esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Road Design for Wind Farms in China
## 5 Research on Water and Soil Loss
### Characteristics of Water and Soil Loss in the Wind Farm
#### 5.2.3 Spatial Distribution Nonuniformity  
[PERSON] [51], taking the Jiyuan Wind Power Plant as an example, predicted the amount of water and soil loss in different blocks of the wind power project (Figure 14). It can be seen in the figure that the water and soil loss of the wind power project is mainly concentrated in the wind turbine equipment area and road engineering area, while the amount of water and soil loss in the booster station, transmission line area, and construction production and living area is less.  
[PERSON] [52] and others took a wind farm in Dali, Yunnan as the project background and calculated the area and proportion of water and soil conservation zones of the project (Table 5). Among them, the wind farm road project accounts for the largest proportion and area, followed by the waste disposal area, and then the fan unit area.  
Figure 14: Distribution of water and soil loss in the predicted areas.  
Figure 13: Cumulative sediment yield of each month after a disturbance in each division [50].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Serial No.**} & **Water and Soil** & **Area Covered/hm2** & **Proportion/\%** \\  & **Conservation Zoning** & & \\ \hline
1 & Fan unit area & 8.7 & 13.84 \\
2 & Collecting line area & 0.94 & 1.49 \\
3 & Booster station area & 0.94 & 1.53 \\
4 & Road works area & 37.56 & 59.73 \\
5 & Waste disposal area & 13.15 & 20.91 \\
6 & Construction production and & 1.57 & 2.50 \\  & living area & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Area and the proportion of water and soil conservation zones in the project area.  
[PERSON] [53] took the Shuangmiao Wind Farm in Kazuo County as an example to discuss the impact of the construction and operation of the wind farm on the surrounding ecological environment and proposed specific methods and measures for the restoration of the ecological environment of the wind farm. By comparing the area and amount of water and soil loss in different areas of the project (Table 6 and Figure 15), it is concluded that during the construction period, the construction road area is the main area producing water and soil loss, accounting for 59% of the total water and soil loss.  
To sum up, the water and soil loss in the wind farm is mainly concentrated in the road works and the fan unit area. There is less water and soil loss in the booster station, the power collection line, and the production and living areas. The road works and the fan unit area should be the key prevention areas for water and soil loss in the wind farm, and the booster station, the production and living areas, and the power collection line area should be the secondary key prevention areas.
## 3 LCA of Pineapple Cultivation and Processing  
### Objectives and Scope  
As mentioned above, the objective of the current LCA was to quantify carbon, water, and energy footprints from the supply chain, and the operations involved in the cultivation of pineapple and its industrialization, such as (i) packed as fresh fruit, (ii) dehydrated pulp, and (iii) pineapples in syrup. The functional unit was defined under the following situations: (i) in the agricultural stage, 1 kg of harvested pineapple; (ii) in the three industrialization scenarios, 1 kg of packed pineapple/dehyd Pineapple/pineapples in syrup. This study was carried out from \"the cradle to the gate\" since it considered the production (agricultural stage) and fruit processing (industrial stage), which included the extraction, transformation, use, and consumption of raw materials during the supply chain up to obtaining the final products.  
For the achievement of this LCA, the material and energy flows were tracked and analyzed, which included intermediate flows, outflows as products, co-products, residual flows to treatment, and flows released to the environment such as gas emissions and liquid or solid effluents. Capital goods were considered, such as the use of machinery for the land preparing and the transportation of supplies [49], while others were excluded, such as the construction of facilities, since their contribution was not very significant in the LCA result [50, 51]. The following procedure was used in the quantification of the inventory of capital goods: (i) the main materials that make up the capital good were identified, as well as their quantity; some examples include steel, plastic, non-ferrous metals, among others; (ii) the useful life of the product was established, generally 20 years; (iii) the functional unit was quantified from the amount of product produced, for example packaged pineapple during the product's useful life; and (iv) the amount of material calculated (i) was divided by the functional unit quantified in (iii). Therefore, each material amount from the capital good was quantified per kg of packed fruit. This data was compiled and included in LCI using this procedure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Supplies**} & \multicolumn{6}{c}{**Energy Consumption by Source (\%)**} \\ \cline{2-7}  & **Coke** & **Diesel** & **Fuel Oil** & **LP Gas** & **Natural Gas** & **Biomass** & **Electricity** \\ \hline Cans & 21.71 & 0.31 & 0.22 & 0.004 & 69.32 & - & 8.44 \\ Cardboard & - & 2.87 & 5.11 & 0.90 & 68.18 & - & 22.94 \\ Fertilizers and pesticides & - & 12.87 & - & - & 25.73 & - & 61.40 \\ Glue & 5.12 & 3.32 & 0.63 & 0.67 & 76.43 & - & 13.83 \\ Plastic and Wax & - & 21.23 & 2.73 & 0.20 & 57.59 & - & 18.25 \\ Sugar & - & 0.02 & 0.75 & - & - & 91.33 & 7.90 \\ \hline \hline \end{tabular} Source: own elaboration with information from [39, 46, 47, 48, 49, 42].  
\end{table}
Table 2: Energy consumption for manufacturing supplies used in the product system.
## 5 Service Discovery Mechanism
### SDM_TYPE TLV Generation  
Services announced by each node are part of the HELLO and TC messages defined in the OLSRv2 protocol. Besides the TLV structures defined in OLSRv2, we have defined a new one for service discovery.  
Taking into account the size of the OLSRv2 header and the size of the HELLO and TC messages, the size of an OLSRv2 packet with a TLV service discovery structure is the following:  
Figure 8: Topology control (TC) message format.  
Figure 7: HELLO message format.  
OLSRv2 packet header + HELLO message + SDM_TYPE TLV = 34 bytes OLSRv2 packet header + TC message + SDM_TYPE TLV = 39 bytes In OLSRv1, when a HELLO and SDM message were transmitted, there was an overhead in the network of 54 bytes. In the case of TC messages, the number of bytes transmitted was 52. HELLO messages with a SERVICE_DISCOVERY TLV structure are sent whenever: (1) a node has to announce a new service; (2) a node leaves the network, in this case the VALIDITY_TIME TLV structure will have a value of zero; and (3) periodically, when the timer in the INTERVAL_TIME TLV structure expires.  
The shorter the time defined in the INTERVAL_TIME attribute is, the more accurate the services stored at the remote service table of each node are, but the bandwidth consumption in the network will be greater. On the contrary, the higher the time defined in the INTERVAL_TIME attribute is, the lower the bandwidth consumption is, but the nodes will not have a service table updated as desired. This implies that the false service discovery ratio will be increased. As INTERVAL_TIME is a user-configurable parameter, we modify it depending on the user requirements needed every moment.
## 3 Methodology
### Deep Q-Network Algorithm
Through experience replay, the offline update strategy can avoid excessive fluctuations in the algorithm and maintain high stability. Furthermore, a target network and estimation network are introduced. When using a nonlinear function approximator during the training process, the Q-value will always change. However, if a set of changing values are used to update the Q-network, the value estimation may get out of control, which may lead to instability. In order to solve this problem, the estimated Q-network is updated every round through the stochastic gradient descent method, while the parameters of the target Q-network are updated frequently and slowly. The details of the DQN procedure for the considered problem is presented in Algorithm 1. ```
1:Initialize weight parameter \(\theta\) for Q-function
2:Initialize weight parameter \(\theta^{-}=\theta\) for target \(\widehat{Q}\)-function
3:Initialize experience replay buffer \(D\) with capacity \(N\)
4:for\(i=1,2,\dots,M\)do
5: Initialize the state \(s_{1}\), and pretreat the state \(\varphi_{1}=\varphi(s_{1})\)
6:for\(t=1,2,\dots,T\)do
7: Select an action \(a_{t}\) with probability \(\epsilon\) randomly
8: Otherwise select \(a_{t}=\arg\underset{a}{max}Q(\varphi(s_{t}),a;\theta)\) with probability \(1-\epsilon\)
9: Adopt action \(a_{t}\) and observe reward \(r_{t}\) and next state \(s_{t+1}\)
10: Pretreat the state \(\varphi_{t+1}=\varphi(s_{t+1})\)
11: Store transition \((\varphi_{t},a_{t},r_{t},\varphi_{t+1})\) in \(D\)
12: Sample minibatch \(b\) of transitions \((\varphi_{j},a_{j},r_{j},\varphi_{j+1})\) from \(D\), \(j=1,2,\dots,T-1\) randomly
13: Set \(y_{j}=r_{j}\), if episode terminates at slot j+1
14: Otherwise, set \(y_{j}=r_{j}+\gamma\underset{a_{j+1}}{max}\widehat{Q}(\varphi_{j},a_{j+1}; \theta^{-})\)
15: Perform gradient descent slot on \((y_{j}-Q(s_{j},a_{j};\theta))^{2}\) with respect to the network parameters \(\theta\)
16: Update \(\theta^{-}=\theta\) every \(B\) slots
17:endfor
18:endfor
```  
**Algorithm 1** The Proposed Algorithm
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Key Security Analysis
#### 5.3.2 Sensitivity of the Key  
The level of security provided by the encryption algorithm can be understood from the sensitivity of the key. A change in a single bit of the key can result in significant changes to the encrypted image. In order to investigate the sensitivity of the secret keys, we calculate the _UACI_ and _NPCR_ for the encrypted images obtained for the original chaotic keys \(x_{0}\) and \(y_{0}\), and the modified keys obtained by adding \(1\times 10^{-16}\) to the original chaotic keys. For our analysis, we consider the modification of key \(x_{0}\) to \(x_{0}+10^{-16}\) and \(y_{0}\) to \(y_{0}+10^{-16}\). The tabulated values are presented in Table 11. In order to visualize the impact of a very small change in a key on the decryption process, we present images encrypted with different keys in Figure 15. In the encryption phase, the change in one bit of key _NPCR_ (\(\%\)) of the two encrypted images is high, indicating the dissimilarity of the encrypted image, and similarly, in the decryption process, the change of the key results in a completely different image rather than obtaining the input image. Thus, it is clear that the algorithm proposed for encryption is highly sensitive to slight changes in the key.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(x_{0}+10^{-16}\) & \(y_{0}+10^{-16}\) \\ \hline \(NPCR\) (\(\%\)) & 99.620056 & 99.632263 \\ _UACI_ & 33.482385 & 33.582177 \\ _PSNR_ (\(dB\)) & 27.074867 & 27.085761 \\ _SSIM_ & 0.009156 & 0.011158 \\ \hline \hline \end{tabular}
\end{table}
Table 11: _NPCR, UACI, PSNR, SSIM_ values for the sensitivity of the keys with precision \(x_{0}+10^{-16}\) and \(y_{0}+10^{-16}\).  
Figure 14: Extracted secret images of [PERSON] and the Fingerprint at different Spn densities at 0.005, 0.001 and 0.01.  
Comparison of the Cryptographic Analysis of the Proposed Algorithms with Results from Recent Literature  
The analysis of the algorithm is compared to recent literature, and the results are tabulated in Table 12. The encrypted image generated by our algorithm exhibits a high level of randomness, as evidenced by its information entropy value of 7.9977. Furthermore, the _NPCR_ achieves a value of 99.6902, indicating a significant change between the input and encrypted images. Additionally, the _UACI_ value of 33.5929 surpasses the values reported in the compared articles, highlighting the effectiveness of our algorithm. The random distribution of pixels in both the input and encrypted images is demonstrated in the latter part of the image. The correlation coefficients obtained are close to zero, further indicating the lack of correlation between the pixel values. These results provide clear evidence that our proposed algorithm possesses the ability to encrypt images effectively and withstand various attacks.
# Novel Fault Injection Attack without Artificial Trigger
## 4 Experiment
### Experimental Setup
#### 4.1.2 Electromagnetic Fault Injection Attack  
Figure 8 represents the experimental environment configuration constructed to conduct an electromagnetic fault injection attack on AES implemented on the Arduino UNO board. The electromagnetic fault injection attack environment included the oscilloscope, Riscure's EMFI(Electromagnetic Fault Injection) transient probe, EM(Electromagnetic) probe station, EM probe tips, and the spider. The fault injection attack was conducted by applying the side-channel analysis software Inspector. In this study, a fault was injected randomly in the range of 655\(\sim\)675 us from the start of encryption to identify the 16-byte 10-round key. The location of electromagnetic fault injection on the Arduino UNO board is represented in Figure 8, and the chip area is divided into ten equal parts horizontally and five equal parts vertically. Fault injection was performed 20 times for each point, thereby executing a total of 1000 fault injection attacks.
## 2 Experimental Methodology
### Characterization of the Nanoparticles  
The size distribution, crystal phase, and morphology of the TiO\({}_{2}\) NPs contained in the NFs were determined experimentally. The size distribution was determined by the dynamic light scattering (DLS) technique (model NanoBrook 90 Plus PALS, Brookhaven Instruments Co., Holtsville, NY, USA). The accuracy of the particle size measurements was corroborated on standard-sized polystyrene spheres with a diameter of 216 \(\pm\) 4 nm (part number 3220A, Thermo Fisher Scientific, Waltham, MA, USA). For DLS analysis, the sample must be clear to slightly hazy. To achieve the required transparency, an aliquot from the stock solution was diluted to 1:1000, and then sonicated in a Branson 5800 ultrasonic bath for 1 h. The crystalline phase and the structural purity of the TiO\({}_{2}\) NPs were determined by X-ray diffraction (XRD) on an Empyrean diffractometer (Malvern Panalytical, Worcestershire, UK). A powdered sample was obtained by drying 15 mL of stock solution at room temperature. The powder was ground and placed in a non-glass sample holder, and then the diffraction patterns were referenced to the ICSD database. The morphology of the NPs was determined by the scanning electronic microscopy (SEM) technique (model EVO MA25, Zeiss Microscopy, Munich, DE). The sample was prepared for SEM by depositing a droplet of the diluted solution used in the DLS measurements on a carbon film mounted on a specimen stub. The droplet was evaporated in a desiccator.
## 2 Results and Discussion
### Profiles of Aldehyde Compounds  
Volatile compounds detected using SPME pretreatment combined with GC-MS included 16 aldehyde compounds (Table 1). Acetaldehyde is the key aroma compound in fermented milk products and can improve flavor [20]. _Lactobacillus delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03 are known to play important roles in producing aromatic compounds in fermented milk [9; 19]. Acetaldehyde can be produced directly from ethanol by the activity of alcohol dehydrogenase as an intermediate in the metabolism of sugar [21; 22]. Several metabolic pathways in _L. delbrueckii_ subsp. _bulgaricus_ and _S. thermophilus_ have been reported to result in the production of acetaldehyde in fermented milk [21]. For example, threonine is readily converted to acetaldehyde by catalysis of threonine aldolase [23; 24; 25]. In this study, high levels of acetaldehyde were present in all combinations of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03, particularly 1:1000 treatment. Similar results were reported by [PERSON] et al. (1973), who reported that a more abundant _S. thermophiles_ population stimulated the production of aldehyde compounds in yogurt [26]. 3-Methylbutanal is a branched-chain aldehyde compound derived from isoleucine and leucine by the action of enzymes [27]. As this aldehyde compound has a low taste threshold (1.2 ug /L), trace concentrations can be important characteristics in fermented milk [28; 29]. In this study, a high concentration of 3-methylbutanal was present in the volatile fraction of milk fermented by all combinations of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03, particularly in the 1:100 and 1:1000 treatments, where the relative concentrations reached 8.23 and 7.8 ug/L, respectively. Hexanal and heptanal are also important aroma compounds that contribute to good flavor in fermented milk products [23]. In this study, hexanal concentration reached 5.2 ug/L in the 1:1000 combination; the levels reached 1.43, 1.29, 2.51, and 1.24 ug/L in the 1:1; 1:10, 1:50, 1:100, and 1:10,000 combinations, respectively. As an important flavor compound, hexanal is derived from the oxidation of unsaturated fatty acids and is regularly reported in dairy products, such as fermented milk [30]. In this study, the concentration of heptanal ranged from 1.55 to 2.23 ug/L in the different combinations of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03; there was no detectable heptanal only in the 1:10,000 combination, which may have been due to the smaller initial proportions of _L. delbrueckii_ subsp. _bulgaricus_ in the samples. Similar results were reported by [PERSON] et al. (2017), who reported that heptanal was produced at high levels by _L. delbrueckii_ subsp. _bulgaricus_ during storage [9]. Figure 2: The categories and relative peak areas compared to those of the internal standards for the volatiles in milk fermented with different proportions of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03.
# Detecting Traffic Incidents Using Persistence Diagrams
## 4 Results
If all 6 minimum rankings seem too small, the minimum ranking to include one window from all but one incident could be used instead. 3. Set the first threshold, \(\tau_{1}\) as the largest of the six minimums. 4. Identify which of the windows found in step 1 satisfy the threshold of \(\tau_{1}\). 5. For each of the 5 types of rankings that were not used to determine \(\tau_{1}\), identify the minimum ranking met by all the windows identified in step 4. 6. Set the other 5 thresholds, \(\tau_{2},...,\tau_{6}\) according to the 5 minimums identified in step 5. Once the thresholds \(\tau_{1},...,\tau_{6}\) are set, we classify all windows that have all six rankings each above their respective thresholds as incidents. In the rest of this section, we present the results when this procedure is applied to each of the four data sets. We present the rankings of windows near each incident in the data sets and the thresholds we determined from training data sets. In each phase, we use the thresholds determined by the training data to classify the windows in the corresponding testing data. We measure the performance of these predictions using precision, recall and an F-score. **Remark 4**: _Our performance evaluation using Algorithm 1 exclude any incidents detected on Sundays since the training data did not include any reported incidents on Sunday._  
To provide some comparison, we also apply a standard normal deviates (SND) algorithm to detect incidents in the phase-1 data. The SND algorithm detects an incident in the \(k\)th 5-min time window if the count for that window, \(x_{k}\), satisfies  
\[|x_{k}-\overline{x}|>\tau\hat{\sigma}. \tag{4}\]  
We let \(\overline{x}\) and \(\hat{\sigma}\) respectively denote the mean count and standard deviation of counts from the same treatment group and \(\tau\) denotes some threshold that applies across all treatment groups. In this case, a treatment group is made up of all counts belonging to the same sensor, on the same day of week, at the same time of day. For example, in Phase-1, there were 52 counts taken at sensor S312425, on Monday mornings at 8:00 AM, \(\{x_{k}\}_{k=1}^{52}\). We can compute the mean and standard deviation of this treatment group,  
\[\overline{x}=\frac{1}{52}\sum_{k=1}^{52}x_{k}, \hat{\sigma}=\sqrt{\frac{1}{52-1}\sum_{k=1}^{52}(x_{k}-\overline {x})^{2}}\]  
For a better description of the SND algorithm and its application to incident detection see [11], where incident detection was performed using vehicle velocity rather than traffic flow. The threshold \(\tau\) was determined using the Phase 1-Train data by computing the deviates for each window occurring during a labeled incident. For each of the 15 incidents, we computed the 85 th percentile of the counts that took place during the incident. We chose \(\tau\) to be the second smallest of the 15 percentiles we computed. Thus, for the phase 1 data, we found \(\tau=1.06\), meaning any 5-min window where the count exceeded 1.06 standard deviations from the mean was detected as an incident.
## Materials and Methods
### Performance Parameters  
The performance of the proposed method was tested using a number of qualitative and quantitative parameters [51]. In addition to the yeast strain used during optimization (_S. cerevisiae_ EC1118), another three species were included at this stage (_T. delbrueckii, K. marianus, and M. pulcherrina_). Selectivity was evaluated based on the separation (retention times) and MS spectra using standard mixture spiked into the juice and yeast matrices. The linearity was tested using internal calibrations based on peak area and peak height at seven concentrations of the compounds of interest (0.50-50 mg/L) in the minimal media. LOD (S/N = 3) and LOQ (S/N = 10) were calculated based on the linearity. MS dynamic range also had to be tested due to the concentration and high ionization of the BSTFA-derivatives. The accuracy of the method was evaluated through recovery tests (direct injection _vs_ recovered amount after sample processing) at 1 mg/L and 10 mg/L (_n_ = 4); for calculations, a blank subtraction was performed. Precision was determined through repeatability for the extraction (for juice \(n\) = 4 and for yeast \(n\) = 3, the pellet size being the main limitation in this case), derivatization (_n_ = 4), and instrumental characteristics (retention time and response, \(n\) = 3). Carryover was also evaluated by running an IS blank after the highest calibration level (50 mg/L).  
Due to the length of the procedure, the stability of the yeast extracts was also determined in triplicate (Figure 2). Firstly, extracts were dried, derivatized, and injected on the same day (control); the samples were left on the sample ray for two days at room temperature and then injected. In the second scenario, the extract was dried, derivatized and stored at \(-\)20 \({}^{\circ}\)C for two days and then injected. In a third case, a 100 \(\upmu\)L aliquot of extract was stored at \(-\)20 \({}^{\circ}\)C for two days, then dried, derivatized and injected. Lastly, the dried sample was stored at \(-\)20 \({}^{\circ}\)C for two days and then analyzed.  
Figure 2: Stability tests. Created with BioRender.com.