esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Sensitivity Analysis
### Influences of Backfill with Asymmetrical Compactness on the Pipe  
In the field construction, it is common that backfill has different compaction degrees between the left and right regions, which leads to eccentric compression on the pipe. In this part, four conditions with different degrees of compactness on the left and right sides of the backfill were analyzed in order to evaluate the Von-Mises stress of the pipe. The Von-Mises stress caused by the earth pressure on the pipe was plotted to determine the maximum point of the Von-Mises stress, and the variation in the Von-Mises stress with the traffic load at this point was analyzed.  
Figures 20-23 show that when the compaction degree of the backfill was left-right asymmetrical, the Von-Mises stress distribution was asymmetrical, and the stress was concentrated near the pipe region in contact with the loose backfill. The location and scope of the loose region jointly determined the strain distribution of the pipe, and the positional influence was more prominent (Figures 21-23). The asymmetric stress distribution was more likely to induce the failure of the whole pipe, so the long-term performance of the pipe would be adversely affected by the non-uniform compactness of the backfill. At the maximum point of the Von-Mises stress, the stress fluctuation caused by the traffic load was more significant than the stress under standard conditions, but it was still negligible. This result agreed with the findings reported by [PERSON] and [PERSON] [24].  
Figure 19: The invert Von-Mises stresses of pipes of different hoop stiffness under traffic load: (**a**) change in L1 compaction degree; (**b**) change in L2 compaction degree; (**c**) change in L3 compaction degree.  
Figure 20: _Cont._Figure 21: **(a)** The backfilling schematic of Condition 02; **(b)** the stress distribution of the pipe; **(c)** the time history curve of the left springline under Condition 02.  
Figure 22: **(a)** The backfilling schematic of Condition 03; **(b)** the stress distribution of the pipe; **(c)** the time history curve of the left springline under Condition 03.
## 4 Results and Discussion
### Discussion  
Table 8 shows that the lower the classification probability, the more patches satisfy the classification probability. When the classification probability is 0.80 and 1.00, the number of patches without calcification and with calcification are significantly different: more than 4000 patches without calcification and more than 800 patches with calcification. Table 9 shows that the total number of transference patches learned differs greatly, depending on the classification probability. When the classification probability is 0.80 and 1.00, the total number of patches learned by transfer learning differs by more than 15,000 patches, showing a large difference. Visual inspection of the patches used for transfer learning showed that the patches with low classification probability tended to be incorrectly transferred from the patches with no calcification to those with calcification. Section 4.2 show that transfer learning of new data by semi-supervised learning improved the recall and AUC compared to the initial classifier. Here, we discuss the recall and precision, which showed particularly large changes from the initial classifier. The transfer learning of new data by semi-supervised learning increased the recall compared to the initial classifier. This means that the false negative rate became lower, resulting in a reduction in missed patches with calcifications. The reason for this is thought to be that the transfer learning of new patches increased the diversity of calcifications learned by the classifier. Although the morphology and distribution of calcifications in the breast are diverse, we believe that the classifier was able to learn diverse features of calcification by transfer learning of new patches using semi-supervised learning. On the other hand, the transfer learning of new data by semi-supervised learning resulted in a lower precision compared to the initial classifier. This means that the false positive rate increased, and the patches without calcification were more likely to be incorrectly judged as having calcification. The reason for this is that the trained CNN incorrectly inferred unlabeled patches without calcification as having calcification and learned to transfer them, which increased the false positive rate. There is a trade-off between recall and precision. Mammography is used for screening and is said to be one of the most effective methods for the early detection of breast cancer [41]. The purpose of screening is to improve prognosis through earlier diagnosis and early intervention [42]. Therefore, for early diagnosis, it is important not to miss patches with calcifications, i.e., to lower the false negative rate, making the increase in recall very important. Additionally, the application of semi-supervised learning tended to correctly classify patches with calcifications in areas with dense breast tissue, where the images appear white, indicating that the patches with a small contrast between calcifications and breast tissue were more accurately identified. This suggests that calcifications in dense
# Influence of Approaching Excavation on Adjacent Segments for Twin Tunnels
## 3 Description of Numerical Simulations
### Modelling of Construction Loadings  
As shown in Figure 4, the construction loadings considered in this model from the excavation face until the shield tail are in sequence of the face pressure [36], the friction force between the shield skin and the ground, the jacking force and the grouting pressure [37; 38; 39].  
* It is assumed that the pressure exerted by the TBM on the excavation face varies linearly with elevation according to ground density. The reference pressure at the tunnel axis is usually set as half of the sum of stresses at the tunnel crown and invert [40]. During the actual construction, an additional pressure of about 15 kPa is enforced on the excavation face [41]. As a result, the face pressures at the axes of the first and second tunnels are assumed as 141 kPa and 148 kPa, respectively.
* It is assumed that the grouting pressure acting on both the ground and segment is uniform distribution, and is related to the ground overburden pressure at tunnel crown [42]. As a result, the grouting pressures acting on the first and second tunnels are assumed as 3.3 bar and 3.1 bar, respectively.
* In order to simplify the simulation, the total force of hydraulic jacks is loaded equally on the circumferential surface of the installed segment [27]. For both the first curved tunnel and the second straight-line tunnel, the jacking force per unit area is about 2.1 MPa. However, it is worth noting that the direction of the jacking force vector is not perpendicular to the acting surface for the first curved tunnel. In other words, an angle between it and the central axis of the newly installed segment exists to achieve a curved trajectory.
* In ABAQUS, the friction force indirectly exerted on the soil is simulated via a contact pair consisting of the excavation interface and the shield skin. Furthermore, the Coulomb friction law is assumed, and the friction coefficient is set as 0.25 [30].
## 3 Results
### Coherent Excitonic Quantum Electronic Dynamics in the QD Dimer
For this heterodimer, as there is no exact resonance between two excitonic bands, the efficiency of the Coulomb coupling remains small and the eigenexcitons retain their labeling of the zero order basis. The VIS pump pulse essentially accesses h1 eA and h1 eB, with a small population in h2 eB. The probe IR pulse is applied 100 fs after the pump one. The dynamics can be analyzed in the basis of the zero order exciton states that are not stationary after the first pulse (panel A) or in the eigenexciton basis (panel B), which are stationary when no pulse is present. In the zero order basis set (Figure 5A), one observes population transfers between exciton h1 eA and h1 eB on a fast 17.9 fs time scale, as expected from the range of the Coulomb coupling strength shown in Figure 3A. The two corresponding eigenstates, 1 and 4, are stationary in the absence of pulse. The probe IR pulse leads to additional population transfer between the excitons. Its frequency is resonant with energy differences between intradot and interdot coherences. The dynamics of selected coherences between eigenexcitons, \(\rho_{ij}(t)=2\text{Re}\Big{[}c_{i}^{*}(t)c_{j}(t)\Big{]}\), where \(i\) and \(j\) are the two eigenexcitons involved in the coherence, are plotted in panels C and D. In the absence of pulse, each coherence has a beating frequency given by the energy difference (\(E_{i}-E_{j}\)) between the two eigenstates \(i\) and \(j\). In panel C, we plot the two intradot coherences between eigenexcitons having large weights on the local excitons h1e\({}_{\text{A}}\)-h2e\({}_{\text{A}}\) and h1e\({}_{\text{B}}\)-h2e\({}_{\text{B}}\) and, in panel D, the four interdot coherence between eigenexcitons having large weights on the four pairs of zero order local excitons that are strongly coupled: h1e\({}_{\text{A}}\)-h1e\({}_{\text{B}}\), h1e\({}_{\text{A}}\)-h2e\({}_{\text{B}}\), h1\({}_{\text{A}}\)-h2\({}_{\text{A}}\), h2\({}_{\text{A}}\)-h2\({}_{\text{B}}\). We show the dynamics of the coherences between eigenexcitons because these are the ones probed by the probe pulse, and observed in the pump-probe transient absorption spectroscopy as reported below. The interdot h1e\({}_{\text{A}}\)-h1e\({}_{\text{B}}\) coherence (panel D) is the most intense interdot one, because the two eigenexcitons are excited by the pump pulse. Its beating frequency corresponds to that of population exchange between the two zero order exciton involved in the coherence. This coherence is only weakly excited by the IR pulse, because its frequency is to the red of that of the IR pulse. On the other hand, the IR pulse excites the two intradot coherences and the other three interdot ones because it is resonant with their frequency. Coherences involving eigenexcitons having large weights on local and CT states have a much smaller amplitude. We show an example in Figure S13 of Section 6 of the SI.
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
## 3 Proposed Intelligent Dynamic Power Control with Cell Range Expansion
2:for\(n=1:N\)do
3:for\(s=1:S\)do
4: MBS \(m\) calculates \(\zeta_{mn}^{MBS}\) and \(\zeta_{sn}^{ SBS}\);
5:if\(\zeta_{mn}^{MBS}<=\zeta_{sn}^{ SBS}+\eta\)then
6: MBS \(m\) offloads MUE \(n\) to SBS \(s\) as a CUE;
7:for\(t=1:T\)do
8: Calculate \(\gamma_{mst}^{k}\) using the same SG as MBSs according to (2);
9:if\(\gamma_{mst}^{k}<\Gamma_{st}\)then
10: SBS \(s\) allocates different SGs from the MBS to SUE \(t\);
11:else
12: SBS \(s\) allocates the same SG of the MBS to SUE \(t\);
13:endif
14:endfor
15:endif
16:endfor
17:endfor
18:// SBSs dynamically assign the appropriate transmission power to SUEs and CUEs. 19:for\(s=1:S\)do
20: SBS \(s\) sorts SUEs in order of their SINR;
21:for\(t=1:T\)do
22:for\(k=1:K\)do
23: SBS \(s\) calculates \(\gamma_{mst}^{k}\) according to (2);
24:if\(\gamma_{mst}^{k}<\Gamma_{th}\)then
25: repeat \(p^{SBS}=p^{SBS}+\Delta p\) and calculate \(\gamma_{mst}^{k}\) until \(\gamma_{mst}^{k}==\Gamma_{th}\);
26:else
27: repeat \(p^{SBS}=p^{SBS}\) - \(\Delta p\) and calculate \(\gamma_{mst}^{k}\) until \(\gamma_{mst}^{k}==\Gamma_{th}\);
28:endif
29:endfor
30:endfor
31:endfor
```  
**Algorithm 1** Proposed DPC scheme with CRE. Figure 4: Comparison of the transmission power operation for conventional (no power control), REP, and proposed DPC schemes (in the proposed DPC scheme, SBSs of site 1 assign subchannel group A with power control to each of SUEs and CUEs in the inner zone, and assign subchannel group B and C to SUEs and CUEs in the outer zone).
## 4 Approach
### Sampling-Based Configuration Generation Method
As shown in Figure 4, the images on the left illustrate the distribution of mobile base positions that can be obtained for a valid mobile manipulator configuration when the height of the end-effector is 0.3 m. Correspondingly, the images on the right illustrate the distribution of mobile base positions that can be obtained for a valid mobile manipulator configuration when the height of the end-effector is 0.9 m. The results were obtained from one thousand random sampling experiments. It can be seen that there are regions where feasible mobile base locations are more densely populated, and sampling in these regions has a higher probability of obtaining feasible mobile base locations. These areas are circular in shape and can be represented by their linear distance from the end-effector. Since moving the mobile base too far or too close to the end-effector reduces the manipulating flexibility of the manipulator, we can define a small area where 50% of the feasible mobile base positions are located, which is called the High ManipulatingFlexibility Area (HMFA) in this paper. As shown in Figure 4, the areas are the rings between R1 and R2. The R1 and R2 of the HMFA corresponding to each height of the end-effector can be derived from the results of the sampling experiment, which indicates the distributions of the valid mobile base positions corresponding to different end-effector heights (as shown in Figure 5). When sampling the mobile base positions, sampling in the HMFA corresponding to the end-effector height should have a higher priority than sampling in the other areas. This strategy increases the probability of sampling a feasible mobile base position and reduces the number of sampling repetitions, thus increasing the efficiency of sampling. The sampled configuration of the mobile base should be checked for collision with the environment using ISMBCONFIGVALID() in Algorithm 2. After sampling the configurations of the end-effector and the mobile base, the configuration of the manipulator could be calculated with CALMANIPCONFIG(). In this paper, we use the Kinematics and Dynamics Library (KDL) [39] to solve the inverse kinematics problem. The SCG process is repeated until a valid configuration, checked by ISMMCONFIGVALID(), is generated for the mobile manipulator. Figure 4: Valid mobile base positions with different end-effector heights. (**a**) The High Manipulating Flexibility Area (the ring between R1 and R2) of the mobile manipulator when the height of the end-effector is low. (**b**) The High Manipulating Flexibility Area (the ring between R1 and R2) of the mobile manipulator when the height of the end-effector is high. Figure 5: Valid mobile base position distribution.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Command and Data Management System
#### 3.5.4 InnoCube Apps  
As outlined in the former subsection, all apps are developed in Corfu and each app can be configured to fit different nodes, before being compiled into a specific Rodos image. A set of vital apps are present on all nodes enabling basic overview and operations, e.g., the _AnomalyReporter_, _Housekeeper_, _Surveillance_, and _Watchdog_, which are continuously monitoring and reporting on the state of each node. Furthermore, there are the apps needed to upload operating system images, write them to flash memory and boot the node, namely the _FirmwareUploader_, _FlashManager_ and _BootManager_. Finally, there is always a _TimeManager_ client present for CubeSat-wide time synchronisation. There are six different configurations of SKITH nodes that come with the following apps in addition to the vital apps mentioned above: The OBC node software features the _Commander_, which receives the telecommands sent from ground segment, verifies their signatures and publishes them to the Telecommunand topic. The more complex _TimedCommander_ stores and distributes lists of time-tagged commands, which can also be edited via telecommand before activation. Reacting on specific telecommand lists for certain satellite modes, the _Navigation_ and _PowerManagement_ apps issue high level commands to the SKITH nodes which are controlling the respective subsystems. The PCDU node image includes the _BatteryVoltageManager_, the _PowerManagementExecutor_ implementing the commands issued by the _PowerManagement_ as well as the _RedundancyManager_ ensuring that only one of each hot redundant SKITH nodes is taking lead. In addition, this image comprises a _MasterWatchdog_ and the _SKITHRadioMaster_, which issues communication frames to each node.  
The up/downlink node is given the Uplink and Downlink apps needed to interact with the transceiver, as well as the _DownlinkManager_, which manages different queues of telemetry to be sent down and the _TelemetryHistory_ app, holding older telemetry to be sent down when the link is otherwise unused. The attitude control node comes with the _AttitudeDetermination_ and _AttitudeControl_ apps, including the _FDIR_ apps, which are explained in the ADCS section.  
The payload computers for EPISODE and Wall#E will have dedicated apps to interact with each payload and forward the respective telecommands and telemetry.
## 2 Antenna Configuration
EBG structures can be considered a periodic dielectric or metallic materials, which have the capability of passing or stopping the propagation of electromagnetic waves at a known frequency. PBG materials can be applied to increase the behaviour of a single element patch antenna to obtain a higher gain. EBG, like PBG, structures can be also exploited to make a low-profile and high-efficiency antenna. Moreover, their high surface impedance cooperates to suppress the surface waves. EBG can be etched from the ground for added as load for the antenna to improve the performance by creating passbands in the working BW of the multiband antennas [44]. An important capability of EBG structures is their ability to decrease back radiation and the SAR (specific absorption rate). The SAR shows the rate of absorption of RF energy in the body; according to FCC guidelines, it should be less than 2 W/kg. Designing of wideband and UWB antennas usually use conventional monopole with a ground length of \(\lambda/8\). Using a truncated ground in designing a wearable UWB antenna significantly increases the SAR of the antenna, which affects the antenna's performance and  
Figure 3: The surface current distribution (SCD) at 5G, sub-6 GHz, lower-end, and higher-end of the working band. has negative, unacceptable impacts on the body. Therefore, the ground should be complete to reduce the SAR value. However, utilizing a full ground utterly disturbs the wideband working of the antenna, making it a narrowband antenna. For further improvement, the SIW structure is used to both improve the impedance matching of the antenna and degrade the SAR level.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 7 Assessment of the Available Architectures
### Scheduling and Control Category  
This category gathers all the use cases regarding the smart usage of customer's appliances and local distributed generation. These include scheduling, peak shaving, and load shifting through local energy balances for the purpose of limiting the consumption below the contractual threshold or for the purpose of reducing the total bill. In this scenario, the customer appliances and generators are the IHDs. Architecture 1 for these services requires point-to-point connections for each IHD. Short-range wireless technologies, such as BLE or Wi-Fi, are the most suited for this architecture, but a PLC solution can also be used, except for use case SC1, which has a strict latency requirement.  
Smart appliances could also exploit information coming from the market, network requests, or other sources for increasing the accuracy of their scheduling algorithms. They could also need to communicate and share data among themselves for coordination purposes. In this case, Architecture 2 or 4 is required. It is worth noting that extra time is needed by smart appliances or EMSs to process data, calculate results, and, eventually, communicate with the surrounding smart appliances. If a third party is involved, metering data can be transferred through WAN, processed and returned with energy prices, environmental hints, and customized contractual changes. In this case, the increase in latency from SM to the customer has to be evaluated. A scheme for this configuration using Architecture 4 is reported in Figure 7.  
Architecture 3 makes it possible for an authorized third party to withdraw metering data directly from the SM with the scope of controlling and scheduling his/her connected smart appliances. This architecture simplifies service provisioning by moving the algorithms needed for coordination and scheduling into the service-provider domain. It requires all the controlled appliances to be connected to the public network through Cellular or IoT-dedicated technologies. It is worth noting, though, that scheduling and control services require a fast and constant refresh of data exchanged, which typical WAN technologies might not be able to provide.
## 2 Innovative Methodology
### Data Redundancy Elimination  
After the coordinates of estimated point \(P_{est}\) are determined, we use \(P_{est}\) to replace \(P_{i,j+1}\) in scanning line \(i\). Afterwards, the new point set that contains \(P_{est}\) is used for bi-Akima interpolation, and there is a deviation \(h_{i,k}\) between the interpolated curve and each initial sampled point \(Q_{k}\), where \(i\) is the scanning line number and \(k\) is the serial number of initial point cloud in line \(i\). As mentioned earlier, the initial point cloud is obtained by 3D scanning measuring devices using the isochronous or equidistant sampling method in Step 1 as shown in Figure 1. The deviation \(h_{i,k}\) can be obtained by  
\[h_{i,k}=\underset{x\in(X_{j},X_{j+1})}{\text{MIN}}(s)=\underset{x\in(X_{j},X_{ j+1})}{\text{MIN}}\left(\sqrt{\left(x-x_{k}\right)^{2}+\left(y-y_{k}\right)^{2}+ \left(z-z_{k}\right)^{2}}\right), \tag{15}\]  
where point \(Q_{k}(x_{k},y_{k},z_{k})\) is an initial sampled point between \(P_{i,j}(X_{j},Y_{j},Z_{j})\) and \(P_{i,j+1}(X_{j+1},Y_{j+1},Z_{j+1})\), and \(P_{curv}(x,y,z)\) is the point in interpolated curve that makes the distance \(S\) shortest. Then, the max deviation \(d_{\text{max}}\) of the whole curve (i.e., from \(P_{i,1}\) to \(P_{est}\)) can be calculated by the following formula:  
\[d_{\text{max}}=\text{MAX}(h_{i,k}), \tag{16}\]  
which is compared with the required accuracy \(\varepsilon\). If \(d_{\text{max}}>\varepsilon\), discard point \(P_{est}\). If \(d_{\text{max}}<\varepsilon\), delete current compressed point \(P_{i,j+1}\) which is input from Step 2. Next, create an estimative flag \(F_{i,j+1}=1\) to replace point \(P_{i,j+1}\). This flag takes up only one bit of data storage space. After completing the above process, output the final compressed point cloud data flow, which contains the point coordinate and estimative flag information to the data storage devices. Afterwards, make \(j=j+1\), build a new shape-preserving piecewise bicubic Hermite curve to predict the shape and direction of the current scanning line, and create a new estimative point \(P_{est}\) to loop through the above data redundancy identification and elimination process until \(P_{i,j}\) is the end point of the current scanning line \(i\) or the data sampling is over. In addition, when \(P_{i,j}\) is the end point of line \(i\), make \(i=i+1\) and continue to loop the above algorithm until the measurement is completed.