esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 4 Verification of Developed Impact Formula
### Penetration Depth Assessment  
The tested penetrated depth to predicted penetration depth ratios are compared in Table 2 and Figure 10, under the application of the proposed formula, modified NDRC, ACE, Haldar, Hughes, UKAEA, UMIST, and Li-Chen. The penetration depths of the perforated specimens are also used. In Table 2, the data include the specimens in which the penetration depth is measured. The overall average ratios are found to be 1.00, 0.87, 0.85, 1.2, 0.74, 1.13, 1.97, and 1.36 under the application of the proposed formula, modified NDRC, ACE, Haldar, Hughes, UKAEA, UMIST, and Li-Chen, respectively. Even if the overall average ratio for each formula seems to be good, the average value in each test varies (Table 2). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multicolumn{6}{c}{**Projectile**} & \multicolumn{6}{c}{**Concrete panel**} \\ \cline{2-10}  & \(d_{p}\) & \(l_{p}\) & \(M_{p}\) & \(V_{imp}\) & \(N_{p}\) & \(f_{c}\) & \(h\) & \(V_{f}\) & \(\rho_{steel}\) (\%) \\ \hline Abdel-Kader and & 23 & 64 & 175 & 201–354 & Blunt & 26 & 100 & - & 0.4 \\ [PERSON] [59] & 40 & 115 & 800 & 91–135 & Sharp & 29–71 & 90 & 0–0.9 & 0.75 \\ [PERSON] et al. [57] & 13 & 24 & 15 & 306-658 & Ogive & 45–90 & 55 & 0–2.33 & \\ [PERSON] et al. [54] & 76 & 531 & about 13,000 & 164–335 & Ogive & 23 & 910–1830 & \\ [PERSON] et al. [56] & 12.6 & 24 & 15 & 620–704 & Ogive & 45–237 & 150 & 0–1.5 & \\ [PERSON] et al. [53] & 76 & 531 & about 13,000 & 200–448 & Ogive & 23–39 & 1220–1830 & - & \\ [PERSON] et al. [55] & 20–31 & 203,305 & 478,1600 & 442–1225 & Ogive & 58 & 940–3050 & \\ [PERSON] et al. [52] & 20–31 & 203,035 & 478,1600 & 405–1358 & Ogive & 51–63 & 910–2740 & \\ [PERSON] et al. [24] & 27 & 242 & 906 & 277–800 & Ogive & 32–108 & 760–1830 & - \\ [PERSON] [60] (first test) & 20 & 20 & 33 & 270,350 & Hemisphere & 29–42 & 30–70 & 0.5–2.0 & \\ [PERSON] [60] (second test) & 20 & 20–40 & 33–90 & 95–200 & Hemisphere, Sharp & 41–103 & 50–100 & \\ [PERSON] [60] (third test) & 20 & 20 & 33 & 200 & Hemisphere & 168 & 10–30 & 1.5 & \\ Total & 20–76 & 20–531 & 15–13,000 & 91–1358 & & & & & \\  & & & & & & & & & \\ \hline \hline \end{tabular} Note: \(d_{p}\) is the diameter of a projectile (mm); \(l_{p}\) is the length of a projectile (mm); \(M_{p}\) is the mass of a projectile (kg); \(V_{imp}\) is the impact velocity of a projectile (m/s); \(N_{p}\) is the nose shape of a projectile; \(f^{\prime}_{c}\) is the compressive strength of concrete (MPa); \(h\) is the thickness of a concrete panel target (mm); \(V_{f}\) is the steel fiber volume fraction (%); \(\rho_{steel}\) is the bottom reinforcing ratio in \(b_{puncl}d_{puncl}\) for each face; \(b_{puncl}\) is the width of the specimens (mm); and \(d_{puncl}\) is the average effective depth (mm).  
\end{table} 
Table 1: Summary of impact test programs. Figure 10: Ratio of tested to predicted penetration depth (Avg. is the average and SD is the standard deviation).
## 3 Key Technologies of UAV-Assisted SAGINs
### Routing Protocols
#### 3.1.1 Static Routing Protocols  
A static routing protocol is an automatic route discovery mechanism preconfigured by network administrators, which does not rely on dynamic routing protocols [42]. In SAGINs, this means that network administrators need to manually set up routing tables and specify the path of data packets from the source node to the destination node based on the predetermined flight path and mission requirements of the UAV. In UAV-assisted SAGINs, the implementation of a static routing protocol begins with network administrators preconfiguring routing tables, including setting fixed routing paths as communication links between UAVs and ground stations, satellites, or other network nodes [43]. [PERSON] et al. [44] proposed a communication topology-preserving motion planning method to enable static routing in UAV networks. When a packet arrives at a router or drone node, the device forwards the packet to the next designated node based on the preset routing table entry. This process does not involve complex algorithm calculations but is directly carried out based on the administrator's configuration. In addition, static routing protocols provide a high degree of stability and predictability, which is particularly important for the control instructions and critical data transmission of UAVs. In situations where the network topology is relatively fixed, static routing protocols can ensure the reliability of data transmission.
## 3 Obstacle Avoidance Motion Planning Framework
### Compliance-Oriented Potential Field  
Passing through intersections involves multiple traffic laws. Although specific provisions in the traffic laws differ across countries and regions, a significant degree of consistency in fundamental principles is shared. Through analysis, seven common types of violations at intersections are categorized: speeding, failure to stop, failure to yield, improper turn, marked lane violation, illegal stopping, and illegal reversing. Based on the standardized mathematical description process proposed in the previous work [32], the traffic laws involved at intersections are digitized to form logical propositions for violation judgments. The basic violation propositions of common violations are extracted. The expressions are shown in Table 2.  
Three basic violation propositions are constrained by three types of compliance-oriented potential fields: lane-marking potential field, stop-line potential field, and speed-limit potential field. In this study, the design of the compliance-oriented potential field is based on China's traffic law, specifically, the _Regulation on the Implementation of the Law of the People's Republic of China on Road Traffic Safety_[33] and the _Road traffic signs and markings  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Violation Proposition** & **Digitized Expression** \\ \hline Crossing the lane line & \(overlap(area(ego),laneline)\) \\ Crossing the stop line & \(overlap(line(mfrntp(ego),crdn(ego)),stopline)\) \\ Speed violation & \(\big{(}vx(ego)>vx_{upper}\big{)}\vee(vx(ego)<vx_{lower})\) \\ \hline \hline \end{tabular} Where _ego_ denotes the ego vehicle; the atomic proposition _crdn(obj)_ denotes the geometric center coordinates of the traffic participant _obj_; _mfrntp(obj)_ denotes the coordinates of the midpoint of _obj_’s front end; _rx(obj)_ denotes the longitudinal velocity of _obj_; _rx\({}_{appr}\)_ and _rx\({}_{iaour}\)_ denote the upper and lower bounds of the speed limit, respectively; _ara(obj)_ denotes the area structure that includes the vertex coordinate structure of obj; _line(\(p_{1}\), \(p_{2}\))_ denotes the line between points \(p_{1}\) and \(p_{2}\); _overlap(\(p_{1}\), \(r_{2}\))_ judges the overlap status of areas \(r_{1}\) and \(r_{2}\); _stopline_ and _landeline_ denote stop line and lane line, respectively.  
\end{table}
Table 2: Basic violation propositions and their digital representations.  
_Part 3: Road traffic markings_[34]. The overall compliance-oriented potential field is the sum of the compliance-oriented potential functions (PF):  
\[U_{\text{com}}=\sum_{i}U_{\text{laneline}_{i}}+\sum_{j}U_{\text{ stopline}_{j}}+\sum_{m}U_{\text{speed}_{m}} \tag{15}\]  
where \(U_{\text{laneline}}\), \(U_{\text{stopline}}\), and \(U_{\text{speed}}\) are lane marking potential PF, stop line PF, and speed limit PF; \(i,j\), and \(m\) denote the index of the \(U_{\text{laneline}}\), \(U_{\text{stopline}}\), and \(U_{\text{speed}}\), respectively.
## 3 Results and Analysis
### The Basic Description of the Virtual Community Case
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sector**} & \multirow{2}{*}{**Archetype**} & \multirow{2}{*}{**Description *} & \multirow{2}{*}{**N**} & \multicolumn{2}{c}{**DS3 *} \\ \cline{5-5}  & & & & \(\lambda\) & \(\xi\) \\ \hline \multirow{4}{*}{R.S.} & R1 & One-story single-family residential building & 500 & 1.173 & 0.278 \\  & R2 & One-story multi-family residential building & 200 & 0.589 & 0.439 \\  & R3 & Two-story single-family residential building & 200 & 1.512 & 0.240 \\  & R4 & Two-story multi-family residential building & 100 & 1.214 & 0.288 \\ \hline \multirow{4}{*}{BS} & B1 & Multi-unit retail building & 7 & 0.450 & 0.860 \\  & B2 & Small multi-unit commercial building & 2 & 0.456 & 0.724 \\  & B3 & Super retail center & 1 & 0.412 & 0.913 \\ \hline \multirow{2}{*}{ES} & E1 & One-story school & 2 & 0.824 & 0.531 \\  & E2 & Two-story school & 2 & 1.388 & 0.435 \\ \hline \multirow{4}{*}{PS} & P1 & Small grocery store/gas station with a & & & \\  & & convenience store & & & \\  & P2 & Hospital/clinic & 5 & 1.034 & 0.742 \\ \hline \hline \end{tabular}  
* From [6], \(A_{DS_{I}}\) and \(\xi_{DS_{I}}\) are the logarithmic median and standard deviation of each damage state, respectively, when IM = x.  
\end{table} 
Table 3: Lognormal parameters of the developed fragility for the selected building archetypes.
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 4 Proposed Method  
### Objective Function Using the Maximin Principle  
Assuming total \(k\) commodities exist, the constraint equation and objective function applying the maximin principle [18] are shown below.  
**constraint equation:**:  
\[\sum_{e\in\delta^{+}(s^{n})}x_{e}-\sum_{e\in\delta^{-}(s^{n})}x_{e}\geq v\ \ \ (n=1,\ldots,k) \tag{1}\]
**objective function:**:  
\[\text{Max. }v \tag{2}\]  
Equation (2) is intended to maximize the minimum flow among all commodities, not the maximum flow of each commodity. To increase the flow in a state where the flow of each commodity is fair, the objective function in Table 1 and the new weights \(w_{1}\) and \(w_{2}\) are combined into Equation (3). The \(w_{1}\) and \(w_{2}\) values are manually changed by the administrator according to the actual network conditions.  
\[\text{Max. }w_{1}v+w_{2}\sum_{n=1}^{k}\left(\sum_{e\in\delta^{+}(s^{n})}x_{e}- \sum_{e\in\delta^{-}(s^{n})}x_{e}\right)\ \ (w_{1}+w_{2}=1) \tag{3}\]
# Research on Variable-Step-Size Adaptive Filter Algorithm with a Momentum Term
## 2 Algorithm Principle  
### Principle of LMS Algorithm  
The adaptive LMS algorithm is based on the Wiener filter and incorporates the gradient descent method. It minimizes the error between the filter output \(y(n)\) and the desired output \(d(n)\) by adjusting the weight vector. The cost function used is obtained as follows:  
\[J(n)=\frac{1}{2}e^{2}(n) \tag{1}\]  
By taking the gradient of \(J(n)\) with respect to \(w(n)\), the weight update formula for the standard LMS algorithm can be obtained as follows:  
\[\begin{array}{ll}w(n+1)&=w(n)+\mu(-\
abla J(n))\\ &=w(n)+\mu e(n)X(n)\end{array} \tag{2}\]  
The schematic diagram of system identification based on the LMS algorithm is shown in Figure 1.  
In summary, the LMS algorithm consists of two main processes: filtering and adaptive adjustment. The specific steps are as follows:  
Figure 1: Schematic diagram of system identification based on LMS algorithm.  
Figure 2: Schematic diagram of LMS algorithm model.  
1. Determine the parameters: the global step size parameter \(\beta\) and the number of taps in the filter.
2. Initialize the initial value of the filter.
3. Algorithm operation process:  
Here, \(X(N)=\left[x(n),x(n-1)\cdots,x(n-k+1)\right]^{T}\) represents the input of each tap of the \(k\)-th order filter.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 5 Conclusions  
This study compared the performance of static and SLAM scanners from various generations in a typical commercial indoor environment characterized by long, straight corridors with recesses. Although the static scanners achieved the highest accuracies (overall RMSE of 1.2 mm for Leica RTC360 and 5.4 mm for Trimble X7), new-generation SLAM scanners approached this accuracy (7.3 mm for Emesent Hovermap ST-X and 8.8 mm for FARO Orbis). This was a significant improvement when compared to the older generation scanner GEOSLAM ZEB Horizon RT with overall RMSE of 36.2 mm. These results were further confirmed in deeper analysis of local accuracies. Considering the fact that denoising of SLAM point clouds leads to further improvement of the accuracies and the rapid scanning when employing this type of instruments, we conclude that the use of new generation scanners already provides a highly suitable alternative to static scanners in indoor building environments.  
Conceptualization, M.S. and R.U.; methodology, M.S.; software, O.M.; validation, J.B., O.M. and M.S.; formal analysis, A.C.; investigation, T.K.; resources, A.C.; data curation, A.C.; writing--original draft preparation, R.U.; writing--review and editing, M.S.; visualization, A.C.; supervision, M.S.; project administration, R.U.; funding acquisition, R.U. All authors have read and agreed to the published version of the manuscript.  
The research was co-funded by the European Union under the project INODIN, no. CZ.02.01.01/00/23_020/0008487 and The Technology Agency of The Czech Republic under the project MESYVVED no. SQ01010105.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Hereby: we would like to thank the technicians performing the measurement and primary data processing, namely [PERSON] (the company 3 gon Positioning s.r.o., instruments GeoSLAM ZEB Horizon RT and FARO Orbis); [PERSON] (Geotronics s.r.o., Emesent Hovermap ST-X), [PERSON] (Gefos s.r.o., Leica RTC 360. For the purpose of Open Access, a CC BY 4.0 public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.  
The authors declare no conflicts of interest.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.3 A New High-Resolution Nation-Wide Aboveground Carbon Map for Brazil  
Based on existing biomass maps and an up-to-date Land Use and Land Cover (LULC) map, [PERSON] et al. (2014) presented a new high resolution (50 m) nation-wide aboveground carbon map for Brazil. They evaluated this map with an independent reference dataset from [PERSON] et al. (2013), and found high consistency between the two datasets with a corresponding R\({}^{2}\) of 0.59.
## 1 Introduction  
Diabetes mellitus (DM) is closely related to blood glucose, and blood glucose regulation is closely related to \(\alpha\)-glucosidase activity [1]. An important research direction for hypoglycemic drug research is to screen \(\alpha\)-glucosidase inhibitory active components [2]. Voglibose, miglitol, and acarbose are \(\alpha\)-glucosidase inhibitor drugs that have been successfully marketed for the treatment of type II DM [3]. However, these drugs can also cause side effects such as diarrhea, abdominal distention, and flatulence [4]. In recent years, it has been reported that the \(\alpha\)-glucosidase inhibitory activity effects of wild blue honeysuckle fruit [5], raspberry [6], and mulberry [7] are remarkable, making them potential targets for \(\alpha\)-glucosidase inhibitors. Several \(\alpha\)-glucosidase inhibitory active components from natural products have been reported, such as flavonoids [8], alkaloids [9], polysaccharides [10],and so on, which have become research topics due to their milder side effects compared with voglibose, miglitol, and acarbose.  
_Zanthoxylum bungeanum_ is a deciduous tree, and its fruit is an important food condition for humans. The aroma of _Zanthoxylum bungeanum_ fruit is due to the high amount of essential oil. It has been reported that _Zanthoxylum bungeanum_ essential oil has hypoglycemic [11] and antioxidant [12] activities. Phenols and alkylamides (ethanol extract) have been identified by HPLC-ESI-MS and are considered the main components underlying \(\alpha\)-glucosidase inhibition [13]. However, the contribution of the volatile components (essential oil) of _Zanthoxylum bungeanum_ to \(\alpha\)-glucosidase inhibition has not been determined.  
Steam distillation is the most common extraction method for plant essential oils [14]. In recent years, deep eutectic solvent (DES) has also been used to improve the extraction yield of plant essential oils [15]. DES can dissolve the plant fiber structure so that the active ingredients can be more easily released from cells [16]. DES, a new ionic liquid analog discovered by Abbott, is a green, environmentally friendly, and efficient extraction solvent [17]. It is a low-eutectic mixture composed of two or three components, containing a certain proportion of hydrogen bond donors and receptors.  
Ligand molecules and receptor molecules are simulated and connected by molecule docking technology so as to find the best binding mode between them, as well as to determine the active sites and reasonable conformation [18]. Nowadays, molecular docking is a powerful tool that is used to reveal the mechanism of biological activity [19; 20; 21].  
The present study aims to provide a comparison of the inhibitory activity of _Zanthoxylum bungeanum_ essential oil and other plant essential oils against \(\alpha\)-glucosidase. In addition, the study aims to determine how the components of _Zanthoxylum bungeanum_ essential oil induce their inhibitory action against \(\alpha\)-glucosidase, thus, contributing findings that may be beneficial to the utilization of _Zanthoxylum bungeanum_ as a health food.
## 2 Formulation of the Effectiveness Criterion
### Second Condition: Isolation Effectiveness  
The relationship between the seismic force acting on the not isolated substructure and the elastic capacity of the pier is expressed, in terms of spectral acceleration, by comparing the Equivalent Substructure acceleration with the Elastic Limit acceleration. From this comparison, the second condition (isolation effectiveness) follows:  
\[a_{ES}\leq a_{EL} \tag{12}\]  
Inequality (12) expresses the fact that, if \(a_{ES}\) is less than or equal to \(a_{EL}\), then the elastic capacity of the pier is sufficient to resist the seismic force associated with the not isolated mass of the pier. Unlike Inequality (7), Inequality (12) must be satisfied in both principal directions of the bridge; otherwise, deck isolation alone is not effective in reducing the vulnerability of the structure, and other approaches must be implemented, such as local strengthening, etc.  
By combining Inequalities (7) and (12), the criterion of effectiveness for deck isolation can therefore be formulated as  
\[a_{ES}\leq a_{EL}\ \leq a_{As-Built} \tag{13}\]  
This criterion is graphically represented in Figure 5.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments  
### Datasets  
To evaluate the performance of our proposed MMCL, we conduct experiments on three challenging video temporal grounding datasets:  
**Charades-STA**[1] consists of videos portraying routine activities, sourced from the Charades dataset [37]. It encompasses 6672 videos, 16,128 annotations, and 11,767 temporal moments. The uneven distribution of clip lengths and a high degree of overlap place considerable demands on local feature modeling. Of the annotated moments, 12,408 are allocated for training and 3720 for testing.  
**ActivityNet Caption**[38] was initially constructed for dense video captioning. It comprises around 20,000 YouTube videos with diverse contents, averaging 120 s in length. The dataset features semantic variety and broad domains, placing higher demands on the multimodal representation capability of models. Following the experimental setup in [22, 23], val1 is used as the validation set and val2 as the test set, containing 37,421 and 17,505 annotated temporal moments for training and testing, respectively. Currently, this is the largest dataset in this task.  
**TACoS**[39] is a video temporal grounding dataset focusing on kitchen scenarios, derived from the MPII Cooking Composite Activities dataset [39]. It comprises 127 videos with annotated time segments, totaling over 18 h in duration. The dataset features a large number of consecutive actions, with dense temporal distributions and a high overlap ratio, posing greater challenges for boundary-based models. Following [22, 23], 10,146, 4589, and 4083 temporal annotations are used for training, validation, and testing, respectively.
# Quality of Service Based NOMA Group D2D Communications
## 1 Introduction
### Mathematical Preliminaries on Gauss-Poisson Process  
The generalized GPP [21] is defined as the Poisson cluster process with homogeneous independent clustering. Denote \(\lambda\) as the parent process intensity. Then, each cluster in GPP can be classified as single-point or two-point cluster. Let \(1-a\) and \(a\) denote the probabilities that a cluster is single-point, and two-point, respectively. When the cluster is single-point, the point is located at the position of the parent. When the cluster is two-point, one point is located at the position of the parent while the other is randomly distributed around the parent with some probability density function (PDF) \(f_{u}(\cdot)\), where \(u\) is the inter-point distance in two-point cluster.
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 4 Isothiocyanates Antimicrobial Properties against Human Pathogens  
Since the early 1900s, great interest has been shown in plant antimicrobial properties. [PERSON] reported in 1925 on plants' resistance to infection, providing a scientific contribution describing the ability of onion bulbs to resist fungal infections [40]. The interest in plant antimicrobial properties is related both to improving the cultivation and conservation of edible plants and to understanding their role in human health. For this reason, many articles focus on plant metabolites with antibacterial properties [41; 42]. Only recently, the lack of new antibiotics and subsequent failure to provide resolute therapy against multi-drug-resistant microbes has increased the need of an intense search for innovative solutions. This review focuses on ITCs' antimicrobial properties against human pathogens, especially against bacteria with multi-drug-resistance phenotypes for which the discovery of a new therapeutic solution is more pressing. The antimicrobial activity of ITCs against the microbes selected in this review is summarized in Table 2.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**ITCs** & **ITCs Source** & **Sensible Bacterial** & **Mechanism of Action** & **Reference** \\ \hline
**Sulforapahane** & broccoli seed & _H. pylori_ & NA & [43] \\ \hline
**Sulforapahane** & Synthetic & _P. aeruginosa_, EHEC _. coli strains, S aureus_ & SFN inhibits bacterial quorum sensing, affects the pyocyanin production and exerts anti-biofilm activity against _P. aeruginosa_. & [44; 45; 46] \\ \hline
**Phenethyl isothiamate PEITC** & _Sinapis albt_ & _C. difficile_ ATCC 9689 and C. _perfringus_ and ATC 13124 & PETIC mechanism of action against _Clostriila_ species appears to be correlated with aromatic structure. & [47] \\ \hline
**Benzyl isothiamate PEITC** & _Salvatora persica_ root & _S. enterica_ Typhimurium & Essential oil rich in BITC, induces the loss of bacterial membrane integrity. BITC inhibits EHEC _. coli_ Shiga toxin production. & [52] \\ \hline
**Benzyl isothiamate BITC** & _Synthetic & _C. difficile_ C. _perfringus_; _EPfringus_; _