esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Model
### Theoretical Calculations  
The materials of the stationary and rotating seal rings are stainless steel and graphite, respectively, and their performance parameters are listed in Table 2. The thermo-hydrodynamic parameters set out in the theoretical calculations are shown in Table 3. Additionally, the thermophysical properties of helium could refer to Ref. [35], in which the compressibility coefficient, the heat capacity, and the viscosity of helium were discussed under different operating conditions, based on the data base of NIST REFPROP. The viscosity of helium changes markedly, while the compressibility coefficient and the heat capacity change little for the temperature range between 100 K and 400 K, and the pressure range between 0.1 MPa and 5 MPa. In addition, the density is also an important factor influencing the thermo-hydrodynamic characteristics of the face seal. According to Figure 2, the density of helium varies distinctly under different operating conditions. For different temperatures, the density increases almost by a factor of 7 with an increase of pressure from 0.6 MPa to 5.0 MPa, while its density decreases to 70% in values with an increase of temperature from 100 K to 400 K for all pressure conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Graphite** & **Stainless Steel** \\ \hline Material density/kg\(\cdot\)m\({}^{-3}\) & 1800 & 7930 \\ Young’s modulus/GPa & 300 & 400 \\ Poisson’s ratio & 0.17 & 0.17 \\ Specific heat capacity/J\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\) & 710 & 500 \\ Thermal conductivity/W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\) & 129 & 16.2 \\ Linear thermal expansion & 2.75 & 5.5 \\ coefficient/10\({}^{-6}\) K & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material performance parameters of seal rings [35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Symbol** & **Specification** \\ \hline Convection heat transfer coefficient of solid rings & \(k_{1},k_{2}\) & 8.0 \\ at ambient condition/W\(\cdot\)m\({}^{-2}\)\(\cdot\)K\({}^{-1}\) & \(k_{c}\) & 0.024 \\ Thermal conductivity of helium/W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\) & \(k_{c}\) & 0.024 \\ Degrees of freedom of motion of gas molecules & \(i_{d}\) & 3 \\ Sealing temperature/K & \(T_{0}\) & 100\(-\)350 \\ Sealing pressure/MPa & \(p_{0}\) & 0.1\(-\)5.0 \\ Basic film thickness/\(\upmu\)m & \(h_{0}\) & 1.0\(-\)5.0 \\ Rotational speed/rpm & \(n\) & 3000\(-\)50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermo-hydrodynamic parameters used in the theoretical calculations [35].  
In calculations, except for the thermal boundary conditions, the pressure adopted the forced boundary conditions, and the temperature adopted the dynamic boundary conditions [10]. Meanwhile, the reliability of the thermo-hydrodynamic analysis had also been validated in Ref. [10], in which the numerical results of the temperature distribution were compared with the experimental date of [PERSON]'s work, and they were in good agreement.
# A High Performance 0.3 V Standard-Cell-Based OTA Suitable for Automatic Layout Flow
## 8 Comparison  
The small-signal and large-signal Figures of Merit (\(FOM_{S}\) and \(FOM_{L}\)) are very popular metrics for comparing OTAs. However, these metrics do not take into account the area footprint of the amplifiers. Therefore the area-normalized \(FOM_{S,A}\) and \(FOM_{L,A}\) have been proposed in [39] to allow for a more comprehensive comparison. These FOMs are defined as:  
\[FOM_{S,A}=\frac{GBW\cdot C_{L}}{P_{D}\cdot Area};\quad\quad FOM_{L,A}=\frac{SR _{avg}\cdot C_{L}}{P_{D}\cdot Area}. \tag{26}\]  
The proposed OTA has been compared against several ULV (i.e., operating with a supply voltage as low as 0.3 V) OTAs from the literature, and the results of this comparison are reported in Table 5. The proposed amplifier results in the highest \(FOM_{S}\) and \(FOM_{S,A}\) outperforming all the other ULV OTAs. In addition, it exhibits the highest dc gain among the standard-cell-based OTAs. Large-signal performance is in line with the state of the art. Indeed, the \(FOM_{L}\) and \(FOM_{L_{exc}}\) of the proposed OTA are higher than the ones in [33; 51], whereas they are comparable with those shown by [11; 12; 66]. Power consumption of the proposed OTA is relatively high, and this is mainly due to two main reasons: (1) the usage of non-minimum-sized standard cells to minimize the offset standard deviation under mismatch variations; (2) the adoption of a standard-cell library built with the low threshold voltage transistors of the technology (which allow us to achieve the best GBW). If higher offset and lower GBW are acceptable for the application, wherein the minimization of power consumption is the most important design goal, the following strategies can be combined: (1) reduce the size of the reference standard-cell; (2) choose the standard-cell library built with the high-threshold voltage transistors (recent CMOS technologies typically offer two or three threshold voltage options); (3) further reduce the supply voltage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Typical** & **Mean** & **Std** \\ \hline \(A_{V_{D}}\) [dB] & 50.33 & 50.24 & 1.12 \\ \(A_{V_{C}}\) [dB] & 7.45 & 16 & 9.12 \\ \(GBW\) [MHz] & 10.4 & 10.56 & 1.23 \\ \(m\varphi\) [deg] & 56.33 & 55.92 & 3.54 \\ \(P_{D}\) [\(\mu\)W] & 12.09 & 12.47 & 0.2 \\ \(Offset\) [mV] & \(-2.2\) & \(-2.12\) & 3.94 \\ \(SR_{p}\) [V/\(\mu\)s] & 631.4 & 634 & 21.97 \\ \(SR_{m}\) [V/\(\mu\)s] & 811.3 & 824.4 & 42.91 \\ \(THD\) [\%] & 0.24 & 0.27 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the proposed OTA under mismatch variations.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 2 Preliminaries
### Shaping Architecture vs. Shaping Algorithm  
The aforementioned shaping schemes have in common that they are aiming at solving an indexing problem, which is that the binary input of the shaper determines an amplitude sequence. At the receiver side, the inverse operation is carried out. For proper characterization and categorization of this indexing problem, it is insightful to differentiate between architectures and algorithms.  
When we speak of the architecture, we mean the underlying principle behind the mapping operation, which in turn can be realized with various different algorithms as shown in the fifth layer of Figure 1. For instance, the principle of the CCDM architecture is that the sequences at the shaper output have a fixed number of occurrences of each amplitude, i.e., they have a fixed composition. Furthermore, the mapping algorithm can operate on one nonbinary or several binary subsets of the output sequence. Bit-level [51, 57] and parallel-amplitude [58] designs are modifications to the conventional CCDM architecture that carry out such a transformation from one nonbinary to several binary DMs. Among all algorithms, a LUT is probably the simplest way to solve the CCDM indexing problem, yet the LUT size table is prohibitively large as it reaches gigabit size already for short blocklengths [76]. The original mapping method for a nonbinary-alphabet CCDM is AC (Section IV in [52]) which is modified from [53]. For binary-output CCDM, SR has recently been proposed as a low-serialism alternative to CCDM. MPDM [55] extends the CCDM principle (and thus architecture) by using variable-composition DM, yet internally uses CCDM methods for mapping and demapping.  
As another example, the principle of SpSh architecture is that the sequences at the output of the shaper satisfy a maximum-energy constraint, i.e., they satisfy (4). The problem of indexing these sequences can be solved again by a using a LUT. On the other hand, ESS [22], SM [23] and (Algorithm 1 in [23]) are constructive algorithms to index sequences inside a sphere. The required storage and computational complexity of these algorithms are compared in Section 6. For further discussion on SM, we refer the reader to [23; 81], (Chapter 8 in [82]) and (Section 4.3 in [10]).
## 3 Results and Discussion
Water present in biological tissues has been defined as bound or free water depending upon its proximity to neighboring macromolecules, membranes, or other interfaces. In a classical paper [51] entitled, \"What retains water in living cells?\" [PERSON] & [PERSON] concluded that cell proteins located on the surface of a biological membrane polarize most of the cell water in the form of multilayer layers. Water forming the hydration shell of the macromolecule is considered as bounded. The remaining water in the system is defined as bulk. As we can expect that the number of water layers in which the mobility of the molecules is limited  
Figure 2: Comparison of dielectric parameters \(\Delta\varepsilon\) (**a**), \(\alpha\) (**b**), \(\tau\) (**c**) for fresh CPDA-1 with storage medium for three samples A, B, and C. Each datum is mean \(\pm\) SEM from six repeated measurements. (differs from) does not exceed 4-5. This should mean that the volume of such water, in comparison with the intracellular water volume, is insignificant. Therefore, the presence of a membrane in the hemolysisate should not lead to a change in its dielectric spectrum. Limitation of the study: It is well known that the PRBCs lesion is affected by the protocol of PRBCs preparation and storage [1; 5; 26]. The results presented here, were obtained for units of PRBCs that are non-leukoreduced and cells are stored in CPDA-1. In the future, we plan to examine PRBCs units that are produced with leukoreduction and gamma-irradiation, in which cells are storage in SAGM (Saline, Adenine, Glucose, Mannitol). Thus, from the results demonstrated in this study, and based on our previously published data [48], it follows that:  
1. The main factors determining the microwave dielectric response of the PRBC suspension is the condition of water molecules in the cytosol and storage medium;
2. The contribution associated with the water molecules located near the surface of the RBC membrane can be neglected in microwave dielectric measurements. Thus, having summed up our results, we conclude that using MDS, it is possible to control the functionality of PRBCs by the response of water molecules located both inside and outside the cell.
# Propagation Characteristics of Modulated EHF Signal in the Wake Region of Plasma Sheath
## 3 Numerical Simulations on the BER
The BER performance is better when channel 6 is adopted and with a carrier frequency of 140 GHz and 225 GHz. When 2 FSK is adopted, in channel 3, the minimum BER magnitude is between \(10^{-2}\) and \(10^{-3}\) when the carrier center frequency is 94 GHz, and between \(10^{-3}\) and \(10^{-4}\) when the carrier center frequency is 140 GHz and 225 GHz. In channel 6, the minimum BER magnitude is between \(10^{-3}\) and \(10^{-4}\) when the carrier center frequency is 94 GHz and 225 GHz, and between \(10^{-4}\) and \(10^{-5}\) when the carrier center frequency is 140 GHz. When channel 6 and a 140 GHz carrier center frequency are selected, the BER performance is the  
Figure 3: Under the same noise settings, BER performance of (**a**–**c**) channel 3 and (**d**–**f**) channel 6 when the carrier frequencies are 94 GHz (**a**,**d**), 140 GHz (**b**,**e**), and 225 GHz (**c**,**f**), respectively. best. Under the same channel and noise level, the BER performance of 2 FSK is better than 2 ASK, but worse than 2 PSK. When using 4 QAM (QPSK), the minimum BER magnitude is between \(10^{-2}\) and \(10^{-3}\) when the carrier frequency is 94 GHz, and between \(10^{-1}\) and \(10^{-2}\) when the carrier frequency is 225 GHz. When the carrier frequency is 140 GHz, the minimum BER magnitude is between \(10^{-3}\) and \(10^{-4}\) in channel 3, and zero in channel 6. The BER performance is the best when channel 6 and a 140 GHz carrier frequency are selected. In summary, when 2 PSK modulation with a carrier frequency of 140 GHz or 225 GHz, or 4 QAM (QPSK) modulation with a carrier frequency of 140 GHz is adopted, better BER performance can be obtained from channel 6.
## 3 Problem Statement and Results
### Kalman Filter for Trajectory Smoothing  
Until now we have been dealing with the optimization of trajectories for a single vehicle according to two possible performance criteria. Needless to say, once the optimum trajectory is determined (here for the sake of simplicity, we disregard the time to compute the trajectories), the vehicle obviously start running the calculated path. In real cases, the vehicle will have to face undesirable phenomena during the maneuver's execution, which could perturb the previously calculated optimum trajectory and make it divert from the calculated path. This is usually called the _Random Noise Disturbances_[12] in the literature. In this subsection we will extend the initial System of Equations describing the mobility of our one-dimensional vehicle to accomplish for this variability in the execution of the maneuvers. We will model such phenomena by assuming that an additive Gaussian noise process produces this variability by influencing:  
* The shape of the traced path due to possible deviations from the optimum course, see Subsection 3.5.1.
* The sensors' measurement on the position and speed at a fixed time \(t\), see Subsection 3.5.2.  
Kalman Filter theory has been proven to be a very reasonable option for state estimation and path reconstruction under the aforementioned circumstances. Moreover, it is the core technology that implements basic car localization and motion on autonomous vehicles, like the famous Google Car [13]. Before discussing in detail the characteristics of the Kalman Filter, we will first express our extended model for the System (1) by accounting for the two influencing factors presented earlier:
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 4 Results  
We executed each algorithm with a fixed number of iterations using the parameters in Table 2. The executions were run three times in the bands SCL for commonly optical interconnected applications and OESCL for ultra-wideband applications. We used this differentiated frequency window approach to test the robustness of the transmittance integral defined in Equation (2). Lumerical's 2.5D variational FDTD was run on the solutions. All optimized designs depend on the initial population and thus on the initial guess. Before our work evaluation, we performed an exploratory analysis for validation, executing an optimization for all algorithms four times each. This was performed individually with a different initial random guess because we do not want to make additional assumptions about the final design and wanted to simply let the optimization algorithms find some patterns.  
Our results are presented in three main terms:  
1. **Convergence rate** is the algorithm's speed in achieving FOM stability, with a variation \(<\)2%. To calculate it, we used the mean (Geomean) of the FOM trend.
2. To evaluate **the similarity of the FOM**, we defined the deviation factor \(\Delta FOM\) as the absolute difference between the geometric mean, Geomean, and the actual FOM trend value, \(\Delta FOM_{i,j}=|\text{Geomean}_{i}-FOM_{ij}|\), where \(j\) is each algorithm execution, i.e., PSO\({}_{1}\), PSO\({}_{2}\), and PSO\({}_{3}\), and \(i\) is the type of algorithm, i.e., Geomean\({}_{PSO}\), Geomean\({}_{GA}\), or Geomean\({}_{CMA-ES}\). Additionally, the maximum FOM was also considered to characterize the performance of each method.
3. The **waveband analysis** was performed over a defined band, either SCL or OESCL. We analyzed the maximum and minimum transmittance values to characterize a planar response of the device over the given frequency. We considered variations \(\leq\)1% of the transmittance to be under the fabrication error limit.  
The average optimization total execution time was 12 h for a 100-iteration analysis for each algorithm. Our experiments were performed on an individual workstation with an Intel i7 processor 4 th gen and 32 GB of RAM.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 2 Materials and Methods
### Rate-Dependent Hysteresis Model Based on P-I  
To establish the rate-dependent model of the PZT FSM, this paper adopts a Hammerstein structure, which combines the static nonlinear P-I model with the dynamic linear model in series. The structure is shown in Figure 5. \(u\) is the input signal and \(y\) is the output signal.  
The dynamic linear model's equation is:  
\[G_{0}(z)=\frac{A(z^{-1})}{B(z^{-1})} \tag{4}\]  
where \(B(z^{-1})=b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+...+b_{n\
atural}z^{-nb}\), \(A(z^{-1})=a_{0}+a_{1}z^{-1}+a_{2}z^{-2}+...+a_{n\
atural}z^{-n\
atural}\).  
Figure 6 depicts the hysteresis curve of the P-I rate-dependent model when the input signals are the same amplitude but different frequencies. Thus, it can be seen that it has the same characteristics as the PZT FSM when the frequency of the input signal changes.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 5 Hierarchical Manipulation Planning
### Graph Searching  
Given the initial and goal poses of the object, grasps of these object poses can be determined by data provided by object analysis introduced in Section 4. Then, we look for the same object and grasp configurations that are embedded in nodes of the graph. If the new object poses and their grasp configurations can not be found in the graph, new nodes will be created and be connected with the graph. The reachability of new nodes can be guaranteed by the controllability of pivoting motion [18], which proved that a sequence of three pivoting motions can move the object to an arbitrary object configuration with the same base surface in the neighborhood.  
Weights are manually assigned to edges in the graph. As the object can be efficiently moved by pivoting and we expect the robot finishes the placement of the object quickly, we set the weight of edges representing pivoting to a small value 0.1. The weight of edges indicating regrasping is set to 0.5 and the weight of edges indicating tumbling is set t 1. A relative large weight is designed to edges related to tumbling because the tumbling is mainly designed for changing the object's supporting base instead of moving the object. Knowing the initial and goal configurations, we search the weighted graph to find a path. A solution path includes sequences of discrete object and grasp configurations. Intermediate configurations are added to ensure a smooth motion during pivoting and tumbling by the MPC introduced in Section 5.2. In a lower-level motion planning, we use rapidly exploring random tree (RRT) to sample the space considering grasp configurations. At this level, if configurations with feasible inverse kinematics and obstacle clearance cannot be found, we go back to the graph, remove certain nodes and edges which caused the problem and re-search the graph for a new path.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories
#### 6.3.5 Energy Savings from a TowerCo's Perspective  
In this subsection, we analyzed the impact of different TDLs and the percentage of overlapping actions on the TowerCo's energy performance. In the RANaaS implementation, the TowerCo did not control resources used by MNOs. Rather, it only distributed energy consumption costs based on resources used by MNOs. As a result, across the three scenarios tested here (S1-S3), similar energy savings of around 65 percent were obtained. The average TowerCo's energy consumption over 24 h in S2 was two percent lower, while that in S3 was one percent higher compared to S1.  
As described in Section 6.3.2, RAN services could bridge the percentage difference of simulation outcomes in category one from the operators' and TowerCo's perspectives. It provides a way forward to serve multiple operators based on traffic demands, user distribution, and network configuration of all MNOs. Furthermore, if the operators opt for the RANaaS offering, the energy cost distributions must consider how much their actions overlap.
## 4 Experimental Results and Discussion
### Subjective Evaluation
#### 4.5.3 Ranking and Raw Data Processing  
After the stereoscopic images were scored by the participants, the Difference Mean Opinion Scores (DMOS) between each pair of stereoscopic images, which include the original stereoscopic image and the reconstructed stereoscopic image, were calculated. According to ITU-R BT.500, the value range of DMOS is from 0 to 100. The formula is as follows:  
\[DMOS_{j}=\frac{1}{N_{j}}\sum_{i=1}^{N_{j}}d_{ij} \tag{13}\]  
\[d_{ij}=r_{iref(j)}-r_{ij} \tag{14}\]  
where \(r_{ij}\) denotes the raw quality score of the \(j\)-th reconstructed stereoscopic image evaluated by the \(i\)-th participant, \(r_{iref(j)}\) is the raw quality score assigned by the \(i\)-th participant to the \(j\)-th original stereoscopic image, and \(N_{j}\) denotes the number of participants involved in assessment of the \(j\)-th stereoscopic image. Then \(DMOS_{j}\) is the mean of the raw difference scores \(d_{ij}\).  
Before calculating the final DMOS of each of the reconstructed stereoscopic images, the data of the participants with poor score stability should be removed. Specially, if the value of \(d_{ij}\) is beyond the 95% confidence interval of \(DMOS_{j}\), then \(d_{ij}\) is called an outlier.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 5 Proposed Scheme  
As stated above, since uIP TCP and reliable CoAP are similar in that both allow only one outstanding packet by default, the previous studies on congestion control mechanisms of CoAP inspired our proposed scheme. However, we note that uIP TCP and reliable CoAP have different requirements. The most distinctive difference is how to handle a failure of packet delivery after the maximum retransmission attempts. In the case of CoAP, if a message delivery fails even when the number of retransmissions reaches the maximum, the application is notified that the message was not delivered successfully. That is why the delivery ratio is included in the performance evaluation metrics in some CoAP studies. In contrast, it is expected that TCP does not allow delivery failure of a part of the byte stream. As long as the TCP connection is alive, TCP must guarantee that every single segment is delivered to the upper application in order. If a segment fails to be delivered even when the number of retransmissions reaches the maximum number of retrials, a broken network link is notified to the upper layer. Consequently, under the same network conditions, TCP requires that the maximum number of retransmissions be set to a higher value compared to CoAP. However, since retransmission by an end-to-end protocol requires high energy consumption in an energy-restricted network, our main focus is on reducing TCP retransmissions without decreasing throughput.  
Figure 3: Changes of RTT, RTO, actual retransmission timer, and retransmission counter for some nodes in a \(5\times 5\) grid topology with ContikiMAC enabled. (**a**) This node had few chances to measure RTT without taking sample RTTs using retransmitted segments. (**b**) In this node, a segment was successfully delivered after 10 retransmissions.  
Figure 2: Changes of RTT (blue), RTO (black), actual retransmission timer (pink), and retransmission counter (red) for a node in a \(5\times 5\) grid topology: (**a**) with NullRDC enabled (i.e., no RDC enabled); (**b**) with ContikiMAC enabled.  
As discussed in the previous section, an RDC mechanism may cause large RTT fluctuation, which may lead to many retransmissions. While the purpose of RDC is energy efficiency, inducing many retransmissions can nullify the benefits of RDC. Obviously, our primary goal is to reduce retransmissions with and without RDC enabled. To this end, we consider weak RTT estimation, backoffs with variability, and dithering.