esa-sceva/satcom-chunk-collection huggingface dataset:
# Gain-Scheduled Model Predictive Control for
## 3 Controller Design
### Design of Gain-Scheduled MPC  
In this subsection, a series of MPCs is first designed based on the linearized models obtained in the previous subsection. Then, a gain-schedule algorithm is proposed to switch among these designed MPCs for controlling the CIPS, based on the measured output pendulum angle of the SIP  
In order to design the GSMPC, a series of single MPCs should be designed first. The block diagram of a single MPC is shown in Figure 5. According to [46], an MPC mainly consists of three parts, i.e., model prediction, rolling optimization, and feedback. The MPC enables the optimizer to determine the control strategy by solving the constrained finite-time optimal control problem. After computing the optimal control input sequence, only the first element of the control sequence is applied. In the next time step, the state of the system is measured, and a new constrained finite-time optimal control problem is solved on a rolling basis. This rolling time-domain control scheme introduces dynamic feedback into the system and is able to deal with modeling errors and uncertainties.  
For a linear system, the MPC can be easily designed via MATLAB's _model predictive control toolbox_. However, the dynamic Equation (14) or (17) of the CIPS represents a complex nonlinear system, which cannot be used directly to design the MPC. So, in this paper, the dynamic Equation (17) of the CIPS is linearized at a series of operating points, resulting in a series of linearized models. Each linearized model can be obtained according to (22)-(25). For each linearized model, an MPC can be easily designed via MATLAB's _model predictive control toolbox_. Based on the above \(n\) linearized models, \(n\) MPC controllers are designed, respectively. The MPC corresponding to the \(i\)th linearized model \((A_{i},B_{i},C_{i},D_{i})\) is denoted as MPC-_1_\((I=i)\).  
Then, a gain-schedule algorithm is implemented to switch among these designed MPCs for controlling the CIPS, based on the measured output pendulum angle \(\theta\) of the SIP. When the measured output pendulum angle \(\theta\) of the SIP is closer to \(\theta_{i}\), the MPC-\(I\) (\(I=i\)) is employed. Mathematically, the gain-schedule algorithm is expressed as  
\[\begin{cases}d_{i}=|\theta-\theta_{i}|,(i=1,\dots,n)\\ d=\{d_{1},\dots,d_{n}\}\\ d_{j}=\min\{d\},(j=1,\text{or}\ j=2,\dots,\text{or}\ j=n)\\ I=\min\{j\}\end{cases} \tag{26}\]  
where \(d_{i}\) is the distance between \(\theta_{i}\) and \(\theta\). \(d\) denotes the set of \(\theta_{i}\). \(d_{j}\) is the minimum value of all elements of \(d\). \(j\) is the corresponding index of this minimum value.  
According to the designed MPCs and the proposed gain-schedule algorithm, the GSMPC is designed. In this paper, we set \(\theta_{o}=\left\{\begin{array}{ccccc}-\frac{4}{9}\pi&-\frac{1}{3}\pi&-\frac{ 1}{6}\pi&0&\frac{1}{6}\pi&\frac{1}{3}\pi&\frac{4}{9}\pi\\ \end{array}\right\}\) to design the GSMPC. In addition, the control system block diagram of the CIPS is shown in Figure 6.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 2 GNSS Signal Model, Meta-Signals, and HO-BOC Signals
### On the ACF of High-Order BOC Signals  
As has been previously stated, an option to increase the code-based observables' precision is the use of single-band HO-BOC signals. Note that the BOC modulation is generally denoted \(BOC(p,q)\), where \(p\) refers to the sub-carrier frequency \(f_{sc}=p\cdot 1.023\) MHz (i.e., \(sc(t)=sign\left[\sin\left(2\pi f_{sc}t\right)\right]\) with \(sign\) the sign function) and \(q\) to the ranging code frequency \(f_{c}=q\cdot 1.023\) MHz [31]. In GPS, an HO-BOC modulation is used in the modern military M signal, i.e., L1-M. In Galileo, the Public Regulated Service (PRS) signal in the E1 band uses also an HO-BOC subcarrier, as well as the corresponding PRS in the E6 band. In the case of BeiDou, the B1A HO-BOC-modulated signal is defined as an authorized service. It is important to notice that even if we provide the discussion and results considering these types of modulation, which are useful to benchmark the interest of GNSS meta-signals, the HO-BOC-modulated signals-in-space available are all regulated/military signals. This implies that these signals are not and will not be available in mass-market receivers.  
In this article, we consider the following representative set of HO-BOC signals:  
* GPS L1-M, BOCsin(10,5)
* Galileo E1 PRS, BOCcos(15,2.5)
* Galileo E6 PRS, BOCcos(10,5)
* BeiDou B1A, BOCcos(14,2)  
The corresponding ACF for these four signals is depicted in Figure 4 and compared to the Galileo E5 ACF. Notice that the ACF shape of the E1-PRS BOCcos(15,2.5) and B1A BOCcos(14,2) is very similar to the one for the Galileo E5; therefore, we can expect a similar achievable performance (i.e., CRB and asymptotic MLE performance). In terms of the MLE behavior, these two signals have much larger ACF  
Figure 3: ACF for other very large bandwidth Galileo, BeiDou (**a**) and GPS (**b**) signal combinations, (i.e., in both plots, the vertical axis represents the normalized autocorrelation). For comparison the Galileo E5 ACF and the Galileo E5A + E6 meta-signal ACF are also shown.  
secondary peaks, which will certainly have an impact on the MLE convergence, as will be discussed in the results. Regarding the L1-M and E6-PRS, the ACF shape is also similar, with a slightly wider main peak w.r.t. Galileo E5 and secondary peaks that are larger, but further apart. Therefore, one can expect the convergence not to be degraded and a slightly worse achievable performance compared to the Galileo E5.  
Time-delay and phase MLE performance results for the four HO-BOC signals are provided in Section 4. The corresponding SPP and RTK positioning results, considering a good satellite geometry, are discussed in Section 7. It is important to notice that, due to the construction of the signal, the bandwidth needed to process these HO-BOC signals will always be lower than the one needed for the Galileo E5 or the different meta-signals.
###### Abstract  
Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (!100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km\({}^{2}\). The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions.  
soil attribute; GIS; ordinary Kriging; rational sampling numbers; spatial heterogeneity +
Footnote †: journal: Applied Sciences
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 5 Privacy Preserving Federated Learning
### Secure Global Model Update with DHC
2:(@CS) generates \(N_{c}\) random vectors \(R_{1},R_{2},\ldots,R_{N_{c}}\) and encrypts the random vectors
3:using the public keys. 4:for\(i\leq N_{c}\)do
5: (@CS) \([S_{i}]_{i}\leftarrow[W_{I}^{i}]_{i}\cdot[R_{i}]_{i}\). 6: (@CS) Partially decrypts \([S_{i}]_{i}\) using \(psk_{i}^{(1)}\)
7:endfor
8:(@CS) Sends partially decrypted vectors \([PD^{1}([S_{1}]_{1}),PD^{1}([S_{2}]_{2}),\ldots,PD^{1}([S_{N_{c}}]_{N_{c}})]\)
9:to the CP. 10:(@CP) Partially decrypts the partially decrypted values using \(psk_{i}^{(2)}\) and obtains
11: \([(W_{I}^{1}+R_{1}),\ldots,(W_{I}^{N_{c}}+R_{N_{c}})]\). 12:(@CP) Calculates \(W_{sum}=\frac{\sum_{k}W_{I}^{k}+\sum_{k}R_{k}}{N_{c}}\). 13:for\(i\leq N_{c}\)do
14: (@CP) Encrypts \(W_{sum}\) using the public key \(pk_{i}\). 15:endfor
16:(@CP) Sends the encrypted values \([[W_{sum}]_{1},[W_{sum}]_{2},\ldots,[W_{sum}]_{N_{c}}]\) to the CS. 17:for\(i\leq N_{c}\)do
18: (@CS) \([W_{g}]_{i}\leftarrow[W_{sum}]_{i}\cdot[\frac{\sum_{k}-R_{k}}{N_{c}}]_{i}\). 19:endfor
20:Output: global weight vectors for the clients \([[W_{g}]_{1},[W_{g}]_{2},\ldots,[W_{g}]_{N_{c}}]\). ```  
**Algorithm 1** Secure averaging local model algorithm  
After updating the global model vector by performing the proposed secure averaging local model vector algorithm, the CP sends the updated global model vector to the clients for the next federated round. The clients execute the local training process using the updated global model vector as shown in Section 5.2 after decrypting the encrypted global model vector using its own private key. Thereafter, they send the newly trained local model vector to the CS, and then the CS and CP work together to update the global model vector. These procedures are repeated until convergence is achieved.
## 3 Testbed of Experiment  
The measurements were carried out at the Universiti Teknologi Malaysia, Kuala Lumpur (UTM-KL) campus, in an indoor to outdoor setting at the Menara Tun Razak Building. The specific I2O environment consisted of corridors surrounded by open and closed offices, conference rooms, and meeting rooms. The floor plan and pictures of the measurement environment are shown in Figure 2a,c. The study environment contained corridors that had two open ends (to the north and the south), as shown in Figure 2a. Figure 2b shows corridor A, where the the Tx antenna was placed, which was open from the north side, and curved to corridor B from the other side. Corridor B, where the Rx was placed, extended from corridor A, and was open from the south side, as shown by Figure 2c. Each of the corridor's walls were made up of a multitude of materials, including concrete, colored glass, and wood. The floor was coated with glazed ceramic tiles, and ribbed metal was formed on the ceilings of the hallways. The Tx antenna (1.7 m in height) was located in corridor A beside a concrete pillar. The direction of the Tx horn antenna is indicated in Figure 2b (toward corridor A, in the direction of the stairs and concrete pillar beside it). The Rx antenna (1.5 m in height) was an omnidirectional antenna located in corridor B at the back of the Tx antenna, as shown in Figure 2c, which rendered the environment completely NLOS. The first location of the Rx antenna was 3.7 m away from the Tx horn antenna. The Rx was then moved by 1 m to the end of corridor B; the Tx-Rx separation distance was then 13.5 m. The measurement configuration is shown in Figure 2a (left side).  
Figure 1: Block diagram of transmitter (Tx) and receptor (Rx) components for a 5G channel sounder.
## 4 Simulations and Results Analysis
### Legitimacy Probability (\(P_{l}\))  
In this section, we evaluate the metric \(P_{L}\) for the case that an alarm is raised against a node, that is \(P_{L}(alarm)\). We first present the behavior of \(P_{L}(alarm)\) according to Equation (19) (and Equations (13) and (14)). Then, we present the accuracy achieved by \(P_{L}(alarm)\) in the simulations.
## 1 Introduction  
In recent years, many countries around the world have been concentrating on the development of green energy. Human power generation systems, which are CO\({}_{2}\) emission free, inexhaustible and the cleanest of all green energy, have become a topical subject. The human power generation system considered here consists of a brushless DC motor (BLDCM), a three-phase bridge rectifier and a power converter. The system uses kinetic energy to drive a permanent magnet BLDCM to generate three-phase AC voltage which is rectified by a full-wave rectifier. The DC voltage is transformed and exported by the DC converter which is a boost device [1; 2; 3; 4; 5] that can be used for the conversion of electrical energy from different permanent magnet BLDCM systems. In a human power generation system uneven manual pedaling may result in unstable power output. All generators have different nonlinear P-V characteristic curves at different rotation speeds, and each of these curves has a best maximum power operating point. No recent studies have been made that have resulted in an effective maximum power point tracking strategy for human power generation systems and currently constant voltage output is assumed.  
However, many current studies have been made of maximum power point tracking strategy for other kinds of new energy applications. These include P&O [6] and Extremum Seeking Control (ESC) [7] which have the advantage of easy and cheap implementation. However, they are not robust and when a steady state is reached output is likely to be subject to perturbation and oscillation around the maximum power point due to external interference. To remedy perturbation at the maximum power point in a human powered system, we propose SMS for MPPT. The precept being that the maximum power point (MPP) condition of INC in the generator P-V characteristic curve is \(\partial P/\partial V\)=0,and the sliding manifold is defined accordingly. The strategy includes two modes, approaching conditions and sliding conditions. In the approaching condition the system trajectory makes timely and effective contact with the sliding manifold. Sliding conditions confine the system to the sliding manifold and the MPP finally reaches the target point. If the operating point is on the left or right side of MPP, the operating point moves against the MPP, this mode operates the system at the MPP effectively. However, SMS is likely to result in high frequency switching or signal input saturation that introduces high frequency noise. This noise can shorten converter and switch life and many studies have been made that propose solutions, including fuzzy theory [8] and extension theory [9]. In this study the extension theory has been applied to effectively avoid the shortcomings of high frequency SMS switching, minimize input saturation, and increase the efficiency of both energy conversion and response time.
## 3 Results
### Effect of EGCG on Ultrastructure Changes of Liver  
An electron microscopy-based experiment was performed to analyze the ultrastructure of liver tissue. The hepatocytes of the liver tissue of the normal control group demonstrated normal mitochondria and most of the mitochondria were spherical or round-shaped, dense granulated cytoplasm, smooth endoplasmic reticulum tubules with cristae normally present and normal rough endoplasmic reticulum whereas the observation of hepatocytes in the DEN-treated group showed extensive cellular damage. It was seen that a swollen and reduced number of mitochondria, distorted shape mitochondria, large clumps of glycogen surrounding cell organelles, broken SER tubules, and dilation of the rough endoplasmic reticulum (\(p<0.05\)). A number of damaged hepatocytes were found in the DEN-only treatment group, and the majority of the cytoplasmic organelles in these cells were degraded. The hepatocytes of liver tissue of the DEN plus EGCG-treated group showed a reduction of the damage in hepatocytes. Mitochondria are spherical or round-shaped but the presence of some dilated mitochondria, presence of glycogen clump, well-developed SER-ER tubule, and few lysosomes. The normal hepatocytes were seen in the EGCG only-treated group (Figure 7a-d).  
Figure 7: **Ultrastructural changes of liver**. Electron microscopy was used to observe the ultrastructure of hepatic cells. (**a**) Hepatocytes of the liver tissue of the control group revealed normal mitochondria and most of the mitochondria are spherical or round-shaped, smooth endoplasmic reticulum tubules with cristae normally present, and normal rough endoplasmic reticulum; (**b**) DEN only-treated tissues displaying extensive cellular damage. Swollen and reduced number of mitochondria, distorted shape mitochondria, large clumps of glycogen surrounding cell organelles, broken SER tubules, and dilation of the rough endoplasmic reticulum was seen. (**c**) EGCG plus DEN-treated tissue—where the liver tissue alteration was significantly reduced as compared to the disease control group (\(p<0.05\)) (DEN only-treated group). (**d**) EGCG only-treated group exhibited normal hepatocytes.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 3 Deep Learning Model for the Prediction of Ocean Weather
### Acquisition of Ocean Weather  
In this study, eight parameters of publicly available ocean weather were accessed and used. Among them, six parameters (i.e., mean wave height (MWH), mean wave direction (MWD), mean wave period (MWP), wind speed (U component) (WU), wind speed (V component) (WV), and sea surface temperature (SST)) were accessed through the ECMWF. ERA-interim was used among the various models. ERA40 does not include data after 2002, and ERA5 includes more detailed information. However, it is not easy to download data for an extended period of time. Therefore, ERA-interim, which includes relatively accurate information over a long period of time, was used in this study. ERA-interim includes 6 h of data at intervals of 0.75 degrees in longitude and latitude. The data for 20 years (from October 1999 to September 2018) worldwide were gathered from ECMWF. Since information about current data is not included in ECMWF, the other two parameters of data (current speed (U component, simply, CU) and current speed (V component, simply, CV)) were accessed through the hybrid isopycnal-sigma-pressure coordinate ocean model (HYCOM). HYCOM provides three kinds of datasets: GLBy0.08, GLBu0.08, and GLBv0.08. GLBy0.08 does not include data before 2014, and GLBu0.08 includes data from 805 to 80N in latitude. In this study, we used the GLbv0.08 dataset, which includes long-term data and covers a large area of the 80S to 90N in latitude. GLbv0.08 includes 3 h of data at intervals of 0.08 degrees in longitude and latitude. The data accessed through HYCOM span a range of 4 years and 4 months (from July 2014 to November 2018) worldwide. For the route planning of a ship, it is necessary to forecast ocean weather from one week to a month. The captain does not change the route in real time during the voyage so that the route is re-determined every 12 h to a day. Therefore, 12 h of data is valid as a unit of time. In a spatial resolution, the denser intervals in longitude and latitude, the better. Generally, any resolution is linearly interpolated from nearby values. In this study, the eight parameters of ocean weather are composed of intervals of 6 h in time and of 1.5 degrees in longitude and latitude.  
The raw data received from the ECMWF and HYCOM data servers are in.nc file format. The original data accessed in this study have very dense spacing. Therefore, we removed redundant data and used only the data necessary to train models, and the data were converted to.h5 file format using the h5 py package. Through this process, the data to be used for the proposed model were generated.
## 4 Discussion
In order to advance this technology towards medical applications, it would be necessary to employ LINuS or other optogenetic tools for which light would lead to p53 accumulation, as the final aim would be to trigger the p53 signaling pathway locally, only in malignant cells. This is not possible with LEXY, as light would have to be constantly applied everywhere else in the body to keep p53 inactive. As LINuS per se works well in HCT116 cells (data not shown), we believe control of PMI with LINuS is achievable with future optimization of the construct. However, blue light penetrates poorly into tissue, thus an approach based on red/infra-red light is highly desirable. The raw data used to generate the figures in the paper are available online as supplementary figures at www.mdpi.com/xxx/. **Figure S1**: The Mdm2-inhibitory peptide (MIP) does not lead to increased p53 levels in HCT116 cells. (a) Western blot showing the levels of p53 in HCT116 cells transfected with the indicated constructs. Mock indicates cells transfected with empty pcDNA3.1(+). \(\beta\)-actin was used as loading control. (b) Bar plot showing the quantification of three independent western blots with samples as in (a). Data indicate mean \(\pm\) standard deviation. NS p \(>=0.05\), \(t\)-test, **Figure S2**: Raw data for the plot shown in Supplementary Figure S1. Only relevant lanes were annotated, **Figure S3**: Raw data for the plot shown in Figure 1c. Only relevant lanes were annotated, **Figure S4**: Raw data for the plot shown in Figure 3c. Only relevant lanes were annotated. **Figure S5**: Raw data for the plot shown in Figure 3c. Only relevant lanes were annotated. Conceptualization, P.W. and B.D.V. ; Methodology, P.W. and B.D.V. ; Software, P.W. ; Validation, P.W. ; Formal Analysis, P.W. and B.D.V. ; Investigation, P.W. ; Resources, B.D.V. ; Data Curation, P.W. ; Writing - Original Draft Preparation, P.W. and B.D.V. ; Writing - Review & Editing, B.D.V. ; Visualization, P.W. ; Supervision, B.D.V. ; Project Administration, B.D.V. ; Funding Acquisition, B.D.V. This work was supported by the BMBF 031L0079 grant to B.D.V. We thank [PERSON] for support with the analysis of LINuS variants in HCT116 cells and members of the [PERSON] lab for helpful discussions. The authors declare no conflict of interest.