esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Improvement Considering the Vibration Deterioration Effect of the Structural Plane in FDEM
### Solution Steps of Dynamic Safety Factor Considering the Vibration Deterioration Effect  
[PERSON] et al. (2023) introduce the basic principle, solution steps, and simulation examples of the dynamic strength reduction method based on FDEM, demonstrating that the improved FDEM can analyze the dynamic stability of slopes under seismic load [4]. When it comes to research on landslides, the focus should be on the evolution process and formation mechanism of landslides. Therefore, considering that most earthquake-induced landslides are generated due to the gradual vibration deterioration of the structural plane, the present study proposes the dynamic strength reduction method based on the vibration deterioration of the structural plane, which can reproduce the formation and evolution mechanism of the earthquake-induced landslides as well as give a quantitative evaluation index of the dynamic safety with reference significance. The specific solution steps are as follows:  
1. Establish the FDEM model and mark the elements at the structural plane.
2. Static calculation stage: the bottom and lateral boundaries are fixed in the normal direction. The ground stress equilibrium is completed when the model kinetic energy reaches the set convergence value.
3. Static stability analysis stage: the tensile strength, friction angle and cohesion of the model are reduced, and the reduction factor is the static strength reduction factor (SSRF). The static safety factor (SSF) is denoted as SSRF when the landslide occurs.
4. Dynamic calculation stage: the bottom and lateral boundaries are converted to viscous and free-field boundary conditions, respectively. The dynamic loads are input from the bottom of the model.
5. Dynamic stability analysis stage: Since the slope stability under dynamic load is weaker than that under static condition, the initial value of the dynamic strength reduction factor (DSRF) is slightly smaller than that of SSF. After DSRF is selected, seismic waves are applied to the model, and the joint and adjacent triangular elementsat the structural plane update the values of friction angle and cohesion in real-time according to the cumulative displacement, the relative velocity and the shear cycle number to simulate the vibration deterioration effect of the structural plane. (6) DSRF is reduced sequentially, and Step (5) is repeated until the landslide is observed. The DSRF, at this point, is denoted as the dynamic safety factor (DSF).
## 2 Spatial Navigation Cell Model
### Grid Cell Update Model Based on Boundary Information
This mechanism also allowed us to generate a grid cell score table, which is shown in Table 1. The trends of the grid cell scores in the three different environments showed that the grid cell scores gradually increased and eventually stabilized as the exploration time increased. This pattern of data change is due to the positive effect of the boundary cells that was activated by the rats visiting the boundary of the environment, which thus updated the grid cell distribution. The reason for the higher grid cell scores, which were obtained by exploring the nine-sided environment rather than the pentagonal and trilateral ones, is explained by the fact that the nine-sided environment provides richer boundary information. \[r\big{(}\tau_{x},\tau_{y}\big{)}=\frac{n\sum\limits_{x,y}\lambda(x,y)\lambda \big{(}x-\tau_{x},y-\tau_{y}\big{)}-\sum\limits_{x,y}\lambda(x,y)\sum\limits_{ x,y}\lambda\big{(}x-\tau_{x},y-\tau_{y}\big{)}}{\sqrt{\left[M\sum\limits_{x,y} \lambda(x,y)^{2}-\left[\sum\limits_{x,y}\lambda(x,y)\right]^{2}\right]\left[n \sum\limits_{x,y}\lambda\big{(}x-\tau_{x},y-\tau_{y}\big{)}^{2}-\left[\lambda \big{(}x-\tau_{x},y-\tau_{y}\big{)}\right]^{2}}\right.} \tag{13}\]  
\[HGS=\min\Big{[}\text{cor}\Big{(}r,r^{60^{\circ}}\Big{)},\text{cor}\Big{(}r,r^{ 120^{\circ}}\Big{)}\Big{]}-\max\Big{[}\text{cor}\Big{(}r,r^{30^{\circ}}\Big{)},\text{cor}\Big{(}r,r^{90^{\circ}}\Big{)},\text{cor}\Big{(}r,r^{150^{\circ}} \Big{)}\Big{]} \tag{14}\]  
where \(n\) is the number of grid cells, \(HGS\) is the fraction of grid cells, \(r\) is the auto-correlation plot of grid cells, and \(\lambda(x,y)\) is the firing rate of grid cells at position. Furthermore, \(\tau_{x}\) and \(\tau_{y}\) are the spatial lag coordinates corresponding to the \(x\) and \(y\) coordinates, and \(r^{\beta}\) is the auto-correlation plot rotated by \(\beta\) degrees. \(\text{cor}\big{(}r,r^{\beta}\big{)}\) is the correlation score of the auto-correlation plot \(r\) and the correlation score of the two plots after rotating the auto-correlation plot by \(\beta\) degrees. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Time (s)** & **30** & **60** & **90** & **120** & **150** & **180** & **210** & **240** & **270** & **300** & **330** & **360** \\ \hline Trilateral environment & 0.25 & 0.32 & 0.38 & 0.45 & 0.55 & 0.56 & 0.68 & 0.69 & 0.74 & 0.73 & 0.74 & 0.74 \\ Pentagonal environment & 0.24 & 0.35 & 0.41 & 0.51 & 0.62 & 0.68 & 0.72 & 0.75 & 0.78 & 0.78 & 0.78 \\ Nine-sided environment & 0.20 & 0.34 & 0.40 & 0.49 & 0.58 & 0.71 & 0.80 & 0.84 & 0.86 & 0.84 & 0.85 & 0.85 \\ \hline \end{tabular}
\end{table} 
Table 1: Grid cell scores in different geometric environments.
## 2 Materials and Methods
### Modeling and Control  
#### 2.2.1 Position Analysis  
Our design focuses on minimizing the CoG shift. The low power servo motors, the low operational speeds, and the lightweight design led to the assumption that torques created by actuation and inertial movement are negligible. Furthermore, the system design inherently balances the created torques by moving the counterbalance mechanism in an opposite direction to the manipulator. Thus, the created forces can be taken as disturbances cancelled out by the UAV's stabilization system. As a result of the physical design, the manipulator undergoes planar motion, with the 3D rotation controlled by the UAV's movement and rotation. To match the Denavit-Hartenberg (D-H) parameters and the inverse kinematic equations [21], the notations in Figure 8 are introduced.  
Figure 8: Kinematic conventions of the manipulator.  
Figure 7: Final assembly: cable aerial manipulator with a CoG compensation mechanism.  
To compute the counterbalance weight displacement required to compensate for the CoG shift by the payload, the following equations are derived for the forward kinematics of a 3 DoF 2-link aerial manipulator:  
\[x_{0}=l_{1}\cos(\theta_{1})+l_{2}\cos(\theta_{1}+\theta_{2}),y_{0}=l_{1}\sin( \theta_{1})+l_{2}\sin(\theta_{1}+\theta_{2}), \tag{1}\]  
where \(x_{0},\ y_{0}\) represent the payload coordinates, \(\theta_{1},\ \theta_{2}\) the link angles, and \(l_{1},\ l_{2}\) the link lengths. Another angle, \(\theta_{2,\ \text{absolute}}\), was defined to account for the limits of the actuation mechanism. This angle measures the angle of link 2 relative to the drone axis, rather than link 1. It is equivalent to the sum of \(\theta_{1}\) and \(\theta_{2}\).  
Similarly, these equations are applied to the links to identify the effect of the link mass and location on the CoG. Once all mass locations are obtained, the following equation is used to obtain the horizontal CoG position:  
\[x_{cg}=\frac{\sum_{i}m_{i}x_{i}}{\sum_{i}m_{i}}, \tag{2}\]  
where, as shown in Figure 9, \(x_{i}\) represents the horizontal position and \(m_{i}\) represents the mass of each component \(i\). By setting the CoG displacement to zero and solving Equation (2) for the counterbalance weight (battery) position (\(x_{b}\)),  
\[x_{b}=-\frac{m_{1}x_{1}+m_{2}x_{2}+m_{p}x_{p}}{m_{b}}. \tag{3}\]  
Here, \(x_{1}=\alpha_{1}l_{1}\cos(\theta_{1}),\ x_{2}=l_{1}\cos(\theta_{1})+\alpha_{2}l _{2}\cos(\theta_
## 5 Conclusions  
Studies using sterile model food systems can give us valuable information on the growth and metabolic potential and activity of microorganisms of interest. Obviously, the growth parameters cannot be the same as those that occur in real food systems due to various implicit factors (e.g., interaction and antagonism between microorganisms, effect of food structure on growth, etc.). However, there is no other way to study microorganisms of the same genus or species; hence, such compromises cannot be avoided. On the other hand, the study of the metabolic activity of single strains on sterile model systems is the only tool for elucidating the compounds produced by those strains. In our study, various VOC were produced in inoculated models only, indicating their microbial origin from single strains, while others were produced in both inoculated and controls indicating the non-microbial origin in most of the cases. Additionally, several VOCs (e.g., methanethiol, dimethyl sulfide, dimethyl disulfide, 2-pentanone, ethyl acetate, 2-nonanone, n-nonanone, 2-undecanone, 3-octen-2-one, 2-heptanone, 2-heptanone, hexyl acetate, ethyl-2 methylbutyrate, ethyl iso-valerate, ethyl iso-valerate, ethyl iso-valerate) were found to increase during storage depending on strain and/or temperature. Such compounds are proposed herein as CSI in seafood. Certainly, the metabolic activity might again be affected in complex food systems in which other microorganisms are also present, but in such cases a comparison with other studies on real foods can give us insights on food spoilage mechanisms.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/microorganisms11010189/1](https://www.mdpi.com/article/10.3390/microorganisms11010189/1)]([https://www.mdpi.com/article/10.3390/microorganisms11010189/1](https://www.mdpi.com/article/10.3390/microorganisms11010189/1)), Table S1. VOC produced by seven _Pseudomonas_ strains in single and mixed cultures in fish juice agar model substrates at 0 \({}^{\circ}\)C (a), 4 \({}^{\circ}\)C (b) and 8 \({}^{\circ}\)C (c). Control is also presented. Figure S1. Population changes in _Pseudomonas_ strains during storage of inoculated fish juice agar (FJA) model substrates at 0 \({}^{\circ}\)C (a), 4 \({}^{\circ}\)C (b), and 8 \({}^{\circ}\)C (c). Results are expressed as means \(\pm\) stdev of three replicates.  
Conceptualization, I.S.B. and F.F.P.; methodology, D.A.A., E.K., F.F.P. and A.M.; validation, D.A.A., F.F.P., E.K. and A.M.; formal analysis, D.A.A., E.K. and A.M.; investigation, D.A.A., F.F.P. and E.K.; resources, I.S.B. and S.A.H.; writing--original draft preparation, D.A.A. and F.F.P.; writing--review and editing, I.S.B., S.A.H. and A.M.; visualization, D.A.A., F.F.P., E.K. and A.M.; supervision, I.S.B. and F.F.P.; project administration, I.S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets generated for this study are available on request to the corresponding author.  
The authors declare no conflict of interest.
# Marine Gelatine from Rest Raw Materials
## 3 Production Strategies for Gelatine
### Pre-Treatment and Extraction Strategies
#### Enzymatic Pre-Treatment  
Treatment with proteolytic enzymes, either alone or in combination with other pre-treatments (alkaline, acidic, etc.) is another option for improving extraction yield and quality of the obtained product. Enzymes are catalyst biomolecules which can speed the rate of biological reactions by catalyzing a transition state with a lower energy of activation. They can also hydrolyze the covalent cross-links in the terminal regions of proteins and facilitate the transformation of collagen to gelatine, while producing less waste compared to the chemical treatments [61]. [PERSON] et al. [46] have compared properties of gelatine extracted from bones of mackerel and blue whiting obtained using non-enzymatic (HCl) and enzymatic pre-treatment using Flavourzyme (fungal protease/peptidase complex obtained from _Aspergillus oryzae_). They concluded that gelatine obtained by enzymatic pre-treatment of bones showed significantly higher emulsifying activity (EAI) and stability (ESI) indices in comparison to acid pre-treatment. Gelatin extraction from bigeye snapper (_Priaanthus tayenus_) skin was developed by [PERSON] et al. [62], using a pepsin-aided process (big eye snapper pepsin, BSP) in combination with a protease inhibitor (pepstatin A and soybean trypsin inhibitor). The bloom strength of pepsin-treated gelatine was greater than the gelatine extracted from bigeye snapper skin by the conventional process, which had a substantial degradation of gelatine components, and soybean trypsin inhibitor added during the extraction process significantly reduced the degradation of \(\alpha\)- and \(\beta\)-chains in the gelatine. Since most proteolytic enzymes are usually able to cause significant degradation of gelatine \(\alpha\)- and \(\beta\)-chains, careful optimization of pre-treatment conditions is required to avoid this. [PERSON] et al. [61] have investigated pre-treatment optimization of grass carp fish (_Ctenopharyngodon idella_) scales by protease A2G enzyme utilizing the response su rface methodology (RSM). The resulting gel strength (276 \(\pm\) 12 g) and viscoelastic properties were comparable to porcine skin gelatine at lower temperatures, while the imino acid content, gelling and melting points were lower. Since surimi processing wastes represent composite material of skin, scale, bone and muscle, enzymatic pre-treatment may be a good solution for removal of non-collagen proteins prior to gelatine extraction. Enzymatic digestion can also be used as part of the pre-treatment, to remove interfering tissues before a more conventional chemical treatment is used. [PERSON] et al. [45] have used alkaline protease from Bacillus mojavensis in their work on extracting gelatine from tuna (_Thumus thumus_) heads, where the enzyme was used to obtain clean bone material before demineralisation with HCl and subsequent treatment with Ca(OH)\({}_{2}\).
## 3 A Case Study for Validating the Proposed Model and Process  
The peridynamic theory was implemented in this study to characterize the crack growth in a compact tension (CT) specimen with an initial precrack. The present study is modeled after the experiments by [PERSON] et al. [23] and peridynamic simulations by [PERSON] et al. [18]. [PERSON] et al. [23] conducted research into the fracture behavior of a standard CT specimen used for determining fracture toughness material parameters, but modified their tests with the addition of a hole. Four unique variations of this \"modified\" CT specimen were defined which exhibit unique behavior in the presence of a growing crack. Each of the variations altered the spatial position of the added hole relative to the initial crack tip. By altering the parameters X1 and Y1 from Figure 5, the authors showedthat growth of the crack would either sink into the hole or bypass it along a unique path. In Figure 5, the dimension unit is mm.  
This paper implemented the peridynamic theory for solution of the crack growth problem from the experiments by [PERSON] et al. [23] in a commercially available finite element package. At present, most solutions for the governing equation of peridynamics have been via Matlab or Fortran codes developed specifically for that purpose, or via open-source peridynamic codes in the studies [18; 20; 24; 25; 26]. Implementing a peridynamic solution in a finite element framework would utilize the inherent efficiencies of the nonlinear solvers contained in commercial FE code and would allow for a wider array of problems to be solved. Peridynamics has been successfully implemented into ABAQUS software for the case of dynamic brittle fracture [8] and for the case of ductile fracture [9]. The present work implemented peridynamics into ABAQUS for a high cycle fatigue analysis.  
The specimen CT1 was selected from the available configurations analyzed by [PERSON] et al. [23] for the present work. Geometry of the specimen can be found in Figure 5. The problem was formulated according to the 2D bond-based theory for plane strain. The experimental sample was an SAE 1020 steel with modulus E = 205 GPa and [PERSON]'s ratio of 0.29 used in the research by [PERSON] et al. [23]. The sample contained a precrack of length 0.9 mm.  
A convergence study was completed for \(\delta\) convergence. Horizon sizes of 1.2 mm, 0.6 mm, and 0.5 mm were utilized for the present work which correspond to model sizes of 15,000, 60,000 and 90,000 nodes. Plots of the crack path show that for the coarsest mesh the crack propagates into the hole rather than bypassing it. Good agreement is achieved between the crack paths for horizon sizes of 0.6 mm and 0.5 mm. Fatigue life predictions are reported for the model with horizon size of 0.5 mm.
# Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site
## 2 WSN
### Routing Algorithms
#### 2.2.1 XMESH  
XMESH is a full-featured, multi-hop, ad-hoc, mesh networking protocol developed by MEMSIC (Andover, MA, USA) for wireless networks [28]. An XMESH network consists of nodes (motes) that wirelessly communicate with each other and are capable of hopping radio messages to a base station where they are passed to a PC or other client. The hopping effectively extends the radio communication range and reduces the power required to transmit messages. By hopping data in this way, XMESH can provide two critical benefits: improved radio coverage and improved reliability. Two nodes do not need to be within direct radio range of each other to communicate. A message can be delivered to one or more nodes in between, which will route the data to its final destination. Likewise, if there is a bad radio link between two nodes, that obstacle can be overcome by rerouting around the area of bad service.
# Noise Control in Air Mechanical Ventilation Systems with Three-Dimensional Metamaterials
## 5 Discussion  
Mechanical ventilation systems are the cause of noise that many people find annoying. If noise control is conducted in the traditional way, the use of passive filters made of sound absorbing material leads to a certain loss of load. This phenomenon consequently increases the air speed of the fan to compensate for the pressure drop, and consequently the effective noise, which results in higher level than the estimated levels. The sound attenuation produced with three-dimensional metamaterial spheres arranged in layers with different space distances between each other indicates significant IL values at certain frequency bands. At constant diameter of the spheres, the most significant sound attenuation was measured between 4 kHz and 6.3 kHz [36; 37; 38].  
If three layers of elements are in place, the frequency interval of sound attenuation can be increased. The research studies conducted with three-dimensional metamaterial spheres are considered to be more material-saving efficient, in line with the principles of environmental sustainability. In addition, the material for the spheres can be made of hard plastic or resins with a certain number of chipboards obtained from recycled building materials. When planning the life cycle of the material from which this type of acoustic filter is made, the use of recycled waste would be a 100% sustainability goal if at the end it can also be disposed of in the environment at the end.  
The advantage of this proposed acoustic filter is its suitability to be used in small spaces, such as those offered by the enclosures of the mechanical ventilation systems next to the primary units, and the simplicity of its design together with the reduced pressure drop.  
As mentioned, this study does not consider the behavior of sound between the interstices, which is a limitation of this research study, as well as the scattered and polarized effect of sound rays at the boundaries and within the small volume of the enclosure [7]. On this basis, the measured results should be considered optimistic, which may change when material properties are considered, as other factors and effects may occur.  
Future research will focus on performing acoustic measurements on filters installed in real systems. This will provide the opportunity to compare the IL values of different fan types, characterized with unique spectrum and specific features, including fan diameter, number of blades, and overall speed. In addition, the real system would consider the effects of the material properties of the enclosure, so a different measurement method shall be in place, in which multiple microphones will be arranged at regular distance, along a grid disposition, homogeneously distributed over the surface of the three-dimensional architecture.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 3 Preliminaries
### Problem Formulation  
In the work, we attempted to develop a follow-me cloud-cloudlet approach for the mobility management. The main purpose is to reduce the transmission time during the handover by increasing the cache hit rate. As defined previously, The proposed follow-me cloud-cloudlet architecture contains a cloud-cloudlet (\(CL\)) set as \(CL=\{c_{1}\to l_{1},c_{2}\to l_{2},\cdots,c_{s}\to l_{d},\cdots,c_{d}\to l_{s}, \cdots,c_{n}\to l_{n}\}\), where \(l_{s}\), \(l_{d}\), \(c_{s}\), and \(c_{d}\) are determined by FMCC during the handover operation. When FMCC decides the \(l_{d}\), we try to pre-cache of the \(l_{d}\) of the required data packets. Let \(\alpha\) be denoted as the packet quantity existed in cache of the fog-computing-based RAN architecture. A data file \(F\) considered in this work is  
Figure 2: (**a**) Fog-RAN architecture using follow me cloud-cloudlet and (**b**) its simplified architecture.  
divided into \(n\) packets, \(F=\{p_{1},p_{2},\cdots,p_{s},\cdots,p_{s},\cdots,p_{d}\}\), where \(n\) is the total packet number of \(F\). In our mobility protocol design, the total data transmission time when switching to different access point is \(T_{FM}(p_{s})+T_{FML}+T_{FMC}+T_{FML}(p_{d})\), where \(T_{FM}(p_{s})\) is the time cost of the _follow me (FM)_ phase, \(T_{FML}\) is the time cost of the _follow me cloudlet_ (\(FML\)) phase \(T_{FMC}(p_{d})\) is the time cost of the _follow me cloud_ (\(FMC\)) phase, and \(T_{FML}(p_{s})\) is the time cost of the _follow me cloud-cloudlet_ (\(FMCL\)) phase (as described below). The problem is formulated to minimize the total data transmission time as:  
\[\begin{split}\text{minimize}\ \ \{\text{min}\ \sum_{1\leq s\leq k}T_{FM}(p_{s})+T_{FML}+T_{FMC}+\text{min}\ \sum_{k\leq d\leq n}T_{FML}(p_{d})\}\\ \text{subject to}\ \left\{\begin{array}{l}T_{FM}(p_{s})= \left\{\begin{array}{l}T_{c},\text{if}\ p_{s}\,\epsilon\,l_{s}\\ T_{d},\text{otherwise}.\end{array}\right.\\ T_{FML}(p_{d})=\left\{\begin{array}{l}T_{c}^{\prime},\text{if}\ p_{d}\, \epsilon\,l_{d}\\ T_{d}^{\prime},\text{otherwise},\end{array}\right.\end{array}\right.\end{split} \tag{1}\]  
where \(T_{c}<<T_{d}\), \(T_{c}^{\prime}<<T_{d}^{\prime}\). Time \(T_{c}\) and \(T_{c}^{\prime}\) denoted that the data transmission time of packet \(p_{s}\) and \(p_{d}\) is directly accessed from cache of cloudlets \(l_{s}\) and \(l_{d}\), \(T_{d}\) and \(T_{d}^{\prime}\) denoted that the data transmission time of packet \(p_{s}\) and \(p_{d}\) must be transmitted from data center of clouds \(c_{s}\) and \(c_{d}\), respectively.  
In addition, [PERSON] et al. [34] generally discussed two kinds of caching techniques; web caching and redundancy elimination (RE). There are three types of RE technique; chunk-level RE, TCP-level RE and the packet-level RE. In the paper, we adopted the packet-level RE as our caching technique [34].