esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Energy Harvesting from Plates by Means of Piezoelectric Dynamic Vibration Absorbers
## 5 Experimental Results
### Tests with Impulsive Excitation
Measured acceleration and voltage generated by the simple patch are shown in Figure 15, and the amplitudes of the FRFs obtained from these measures are shown in Figure 16.  
Analysis of time-domain data shows that the maximum voltage generated by the patch is about three times smaller than the maximum voltage generated by the CPDVA and very quickly reaches values lower than \(\pm 1\) V. The experimen  
Figure 16: Magnitude of the FRF between the acceleration at the center of the plate and the impulse (**left**) and the magnitude of the FRF between the OC voltage generated by the piezoelectric patch bonded to the plate and the impulse (**right**). Experimental and predicted FRFs for the impulse applied to the center of the plate. Figure 15: Experimental results obtained by means of an impulse applied at the center of the plate with the piezoelectric patch directly bonded to the plate: impulse (**left**), acceleration at the center of the plate (**center**) and OC voltage generated by the patch (**right**). peaks at the first natural frequency of the plate. The height of the peak of the voltage FRF obtained with the patch is one order of magnitude smaller than the peaks obtained with the CPDVA. In order to quantify the performance of the patch and of the CPDVA, the root mean square value (RMS) of the OC voltage was calculated for each test, and then, the mean RMSs of both harvesting systems were calculated. In particular, the mean RMSs of the OC voltage generated by the patch and by the CPDVA are 0.75 V and 3.86 V, respectively. In Figure 16, a small peak corresponding to the second natural frequency of the plate can be observed. Indeed, the patch was bonded with an offset in the y direction with respect to the center of the plate. Moreover, the hammer hit was manually generated and was not exactly centered nor aligned to be perpendicular to the plate. Thus, it was able to weakly excite the second mode of the plate. Finally, the robustness of CPDVA was assessed by carrying out experimental tests using a CPDVA with a small tuning error \(\Delta f\approx-1\) Hz. The results, which are reported in Figure 17, show some modification to the OC voltage FRF that do not decrease the effectiveness of the vibration harvesting system. In particular, the two peaks move to lower frequencies (13.31 Hz and 16.13 Hz, respectively). The amplitude of the first peak decreases, whereas the amplitude of the second peak increases and becomes larger than the amplitude of the first peak. These results are in agreement with the calculated results of Section 3 (which refer to a uniformly distributed load).
# Effective Techniques for Protecting the Privacy of Web Users
## 5 Experimental Results
To the best of our knowledge, this is the first analysis study of the PPTs in terms of their effectiveness in detecting obfuscated JavaScript programs. Obfuscation in JavaScript involves transforming the visual representation of the code into an altered version that is extremely difficult to read, understand, and reverse-engineer without changing its functional characteristics [26]. Web trackers commonly apply obfuscation methods in JavaScript codes to avoid detection by various privacy protection software and conceal their malicious intentions. Web developers may also use obfuscation and code protection methods to protect their code. However, obfuscation can be utilized by malware writers and attackers to bypass security measures. In (Figure 8) we observed that 30% of the tracking JavaScript programs are obfuscated while 70% are plain-text code. We also observed that 36% of the functional JavaScript programs are obfuscated while 64% are plain-text code. Figure 6: The average error rate (AER) of the PPTs.
## 4 A Case Study of Hidake Tunnel in Japan
### Basic Condition of Hidake Tunnel  
The Hidake tunnel (as shown in Figure 8) was constructed in 1982 with the poling-board method. The tunnel length is 780 m, with a total of 65 spans. The maximum depth is 25 m. The thickness of the concrete lining is 0.7 m at a part of the tunnel, and 0.55 m at the other part. The material of the concrete lining is C20 concrete. No surface prep was conducted on the concrete lining. No special anchorage between the tunnel and its lining was used. The surrounding rocks are sandstone, sand tuff, lapilli tuff, basalt, tuff breccia and lapilli tuff in different segments.  
Due to the alteration of geological conditions and the deterioration of lining concrete, some defects such as cracks and seepage began appearing in the concrete lining after twenty years of service. The tunnel was also affected by the Kumamoto earthquakes, including a magnitude 7.0 main shock, on 16 April 2016.
## 3 System Model
### Energy Consumption Model  
As shown in Figure 6, the system has L + 1 time slots in each frame. It contains an access control slot and L data transmission slots. The duration is T units. CH and CM control information is exchanged through access control slots. A packet need a frame transmission, so the packet size is B \(\times\) T \(\times\) L. The cluster operation determines B and T. In order to efficiently utilize the battery of each sensor, L is adaptable according to the time-varying channel conditions. The CMs transmit data packets according to control information in data transmission slot [36, 37, 38].  
Since the CH has a long-duration power supply, it is not subject to energy constraints. This paper considers only the energy consumption of CMs when they exchange control message and transfer data.  
Each sensor has the same initial energy \(\mathrm{E_{in}}\) which is obtained from a non-rechargeable battery in a cluster. Our work is only concerned about the impact of dynamic spectrum access protocol for energy efficiency, so that the communication channel only considers the impact of the path loss. The energy consumption is \(E_{\mathit{exp}}+\varepsilon d_{i}^{\alpha}\) in data transmission. \(\varepsilon\) is power amplifier at the receiving end. The distance  
Figure 5: State diagram of the power model.  
Figure 6: the frame time-slotted structure.  
between the cluster head and the cluster members is \(d\). E\({}_{\rm{cir}}\) is the energy consumption of the circuit. E\({}_{\rm{cir}}\) and \(\epsilon\) are computed by per bit. \(\alpha\) is the path loss coefficient. Its value depends on the channel quality. L represents the continuous transmission data of the number of slots [39]. Hence, the total energy consumption is:  
\[E_{i}^{tr}(I)=(E_{cir}+\epsilon d_{i}^{2})\times B\times T\times l \tag{10}\]  
where E\({}_{\rm{cir}}\) is nJ/bit. \(\epsilon\) is in pJ/bit/m\({}^{2}\), B is the transmission rate (bit/s), T is the slot length (seconds). Since CMs receive the CH messages, reception energy consumption also needs to be considered. The expression is as follows:  
\[E_{i}^{tr}(I)=E_{cir}\times B\times T\times l \tag{11}\]
## 5 Hardware Deployment  
During a rescue mission, rescuers are always on a limited time schedule to detect health complications and initiate possible treatment paths. Hence, becoming time-efficient is one of the major requirements in a rescue situation. After developing the detection model with ML algorithms, our goal was to accelerate it in an FPGA-based hardware prototype that would provide not only the same accuracy achieved in previous steps but also be very time economical, i.e., small inference time. Programmable hardware like FPGA was chosen due to its flexibility in hardware-level compilation, reprogramming ability, and impressive application performance even while processing multiple workloads at the same time. The Summary of the acceleration steps can be written below:  
1. Selection of software framework for FPGA
2. Compilation of the ANN model with an open-source machine learning compiler like Apache Tensor Virtual Machine (TVM)
3. Quantizing and accelerating the ANN model with Versatile Tensor Accelerator
## 4 Results
### Time-Domain Analysis
#### 4.1.2 Time-Domain Analysis for Extracted Noise  
Figure 11a shows the reference noise gauged in the time domain. Figure 11b-f show noises extracted by five de-noising algorithms in channel no. 25. The results suggest that noises extracted by WRPCA and cross-channel DWRPCA had similar amplitudes to the reference noise.
## 3 Method and Data
### Measurement of RGTI, NQP, and CRCI
\[y_{ij}=\frac{x_{ij}-\min(x_{ij})}{\max(x_{ij})-\min(x_{ij})},\ y_{ij}\ \text{is a positive indicator} \tag{3}\]  
\[y_{ij}=\frac{\max(x_{ij})-x_{ij}}{\max(x_{ij})-\min(x_{ij})},\ y_{ij}\ \text{is a positive indicator} \tag{4}\]\[p_{ij}=y_{ij}/\sum_{j=1}^{n}y_{ij}\,e_{ij}=-k\sum_{j=1}^{n}p_{ij}\times\ln\bigl{(}p_{ij} \bigr{)},d_{j}=1-e_{j},w_{j}=d_{j}/\sum_{j=1}^{n}d_{j} \tag{5}\]  
In Equations (3)-(5), \(Y_{ij}\) represents the normalized value of the \(j\)-th indicator for system \(i\); \(X_{ij}\) denotes the raw value of the \(j\)-th indicator for system \(i\); and \(x_{ij}\) refers to the information entropy of the \(j\)-th indicator for the \(i\)-th province. In Equation (3), \(p_{ij}\) is the normalized value, and \(m\) represents the number of provinces. Additionally, \(e_{j}\) denotes the information entropy of indicator \(j\), \(\mathrm{d_{j}}\) represents the redundancy of the information entropy, \(w_{j}\) is the weight of indicator \(j\), and \(n\) is the total number of indicators.
## 6 Simulation Results
### Simulation Settings  
We evaluated the performance of the proposed algorithms in the simple mesh network topology, the Japan network topology, and the NSF network topology using MATLAB. The characteristics of the three network topologies are shown in Figure 4 and Table 4. We simulated the SCN based on the sub-core-selective-switch-based SXC architecture, which does not support spatial channel switching. Each SDM link consists of five 4-core MCFs, i.e., one SDM link contains 20 SLs, and each SL is divided into 32 SBs. Then, 112-Gbaud transceivers are deployed in the whole network without considering the complexity and cost, which transmit/receive an OCh that occupies 125 GHz. The bit rate and transmission distance that can be supported by each transceiver using different MFs are shown in Table 2. Suppose four traffic request sets are generated, each with 50 traffic requests of possible traffic rates of 2 Tb/s, 4 Tb/s, 6 Tb/s, 8 Tb/s, and 10 Tb/s. The source and destination nodes of each traffic request are randomly and uniformly generated and the sum of the traffic rates in the four sets is 200 Tb/s, 250 Tb/s, 300 Tb/s, and 350 Tb/s, respectively. The LBMSA algorithm and its comparison algorithms are used to allocate network resources to traffic request sets in different networks, and the occupancy of spatial and spectral resources in the corresponding cases is analyzed. The usage of MF and MFC when applying the LBMSA algorithm for resource allocation is also analyzed. To further demonstrate the effectiveness of the LBMSA algorithm, we also tested the maximum sum of traffic request rates that the three networks can carry, and analyzed the results.  
no CN is set on the routing path, the MF is selected based on the total length of all SDM links contained in the routing path, and the same MF is used for the whole connection. MFC is not considered in the KSP-CNs algorithm. Similar to the LBMSA algorithm, after determining the routing path for a traffic request, the KSP-CNs algorithm calculates all possible options of CNs and MFs on that routing path. However, CNs do not perform MFC, but only determine one MF to be applied to the whole routing path based on the length of the longest MFS. We use network resource utilization as a metric to evaluate these algorithms.
## 4 Fiber Sensing Technologies
### Optical Sensing for Gas Detection
#### 4.3.1 Wavelength Modulation Spectroscopy (WMS)  
Absorption spectroscopy is based on a unique signature attenuation of spectral intensity caused by the gas to be monitored. It happens when light radiation interacts with molecular species and is represented by the Beer-Lambert Law [6; 43; 44]:  
\[I_{t}(\lambda)=I_{0}(\lambda)\varepsilon^{-\alpha(\lambda)L},\]  
where \(I_{t}(\lambda)\) and \(I_{0}(\lambda)\) are the transmitted and incident light intensity at wavelength \(\lambda\) of the laser diode, respectively, and \(L\) is the interaction length or path length that the light has to pass through the gas. The _absorption_ coefficient \(\alpha(\lambda)\) is \(\alpha(\lambda)=C\times\varepsilon(\lambda)\), where \(C\) is the gas concentration and \(\varepsilon(\lambda)\) the specific absorptivity of the gas [6; 43; 44]. A typical experimental configuration of WMS is composed by a DFB laser diode, which is wavelength-tuneable, a function generator, a gas chamber, a photodetector,and a lock-in amplifier, as illustrated in Figure 3.  
In this technique, the laser wavelength and intensity are modulated by applying at the laser diode injection current a sinewave signal at a frequency in the order of kHz. A ramp signal is added to sweep the laser frequency across the gas absorption line. The sinusoidal modulation in the operational kHz range allows the shift of the detection bandwidth to higher frequencies where laser intensity noise is  
Figure 3: Wavelength Modulation Spectroscopy (WMS) basic configuration; (**a**) laser scan through a single gas absorption line and (**b**) synchronous detection of first and second harmonic of the laser modulation through a lock-in amplifier.  
reduced. From Figure 3a, the modulated laser light interacts with the absorption line of the target gas and two possible situations may be observed: (i) the modulated frequency is doubled when the laser light is tuned to the center of absorption line; and (ii) the frequency remains unchanged when the laser is tuned out of the center of the absorption line. Therefore, this interaction between the modulated laser light and absorption line of the target gas generates signals at different harmonics of the applied modulation frequency (see Figure 3b).  
Gas concentration is typically detected by recovery of the second harmonic (2f) of the laser modulation frequency through a synchronous detection, using a lock-in amplifier. The first and second demodulated harmonics are shown in Figure 3b, where the amplitude of the second harmonic (2f) of the laser modulation frequency is proportional to the gas concentration. The first harmonic (f) is proportional to the first derivative of the gas absorption line and equals zero when the wavelength modulated laser light is centered in the target gas absorption line.
## 3 Close-Proximity Concepts Design and Analysis
### Phases  
A general Phase is defined as a predefined mission operations segment with an explicitly defined start. The phases of the Concept of Operations considered in this paper are Client Phasing (CP), Far Rendezvous (FR), Close Rendezvous (CR), and Capture (CAP). In the scope of the \"Verification and Validation of Rendezvous and Proximity Operations Safety\" project developed for ESA, the following definitions were proposed and adopted [11], as illustrated in Figure 4:  
* Client Phasing Phase: The mission phase outside of the Approach Zone (AZ) where the orbital parameters of the client and servicer are brought closer to one another. This phase is usually led by the ground segment and ends at the GO for the AZ decision point (i.e., before the GO decision to enter the AZ).
* Far Rendezvous Phase: The first phase of the Close-Proximity Operations. This phase is initiated at the GO for the AZ decision point after a GO/NO-GO decision is taken with a positive result to enter the AZ, and ends at the GO for the Keep Out Zone (KOZ) decision point, i.e., before initiating the entry into the KOZ. As a higher level of on-board autonomy is required during CPO, the Far Rendezvous Phase usually corresponds to a handover from the Ground Segment to Space Segment.
* Close Rendezvous Phase: The mission phase that is initiated at the GO for the KOZ decision point after a GO/NO-GO decision is taken with a positive result to enter the KOZ through the Approach Corridor, and ends at the GO for the Capture decision point.  
Figure 4: Illustration of the Mission Concept, extracted from [10].  
For the sake of completeness, it should be mentioned that the capture phase is the mission phase that begins with the GO decision at the Capture decision point. After the GO, the maneuvers proceed beyond the Point of No Return, and a physical connection is initiated to accomplish a stable stack. The phase ends when the stack configuration is confirmed by the Servicer.
## 3 Fault Tree Analysis (FTA) Analysis
The probability of the leakage of the slurry out of the working vessel due to an inappropriate setting of the autoclave is the factor X and the effect of overloading the vessel is the factor Y. The G4 gate combines the probability of failure of the autoclave measuring devices. Uncontrollable and potentially a serious accident can occur due to the extreme pressure and temperature increase in the vessel (intense overflow or \"explosion\") if the measuring devices fail. The temperature of the slurry, its pressure and the reaction time are measured during autoclave operation. The temperature is measured by a thermocouple whose thermoelectric voltage measures a suitable electronic thermometer. The probability of failure of a thermometer device indicates the gate G41. The gate G41 joints the probability of failure of the electronic thermometer (factor Z), the probability of breakage (mechanical damage) of the used thermocouple (factor AA), damage to the ceramic cover of the thermocouple (factor AB), and the short-circuit (unwanted contact between thermocouple wires out of the isolation) of the thermocouple (or connecting wires) AC. Gate G42 indicates the probability of failure of the manometer due to the jamming of the mechanical parts with the probability of AD or damage due to the aggressive leaching media AE. The most recent underlying cause is an incorrect indication of the start time of the experiment and its end (or its absence) with the probability of AF. All the gates considered are \"OR\". The probability of gate G1 is 0.02622, G2 gate: 0.00748, G3 gate: 0.01460 and Gate G4: 0.00936. The riskiest G1 gate appears to characterize the device for rotation of the autoclave vessel. The probability of the top event is 0.05653, i.e., 5.7%. Thus, the probability of failure of the leaching process--the top event--is relatively high, so roughly every 17 th leaching is terminated prematurely. This results in time, material and consequently economic losses, possibly (e.g., leakage of the slurry with high temperature and usually with a basic reaction) can also lead to damage to the health of \begin{table}
\begin{tabular}{c c c c c c} \hline
**Code of Factor** & **Probability** & **Code of Factor** & **Probability** & **Code of Factor** & **Probability** \\ \hline A & 0.001 & B & 0.001 & C & 0.005 \\ D & 0.004 & E & 0.0095 & F & 0.001 \\ G & 0.002 & H & 0.003 & I & 0.0005 \\ J & 0.004 & K & 0.001 & L & 0.002 \\ M & 0.001 & N & 0.001 & O & 0.002 \\ P & 0.0005 & Q & 0.0005 & R & 0.001 \\ S & 0.002 & T & 0.001 & U & 0.001 \\ V & 0.002 & W & 0.0002 & X & 0.0005 \\ Y & 0.002 & Z & 0.001 & AA & 0.0005 \\ AB & 0.001 & AC & 0.00239 & AD & 0.001 \\ AE & 0.0005 & AF & 0.0005 & & \\ \hline \end{tabular}
\end{table} 
Table 2: The probability of individual basic events. the appraiser. Therefore, it is advisable to reduce the probability of a top event. The \"tree of failures\" makes it easy for us to find a \"critical cut\", which is to take the direction of eliminating the causes of the failure. In our case, they are branches that connect in the G1 gate.
# Microscopic Experiments to Assess the Macroscopic Sweep Characteristics of Carbon Dioxide Flooding
###### Abstract  
The Lunnan oilfield in the Tarim Basin, one of China's major onshore oilfields with substantial geological reserves, faces particular challenges due to the complexity of its reservoir environment and the dispersion of remaining oil. Carbon dioxide, a greenhouse gas, presents an opportunity for enhanced oil recovery (EOR) and geological storage. In this context, the use of carbon dioxide for EOR can simultaneously address environmental concerns and improve oil recovery rates. This study focuses on the TI reservoir in the No. 2 well area of the Lunnan oilfield, employing advanced techniques to analyze the micro- and macro-characteristics of carbon dioxide flooding. Results: From the microscopic point of view, carbon dioxide flooding is mainly miscible with crude oil, which has a strong component exchange effect and can be displaced in the form of full pores, and the microscopic displacement efficiency is close to 100%. Macroscopically, under the combined injection and production of different injected hydrocarbon pore volume multiples (HCPVs), it is injected at the upper and lower layers of the interlayer and produced far away from the lower layer of the interlayer, with a total recovery rate of 52.83%. With the increase in the HCPV, the recovery increased rapidly at first and then slowly, and the HCPV at the demarcation point was 0.5, while the oil production rate increased in a wave-like manner and then decreased rapidly, and the HCPV at the breakthrough point of TI gas was 0.5. However, when the upper and lower layers far away from the interlayer are injected at the same time, the upper and lower layers of the interlayer are produced at the same time, and the total recovery rate can reach 83.02%. With the increase in the HCPV, the recovery rate increases rapidly at first and then slowly, and the HCPV at the turning point is 6.52. The oil production rate increases in a wave-like manner, then decreases rapidly, rises rapidly, and then decreases slowly in a wave-like manner. The HCPV at the breakthrough point of TI gas is 0.63, and the HCPV at the injection-production transition point is 0.63. The total recovery rate of carbon dioxide miscible displacement can reach 88.68% under the condition of separate injection and combined production with different injected hydrocarbon pore volume multiples. With the increase in the HCPV, the recovery increased rapidly at first and then slowly. The HCPV at the demarcation point was 6.5, the oil production rate increased in a wave-like manner, then decreased rapidly, increased rapidly, and then decreased slowly in a wave-like manner. The HCPV at the breakthrough point of TI gas was 0.63, and the HCPV at the injection-production transition point was 6.5. The research results provide data support for the physical reality of the microscopic and macroscopic sweep characteristics of carbon dioxide flooding in the Lunnan oilfield, Tarim Basin.  
L Lunnan oilfield; carbon dioxide flooding; micro-sweep characteristics; macro-sweep characteristics +
Footnote †: journal: Applied sciences