esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Open Issues and Challenges
### Relative Motion between the UAV and the Sensors
#### 4.2.1 Contact Duration between the UAV and the Sensor  
In U-WSN with mobile sensors, the node has an opportunity to communicate with the UAV when they are within range of each other. The link duration is named as _contact duration_[7; 17; 18; 19]. As the \"one UAV and one sensor\" is the min-unit in U-WSN, we take this scenario where the UAV moves along a predefined path to maintain the network connectivity, for example, to calculate the _contact duration_ (which is denoted as \(T_{cd}\) in this article).  
According to the existing research, we refined the scenario into two categories: (a) both the UAV and the sensors move along a straight path, and (b) they move along a curved path. The other cases, e.g., the \"trajectories\" of the UAV and the sensors have some intersections, \(T_{cd}\) still need further study.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Control and Data Channel  
Owing to the restricted penetration of mmWave-based 5G networks, control overhead for connection and mobility management is implemented on microwave communication bands, whereas data channels are established on mmWave communication bands. This complex integration has a considerable impact because numerous goals must be considered while creating a routing strategy. These objectives include enhancing the average sum rate, reducing the overall delay, and appropriately balancing the load. Thus, they are lacking in adequate research which can incorporate control channels into mmWave bands while maintaining a low overhead. Separating the control and the data channels was not necessary in the traditional 3G and 4G networks. To sum up, the traffic management of the separated channels in mmWave communication is still an open research direction, and focusing on it may pave the route for novel initial call setup and resource allocation protocols.
## 4 Discussion
### Muscle Strength and Physical Fitness Variables  
As mentioned, CrossFit has actions that contemplate muscle strength and power [20]. In this regard, an intervention analyzed the relationship between CrossFit performance in the back squat exercise, primarily during WOD training sessions. It concluded that this exercise has a moderate to strong positive correlation (ranging from r = 0.47 to 0.69) for training performance [20]. Similarly, an experimental study with a 6-month intervention (60 min sessions) obtained a significant increase in back squat exercise performances (_p_ < 0.001) [15]. Another 8-week intervention program in healthy adults who had greater CrossFit experience (greater than 12 months) obtained a significant increase in results after the intervention for back squat exercises (_p_ < 0.001) [33]. A study that performed an intervention in healthy adults with little experience for less than 6 months in CrossFit, which had a duration of 9 weeks, concluded that muscle strength in the back squat exercise is significantly improved (_p_ < 0.001) [12]. On the other hand, in our study, there were no significant changes in deadlift, military press, CMJ, sit-ups, push-ups, or 30 m sprint speed in novice and advanced CrossFitters. A study with similar characteristics over a 4-week period did not obtain significant changes in physical fitness [30]. Nevertheless, the novel response of significant improvements in the back squat from our program supports the importance of this exercise as a key factor for enhancing performance in short-term training cycles. Moreover, the results suggest that these improvements can be optimized by adjusting workloads according to each individual's 1 RM in 4-week intervention; this information could be highly valuable for coaches in planning and prescribing training periods in CrossFit [15; 20]. The existing literature indicates that novice athletes (those with less experience) tend to show rapid and notable improvements due to their lower baseline physical fitness levels. In contrast, advanced athletes experience more gradual and specific improvements, requiring more precise adjustments in their training to continue progressing [15]. Therefore, these results could be explained by the possibility that the stimulus was insufficient to induce changes in the experienced participants, who had a longer training history. In contrast, the novice group was likely in an initial phase of performance improvement during the training intervention.
## 3 Results and Discussion
### Residual Moisture Content and Water Activity  
The freeze-drying technique is one of the drying methods that allows low residual moisture to be reached in the final product. Figure 2 shows the residual moisture content, \(RM\), of freeze-dried pumpkin that was untreated and PEF-treated with different specific energies. As it can be seen, the \(RM\) content in the untreated pumpkin dried at 60 \({}^{\circ}\)C is lower than that of the one dried at 40 \({}^{\circ}\)C. That can be explained by the enhancement of sublimation due to the increased heat flow from the heating plate of the freeze-dryer, resulting in deeper water removal from pumpkin tissue. What is interesting is that the application of PEF\({}_{1}\) in the case of drying at 40 \({}^{\circ}\)C resulted in a reduction of \(RM\) by 20% compared to the untreated sample. However, the PEF\({}_{2}\), PEF\({}_{3}\), and PEF\({}_{4}\) treatments did not show any additional reduction of \(RM\). The opposite behavior has been observed for pumpkin dried at 60 \({}^{\circ}\)C. In this case, PEF\({}_{1}\) treatment increased the \(RM\) of samples after freeze-drying by approx. 3-fold. What is more, the PEF\({}_{2}\), PEF\({}_{3}\), and PEF\({}_{4}\) treatments were able to reduce \(RM\) by approx. 45, 11, and 5%, respectively, compared to the untreated freeze-dried samples. A similar reduction of \(RM\) induced by PEF treatment has been reported previously for apples [19]. The water activity of all investigated samples varied between 0.143 and 0.149. Lower values of water activity were found for samples dried at 60 \({}^{\circ}\)C, but no clear impact of energy input during PEF application was found. However, samples pretreated with 2 kJ/kg, PEF\({}_{4}\), exhibited the lowest values, 0.145 and 0.143 for 40 and 60 \({}^{\circ}\)C, among other materials dried at the same temperature.
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
## 3 Results and Discussion  
### Statistics on Investigated Apartments and Occupants  
The residential high-rise buildings under investigation were constructed between 2010 and 2018. The exterior walls are mainly constructed with burnt clay bricks without thermal insulation. Single-glazed, top-hung windows dominate with transparent components without moveable or fixed external shading systems. Around 80% of the apartments investigated have a floor area between 50 m\({}^{2}\) and 100 m\({}^{2}\). More than half of the households have four family members (parents and two children).  
Every household has at least one AC unit, with 81% owning two or more units. These are split-type AC systems that do not take in outdoor air. Additionally, most households have more than two electric fans, which are frequently used in conjunction with window ventilation and AC cooling. On weekdays, 80% of respondents leave home between 6:30 and 7:30 and return between 17:00 and 18:00.  
In a mixed-mode building, occupants can maintain thermal comfort through mechanical cooling with AC, opening windows, or using electric fans. Figure 3 shows the respondents' cooling preference rankings, where \"Level I\" indicates a strong preference, while \"Level IV\" represents a less favored option.  
The results indicate that electric fans are the most preferred cooling measure, followed closely by split-type ACs. Conversely, opening windows is the least favored option, as outdoor temperatures in hot and humid climates are typically higher than indoor temperatures. Regarding the factors influencing cooling choices, 60% of respondents prioritize energy cost, whereas 40% consider thermal comfort the primary factor. These varying cooling preferences contribute to differences in indoor climate conditions and AC-related energy consumption across apartments.
# Revisiting the Detection of Lateral Movement through Sysmon
## 5 Lateral Movement Categories on Windows Environments
### Golden Ticket  
Golden Ticket is another Active Directory attack related to Kerberos authentication exploitation techniques. While access with administrative privileges is important for the adversary, persistence during lateral movement within a targeted network is the real challenge. This aspect is crucial for an opponent to reach its target within network environments in which the domain's admin password changes frequently.  
Figure 2: High-level view of Kerberos Authentication procedure, along with the NTLM hash extraction vulnerability, that makes KDC prone to the Hashing Exploitation Attacks of Section 6.2, namely, PtH, PtT, Golden/Silver Ticket.  
Golden Ticket migrates features from both PtH and PtT LM methods. Precisely, it exploits the Kerberos authentication protocol's lack of validation procedures during the impersonation of a legitimate host. This is due to the design of the Kerberos protocol to allow users that hold a TGT for a session to be considered trusted for any other network resource related to ticket authentication.  
In the context of this work, Mimikatz is used as a standalone tool for the realization of GT. There is also the possibility of the Metasploit Framework's automated _meterpeter_ extension as a faster solution for the execution of the attack.
## Materials and Methods
### Processes of Results Analysis  
The basic analysis unit was a pixel with a resolution of 1-km. Multi-phase images were all geo-registered, and pixels at the same location in the multi-phase images can ensure spatial matching. The area of different land use types in different scenarios was calculated by the aggregation (average) of multiple pixels belonging to the same land use type. The mutual transfers between land use types indicate that the pixels in the same location might be transformed into the other five categories or remain unchanged in the next period. The pre-matrix and post-matrix were compared using MATLAB (Version 2019b) and mapped using ArcGIS (Version 10.2) to obtain land conversion maps for each scenario. Similarly, the ecosystem functions of different land use types in different scenarios were also calculated based on pixel aggregation.
## 2 Irrigation System Modeling
### Water Balance Modeling  
The water balance estimates the water content in the root zone at a given time, water increases in the presence of rain, irrigation and capillary rise (see inputs in Figure 1), while the amount of water in the soil decreases due to evapotranspiration, runoff and deep percolation (see outputs in Figure 1). The AD represents the available water between the field capacity and the permanent wilting point. Within these limits there is a threshold which allows the plant to have readily available water (_RAW_) in the root zone [10].  
To predict the water content in the soil, the daily moisture balance for the root zone of the soil is needed. The dynamics of water content in the root zone is described by Equation (1), which represents the water balance [10; 27].  
\[H(t+1)=H(t)+P(t)+R(t)-ET_{c}(t)-PP(t) \tag{1}\]  
where \(H(t+1)\) is the water lamina in the root zone at the end of the day in mm, \(H(t)\) is the water content in the root zone at the beginning of the day in mm, \(P(t)\) is the precipitation occurring in the day in mm, \(R(t)\) is the irrigation applied in mm, \(ET_{c}(t)\) is the crop evapotranspiration in mm, and \(PP(t)\) is deep percolation in mm.  
In the root zone, root growth, root depth, crop evapotranspiration estimation, rainfall and irrigation must be taken into account to determine the moisture in the soil, which is limited by field capacity and permanent wilting point [27].
## 2 Experimental Details
### Processing  
Milling was carried out for 2 h at a speed of 200 rpm in an Argon atmosphere to ensure uniform elemental distribution. The ball-milling parameters have been reported elsewhere [27]. The milled B+GNPs powder was mixed with Al powder (overall molar ratio 1:24) for an additional 5 h using a ball mill and subsequently pre-heated (overnight at 80 \({}^{\circ}\)C) and green compacted (60 vol.%). To prepare the corresponding Al\({}_{3}\)BC/Al composite, the green compact was then sintered in a vacuum sintering furnace (tubular furnace) at 1000 \({}^{\circ}\)C. The sintering process maintained a slower heating and pressurization process, e.g., heated at a rate of 5 \({}^{\circ}\)C/min, kept for 1 h (soaking time) below 5 \(\times\) 10\({}^{-2}\) mbar pressure, and then slowly cooled to room temperature. This ensures Al will maintain formability favorably and even become liquid at 680 \({}^{\circ}\)C. Finally, a low-pressure (40 MPa) and high-temperature (400 \({}^{\circ}\)C) hot-press deformation was applied for 20 min in a graphite mold (\(\varphi\) 20 mm) to achieve the final consolidated specimens. During hot pressing, the heating rate was maintained at 25 \({}^{\circ}\)C/min, followed by water cooling. The experimental procedure employed in this research is shown schematically in Figure 1.  
Figure 1: The schematic process flow chart employed for the preparation of the Al\({}_{3}\)BC/Al composite through the powder metallurgical processing route.
## 3 Materials and Methods
### Study Area Generalization and Model Establishment  
#### 3.2.1 Catchment Generalization  
Catchment partitioning can be conducted by two primary methods: the Thiessen polygon method and the manual partitioning method. In the Thiessen polygon method, the software automatically identifies and divides study areas, making it a fast and efficient approach for large-scale catchment divisions. However, it is less suitable for highly developed  
Figure 2: Flowchart of the SWMM simulation of CUG.  
areas. In contrast, the manual partitioning method can more accurately determine surface runoff flow directions and divide study areas into catchments based on landform, topography, and the spatial distribution of maintenance and rainwater wells across different sectors. This method is particularly effective in densely developed areas, though it has the drawback of lower computational efficiency compared to the Thiessen polygon method.  
Since CUG's west campus is extensively developed, with lecture halls, roads, and public facilities occupying much of the original land and given the relatively compact campus area, we employed the manual partitioning method to divide the catchments. Based on the land use types and layout of the campus, as shown in Figure 1, building areas, playgrounds, and roads were classified as impervious zones, while green spaces and ponds were categorized as pervious zones, with different permeability coefficients assigned accordingly. The internal drainage network is primarily located beneath the main roads, which serve as boundaries for watershed division. Sub-watersheds were further delineated based on the land use types within these areas.  
The catchment sizes were determined according to the actual conditions of the network plan and land use types. Following the proximity principle, runoff from each catchment flows into the nearest pipe network node. The campus area contains 75 catchments, with watershed areas ranging from 0.029 \(\times\) 10\({}^{-2}\) km\({}^{2}\) to 1.059 \(\times\) 10\({}^{-2}\) km\({}^{2}\), and impervious area percentages ranging from 0% to 95.90%.
## 5 Discussion  
The seismic refraction method measures the travel time of the waves refracted at the interface between different sublayers with different wave velocities or impedances. Thus, the thickness of each layer can be calculated using the wave velocities of two consecutive layers and the time intercept in the travel time-distance curve as:  
\[Z_{i}=\frac{t_{i}\cdot V_{i}\cdot V_{i+1}}{2\sqrt{(V_{i+1}{}^{2}-V_{i}{}^{2})}} \tag{2}\]  
where \(Z\) = thickness of layer; \(t\) = time intercept; \(V\) = wave velocity; and \(i\) and \(i+1\) = different layers. The thickness of the soil layer (or the first layer) can be determined by the velocities of the first layer and the second layer (\(V_{1}\) and \(V_{2}\)), and the time intercept (\(t_{1}\)). However, the clear determination of \(V_{1}\), \(V_{2}\), and \(t_{1}\) is not easy because wave velocity in a given layer is not constant, resulting from the natural soil deposits rarely being homogeneous and wave velocity slightly increasing with an increase in depth at a given layer. Note that seismic waves propagate in a medium through connected particles, and seismic wave velocity depends on soil structure and stress condition. Seismic wave propagation is primarily affected by the stiffness of the fabric in a particular material, and thus seismic wave velocity and effective stress (\(\sigma^{\prime}\)) have a certain relationship with experimentally determined coefficients (\(\alpha\) and \(\beta\)). \[\text{Seismic wave velocity}=\alpha(\sigma^{\prime})^{\beta} \tag{3}\]  
The \(\alpha\) and \(\beta\) coefficients are dependent on the packing type (porosity and coordination number) and the contact force (Hertzian contact and Coulombic force). The seismic wave velocity slightly increases with depth even in one layer because of the increase in effective stress (Equation (3)), which is the function of mass density and depth. However, the wave velocity changes rapidly at layer boundaries due to the change in material type, mass density, particle size, and others. Hence, the relation between seismic wave velocity and depth can produce different slopes along the depth. Figure 7 shows the seismic wave velocity with the depth at which the geophones are installed. It can be observed in Figure 7 that there are four different lines \"a\", \"b\", \"c\", and \"d\": Line \"a\" indicates the initial slope between wave velocity and depth; thus, it is related to the first soil layer; Line \"b\" indicates the second slope between wave velocity and depth; thus, it is related to the second layer. Therefore, the intersection point between lines \"a\" and \"b\" may correspond to the thickness of the first soil layer, which is point \"e\". Figure 8 shows the calculated soil depths based on the suggested graphical bilinear method at the selected testing sites. It is shown in Figure 8 that
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Titanium Oxide Films  
Titanium (III) chloride and ammonium hydroxide (NH\({}_{4}\)OH) solution were used to produce TiO\({}_{2}\) films on ITO glass substrates at 300 K, under 250 deposition cycles [137]. The growth rate was 0.6 nm/cycle during the experiment, based on the XRD analysis, the presence of TiO\({}_{2}\) with Ti\({}_{3}\)O\({}_{5}\) and beta phase titanium dioxide or titanium (IV) oxide (TiO\({}_{2}\)) in as-deposited films and annealed films, respectively. SEM studies confirmed compact morphology with spherical grains (20-50 nm). In photoelectrochemical studies, annealed films (450 \({}^{\circ}\)C, 6 h) indicated more photoactivity and stability in electrolytes than as-deposited films. [PERSON] and co-workers [138] reported the growth rate was 0.01-0.02 nm/cycle, with the film thickness about 0.3 to 1.2 \(\upmu\)m. The films annealed at 300 \({}^{\circ}\)C showed amorphous phases, while annealed films (400 \({}^{\circ}\)C) successfully improved the crystallinity of the films in the XRD studies. Based on the optical characterization, direct (3.4 to 3.8 eV) and indirect band gap values (2.1 to 3.8 eV) increased with the annealing process. [PERSON] and co-workers [139] explained the mechanism of film formation via titanium (III) chloride (TiCl\({}_{3}\)) (source of Ti\({}^{3+}\)) and hot water (source of oxygen) under 150 cycles, at 353 K, as highlighted in Equations (45)-(47).  
\[4\mathrm{Ti}^{3+}+\mathrm{O}_{2}+2\mathrm{H}_{2}\mathrm{O}\to 4\mathrm{H}^{+}+4 \mathrm{TiO}^{2+} \tag{45}\]  
\[\mathrm{TiO}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2}\to\mathrm{H}_{2}\mathrm{O}+ \mathrm{Ti}(\mathrm{O}_{2}\ )^{2+} \tag{46}\]  
\[\mathrm{TiO}^{2+}+\mathrm{OH}^{-}\to\mathrm{H}^{+}+\mathrm{TiO}_{2} \tag{47}\]  
SEM studies confirmed bigger grain sizes in as-deposited films (228 nm) compared to annealed films (203 nm) because of the water evaporation in the annealing process. Moreover, flower-like morphology could be detected because grain coalescence caused them to grow. Fluorine-doped tin oxide-coated glass was used as the substrate during the formation of films [140]. Spherical grains with irregular shapes could be observed in SEM images. In the surface wettability analysis, these films were hydrophobic in nature (water contact angle more than 90\({}^{\circ}\)). TEM images confirmed chain-like structures with small particles as reported by [PERSON] and co-workers [141]. [PERSON] and co-workers [142] described the synthesis of films by using TiCl\({}_{3}\) and sodium hydroxide (NaOH). They observed (a) a higher growth rate when a higher concentration of precursor was used, and (b) poor quality of the films due to powdery deposits. They suggested that the obtained films be heated at 673 K for 120 min to remove hydroxide and improve the crystallinity.  
Figure 8: The conduction pathway of electrons (at grains and grain boundaries in the SILAR-deposited ZnO thin films using (**a**) distilled water, and (**b**) ethanol reproduced with permission from [135], [PERSON], 2022.
## 4 Results
Thus, adjacent channel interference can be calculated using the following expression:  
\[I_{BS/ULE}=P_{TX}+G_{IMT}+G_{FS}-L_{p}-L_{xpr}-FDR(\Delta f) \tag{9}\]  
The following \(I/N\) curves in Figure 10 were obtained for the deployment scenario when 5G network and FS receiver were located within the same area, with adjacent channel frequency separation, where the curves mean the following:  
* The red curve is for urban deployment, using 60 m FS stations;
* The blue curve is for urban deployment, using 20 m FS stations;
* The green curve is for suburban deployment, using 60 m FS stations;
* The yellow curve is for suburban deployment, using 20 m FS stations;  
As can be seen from the figure, \(I/N\) criteria was exceeded, from 10 to 45 dB, for every deployment scenario of 5G and for every height of the FS station. The results clearly indicate that co-location of 5G network and FS stations, even in adjacent channels, might be problematic if compatibility is estimated using \(I/N\) criterion. The following \(C/I\) curves in Figure 11 were obtained for the deployment scenario, when 5G network and FS receiver were located within the same area, with adjacent channel frequency separation. Where the red curve is for urban deployment using 60 m FS stations, the blue curve is for urban deployment using 20 m FS stations, the green curve is for suburban deployment using 60 m FS stations, and the yellow curve is for suburban deployment using 20 m FS stations. Figure 10: \(I/N\) curves for adjacent channel interference when 5G network and FS receivers are located in the same area. As can be seen from the above figure, \(C/I\) criteria is met in 100% of snapshots, and margins are from 5 to 50 dB. The results indicate that planning 5G and FS co-location scenarios, using \(C/I\) criteria, allows for compatibility in shared locations.