esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Transmission
### DS-SS proposal
Then, assuming a 3 kHz bandwidth the maximum achievable uncoded bit rate is 23.6 bits-per-second (bps). To overcome this problem we propose a physical layer based on DS-SS signaling. DS-SS signaling consists in mapping \(m\) data bits into one of the \(M=(2^{m})\) spreading codes. At the receiver, the transmitted bits are obtained by correlating the received time-frequency synchronized signal with the \(M\) spreading codes. Maximum correlation value determines the most probable transmitted spreading code, _i.e._, the most probable transmitted group of \(m\) bits. This technique was proposed to reduce the multiple access interference in DS-CDMA systems, in [37] a performance evaluation of this system employing Walsh M-ary orthogonal signaling is done. The advantages of this technique are that the spectral efficiency can be increased by hardly decreasing the SNR and that accurate channel estimation is not required. Other physical layer candidates such as conventional DS-SS and DS-CDMA require accurate channel estimation in order to avoid large performance degradation, specially if a RAKE receiver is used. The downside is that the computational complexity of the receiver is high, of order \(\mathcal{O}(2^{m})\). It is important to notice that DS-SS and DS-CDMA achieve higher spectral efficiency by scarcely increasing the complexity at the expenses of suffering from a large degradation of the BER performance. On the other hand, DS-SS signaling achieves higher spectral efficiency by scarcely increasing the BER at the expenses of a large increase of the system complexity. To further increase the spectral efficiency of DS-SS signaling one can modulate the spreading code by using \(n\) more data bits. In that sense the number of bits per spreading code is increased from \(m\) (DS-SS signaling) to \(m+n\) (DS-SS signaling plus modulation). The \(m\) bits corresponding to DS-SS signaling are obtained at the receiver as described previously, the \(n\) bits corresponding to the modulation are obtained as in conventional DS-SS systems assuming the spreading code estimated from the DS-SS signaling detection. Note that to detect the \(n\) modulation bits it is required to have an estimate of the channel response.
# Synthesis and Preliminary Screening of the Biological Activity of Sulindac Sulfoximine Derivatives
## 1 Introduction
The study of CDK inhibitors with sulfoximines has led to the identification of a compound (whose acronym is BAY 1000394) endowed with remarkable antiproliferative activity, good solubility, and specificity, and which has also been studied in the clinical phase on patients with solid tumors at an advanced stage [8]. In the past work of some of us, we reported the synthesis of chiral sulfoxides by enantioselective oxidation of sulfides with _tert_-butyl hydroperoxide (TBHP) in the presence of a 1:2 complex between Ti(_i_-PrO)\({}_{4}\) and (_S_,_S_)- or (_R_,_R_)-hydrobenzoin. This oxidation protocol has proved to be very useful for the synthesis of different types of chiral sulfoxides obtainable in good yields with high enantiomeric purity. In this context, we reported the synthesis of chiral sulindac esters obtained by enantioselective oxidation of the corresponding sulfides. The products were obtained with moderate yield and with a high enantiomeric purity. Then, the esters were hydrolyzed to yield chiral sulindac (2a) [20]. Taking into account all these aspects, and our experience of the synthesis of racemic and enantiopure sulfoxides [15; 19; 20], and in particular of sulindac alkyl esters [20] and of sulfoximine [19], we decided to synthesize the sulfoximines of sulindac derivatives and to test the bioactivity  
Figure 1: Structures of sulindac, sulindac sulfide and sulindac sulfone. of these compounds, in which the anti-inflammatory sulindac is combined with the new possibilities connected with the sulfoximine moiety.
# M-Shape PV Arrangement for Improving Solar Power Generation Efficiency
## 7 Results
Another important factor is the geographic location. The figure shows the amount of power generated by Naha. From Figure 12, it can be concluded that with increasing latitude, the M-shape will have a better power generation effect than this result. Figure 14 shows the area usage of two types of PV panels. The M-shape PV design uses less area space compared to the S-shape design. This is due to the concept that M-shapes are continuous, while S-shape uses intervals. The horizontal line in Figure 14 indicates the increase of area without power generation. This will lead to increased costs with less revenue. As the number of solar panels increases, the efficiency of the M-shape becomes larger. Table 13 shows the detailed area usage for the two PV arrangements for Naha and Shenyang. Another point is that as latitude increases, the sun's elevation angle becomes smaller. This makes the shadow length larger and verifies that the M-shape will greatly reduce the area in mid-latitude. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Shenyang**} & \multicolumn{4}{c}{**Naha**} \\ \cline{2-9}  & **40** & **80** & **120** & **160** & **40** & **80** & **120** & **160** \\ \hline
**M-shape** & 59.8 m\({}^{2}\) & 119.6 m\({}^{2}\) & 179.4 m\({}^{2}\) & 239.3 m\({}^{2}\) & 62.0 m\({}^{2}\) & 124.0 m\({}^{2}\) & 186.1 m\({}^{2}\) & 248.1 m\({}^{2}\) \\
**S-shape** & 55.4 m\({}^{2}\) & 233.3 m\({}^{2}\) & 411.3 m\({}^{2}\) & 589.3 m\({}^{2}\) & 63.1 m\({}^{2}\) & 179.1 m\({}^{2}\) & 295.2 m\({}^{2}\) & 411.2 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Area comparison of the M-shape and S-shape in Shenyang and Naha. Figure 13: Two types of output power characteristics. Figure 15 clearly shows the the M-shape and S-shape that are considered as the best economical solutions. As the number of panels increases, the power generation increases due to an overload of around 200% leading to a profit. If the overload exceeds 200%, it could lead to a downward trend affecting cost. The descending line represents the cost of land. According to the simulation result, 73 pieces of solar panels used in the M-shape design are optimal. The power generation is 23 \(MWh\), the profit is 3.8 million yen, and the floor area is 114 \(m^{2}\). The overload rate is 182.5%. Similarly for the S-shape, 65 solar panels are an optimal choice. The power generation is 22 \(MWh\), the profit is 3.6 million yen, and the floor area is 162 \(m^{2}\). The overload rate was 172.5%. It can be confirmed that the installation of the west-east setting can save space and be profitable.
# LogInjector: Detecting Web Application Log Injection Vulnerabilities
## 2 Background
### Investigating Known Log Injection Vulnerabilities  
We collected 14 known log injection vulnerabilities from CVE, and these 14 vulnerabilities are distributed in 13 different web applications. We conducted an in-depth analysis of these vulnerabilities and found the following:  
**Finding 1:** Eight of these web applications use databases to store logs, and the remaining five use files to store logs. For web applications that store logs in the database, the corresponding table name of the log storage location will contain the keywords _error_, _log_, etc. For example, Contao [3] uses the _tl_log_ table to record logs. For a web application that uses files to store logs, the file name of the stored log also contains the keywords _error_ or _log_, etc., such as the _webmin.log_ file in Webmin [4].  
**Finding 2:** Among the web applications studied, eight log injection vulnerabilities are caused by web application logs recording user operations, and six are caused by recording program code running error messages, such as when an error occurs in the program code associated with the execution of an SQL statement. These 14 vulnerabilities are all triggered by submitting the corresponding payload to the form on the web page. In particular, for the vulnerability caused by recording program execution error information, it is necessary to input the characters that cause the code execution error to the corresponding web form, such as single quotation marks.  
**Finding 3:** These 13 web applications all have their own input-filtering checks, and the implementations of their respective input-filtering checks are also different. The input-filtering checks of each web application have corresponding bypass methods.  
**Finding 4:** Ten of the log injection vulnerabilities can cause stored cross-site scripting execution (Stored XSS), and the remaining four cause remote code execution (RCE). The damage caused by the log injection vulnerabilities is very serious. An attacker can steal user's cookies with stored cross-site scripting execution. If an attacker succeeds in stealing a website administrator's cookies, the attacker can take over the website. In addition, for remote code execution, an attacker can use this vulnerability to take control of the host and remotely steal private user information, and the impact is more serious.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps Generated Using GIS-Based Methods
#### 3.1.5 Tropical Africa and Southeast Asia 1980 and 2000 Forest Biomass Maps  
The above maps were derived as a function of degradation ratio (DR) and potential biomass density obtained from climate, soil, topographic, and land-use information using rule-based GIS models [55; 56; 57; 58]. Potential biomass was calculated based on land cover data, while the DR was closely correlated with population density. Forest biomass maps for 2000 with a spatial resolution of 0.045 decimal degrees produced by [PERSON] [57; 58] were updated from those for 1980, by including land cover data from GLC2000 and population data in 2000. For both the 1980 and 2000 biomass datasets, the same equations were used to calculate DR, which was estimated by comparing the potential biomass density in 1980 with the corresponding biomass densities obtained from forest inventories. The parameters varied for different forests (open or closed, woodland or savanna), but followed the same patterns.  
\[\text{DR}=\text{a}-\text{b}\,\ln(\text{PD}) \tag{1}\]  
where PD is a population density in people per km\({}^{2}\). Different forest types had different parameter values of a and b, but they were the same for 1980 and 2000 for the same forest type. Because PD in 1980 and 2000 was different, their corresponding DRs were different. Uncertainties of both biomass datasets were not reported, and additional field data are needed to assess the accuracy of this dataset.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 2 Related Work
Moreover, considering energy as a routing metric paradigm, a protocol such as UBERP [26] is not feasible within a real deployment due to its high algorithm complexity while the minimum transmission energy (MTE) [27; 28] routing scheme gives an overall low power consumption at the price of poor network lifetime. The online maximum lifetime (OML) [29] and shortest widest path [20] techniques are better correlated with the Industrial WSN requirements by balancing the minimization of the total energy consumption and the avoidance of energy depleted nodes. Despite the use of the general terminology (i.e., maximization), these paradigms, due to the use of multimetrics, give a theoretical model for improving the network lifetime. These techniques, though more practical, are based only on simulations while PEAR takes a further step by using hardware platforms and considering real deployments requirements. In the context of data intensive D2D (device-to-device) applications facing a spectrum shortage issue [30], the energy efficiency of an IoT system can be enhanced by 5G cellular networks. Combining short-range IoT systems such as WirelessHART and ISA100.11a with 5G capabilities provides expanded transmission range and channel bandwidth while enhancing the energy efficiency [31]. Moreover, within an industrial environment a private 5G aided IIoT systems would also address the privacy and data ownership concerns [32].
## 4 Experiment and Result Analysis
### Evaluation Criteria  
For the detection task, the detection rate and the false-positive rate are two important indicators of the detection effect, which reflect the detection method's ability to cover and judge the objects, respectively. In this paper, pixel-level criteria were used to evaluate the detection performance of the algorithm for unexpected obstacles. Pixel-wise Detection Rate (PDR) can be expressed as:  
\[PDR=\frac{CDP_{uo}}{AP_{uo}}. \tag{7}\]  
Among them, \(CDP_{uo}\) is the number of pixels that are correctly detected as unexpected obstacles; \(AP_{uo}\) is the number of pixels of real unexpected obstacles. Pixel-wise False-Positive Rate (PFPR) can be expressed as:  
\[PFPR=\frac{IDP_{uo}}{AP_{uo}}. \tag{8}\]  
Among them, \(IDP_{uo}\) represents the number of pixels that are incorrectly detected as unexpected obstacles; \(AP_{muo}\) is the number of pixels of real non-unexpected obstacles.
## 3 Results and Discussion
### Broccoli Yields  
Differences in the yield of broccoli plants grown in the analyzed soils are presented in Figure 2. Broccoli plants grown in soil II were characterized by the most favorable biometric parameters, which was also reflected in the highest yields. The average flower head yield ranged from 18.6 t ha\({}^{-1}\) (soil III) to 28.8 t ha\({}^{-1}\) (soil II), and the differences in yield between soil II and soils I and III were significant. The yield of leaves from plants grown in soil I was significantly lower than the yields achieved when plants were grown in soils II and III. The average leaf yield ranged from 40.8 t ha\({}^{-1}\) (soil I) to 45.8 t ha\({}^{-1}\) (soil II). The total yield of broccoli plants ranged from 57.7 t ha\({}^{-1}\) (soil I) to 80.0 t ha\({}^{-1}\) (soil II), and the average value of this parameter reached 59.7 t ha\({}^{-1}\) in soil I, 74.6 t ha\({}^{-1}\) in soil II, and 63.6 t ha\({}^{-1}\) in soil III. The differences in total yield between soil II vs. soils I and III were significant. Broccoli plants grown in soil II were also characterized by the highest marketable yield of flower heads (12.2 t ha\({}^{-1}\) on average), and the difference in this parameter relative to plants grown in soil III was significant. Similar yields of two broccoli hybrids were noted by [PERSON] and [PERSON] [23]. In the cited study, organic fertilizers exerted a particularly positive effect, which indicates that a higher content of organic compounds in soil promotes higher yields.  
Figure 2: _Cont._  
No significant differences were found in the percentage of marketable yield in the total yield of flower heads, but this parameter was highest in broccoli plants grown in soil II (98.2%) and lowest (although still satisfactory) in plants grown in soil III (91.0%).
# Effects of Cement Treatment on Mechanical Properties and Microstructure of a Granite Residual Soil
## 4 Discussion
### Prediction of UCS and Secant Modulus
Given the same experimental trends as that of UCS, the secant modulus of the saturated treated specimens was predicted based on the \(E_{50}\)-\(\eta\)Cvi relationship in Figure 18a:  
\[E_{50}(\eta,C_{\rm{vi}})\ =E_{50}(\eta,C_{\rm{vi}}=0)+\alpha_{\text{E50}}[1-\exp( -\eta C_{\rm{vi}}/\beta_{\text{E50}})]. \tag{3}\]  
Figure 17: Modeling of the UCS of saturated cement-treated GRS (Hollow symbol with cross indicates data excluded in the fitting). (**a**) Fitting of UCS at four curing times; (**b**) \(\alpha_{\text{UCS}}\) dependent on curing time. Similarly, \(E_{50}(\eta=0.4\), \(C_{vi}=0)\) was fixed at 2.8 MPa for the saturated untreated specimen, and a direct fitting of the four data sets gave \(\beta_{E50}\) (9.4 \(\pm\) 1.8) \(\times\) 10\({}^{-3}\). Despite its slightly larger variation than that of the \(\beta_{\rm UCS}\), \(\beta_{E50}\) was fixed at 9.4 \(\times\) 10\({}^{-3}\) for the four curing times. The adjusted fitting is plotted in Figure 18a, and the dependence of \(\alpha_{E50}\) on the curing time is shown in Figure 18b. The \(\alpha_{E50}\) demonstrates a diminished increase with the curing time and an exponential function was established:  
\[\alpha_{E50}(t)\ =\alpha_{E50}(t\ =\ 0)+\lambda_{E50}[1-\exp(\ -t/\gamma_{E50 }t)] \tag{4}\]  
where \(\alpha_{E50}(t=0)\) is the \(\alpha_{E50}\) for the non-cured specimen. The \(\lambda_{E50}\) and \(\gamma_{E50}\) describe the diminished increase of \(\alpha_{E50}\) with the curing time. The \(\alpha_{E50}(t=0)\), \(\lambda_{E50}\), and \(\gamma_{E50}\) were fitted to be 101 MPa, 316 MPa, and 8.0 d, respectively (see Figure 18b). It is noted that the \(\beta_{\rm UCS}\) and \(\beta_{E50}\) are of comparable values. Hence, an approximate value of \(\beta=0.009\) can be adopted for the \(\beta_{\rm UCS}\) and \(\beta_{E50}\). An integrated formula can be established to predict the strength and stiffness of the saturated cement-treated GRS:  
\[\Phi(\eta,C_{vi})\ = \Phi(\ \eta,C_{vi}\!=\ 0)+\alpha(t)[1-\exp(\ -\eta C_{vi}/\beta)] \tag{5}\] \[\alpha(t)\ = \alpha(t\ =\ 0)+\lambda_{t}[1-\exp(\ -t/\gamma_{t})] \tag{6}\]  
where \(\Phi(\eta,\ C_{vi}=0)\) is the strength and stiffness of the saturated untreated specimen. The parameter \(\alpha\) describes the increase of the strength and stiffness with the \(\eta C_{vi}\), and \(\beta\) adjusts its diminished trends. However, \(\alpha\) still depends on the curing time and needs to be determined via Equations (2) and (4).
## 4 Performance Experiments  
### Proposed System Overhead  
To prove the effectiveness of the proposed architecture, we conducted an experiment to calculate the proportion of overhead in the system throughput in terms of time. This proportion shows the extent to which the proposed architecture influences system performance. In this paper, the overhead indicates the total time from receiving to forwarding a particular message excluding message processing time such as parsing, analyzing, and converting. Therefore, the overhead for \(N\) messages, denoted by \(O\)(\(N\)), can be represented as follows:  
\[O(N)=tT(N)-tP(N) \tag{1}\]  
where \(tT\) denotes total time and \(tP\) denotes message processing time. Then the overhead ratio, \(OR\), can be simply calculated by  
\[OR(N)=\ \frac{O(N)}{tT(N)} \tag{2}\]  
The overhead ratio was measured and calculated repeatedly, while increasing the number of the messages, \(N\), from 1000 to 10,000. The result of the evaluation is described in Figure 3. In the experiment, the overhead ratio decreased and stayed stable at about 13.1 percent as \(N\) increased. This proves that the overhead of the proposed architecture has a modest effect on overall performance and is robust to heavy traffic.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Hardware**} & CPU & Intel(R) Core(TM) i7-47/70 CPU @ 3.40 GHz \\  & RAM & 4 GB \\  & NIC & Intel I217-V \\ \hline \multirow{4}{*}{**Software**} & Hypervisor & VMware ESXi 6.5.0 \\  & OS/Kernel Ver. & CentOS 7, 310.0-514.16.1.e17.x86 \\ \cline{1-1}  & DPDK Ver. & 16.11.1 (LTS) \\ \cline{1-1}  & Netlink & Libnl3 3.2.28-3.e17\_3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware and software specifications.
## 3 Methodology
### Data Collection and Normalization (Step 1)  
This step involves collecting and pre-processing data. First, a dataset related to predictive maintenance, proposed by [32] and adopted in the study of [28], was collected from the UC Irvine Machine Learning Repository. This dataset has 10,000 records, each composed of the following attributes: UID, a unique identifier ranging from 1 to 10,000; ProductID, which represents the quality of the product, where L represents 50% of all products, M represents 30% of all products, and H represents 20% of all products; air temperature (K), generated using a random walk process subsequently normalized to a standard deviation of 2 K around 300 K; process temperature (K), generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K; rotation speed (rpm), calculated from the power of 2860 W, overlaid with customarily distributed noise; torque (Nm) values are typically distributed around 40 Nm with \(\sigma=10\) Nm and no negative values; tool wear (min), H/M/L quality variants add \(5/3/2\) min of tool wear to the tool used in the process; target label indicates whether the machine failed or not at this specific data point; and failure type represents a specific type of failure, including no failure, heat dissipation failure, power failure, random failure, overstrain failure, and tool wear failure.  
The data are imbalanced, with a low incidence of some types of failures. There are 9652 no failure samples, 112 heat dissipation failure, 95 power failure samples, 78 overstrain failure samples, 45 tool wear failure samples, and 18 random failure samples.  
In the pre-processing procedure, the data were normalized to the interval \([0,1]\) using the Min-Max approach with the resources of the MinMaxScaler library from Sklearn in Python to avoid differences in the scales of the variables.  
Since real predictive maintenance data are often difficult to obtain, this dataset represents an important source of data, because according to [28,32], it reflects the real predictive maintenance found in the industry.
## 3 Design Methodology
### Multi-Objective Design Problem  
The multi-objective design problem can be defined as follows [39]:  
\[\mathbf{x}^{*}\in\argmin_{\mathbf{x}\in\mathbf{X}}F(\mathbf{x}) \tag{2}\]  
where \(\mathbf{x}=\mathbf{x}_{d}\) (see Section 2) denotes the vector of the design parameters, \(\mathbf{X}\) is the feasible region of the search space, and \(\mathbf{x}^{*}\) represents the optimal design. The objective functions are given as  
\[F(\mathbf{x})=[F_{1}(\mathbf{x}),\ldots,F_{q}(\mathbf{x})]^{T} \tag{3}\]  
where \(q=1,\ldots,Q\). Note that \(F(\mathbf{x})=F(\mathbf{R}(\mathbf{x}))\), where \(\mathbf{R}(\mathbf{x})\) is a vector of responses obtained from EM evaluation of the antenna model at the design \(\mathbf{x}\). The responses may represent the electrical performance (e.g., reflection over a frequency range or isolation between radiators), field (e.g., single-direction gain over the frequency or radiation pattern at the given frequency), or geometry (e.g., footprint or volume) of the structure. If \(Q>1\), the conventional notion of performance improvement known from single-objective optimization (i.e., the one based on direct comparison of the objective function responses obtained for candidate solutions) is not applicable [39]. Instead, designs can be compared using a domination relation [39]. One can say that the design \(\mathbf{x}_{1}\) dominates over \(\mathbf{x}_{2}\) if \(F_{q}(\mathbf{x}_{1})\leq F_{q}(\mathbf{x}_{2})\) for all \(q\) and \(F_{q}(\mathbf{x}_{1})<F_{q}(\mathbf{x}_{2})\) for at least one objective. A design \(\mathbf{x}_{q}^{*}\) is optimal in the Pareto sense if there is no other design \(\mathbf{x}_{r}^{*}\) (\(q\
eq r\)), such that \(\mathbf{x}_{r}^{*}\) dominates over \(\mathbf{x}_{q}^{*}\). The solution to the multi-objective problem is a set of Pareto-optimal designs. For a more detailed description of the MO optimization concept, see [35; 39; 43].