esa-sceva/satcom-chunk-collection huggingface dataset:
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 3 Campaign Test and Data Description
### Collection Procedure  
The strategy for collecting the data are as follows: eight testers participate in the data collection. They are teachers or students of ESME Sudria School (France). All of them use one of the two considered Android smartphones (Xiaomi Note Pro 9 or Samsung A10) and with the same SIM cards (for one single operator). The participants collected the majority of traces during morning and evening hours during working days along  
Figure 1: Overview of the crowdsourcing campaign test.  
work-home trajectory, in addition to some random mobility during the weekend. The \"Drive test\" mode is programmed to execute a set of full test, and each one is composed of a combination of five applications. Figure 2 presents the structure of one complete \"Test cycle\" (full test).  
We will focus later on the analysis of the three most popular services: (i) file downloading, (ii) video streaming, and (iii) web browsing as they generate most of the Internet traffic. Below we present a synthetic description of each selected service:  
* File transfers are carried out in a single thread, representative of customer usage, so as not to artificially maximize throughput. The downloaded files have about twenty different extensions.
* Video streaming service is carried out on the platform of YouTube content. The video is viewed in 720 p resolution for 30 s with no change in quality.
* There are six web browsing tests. Each test tries to request and view pages from international and national web servers for 10 s. The six sites for the web test are selected randomly from a predefined list of 30 popular sites. In Figure 1, you can see an example of six selected web sites.
# Environmental Risk Source Analysis and Classification of Zones: Subway Construction
## 3 Risk Source Analysis of Subway Construction
### Geological Conditions  
Surface subsidence associated with subway construction is closely related to regional geological conditions, affecting the construction methods used for the subway sections. Different construction methods result in varying degrees of surface subsidence during subway construction, leading to varying degrees of subway construction risk [41; 42; 43; 44]. According to the report from the Urumqi Municipal Bureau of Geology, the stratigraphic conditions in the study area can be divided into three categories: a soil layer, a soil-rock composite layer, and a rock layer (Figure 3). The geological structure data come from the Urumqi Municipal Bureau of Geology. Most of the subway construction settlement occurs in the first two geological conditions, particularly in the soil area, where the soil is easily compacted by the upper load during subway construction, resulting in surface settlement. Therefore, a spatial correlation exists between surface subsidence and geological conditions in the Urumqi subway construction. As a result, the distribution of stratum conditions is categorized as the first type of risk source for the subway construction environment.  
Figure 3: Geological structure of the study area includes the soil layer, rock layer, and soil–rock composite layer.
###### Abstract  
In mobile networks, a content server can provide multiple services simultaneously to a mobile device, generating multiple data flows. As the device moves, the transmission path in the wired network may need to be switched to maintain service continuity. However, a single switching path may not be able to accommodate all the flows, potentially leading to congestion and a degraded user experience. To address this challenge, we propose a multi-topology routing-based mobile data scheduling method that dynamically switches flows across multiple paths to enhance flexibility and load balancing. The performance of this method is significantly influenced by the construction of logical topologies. Well-designed topologies provide high-bandwidth, low-latency paths to all possible destination nodes, while poorly designed topologies waste switch capacity and fail to achieve these goals. In this paper, we introduce an efficient multi-topology construction method for scheduling mobile data flows in software-defined networking (SDN). Our approach optimizes and balances transmission capacity for each destination node while adhering to the flow entry constraints of switches. Simulations demonstrate that our method consistently outperforms the single-path switching method and the other two multi-topology construction methods in terms of packet delay, packet loss rate, and network throughput, regardless of the device's new location.  
multi-topology routing; mobility; software-defined networking; flow scheduling +
Footnote †: journal: Applied sciences  
1  
[PERSON]; [PERSON]; [PERSON] An Efficient Multi-Topology Construction Method for Scheduling Mobile Data Flows in Software-Defined Networking. _Appl. Sci._**2024**, _14_, 11568. [[https://doi.org/10.3390/appl142411568](https://doi.org/10.3390/appl142411568)]([https://doi.org/10.3390/appl142411568](https://doi.org/10.3390/appl142411568))
## 3 Preliminary Information
### Meeting Probability  
The probability of the movement direction of a sensor obeys an inhomogeneous distribution in the RWP model. It is more complicated to calculate the standard relative velocity \(\vartheta_{rw}\) in the RWP model than in the RDM model. The value of \(\vartheta_{rw}\) is given as follows:  
**Lemma 4**.: _In Random Waypoint Mobility Model, the standard relative velocity \(\vartheta_{rw}\) between mobile nodes is about 1.754._  
**Lemma 5**.: _In Random Waypoint Mobility Model, the average of the meeting probability in an epoch is \(\overline{P}_{rw}^{M}\):_  
\[\overline{P}_{rw}^{M}=\frac{2R\times\overline{L}}{D}(P_{m}\times V_{rw}+2(1-P _{m})) \tag{12}\]  
_where \(p_{m}=\frac{\overline{r}}{(T+T_{step})}\) is the probability that a node moves at any time._  
**Proof.** Node \(A\) and node \(B\) both move in the RWP model. In the RWP model, the chance that any node is mobile at some moment is \(p_{m}\), the probability that any node is static in some moment is \(1-p_{m}\), so the probability that node \(A\) and node \(B\) are both mobile is \(p_{m}{}^{2}\); the probability that one node moves but another node remains static is \(p_{m}(1-p_{m})\); the probability that both node \(A\) and node \(B\) are static is \(\left(1-p_{m}\right)^{2}\). When one node moves but another node remains static, according to Lemma 3, the average of the meeting probability of both nodes in an epoch is \[p_{rw}^{mp}=\frac{2R\times\overline{v}\times(\overline{T}+\overline{T}_{stop})}{D} \tag{13}\]  
When both node \(A\) and node \(B\) are mobile, according to the relativity of motion, one node can be assumed to be static, then another node moves at the speed of \(\vartheta_{rw}\times\overline{v}\), the meeting probability of both nodes in an epoch is \(p_{rw}^{mm}\):  
\[p_{rw}^{mm}=\frac{2R\times\vartheta_{rw}\times\overline{v}\times(\overline{ T}+\overline{T}_{stop})}{D} \tag{14}\]  
When both of node \(A\) and node \(B\) are static, the meeting probability of both nodes is 0. So the average of meeting probability of both nodes in an epoch is \(\overline{P}_{rw}^{M}\):  
\[\overline{P}_{rw}^{M}={p_{m}}^{2}\times p_{rw}^{mm}+2p_{m}\times(1-p_{m}) \times p_{rw}^{mp} \tag{15}\]  
Simplifying:  
\[\overline{P}_{rw}^{M}=\frac{2R\times\overline{L}}{D}(p_{m}\times\vartheta_{rw }+2(1-p_{m})) \tag{16}\]  
\(\square\)
## 3 Autonomous System
### Modular Approach to Fault Propagation in Wireless Sensor Networks
Communication in wireless sensor networks is highly critical because links are highly volatile to faults and errors. One source of link failure in wireless sensor networks is the collision of network packets. For instance, the authors in [23; 43; 44; 45; 46] studied the potential for network packet collisions in a nearby network caused by phase change and overlap. On the other hand, sensor nodes might have a perfect link connection, but the packets might not be transmitted to their correction destination because of path errors. Finally, a software bug in the routing function can create deadlocks or transmit the packets to the wrong destination [10; 11; 12]. **Network sink faults:** The function of the network sink is to collect all sensor data generated in the network and transmit them to the application module. This part of the wireless sensor network can also be subjected to faults and errors from its components. Sink node failure would not render the collected data useless, unless backup or redundant sinks were present. With a modular approach, sink node failure should not affect the functioning of the entire network, as illustrated in Figure 1.
## 3 Marine-Derived Carotenoid Screening
### In Silico Carotenoid Screening
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Name** & **Database URL** \\ \hline
1 & antiSMASH database & [[http://antismash-db.secondarymetabolites.org/](http://antismash-db.secondarymetabolites.org/)]([http://antismash-db.secondarymetabolites.org/](http://antismash-db.secondarymetabolites.org/)) \\
2 & Bactibase & [[http://bactibase.pfba-lab-tun.org](http://bactibase.pfba-lab-tun.org)]([http://bactibase.pfba-lab-tun.org](http://bactibase.pfba-lab-tun.org)) \\
3 & ClusterMine360 & [[http://www.clustermine360.ca/](http://www.clustermine360.ca/)]([http://www.clustermine360.ca/](http://www.clustermine360.ca/)) \\
4 & ClustScan Database & [[http://csdb.bioserv.pdf.hr/csdb/ClustScanWeb.html](http://csdb.bioserv.pdf.hr/csdb/ClustScanWeb.html)]([http://csdb.bioserv.pdf.hr/csdb/ClustScanWeb.html](http://csdb.bioserv.pdf.hr/csdb/ClustScanWeb.html)) \\
5 & DoBICSUIT & [[http://www.bio.nite.go.jp/pks/](http://www.bio.nite.go.jp/pks/)]([http://www.bio.nite.go.jp/pks/](http://www.bio.nite.go.jp/pks/)) \\
6 & IMG-ABC & [[https://img.jgi.doe.gov/abc](https://img.jgi.doe.gov/abc)]([https://img.jgi.doe.gov/abc](https://img.jgi.doe.gov/abc)) \\
7 & MIBiG & [[https://mibig.secondarymetabolites.org/](https://mibig.secondarymetabolites.org/)]([https://mibig.secondarymetabolites.org/](https://mibig.secondarymetabolites.org/)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Currently available databases for screening secondary metabolites. Figure 5: General workflow of in silico carotenoid screening using databases and tools. Custom machine learning models and algorithms can be used in a more advanced screening pipeline, utilizing the biosynthesis gene cluster sequences available in the public databases (Table 1) as the training data set. For the discovery of novel smBGCs, an approach using machine learning-based methods such as ClusterFinder [67] and DeepBGC [68] is required. However, the drawback of this machine learning-based method is the high false-positive rate [69]. Nevertheless, the combination of these two approaches is expected to provide an accurate and representative list of smBGCs. Previously, genome-based in silico screening has been used to identify two missing genes in the carotenoid biosynthesis pathway. BLAST+ was used to perform similarity searching against the local BLAST Protein database constructed using the makeblastdb program [64]. There was also research conducted using in silico genome-based screening to characterize marine bacterial carotenoid gene clusters using tools and databases as described in Table 1[70; 71; 72; 73; 74; 75; 76]. The most commonly used tool in these research studies is the antiSMASH pipeline. Overall, these \"genome mining\" techniques are used to identify gene clusters, predicting their similarity with available gene clusters in the databases, and constructing a putative pathway of carotenoid biosynthesis.
## 3 Results
### Experiment 1: Evaluation of SMF Model
#### 3.2.1 SMF Model Deduction
(**a**) The results of the forecasting error by using the 2 h irrigation interval. (**b**) The results of 3 h irrigation interval. (**c**) The results of 4 h irrigation interval. (**d**) The results of using all the sampled data. confirmed that a 2 nd-order regression function with window size set to 8 was suitable for the SMF model.
# Realistic Simulation for Body Area and Body-To-Body Networks
###### Abstract
With regards to existing BAN-specific simulators, there are a few options. For example, OMNet++ and its associated frameworks, such as Castalia [13] and MiXiM [14], have some BAN-specific modules. Castalia relies on a real-time measurement campaign to develop channel models, which include space and time variations and the impact of body shadowing and fading [15]. However, the measurements conducted are limited to certain scenarios and applications setups. It considers line mobility, which is not realistic for the diverse mobility patterns of emerging applications [2; 16]. Whereas, MiXiM provides BAN-specific mobility models [17], which mimic mobility structures, including the modeling of posture variations and temporal correlation. With the addition of these mobility models in MiXiM, it is a viable simulation environment for the simulation of PHY and MAC layers. However, since 2011, there has been many new applications, and some are still emerging, where body-to-body (B2B) communication is inevitable [18; 19; 20]. To the best of our knowledge, MiXiM mobility models are still based on [17], which is not developed for body-to-body networks (BBN). In addition, there is hardly any simulator that is compliant with the BAN-specific standard (_i.e._, IEEE 802.15.6).
## 6 Material and Method
### Collision Avoidance  
After the final path for each UAV has been constructed, the array _position_ which contains the \(x\), \(y\), and \(z\) position of the UAV at each time increment, the array _speed_ which contains the speed of the UAV at each time increment and the array _is_stationary_point_ which identifies if the position is a DP or the starting point are available. The time increments for each UAV are the same which means that _position[i]_ for UAV #1 and _position[i]_ for UAV #2 represent the position for both UAVs at the exact same time. The collision avoidance function checks each pair of UAVs for a possible collision. It iterates through the position array of the two UAVs and calculates the distance between them. If the distance is below a set threshold and none of the UAVs is currently at the starting point, a collision has been detected. The condition for a UAV at the starting point is there as multiple UAVs are allowed to be at the starting point at the same time which is also the charging station. Once a collision has been detected at _position[i]_. The function uses the _is_stationary_point_ array and decrement \(i\) until the previous DP or the starting point is reached. The function then inserts values in the _position_, _speed_ and _is_stationary_point_ arrays to force the UAV to wait longer at the DP. Once this is done, the function restarts the check for collision for every pair of UAVs. This process is repeated until no collisions are detected.
## Appendix A Direct Time Integration
### Standard Implementation  
This section presents the algorithm used in [11]. It is a classical implementation of the Newmark-\(\beta\) method. The following makes reference to an equilibrium equation given above (Equation (2)):  
1. The values of \(\prescript{n}{}{\mathbf{q}}_{\mathbf{D}}\), \(\prescript{n}{}{\mathbf{\dot{q}}}_{\mathbf{D}}\), \(\prescript{n}{}{\mathbf{\ddot{q}}}_{\mathbf{D}}\), \(\mathbf{f}_{\mathbf{ED}}(\tau_{\mathrm{n+1}})\), and \(\mathbf{q}_{\mathbf{IP}}(\tau_{\mathrm{n+1}})\)\(\forall i\) are known either from initial conditions or the previous integration step.
2. PREDICTOR: An assumption on one of the unknown vectors at \(\tau_{\mathrm{n+1}}\) must be performed, e.g., \(\prescript{n+1}{}{\mathbf{\widetilde{q}}}_{\mathbf{D}}=\prescript{n}{}{ \mathbf{q}}_{\mathbf{D}}\) is postulated.
3. The values of \(\prescript{n+1}{}{\mathbf{\widetilde{q}}}_{\mathbf{D}}\) and \(\prescript{n+1}{}{\mathbf{\widetilde{q}}}_{\mathbf{D}}\) are computed using [PERSON]'s relations in Equation (10) and the assumption at Step 2.
4. Vector \(\prescript{n+1}{\mathbf{\widetilde{q}}}_{\mathbf{D}}\) is transformed into local displacements at the contact \(\prescript{n+1}{\mathbf{\widetilde{q}}}_{\mathbf{ID}}\) using Equation (4). The same is done with the displacements of the platform contact points, thus obtaining \(\mathbf{q}_{\mathbf{IP}}^{*}(\tau_{\mathrm{n}}+\Delta\tau)\)\(\forall i\).  
The vectors of local displacements at the contacts are fed into the contact model. Thus, the local vectors of contact forces \({}^{n+1}\tilde{\mathbf{f}}_{\mathbf{i}}^{*}\)\(\forall i\) are obtained and transformed into vector \({}^{n+1}\mathbf{f}_{\mathbf{CD}}\) using the transformation matrices from Equation (4).
6. The residual for Equation (2), also known as unbalanced force, is computed: \[\mathbf{R}=\mathbf{M}_{\mathbf{D}}\cdot{}^{n+1}\widetilde{\mathbf{q}}_{ \mathbf{D}}-\left(\mathbf{f}_{\mathbf{ED}}(\tau_{n+1})+{}^{n+1}\widetilde{ \mathbf{f}}_{\mathbf{C}}\right).\] (11)
7. The incremental displacement is computed: \[\Delta\mathbf{q}=\left(\frac{1}{\beta\Delta\tau^{2}}\mathbf{M}_{\mathbf{D}} \right)^{-1}\mathbf{R}.\] (12)
8. CORRECTOR: If the incremental displacement is below a given tolerance \(|\Delta\mathbf{q}|<\text{tol}\), then the equilibrium is satisfied and the integration goes to \(\tau_{n+2}\). Otherwise, the vectors of unknowns are corrected as follows (corrections factors derived from [PERSON]'s relations): \[{}^{n+1}\mathbf{\hat{q}}_{\mathbf{D}} ={}^{n+1}\widetilde{\mathbf{q}}_{\mathbf{D}}+\Delta\mathbf{q}\] (13) \[{}^{n+1}\mathbf{\hat{q}}_{\mathbf{D}} ={}^{n+1}\widetilde{\mathbf{q}}_{\mathbf{D}}+\frac{\gamma}{\beta \Delta\tau}\Delta\mathbf{q}\] \[{}^{n+1}\mathbf{\hat{\tilde{q}}}_{\mathbf{D}} ={}^{n+1}\widetilde{\mathbf{q}}_{\mathbf{D}}+\frac{1}{\beta \Delta\tau^{2}}\Delta\mathbf{q}\]
## 3 Magnetic Circuit Design and Theoretical Modeling
### Calculation of the Stiffness of Permanent Magnet Thrust Bearing  
During the operation of permanent magnet thrust bearing, the rotor is slightly deflected under the axial load \(e\). The axial magnetic force of the permanent magnet bearing prevents the rotor from generating displacement, defining the stiffness of the permanent magnet thrust bearing as the ratio of the magnetic force to the rotor deflection \(e\). Therefore, the stiffness of the bearing can be expressed as follows:  
\[\begin{array}{ll}K&=\dfrac{\partial F_{e}}{\partial e}\\ &=\sum\limits_{k=1}^{N-1}\left\{\dfrac{\left(\dfrac{2\pi RnlB_{r}H_{ d}d_{1}\Lambda_{a}}{2\pi RnlB_{r}+H_{c}d_{1}\Lambda_{a}}\right)^{2}e}{\mu_{0} \pi\left[d+kd_{1}+\left(\dfrac{2+k}{2}\right)h_{g}\right]L_{n}h_{g}}\right\}\\ &+\sum\limits_{k=1}^{N-1}\left\{\dfrac{\left(\dfrac{2\pi RnlB_{r}H_{ d}d_{1}\Lambda_{a}}{2\pi RnlB_{r}+H_{c}d_{1}\Lambda_{a}}\right)^{2}e}{\mu_{0}\pi \left[d+kd_{1}+\left(\dfrac{2+k}{2}\right)h_{g}\right]L_{n-1}h_{g}}\right\} \end{array} \tag{15}\]
## 4 Methodology
### Framework of Proposed Recommendation Model  
As Figure 2 shows, the proposed model contains three main modules: _Preprocessing Module_, _Representation Module_, and _Prediction Module_. In the _Preprocessing Module_, user-location, user-app and app-location interactions are obtained. An undirected bipartite graph \(G\) then is constructed with node attributes attached. Then the graph \(G\) is sent to _Representation Module_. Latent preferences of users and locations are learned via the representation model based on graph convolution. Finally the _Prediction Module_ generates the recommending locations for each user, and evaluations are conducted to ensure its effectiveness.  
Figure 2: Our Proposed Personalized Location Recommending Framework based on app data.
## 4 Analysis and Simulation Verification
### Optimization of Multiple Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(\mathbf{k_{\text{ip1}}}\) & \(\mathbf{k_{\text{ip2}}}\) & \(\mathbf{k_{\text{ip3}}}\) & \(\mathbf{k_{\text{plp1}}}\) & \(\mathbf{k_{\text{plp2}}}\) & \(\mathbf{k_{\text{plplp3}}}\) \\ \hline parameter sensitivity & 2.907 & \(-107.8\) & \(-79.93\) & \(-0.2990\) & \(-0.3447\) & \(-0.3247\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter sensitivity of system B under initial parameters (in Table 10). The parameter optimization is performed to stabilize the multi-converter grid-tied system. As shown in Table 3, the absolute values of parameter sensitivity for \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) are much larger than those for other parameters, so they are identified as the stability-weakest parameters. Following the flowchart shown in Figure 8 the optimization values of \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) are iteratively calculated. The calculation of \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) is \(8.800\times 10^{-4}\). At this point, the \(g(f)\) is illustrated in Figure 17, which exhibits a minimum value of \(4.100\times 10^{-3}\), indicating the system is stable. The waveforms of the PCC voltage before and after the optimization of \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) are illustrated in Figure 18. Figure 18a shows that the PCC voltage oscillates before parameter optimization. After optimizing \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) from \(6.600\times 10^{-3}\) to \(8.800\times 10^{-4}\), system B is stable as shown in Figure 18b. While only optimizing \(k_{\text{ip2}}\) at the same magnitude, which has the highest absolute sensitivity, the PCC voltage continues to oscillate as shown in Figure 19a. Continuing to reduce \(k_{\text{ip2}}\), the PCC voltage is still unstable as shown in Figure 19b. Reducing \(k_{\text{ip2}}\) can significantly improve the PCC voltage oscillation, but it cannot achieve system stability. Figure 19: PCC voltage waveforms after only optimizing the \(k_{\text{ip2}}\) parameter: (**a**) \(k_{\text{ip2}}=8.800\times 10^{-4}\); (**b**) \(k_{\text{ip2}}=2.500\times 10^{-4}\). Figure 17: \(g(f)\) function of system B under final parameter optimization (\(k_{\text{ip2}}=8.8000\times 10^{-4}\), \(k_{\text{ip3}}=8.8000\times 10^{-4}\)). Therefore, when single-parameter optimization is always unable to meet the requirements of system stability, the system can be stabilized by optimizing multiple parameters in combination.