esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 2 Motivation  
The use of handovers for interference mitigation is first advocated. Then, the requirements of a new interference management architecture is discussed and the limitations in applying existing techniques to femtocell networks is identified.
## 4 Conclusions  
In this paper, a new characteristic parameter, the extreme value of the absorption spectrum change rate, is proposed for the first time as a characteristic parameter to determine the characteristic wavelength. On this basis, we proposed to use FDFE to determine the characteristic wavelength. Compared with the traditional methods, this method improved the detection accuracy and stability of the characteristic wavelength. Secondly, a dual-wavelength method was proposed for the first time, which is different from the traditional single-wavelength method. The \(R^{2}\) between the predicted turbidity value and the actual turbidity value was 0.98. Compared with the single-wavelength method, DWM improved the measurement accuracy of turbidity. Finally, a new turbidity compensation method was proposed. The traditional turbidity compensation method is to process the original spectral data. The new method of turbidity compensation compensates for the interference caused by turbidity in the first derivative spectrum. The experimental results showed that the FDPE_PLS with turbidity compensation had good performance.  
Although this paper has made some progress, there are still some limitations. For example, the influence of temperature and PH was not considered, and there was no standard method for selecting the parameters of permutation entropy. These all need to be improved in future work.  
Conceptualization, G.Z. and Q.D.; data curation, G.Z.; formal analysis, G.Z.; investigation, G.Z., X.L., Y.W., and Q.D.; methodology, G.Z.; software, G.Z.; writing--original draft preparation, G.Z.; writing--review and editing, G.Z. and Q.D.; visualization, G.Z.; supervision, Q.D.; project administration, Q.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by Jilin Province Science and Technology Development Plan Project (20190303008 SF), Talent Program of Hainan Medical University(XRC180010,HYPY201905,XRC180006), and Hundred-Talent Program (Hainan 2018). The authors declare no conflict of interest. The authors declare no conflict of interest.
# An Overview of Machine Learning and 5G for People with Disabilities
## 3 5G for People with Disabilities
### High Data Rates  
The current 4 th Generation Long Term Evolution (4G LTE) system supports up to 100 Mbit/s download speeds. Future 5G networks will be able to support higher frequencies (including those higher than 10 GHz). Therefore, transmission with higher rates (on the order of Gbps) will be achieved. Additionally, 5G needs to support applications with high data rates to provide a satisfactory experience to the users, such as real-time video/telepresence for remote healthcare to enable individualized consultations, treatment, and patient monitoring for people with disabilities from their smart homes.  
On the other hand, the International Telecommunication Union (ITU) has identified three different usage scenarios/use cases for International Mobile Telecommunications-2020 (IMT-2020) systems: _enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC)_, and _Ultra Reliable and Low Latency Communications (URLLC)_[46]. They can be summarized as follows:  
* _Enhanced Mobile Broadband (eMBB)_: it addresses use cases for access to multi-media content, services, and data. This usage scenario covers a range of cases, including wide-area coverage and hotspots.
* _Massive Machine Type Communications (mMTC)_: this use case is characterized by a very large number of connected devices, typically transmitting a relatively low volume of non-delay sensitive data. Devices are required to be low cost and have a very long battery life.
* _Ultra Reliable and Low-Latency Communications (URLLC)_: this use case has stringent requirements for capabilities such as latency, reliability, and availability. Some use cases include wireless control of industrial manufacturing or production processes, remote medical surgery, and transportation safety.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 2 Related Work
### Security Mechanisms for WBAN
#### 2.1.1 Cryptographic Authentication Methods  
Cryptographic authentication methods are computationally power-hungry, making them infeasible for constrained WBAN sensor nodes. Elliptic curve cryptography has been successfully deployed in wireless sensor networks. Although these systems are feasible for WBAN, elliptical curve cryptosystems consume higher energy when compared to symmetric cryptosystems [28]. TinySec [29] is an approach for providing authentication in WBAN. In this scheme, every sensor is programmed with a common key before the deployment of the sensor network. Further communication in the network, as a message or packet encryption, is done using a common key. The main drawback of this system is that a compromised sensor can cause the leakage of complete information from the sensors network; hence, the whole system will be at risk. Accordingly, the traditional authentication schemes mentioned above lack security and require high computational power; therefore, they are infeasible for WBANs.
## 4 Taxonomy of MAC Protocols Based on Their Use of WuR Technology
### MAC Protocols without WuR
#### 4.1.3 Hybrid Access Based MAC Protocols for IBSN  
[PERSON] et al. [47] proposed an energy-saving MAC protocol, called DQBAN for BAN as an add-on to the 802.15.6 MAC protocol which suffers from low reliability and limited QoS in real-time environments. The proposed DQBAN is a combination of a cross-layer fuzzy-logic scheduler and energy-aware radio-activation policies. The queuing of access packets and data packets is determined by fuzzy-logic rules, which permit body sensors to find out 'how favorable' or 'how critical' their situation is in a given time-frame. The logic scheduling MAC protocol optimizing QoS and energy consumption by considering cross-layer parameters, such as residual battery lifetime, physical layer quality and system wait time, is presented. The authors tested their proposed protocol on two scenarios: a homogeneous scenario of a body sensor network with 5-35 homogeneous ECG wireless sensors and a heterogeneous scenario of a body sensor network with wireless ECG sensors and four other sensors for clinical doctor PDA, respiratory rate, blood pressure and endoscope imaging.  
In [59], the authors proposed the use of a UWB transmitter for energy-efficient operation of WBANs. Due to the high interference generated by the human body and its environment, one solution to develop low power-output transceivers for radios in the sensor nodes is to optimize the air interface of the network. By creating architectures that exploit features of robust nodes and energy-constrained nodes, low latency and simple network topologies, the authors show that an UWB-based architecture is advantageous over narrowband radio communication.  
In [57], the slotted ALOHA is employed in the contention access period (CAP) to request the slot allocation. A mini-slot method is designed to enhance the efficiency of the contention. Moreover, sufficient slot allocation in the contention-free period (CFP) enables adaptability to different traffic load conditions.
## 3 Results and Discussion
### Water Stability Performance
#### 3.3.1 Immersion Marshall Test  
The Immersion Residual Stability (\(\mathit{IRS}\)) is calculated as follows:  
\[IRS=\frac{MS_{1}}{MS}\times 100 \tag{10}\]  
where \(IRS\) stands for Immersion Residual Stability (%), and \(MS_{1}\) and \(MS\) represent the Marshall stability of the specimen (kN) after immersion for 30-40 min and 48 h, respectively.  
The IRS values of the four WER-EA mixture specimens are shown in Figure 12. The current code does not limit the \(IRS\) value of an emulsified asphalt mixture, but the AASHTO specification requires the \(IRS\) value of an asphalt matrix mixture to be larger than 80%. As can be seen from Figure 12, the \(IRS\) values of all WER-EA mixtures met the requirements except for the emulsified asphalt mixture.  
Compared with the \(IRS\) value of EA, the \(IRS\) value of the WER-EA mixture was closer to 100%, which means that the WER system could improve the resistance of the mixture to water damage. \(IRS_{\text{E20-T}}\) increased by 24.65%, 13.03%, and 21.85% compared with \(IRS_{\text{EA}}\). Among the mixtures, the E20-T mixture had the largest \(IRS\) value, which was 10.3% higher than the lowest of 80.7%.
## 7 Discussion
### Optimal Control of LQRC and LQRIC  
From the simulation results shown in Figure 6 and Table 3, when the supplied DC-link voltage is reduced from 50V to 40V, the compensation performances of HCC and PCC become worse. A large steady-state error and reactive power exists after the HCC- and PCC-controlled \(LC\)-HAPF compensation, whereas the LQRC maintains a good harmonic and reactive power compensation performance. The LQRIC still shows the lowest \(\text{THD}_{i\text{x}}\), \(i_{sn}\) and \(\text{Q}_{\text{Total}}\), which verifies the optimal control of the proposed LQRIC.
## 4 Experiments
### Effectiveness of Enriching Both Intra-Variation and Invariant Features
#### 4.4.2 Results on LFW-a Dataset  
We generated 20 facial images for each person in the galley of LFW-a. The recognition rates of different methods are shown in Table 2. As can be seen, our method overwhelmedmost of the counterpart methods, and was among the top two methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**TrainData\textbackslash{}TestData** & **AgeDB** & **LFW** & **CALFW** & **CPLFW** & **CFP\_FF** & **CFP\_FP** & **VGGFACE2\_FP** & **FERET-b** \\ \hline _Original_ & 50.75 & 77.33 & 63.03 & 58.7 & 69.67 & 60.77 & 61.46 & 84.33 \\ _Synthetic_ & 53.35 & 73.77 & 59.05 & 57.52 & 69.09 & 59.74 & 60.28 & 79.96 \\ _AugD_ & 52.12 & 75.95 & 58.82 & 60.08 & 71.46 & 61.89 & 61.66 & 87.54 \\ _AugI_ & 50.28 & 75.45 & 59.27 & 57.58 & 71.7 & 60.87 & 61.46 & 80.17 \\ _AugE_ & 50.43 & 75.05 & 57.47 & 58.10 & 71.09 & 62.49 & 61.44 & 80.04 \\ _AugPI_ & 52.47 & 78.17 & 60.67 & 60.83 & 73.57 & 62.70 & 62.80 & 90.46 \\ _AugPIE_ & 55.18 & 80.08 & 62.78 & 61.90 & 76.59 & 64.37 & 62.94 & 92.08 \\ _Ori+AugP_ & 56.62 & 82.73 & 69.05 & 65.68 & 78.13 & 66.64 & 68.54 & 90.71 \\ _Ori+AngI_ & 52.63 & 77.48 & 60.93 & 58.63 & 70.83 & 62.07 & 62.84 & 81.17 \\ _Ori+AugE_ & 50.93 & 74.85 & 59.02 & 57.53 & 68.36 & 60.51 & 60.54 & 80.20 \\ _Ori+AugPI_ & **58.82** & **86.08** & **72.03** & **67.97** & 81.96 & **70.44** & **71.02** & 94.54 \\ _Ori+AugPIE_ & 58.65 & 85.98 & 71.02 & 67.15 & **82.69** & 70.23 & 70.22 & **95.54** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Face verification rates (%) of the first implementation of our proposed method on different benchmark datasets when different enriched face images from the CelebA database were used for training. The best result on each dataset is shown in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Database\textbackslash{}Method** & **HOG+SVM** & **CDA** & **G-FST** & **FT-LPP** & **TDL** & **FaceNet** & **KCFT** & **Proposed** \\ \hline FERET-b & 50.17 & 77.25 & 82.08 & 86.08 & 89.33 & 91.42 & 93.17 & **96.41** \\ LFW-a & 40.98 & 51.12 & 57.95 & 62.49 & 74.01 & 89.04 & **98.83** & 97.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rank-1 identification rates (%) of different methods on FERET-b and LFW-a. The best result on each database is shown in bold.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 6 Further Analysis and Discussion
### Robustness of the Deflector Outlet Velocity  
According to the calculation and the simulation, it can be found that the deflector outlet velocity has extraordinary robustness against location deviations of the deflector. Considering the installation error of the deflector, we can express the actual deflector jet distance as  
\[H=H_{s}+\Delta H_{s} \tag{29}\]  
where \(H_{s}\) is the design value and \(\Delta H_{s}\) is the error derived from installation. Our calculations show that when \(\Delta H_{s}\) varies within a range of \(-5\)\(\upmu\)m to \(5\)\(\upmu\)m, the fluctuation of the deflector outlet velocity is less than 0.16%, as shown in Table 5. Moreover, it follows from the numerical simulation that the deflector displacement also has very little effect on its outlet velocity.  
Consequently, the deflector can supply a fairly stable jet for the receivers and the shunt wedge. However, the balance pressure and the pressure gain are much more sensitive to installation and machining errors.
## 3 Results
### Cytotoxicity and Anti-Inflammatory Effects  
The MTT method was used as an indirect measure of cytotoxicity, through the determination of cell viability after treatments with the freeze-dried extracts from SD0 and SD5 (at concentrations of 0.01-0.1 mg/mL). After 24 h of incubation, none of the tested samples showed a significant (\(p>0.05\)) cytotoxic effect on Caco2 cell line, compared to the control.  
qRT-PCR was used to assess the anti-inflammatory potential of the samples, after cell treatment at 10 \(\upmu\)g/mL. TNF-\(\alpha\) and IL-1\(\beta\), two cytokines involved in intestinal inflammation, were used as markers. Compared to positive control (medium + LPS), the treatment with SD5-T24 caused a marked and significant (\(p<0.05\)) anti-inflammatory effect, with a decrease of the TNF-\(\alpha\) gene expression of the 63% (Figure 2A), which was comparable (\(p<0.05\)) to the expression observed in absence of LPS stimulation (Figure 2A). A lower, but also significant (\(p<0.05\)) decrease (\(-17\%\)) was also observed for SD5-T0 (Figure 2A). Compared to positive control (medium + LPS), only the SD5-T24 led to a significant (\(p<0.05\)) decrease (\(-60\%\)) of the _IL-1\(\beta\)_ gene expression after 24 h of incubation (Figure 2B).
# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## Data Mining with Big Data  
### Concepts of Data Mining and Big Data  
There are many concepts such as DM, KDD, and Big Data that are closely related to each other. DM, as an interdisciplinary subject including database design, statistics, pattern recognition, machine learning, and data visualization [6], can be defined in many different ways. [PERSON] and [PERSON] [15] specified DM as \"the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data\". [PERSON] et al. [16] defined DM as \"the process of discovering interesting patterns and knowledge from large amounts of data\".  
Many researcher and practitioners treat DM as a synonym for KDD as IBM [17] deems KDD and DM the same as \"an interdisciplinary area focusing on methodologies for extracting useful knowledge from data\". However, others think that \"KDD refers to the overall process of discovering knowledge from data while DM (in a narrow sense) refers to application of algorithms for extracting patterns from data without the additional steps of the KDD process\" [16], in which the additional steps include data preparation, preprocessing, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining [16]. Here, we take DM as a synonym for KDD whereas DM in a narrow sense refers only to the step to generate a specific pattern using a particular algorithm within an acceptable computational efficiency limit [11; 16].  
There are various definitions of Big Data from 3 Vs to 4 Vs [18]. Volume, velocity, and variety are the well-known 3 Vs and the fourth V can be value, variability, or virtual [8; 18]. Wikipedia specifies that \"Big Data is data sets that are so voluminous and complex that traditional data processing methods are inadequate to deal with them\" [19]. [PERSON] gives a more detailed definition as follows: \"Big Data is high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization\" [20]. Big Data analysis is strongly connected with classical data analysis and DM approaches to access and process these amounts of data very fast [2; 10].  
The flowchart of DM with Big Data is illustrated in Figure 1. The main content of each step includes data preparation, preprocessing, DM in a narrow sense, and evaluation. The interpretation of the results will be discussed in the following sections.
## Results
### Inspection of Soldered Interface
(**X1**) to (**X4**) show X-ray images, (**S1**) to (**S4**) show SAM images from backside with a 100 MHz transducer (images are vertically mirrored), Z1 and Z2 show \(Z_{th}(t)\) plots, and B1 and B2 show the normalized logarithmic derivative, whereby (**X1**), (**X2**), (**S1**), (**S2**), (**Z1**), and (**B1**) belong to two LEDs of Type A and (**X3**), (**X4**), (**S3**), (**S4**), (**Z2**), and (**B2**) belong to two LEDs of Type B. For (**B1**) and (**B2**), the normalization interval was set between 0.1 ms and 1 ms resulting in a peak increase of 0.12 or 0.02. Analogical to LED Type A, the analysis for LED Type B was performed to evaluate the NDTs on voids in soldered interconnects. Therefore, one LED showing little voiding (LED3) and one with strong voiding (LED4) were compared in Figure 6. With X-ray in (X3) and (X4), the voids were quite visible and the void rate was calculated with 1.2% or 8.9%. Evaluation was complicated by the internal structures of the LED, particularly the ceramic vias and the gold bumps appearing as black circles. A detection of small voids behind them was not possible. With SAM in (S3) and (S4), only large voids were detectable and small ones were not detected due to the limited resolution. This effect was directly observable by comparing X-ray and SAM images. The \(Z_{th}(t)\) analysis in (T2) revealed only minimal differences in thermal performance caused by the voids. The curves overlapped until approximately 10 ms and only slightly differed from that point. An inspection with a normalized logarithmic derivative was in this case more sensitive as shown in (B2) with a separation after the minimum turning point at 2 ms and different peaks of 0.02. In conclusion, all NDTs were suitable for inspection of soldered interfaces. The nonwetted solder area of LED2 was detectable with all, whereby, with X-ray, the hoarding of the solder to one side of the pad was also visible unlike with SAM. With TTA, a major reduction in thermal performance was identified for LED2. Void inspection was also possible with all three NDTs, but the achieved resolution with the 100 MHz transducer for SAM was much lower compared to X-ray which also allowed the detection of small voids. A use of higher transducer frequencies was not possible for the required penetration depth. TTA revealed a slightly reduced thermal performance by voids for the two samples, but small variances in voids could not be detected by TTA. However, this revealed that they are not critical for thermal performance under a certain limit. A different aspect is the thermomechanical reliability, e.g., solder crack propagation due to temperature cycles, which can be affected by voids.
## 3 Experimental  
In our experiments, we used lithium-ion cells of various manufacturers, capacities and formats (the manufacturers, models and nominal capacities of the studied cells are presented in Tables 1 and 2).  
The charge procedure consisted of using the constant current/constant voltage (CC/CV) mode. The charge was conducted as follows: the direct current 0.5C\({}_{\mathrm{n}}\) to a voltage value of 4.2 V and step CV until the current falls to 0.025C\({}_{\mathrm{n}}\) (C\({}_{\mathrm{n}}\) is the nominal cell capacity).  
The discharge procedure consisted of applying the constant current (CC) mode. The discharge was conducted as follows: the direct current was applied down to the voltage value 2.75 V. In the course of the cells study, the discharge currents were in the range from 0.2C\({}_{\mathrm{n}}\) to the currents at which the released capacity was approximately C\(\approx\)C\({}_{\mathrm{n}}\)/10. The cells were cycled inside of the climatic chamber Binder MK240 (BINDER GmbH, Germany).  
Experimental studies of the cells were performed in accordance with the following algorithm.  
First, the cells were cycled at least ten times to stabilize cell parameters in connection with the SEI layer formation. The cycling was stopped when, in three consecutive cycles, the measured capacity differed by less than 5%. These training cycles were implemented in accordance with the cell's operation manual (charge: the standard mode described above; discharge: direct current equal to 0.2C\({}_{\mathrm{n}}\) to a voltage value of 2.75 V).  
Second, to decrease a random scatter of the measured cell capacity, the discharge capacity of the cell (at a certain discharge current) was obtained as the average value of the measured capacities obtained for three consecutive charge-discharge cycles. Nevertheless, if in these three cycles the measured capacity differed more than by 5%, additional training cycles were implemented and the experiment was repeated again (or the cell was replaced with another more stable cell and in this case the experiment was implemented from the very beginning).  
Third, to prevent a mutual influence of the charge-discharge cycles (via various residual phenomena), before each value change of the discharge current or the cell temperature, the training cycles were implemented. The training cycles were performed until the measured capacity in three consecutive cycles began to differ by less than 5%.  
In any batch of the same cells, statistical scatter exists of cells measured capacities, which is attributed to many random factors such as the cell's parameter's statistical scatter in the course of the cells production, the cell's operation duration, the cell's operation modes, etc. To decrease this random scatter as much as possible, we standardized the experimental cell's obtained capacities by their top capacity \(C_{m}\) (which was found experimentally for every cell individually). In this case, the above listed random factors were eliminated to a large extent and the empiric curves were constructed more reliably. This method is used in Section 4.2 to study the dependence of cell released capacity on discharge current.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength at Receiving Point  
Since the relay node divides the U-shaped roadway into two symmetrical L-shaped roadways, it is sufficient to analyze the reflection field strength between the relay node and the receiving point to derive the electromagnetic reflection propagation law in the U-shaped roadway. According to the theoretical analysis of the reflected field strength, the strength at the receiving point is affected by the position of the relay node and the receiving point in the roadway, the transmitting frequency of the antenna [26], the relative permittivity of the roadway wall, and the width of the roadway. The transmission frequency of the antenna was 2.5 GHz, and the relative dielectric constant of the roadway wall was 6.