esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With its lossless properties, zero-watermarking has attracted a lot of attention in the field of copyright protection for vector maps. However, the common zero-watermarking algorithm puts too much emphasis on mining for global features, making it vulnerable to cropping attacks, and the robustness is not comprehensive enough. This study provides a vector map zero-watermarking scheme that utilizes spatial statistical information and frequency domain transformation methods in an effort to solve the aforementioned issue. In order to make the scheme more resistant to cropping and compression, it is constructed on the basis of feature point extraction and point constraint blocking of the original vector map. Within each sub-block, feature points are used to build constraint Delaunay triangulation networks (CDTN), and the angular values within the triangle networks are then extracted as spatial statistics. The angle value sequence is further transformed by discrete Fourier transform (DFT), and the binarized phase sequence is used as the final feature information to build a zero watermark by executing an exclusive disjunction operation with the encrypted copyright watermark image, both of which contribute to the scheme's robustness and security. The results of the attack experiments show that the proposed vector map zero-watermarking can restore identifiable copyright images under common geometric attacks, cropping attacks, and coordinate system transformations, demonstrating a high level of robustness. The theoretical basis for the robustness of this watermarking scheme is the stability of CDTN and the geometric invariance of DFT coefficients, and both theory and experiment validate the method's validity.  
zero-watermarking; vector maps; constraint Delaunay triangulation networks; discrete Fourier transform 202325682023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 5 Privacy Preserving Federated Learning
### FL Local Model Training  
The CS selects a machine learning model to be trained on the client's side using local data. A deep neural network model is selected; however, other machine learning models can also be used for the proposed PPFL algorithm. The CS determines the percentage of clients participating in the training process and randomly selects clients to participate in the actual training process. At the first iteration, the CS encrypts the initial global model parameters using the selected client's public keys and sends the encrypted global model parameters to the clients, as shown in step 2. In the following iterations, the CS sends the results of aggregating the local model parameters using homomorphic operations to the clients without additional encryption because the result of the homomorphic operation is also an encrypted value. The global model parameter vector encrypted with the public key of the \(i\)-th client \(c_{i}\) is represented as \([W_{g}]_{i}\), where \(W_{g}\) is a global model vector containing the global model parameters.  
The \(i\)-th client decrypts the encrypted global model vector, \([W_{g}]_{i}\), using its own private key and uses the decrypted global model parameters for the local training process. Each client participating in the proposed PPFL performs the training process using the local data in a deep neural network initialized with the global model parameters, as shown in step 3. After the local training process, the \(i\)-th client obtains local model parameters and proceeds to encrypt a local model vector \(W_{l}^{i}\) containing the local model parameters using its own public key. The client sends the encrypted local model vector to the CS for secure aggregation, as described in step 4. Note that the local model vector encrypted with the public key of the \(i\)-th client is represented as \([W_{l}^{i}]_{i}\). After the CS receives the encrypted local model vector from the clients, the encrypted vectors are used to update the global model vector through cooperation with the CP.
## 10 Software
### Main Program  
The main program awaits data reception through the serial port. Upon receiving and storing RS232 communication data, the main program determines the actions to be taken. The microcontroller then returns to the main program, proceeding from where it was before the interrupt. It verifies whether the received data condition has been met, executes the \"Analyze Command\" function, and acts based on the received command's value.  
To toggle the laser beacon on or off, a 500-millisecond pulse is generated. This pulse both turns on the laser if it is off and turns it off if it is on. Upon receiving the pulse, the laser's state changes.  
For the selection between an internal or external trigger, a pin is set to opt for an \"external trigger\" or cleared to choose an internal trigger.  
To manage battery control, the program employs four double relays that alternate between the two batteries, configuring them to work in parallel during the charging process and switching to series mode when required to supply power to the laser. In the _Standby_ mode, the circuit is completely disconnected from external power. These relays operate in three distinct modes:  
* _Charging_ Mode: Batteries function in parallel.
* _Laser_ Mode: Batteries are configured in series.
* _Standby_ Mode: The circuit is disconnected from external power.  
For optical attenuation control, communication via \(\mathrm{I^{2}C}\) interfaces with a DAC on the power board, allowing the adjustment of the desired attenuation value.  
Another essential feature involves monitoring the battery voltage and container temperature. An integrated ADC in the PIC is employed to measure both of these values.
## 6 Conclusions  
This paper proposes a two-stage approach to schedule EV charging with both G2V and V2G capabilities to improve grid voltage, reduce VUF, and reduce prosumer charging costs. Our study yielded the following findings:  
1. The proposed method provides the lowest cost for customers to charge/discharge their EVs compared with other existing methods for all the scenarios considered in this study. For the summer season, the total cost using the proposed method is around 1.515 times lower than uncontrolled charging and 1.206 times lower than the result obtained using the method which provided the least cost next to the proposed method. A similar trend was seen for other scenarios considered in this study.
2. The battery degradation costs with the proposed method are around 1.756 times lower than Method 1 but 1.64, 1.86, and 1.37 times higher than with uncontrolled charging, and Methods 2 and 3, respectively, when EV penetration is 100%. Similar results were obtained for other case studies taken in this paper.
3. The minimum voltages (p.u) of the critical bus for winter, summer, spring, 100% penetration scenario, weekday and weekend load profiles were 0.97, 0.98, 0.975, 0.97, and 0.99, respectively, and the maximum voltages (p.u) of the critical bus for winter, summer, spring, 100% penetration scenario, weekday and weekend load profile were 1.01, 1.02, 1.01, 1.01, and 1.02, respectively, which shows that the voltage profile lies within the \(\pm\)5% voltage limit. However, the minimum voltage under uncontrolled charging lies below the desired limits for all the scenarios.
4. The values of VUF under uncontrolled charging for winter, summer, spring, 100% penetration scenario, weekday and weekend load profile were 2.1, 2.1, 2.1, 2.1, and 2, respectively. The values of VUF with the proposed method for winter, summer, spring, 100% penetration scenario, weekday and weekend load profile were 1.98, 1.98, 1.95, 1.98, and 1.98, respectively. Thus, it can be seen that the VUF also lies within the prescribed limit (<2% IEEE standard) with the application of the proposed method for all the case studies considered in this study.  
This study is limited to only main two grid requirements, namely VUF and voltage deviation; however, there are many other grid requirements, such as minimization of loss, maximization of reliability, stability improvement, reactive power pricing, etc. Thus,
## 2 Massive MIMO 5G Networks  
### System Model  
A multi-cell massive MIMO network system consisting of L cells, each containing one BS with multiple antenna elements is considered in this study, as shown in Figure 1. This model is similar to the ones proposed in [18; 19; 20] in which each BS communicates coherently with the single-antenna users using the time division duplex (TDD) operating mode. This latter is considered to obtain the downlink signaling informations from the uplink pilots thanks to the channel reciprocity property it relies on. These operations take place during the coherence time \(\tau_{c}\) when the propagation channels are almost stable with negligible fluctuations. At the beginning of the TDD process, the users send the uplink pilot sequences consisting of \(\tau_{p}\) symbols to the BS which estimates the downlink channels before transmitting the requested downlink data. The estimated channels between the \(k\)th user in the \(l\)th cell and the \(m\)th antenna element of the corresponding BS are referred to as the small-scale fading channels which are assumed independent and identically distributed (i.i.d) Rayleigh fading [21].
## 4 Survey Results and Analysis
### Preliminary Data Analysis
#### 4.1.2 Descriptive Statistics Analysis  
In this stage, the values of the mean score ranking technique and standard deviation were adopted to determine the relative significance of the fourteen items. Additionally, for a better analysis of the homogeneity of the experimental data, the coefficient of variation (CV) was also calculated. The coefficient of variation (CV) was calculated as the ratio between the standard deviation and the arithmetic mean; CV = (standard deviation/sample Mean) \(\times\) 100%.  
The scores of the data collected from employees are presented in Table 3.  
In the case of the present research, it is noted that the coefficient of variation (CV) had values between 14.382 (I.01) and 37.673 (I.07), so we can define the parameter reproducibility as good when the CV was between 10-20% for most factors and acceptable (when the CV was between 20-30%) for indicators I.08 and not acceptable when the CV \(>\) 30 (the case of  
\begin{table}
\begin{tabular}{c l c c c} \hline
**Code** & \multicolumn{1}{c}{**Questionnaire Items (QIs)**} & \multicolumn{1}{c}{**Mean**} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **Coefficient of** \\ **Variation [\%]** \\ \end{tabular} & **Rank** \\ \hline I.01 & I use frequently the internet & 4.66 & 0.669 & 14.382 & 1 \\ I.02 & I use internet easily & 4.42 & 0.660 & 14.935 & 2 \\ I.03 & I find easy the registration/training/testing steps in the SSM.ro platform & 4.33 & 0.715 & 16.543 & 3 \\ I.04 & I think that I increase my knowledge through online training, compared to other forms of training & 4.11 & 0.712 & 17.337 & 10 \\ I.05 & I think that using an electronic signature to record & 4.21 & 0.793 & 18.839 & 5 \\ I.06 & I think that online OSH training is a better method & 4.06 & 0.738 & 18.201 & 12 \\ I.07 & (face-to-face)/classroom) is better than & 2.60 & 0.978 & 37.673 & 14 \\ I.08 & better experience than traditional & 3.91 & 0.803 & 20.542 & 13 \\ I.09 & I am satisfied with the online training & 4.11 & 0.596 & 14.530 & 11 \\ I.10 & I am satisfied with understanding the material in the online training & 4.17 & 0.640 & 15.370 & 6 \\ I.11 & I am satisfied with the accountability in complying & 4.12 & 0.651 & 15.822 & 8 \\ I.12 &
\begin{tabular}{c} I am satisfied with the clarification of \\ misunderstandings of OSH legislation provided by \\ the online training \\ \end{tabular} & 4.12 & 0.611 & 14.855 & 9 \\ I.13 & I am satisfied with the online training in order to test & 4.16 & 0.643 & 15.468 & 7 \\ I.14 & I am satisfied with the online training in general & 4.23 & 0.685 & 16.198 & 4 \\ \hline \end{tabular}
\end{table}
Table 3: Data scores collected from employees.  
I.07). So, we can conclude that the indicators have a good/acceptable spread value (except item I.07), and therefore the mean is representative.
## 5 Experimental Setup
### Nodes' Power Consumption  
To simulate the power consumption of nodes, we adopted the energy model proposed in [48]. The power consumption of a node is classified into five states:  
* **Off--**no power consumption as the network node interface is turned off.
* **Inactive--**reduced power consumption as the network node interface is idle.
* **Scan--**the node consumes power while the network node interface detects neighbours.
* **Transmission--**the node consumes power while sending a message.
* **Reception--**the node consumes power because it is receiving a message.  
In our simulations, we assume that all the nodes initially have the greatest level of energy. We consider that the energy level of nodes is measured in units, being 500 units the highest value. We assume that users recharge their device every 24 h. Energy consumption depends on the state of the device and the number of operations using the network interface. For example: if the node is at transmission, reception or scan states, we assume a reduction of 25 energy units for every sent/received message and/or for every scan performed. If the node is in the off or inactive states, we assume there is no energy cost.
## 3 Results and Discussion  
### Donor Film Characterization  
Figure 2 shows SEM images of the surfaces of the (a,b) Fn-CaP/C/PDMS/PET and (c,d) Fn-CaP/C/PET donor samples. By comparing Figure 2a,c, a thick crack was observed on the former film only. Such a crack formation was not obvious when the Fn-CaP/C/PDMS/PET samples were air-dried at room temperature (not freeze-dried). This suggests that cracks might be generated by freeze-drying in the Fn-CaP film on the C/PDMS/PET support most likely due to the relatively high coefficient of thermal expansion of PDMS.  
The high-magnification SEM images (Figure 2b,d) showed that the Fn-CaP film surfaces have a porous structure, which is a typical morphology of CaP films prepared using the biomimetic process [16]. There is no apparent difference in the surface structure between these substrates with and without the PDMS layer. This result is natural from the fact that both films grew on the carbon-coated substrates and the PDMS layer was located under the carbon layer (not exposed to the surface during the film growth). Our previous work confirmed the existence of a hydroxyapatite crystalline phase in the Fn-CaP film prepared under the same conditions on the C/PET support by thin-film X-ray diffraction measurements [16]. Based on these results, it was deduced that both Fn-CaP donor films have a hydroxyapatite crystalline phase.  
Figure 2: SEM images of the surfaces of the (**a,b**) fibronectin-immobilized calcium phosphate (Fn-CaP) on the carbon-preformed optical stamp (Fn-CaP/C/PDMS/PET) and (**c,d**) Fn-CaP on the carbon-preformed PET (Fn-CaP/C/PET) donor samples.
## 5 Results and Performance Analysis
### Data Drop Rate  
Figure 6 compares the average data dropped by E-OLSR, AODV, DSR, OLSR, and GRP when the number of nodes and data rate become varied. As observed in the contrast between Figure 6b,d, when node density is low, the data drop rate increases dramatically. From Figure 6a,b, it can be observed that the data drop rate increases with an increasing number of nodes. The UANET, based on the E-OLSR routing protocol, offers the best data drop rate performance compared to four existing routing protocols. However, DSR shows the worst result in all situations compared to other protocols. Furthermore, GRP also shows an increasing data drop rate at a high data rate Figure 6b,d.  
Figure 5: Average network delay comparison of different routing protocols. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4.
###### Abstract  
The large degradation of the mechanical performance of hollow reinforced concrete (RC) bridge piers subjected to multi-dimensional earthquakes has not been thoroughly assessed. This paper aims to improve the existing seismic damage model to assess the seismic properties of tall, hollow RC piers subjected to pseudo-static, biaxial loading. Cyclic bilateral loading tests on fourteen 1/14-scale pier specimens with different slenderness ratios, axial load ratios, and transverse reinforcement ratios were carried out to investigate the damage propagation and the cumulative dissipated energy with displacement loads. By considering the influence of energy dissipation on structural damage, a new damage model (M-Usami model) was developed to assess the damage characteristics of hollow RC piers. The results present four consecutive damage stages during the loading process: (a) cracking on concrete surface, (b) yielding of longitudinal reinforcements; (c) spalling of concrete, and (d) collapsing of pier after the concrete crushed and the longitudinal bars ruptured due to the flexural failure. The damage level caused by the seismic waves can be reduced by designing specimens with a good seismic energy dissipation capacity. The theoretical damage index values calculated by the M-Usami model agreed well with the experimental observations. The developed M-Usami model can provide insights into the approaches to assessing the seismic damage of hollow RC piers subjected to bilateral seismic excitations.  
Keywords:hollow reinforced concrete piers; seismic damage model; bilateral cyclic loading test; cumulative hysteresis energy; cumulative damage +
Footnote †: journal: Applied Soft. 2019, 9, 1481; doi:10.3390/app9071481
## 2 Materials and Methods  
### Kinematics of DTF and STF Decay Mechanisms  
Ternary fission fragments originated during DTF and STF decays are characterized by different kinematics given the single or dual step nature. Among all the possibilities trajectories, only those belonging to the same plane have been taken into account. A schematic view of both processes is shown in Figure 1.  
Hence, the velocity vector of each fragment is described by 2 variables: the absolute value of velocity and the polar angle (\(\theta\)). Known a priori are the projectile (A\({}_{\rm p}\), \(Z_{p}\)) nucleus, the target (A\({}_{\rm t}\), \(Z_{t}\)) nucleus, and the energy of the projectile that impinges on a fixed target (\(E_{p}\)). For a detailed description of the evolution of the reaction, the mass and the atomic numbers of the fragment have to be fixed by assigning mass values \(M(A_{i},Z_{i})\). From now on, we consider known the masses of the fragments, which we will indicate in synthetic form, not making explicit reference to the dependence on the atomic number and on the mass number, namely \(M(A_{i},Z_{i})=M_{i}\) with \(i=1,2,3\). \(A_{23}\) is the mass number of the intermediate fragment in STF. The actual mass values are taken from [58] and the Q values of the process involved in DTF and STF can be computed.  
By taking into account the energy and momentum conservation laws, we deduced the equations describing the DTF and STF events for fixed tripartitions. In all calculations, we assume that the fragments are cold, meaning that the excitation energy of the system is fully converted into kinetic energy of the fragments. We made this assumption at each separation stage by neglecting the amount of energy dissipated by particle evaporation and/or transformed in deformation of the fragments.  
Assuming that all fragment velocity vectors belong to the same reaction plane, nine variables (mass, energy, and angle of each fragment) describe the kinematics of the exit channel. By fixing the mass tripartition (\(M_{1}\), \(M_{2}\), \(M_{3}\)), the kinetic energy and the emission angle of fragment 1 (\(E_{1}\) and \(\theta_{1}\)) and the emission angle of fragment 2 (\(\theta_{2}\)), and by considering the energy and momentum conservation laws, the remaining three quantities, namely the kinetic energy of fragment 2 (\(E_{2}\)) and the kinetic energy and the emission angle of the fragment 3 (\(E_{3}\) and \(\theta_{3}\)), are calculated. The angles are given with respect to the beam direction and are positive in the anticlockwise direction.
# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
###### Abstract  
Gliomas, one of the most severe malignant tumors of the central nervous system, have a high mortality rate and an increased risk of recurrence. Therefore, early glioma diagnosis and the control of treatment have great significance. The blood plasma samples of glioma patients, patients with skull craniectomy defects, and healthy donors were studied using terahertz time-domain spectroscopy (THz-TDS). An analysis of experimental THz data was performed by machine learning (ML). The ML pipeline included (i) THz spectra smoothing using the Savitzky-Golay filter, (ii) dimension reduction with principal component analysis and t-distribution stochastic neighborhood embedding methods; (iii) data separability analyzed using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The ML models' performance was evaluated by a k-fold cross validation technique using ROC-AUC, sensitivity, and specificity metrics. It was shown that tree-based ensemble methods work more accurately than SVM. RF and XGBoost provided a better differentiation of the group of patients with glioma from healthy donors and patients with skull craniectomy defects. THz-TDS combined with ML was shown to make it possible to separate the blood plasma of patients before and after tumor removal surgery (AUC = 0.92). Thus, the applicability of THz-TDS and ML for the diagnosis of glioma and treatment monitoring has been shown.  
terahertz time-domain spectroscopy; machine learning; glioma; human blood plasma; Support Vector Machine; Random Forest; Extreme Gradient Boosting +
Footnote †: journal: Medical Imaging and Machine Learning
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Structure Design of Leaf Nodes  
The leaf node comprises a MicroController Unit (MCU), a wireless communication module, a sensor module, ports to connect on-demand sensors, and a power management system. The leaf node enables sensing information of the structure and is fixed on the tower staying cables. The standard sensors of the leaf node are for movement (i.e., accelerometer and gyroscope), and environment (i.e., temperature, humidity, ambient light, atmospheric pressure). There is a tension sensor connected directly to the tower staying cable. Some sensors have a programmable threshold trigger to dispatch a wake-up signal to the MCU through the interruption ports. The sleep mode operation helps to keep a low-power operation. Furthermore, the leaf nodes can only connect to the local router node to keep the RF power consumption in a low profile. Figure 6 shows a photo of the leaf node and tension sensor attached to the laboratory mockup tower cable.