confirmed by using the lumbar spine finite element model. Then, the prototype was compared with the composite fusion cage developed by ASTM F2077 and ASTM F2267 methods, and with the commercial PEEK and titanium cages. As a result, the correlation between bone fusion and the porous structure, as well as size of the spine fusion cage composing the composite for porous structure and elasticity, was confirmed. Type 3 structures showed the best performance in bone fusion and the pore size of 1.2 mm was most suitable. In addition, the likelihood of subsidence of a cage with a porous structure was considered to be lower than that of a cage with a solid structure. When the new composite cage combined with two composites was compared with commercial products to verify, the performance was better than that of the existing PEEK material. The subsidence result was superior to the titanium product and showed similar results to PEEK products. In conclusion, the performance value was superior to the existing PEEK material, and the subsidence result was superior to the titanium product and was similar to the PEEK product, and thus, performance-wise, it is concluded that the PEEK product can be completely replaced with the new product.  
additive manufacturing; 3D printing; spinal cage; porous
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 5 System Architecture  
Figure 4 shows the system architecture. The user has an option to enter the input either by text or by audio. The input is processed through the natural language model designed by the authors and keywords are given as the output. If the text within the keywords contains phrases or a combination of multiple words for which the sign language video is present in the database, then those videos are shown for such casesotherwise, the keywords are tokenized further into words or letters.
# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
## 2 Materials and Methods
### Yolo-Pwsl  
#### 2.3.1 Architecture of YOLO-PWsl  
YOLOv5 is an efficient single-stage target detection algorithm, and its lightweight version, YOLOv5s, is widely used in resource-constrained scenarios due to its few parameters and fast inference. Since underwater fish recognition faces challenges such as variable target scales, complex ambient lighting, and strong background interference, targeted improvements for underwater scene characteristics are needed when using the generic YOLOv5s model for detection. In this study, we chose YOLOv5s, which balances computational efficiency and accuracy, as the baseline model, and the motivation for improvement was to solve the three core problems of underwater small-target fish: shape diversity adaptation, fuzzy target feature extraction, and computational resource constraints. To this end, we used the Wise-ShapeIoU loss function to optimize the bounding box regression in the model, adopted the LGFB module to enhance the multi-scale feature fusion capability, and introduced PConv to achieve lightweight computation. The improved network can effectively detect various types of fish targets in underwater images, including occluded individuals, fuzzy targets, and group-dense scenes. The improved architecture is shown in Figure 6. We replaced the original C3 layer with PConv and added three LGFB modules to perform multi-level feature fusion. When these modules are used together, Wise-ShapeIoU improves the localization accuracy of irregular fish bodies, LGFB captures key features through local-global attention, and PConv reduces the number of parameters while maintaining the feature expression capability.
## 4 Evaluation Metrics
### Packet Delivery Ratio  
The PDR is calculated as the total number of received packets divided by the total number of packets sent from sources, expressed as a percentage [58]. Its formula is given as follows:  
\[PDR=\frac{\sum P_{R}}{\sum P_{S}}\times 100. \tag{6}\]  
where \(P_{R}\) represents the total number of received packets and \(P_{S}\) represents the total number of packets sent.
## 3 Field Test Results and Analysis
### Effect of the Vibration Isolation Trend on Ground Vibration
According to the measurement results and statistics, the peak acceleration and velocity amplitudes in different directions on survey line #1 are almost the same as those on line #2, which proves that the vibration isolation trench set in this test had no obvious isolation effect on the Yangtze River dike caused by dynamic compaction. Several factors may lead to the poor isolation effect of the trench, such as the depth of the trench being too shallow, the distance to the dike being too far, the vibration of the Yangtze River dike being relatively small, and so on. The influence of the above factors on the vibration effect of the Yangtze River dike caused by dynamic compaction will be further explored by using the finite element numerical simulation method in the subsequent chapters.
## 3 Results
### Microtomographic Parameters  
The cortical bone area (Ct.Ar), cortical bone area fraction (Ct.Ar/Tt.Ar) and cortical thickness (Ct.Th) values are presented in Table 2 as the mean and standard deviation of each experimental group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Healthy** & **Control** & **Simvastatin** \\ \hline BV & 0.649 \(\pm\) 0.011 & 0.577 \(\pm\) 0.020 & 0.616 \(\pm\) 0.011 \\ TV & 0.728 & 0.728 & 0.728 \\ BV/TV & 0.892 \(\pm\) 0.016 \({}^{\star}\) & 0.788 \(\pm\) 0.021 \({}^{\star\star}\) & 0.847 \(\pm\) 0.017 \({}^{\star\star}\) \\ EV & 0.078 \(\pm\) 0.011 & 0.150 \(\pm\) 0.020 & 0.111 \(\pm\) 0.011 \\ \%BV/TV & 89.260 & 78.88 & 84.700 \\ \hline \hline \end{tabular}  
* Mean difference is significant between healthy and control (\(p\) < 0.05). ** Mean difference is significant between control and simvastatin (\(p\) < 0.05). ** Mean difference is significant between healthy and simvastatin (\(p\) < 0.05).  
\end{table}
Table 1: Bone volume of each experimental group.  
Figure 6: Bone volume fraction (BV/TV) and empty space (EV) segmentation: (**A,D**) ROI showing the entire suture region of the HG group; (**B,F**) ROI showing the entire suture region of the CT group; (**C,H**) ROI showing the entire suture region of the SVT group; (**E,G,I**) reconstruction of the empty space. HG: healthy; CT: Control; SVT: Simvastatin; ROI: region of interest; micrometer bar in micromillimeters (\(\upmu\)m).  
The Ct.Ar/Tt.Ar fraction in the HG group (90.80%; CI: 89.84-91.76) had a larger cortical bone area fraction when compared to the CT (84.58%; CI: 81.46-87.70; \(p\) = 0.001) and SVT group (88.04%; CI: 87.29-88.79; \(p\) = 0.0394). Furthermore, the SVT group showed a higher percentage of cortical bone (\(p\) = 0.0110) when compared to the CT group. In Ct.Ar, the HG group (0.735 mm\({}^{2}\); CI: 0.727-0.743) had a larger cortical bone area when compared to the CT (0.686 mm\({}^{2}\); CI: 0.658-0.715; \(p\) = 0.0005) and SVT (0.715 mm\({}^{2}\); CI: 0.707-0.724); however, with the latter group, there was no significant difference. Furthermore, the SVT group showed a larger cortical bone area when compared to the CT group (\(p\) = 0.0306). In Ct.Th, the HG group (0.906 mm\({}^{3}\); CI: 0.851-0.962) showed greater cortical thickness when compared to the CT (0.649 mm\({}^{3}\); CI: 0.637-0.661; \(p\) < 0.0001) and SVT (0.818 mm\({}^{3}\); CI: 0.736-0.900; \(p\) = 0.0275). Furthermore, the SVT group showed greater cortical thickness when compared to the CT group (\(p\) = 0.0002).
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 3 Definitions and Algorithms
### UAS Flight Planning in a Geofenced UTM Airspace
distance path
5:\([\mathcal{P}_{turn},\mathcal{G}_{turn}]\gets 3 dOperVol(\mathcal{R}_{turn},\mathcal{V},[t_{climb },t_{desc}],\mathcal{N}_{geo},\delta_{vehicle},h_{cruise})\)
6:\(\mathcal{D}_{turn}\gets getDist(\mathcal{P}_{turn})\lhd\) get turn module flight distance
7:
8://climb solution modules
9:\(\mathcal{R}_{intersect}\gets searchIntersect(\mathcal{R}_{VG})\lhd\) get intersections from \([\mathcal{R}_{start};\mathcal{R}_{end}]\) to \(\mathcal{S}_{geo}\)
10:if\(\mathcal{R}_{intersect}\
eq\emptyset\)then
11:\(h_{intersect}\gets extractHeight(\mathcal{R}_{intersect},\mathcal{S}_{geo})\lhd\) get heights at intersections
12:\(h_{max}\gets max(h_{intersect})\)
13:
14://constant cruise altitude
15:\([\mathcal{P}_{const},\mathcal{G}_{const}]\gets 3 dOperVol(\mathcal{R}_{intersect}, \mathcal{V},[t_{climb},t_{desc}],\mathcal{N}_{geo},\delta_{vehicle},h_{max})\)
16:\(\mathcal{D}_{const}\gets getDist(\mathcal{P}_{const})\lhd\) get constant altitude cruise flight distance
17:
18://terrain follower
19:\([\mathcal{P}_{terr},\mathcal{G}_{terr}]\gets 3 dOperVol(\mathcal{R}_{intersect}, \mathcal{V},[t_{climb},t_{desc}],\mathcal{N}_{geo},\delta_{vehicle},h_{intersect})\)
20:\(\mathcal{D}_{terrain}\gets getDist(\mathcal{P}_{terr})\lhd\) get terrain follower flight distance
21:endif
22:
23://cost comparison
24:\([\mathcal{C}_{min},opt]\gets costCompare(\mathcal{D}_{turn},\mathcal{D}_{ const},\mathcal{D}_{terrain})\)
25:if opt == 1 then
26:\([\mathcal{P}_{traj},\mathcal{G}_{traj}]\leftarrow[p_{turn},\mathcal{G}_{turn}]\lhd\) best sol: turn module
27:elseif opt == 2 then
28:\([\mathcal{P}_{traj},\mathcal{G}_{traj}]\leftarrow[p_{const},\mathcal{G}_{const}]\lhd\) best sol: constant cruise altitude module
29:else
30:\([\mathcal{P}_{traj},\mathcal{G}_{traj}]\leftarrow[p_{terr},\mathcal{G}_{terr}]\lhd\) best sol: terrain follower module
31:endif
32:return\([\mathcal{P}_{traj},\mathcal{G}_{traj}]\)
```  
**Algorithm 4** Flight Planning With Geofencing.
###### Abstract  
Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.  
unmanned ground vehicles; unmanned aerial vehicles; mines; VANET; cluster-head election; multicast communication +
Footnote †: journal: Applied  
Article  
UGVs-MDVR: A Cluster-Based Multicast Routing Protocol for Unmanned Ground and Aerial Vehicles Communication