esa-sceva/satcom-chunk-collection huggingface dataset:
# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework
### Design Goals  
We analyzed the requirements for GDPR compliance audit management related to DS requests, requirements for a notarization system in a distributed environment, GDPR principles related to personal data processing, and the feasibility of applying to all SPs in a real environment. Based on the analysis, the design goals of the personal data processing notarization framework for GDPR compliance audit, a solution to the problem, were derived as shown in Figure 5.  
The details of the functional design goals other than the security design goals mentioned previously in the derived design goals are as follows:  
* Delegation for GDPR: DS must have the ability to delegate requests for the processing of personal data to the notarization system and deliver them to DC in an electronic manner. Data creators who want notarization should be easily accessible anytime, anywhere.
* Audit trail for GDPR: Requests and responses must be recorded in the form of 'who, when, to whom, and what' for GDPR compliance, and they must preserve integrity. Records must be stored in a manner such that they can be viewed by an auditor authorized by the DS and DC.  
Figure 4: Requirements to support GDPR compliance audits regarding the DS’s requests.  
* Notarization of request for the processing of personal data: Verification of the personal data processing request and corresponding data through a number of trusted notaries should be enabled, and furthermore, the ability to notarize the integrity of the stored and retrieved data is important. The authenticity and key management of data creators and notaries should ensure that the reliability of the data cannot be denied.
* Managing permission for audit: Only the author or recipient of the data should have access to the relevant data. DSs and DCs must have the ability to authorize auditors to view data for GDPR compliance audits related to requests for the processing of personal data. However, searching for data other than the data of the approver that the auditor has authorized the inquiry authority to view should be disabled.
* Distribution of trust: The authentication of users using the notarization system and the authority that manages the ledger must be performed and mutually verified by certain trusted institutions across countries rather than one.
* Minimum collection: Personal data other than data related to requests and responses should not be collected, and GDPR compliance audits should be possible for requests from DSs without sharing them with DCs or not being provided them from DCs.
## 2 Problem Formulation  
Consider a planar array with dimensions \(M\times N\) and \(M=N\) placed on the \(XY\) plane, where \(M\) and \(N\) represent the number of elements in the \(X\) axis and \(Y\) axis direction, respectively. The elements are evenly distributed with the interval \(d=0.5\lambda\), where \(\lambda\) is the incident wavelength. Assuming the sampling time \(t\), \(t=1,2,\cdots,L\), \(L\) is the number of snapshots, the far-field narrowband sound sources \(s(t)=[s(t)_{1},s(t)_{2},\cdots,s(t)_{N_{s}}]^{T}\) impinging on this array, where \(N_{s}\) represents the number of sound sources, \((\theta_{k},\phi_{k})\) represents the 2D incident wave arrival direction of the \(k\)th sound source, and \(k=1,2,\cdots,N_{s}\), \(\theta_{k}\), and \(\varphi_{k}\) respectively represent the incident azimuth and pitch angle, \(\theta_{k}\in[0^{\circ},90^{\circ}]\), \(\varphi_{k}\in[0^{\circ},360^{\circ}]\).  
The DOA estimation problem can be expressed as a linear equation. A typical array observation model for receiving signals at \(t\) time can be expressed as  
\[y(t)=As(t)+n(t) \tag{1}\]  
where \(y(t)\) denotes the array output signal vector, \(A\) denotes the 2D array manifold matrix, \(n(t)\) denotes the additive noise vector, \(\sigma^{2}\) is the mean square error, and the mean value of \(\sigma^{2}\) is zero, which is independent from the sound source.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 1 Introduction
### Related Work
#### 1.1.1 Multipath Traffic Scheduling and Protection  
Multipath traffic scheduling means allocating network traffic on multiple paths by selecting the appropriate path(s) for data transmission to meet specific constraints or service requirements. A multipath scheduling technique must take into account both traffic protection and bandwidth preservation. However, most of the scheduling techniques proposed in the literature do not take either into consideration. For example, the conventional round-robin (RR) scheduling strategy sends data sequentially over multiple paths and neglects path conditions. The RR scheduler has been found to perform poorly in the Multipath Transmission Control Protocol (MP-TCP) [6]. Although the Weighted Round Robin (WRR) scheduler is improved compared with the (RR) scheduler, the weights it assigns to paths are usually static, which makes it impractical under time-varying conditions such as those in NTN networks. Similarly, the deficit round-robin (DRR) and weighted fair queuing (WRQ) schedulers [6] do not adapt easily to dynamic channel conditions. Path-Aware Networking (PAN) scheduling strategies consider path conditions such as the Round-Trip Time (RTT), Packet Loss Rate (PLR) and bandwidth [7]. The RTT as used in schedulers such as the round-trip time threshold and the lowest-RTT-first schedulers [6] enables traffic to arrive before the expiration time [8; 9], but the _head-of-line blocking_ can affect connections that differ greatly in latency. When PLR [9; 10] is used for scheduling, undelivered and delayed traffic is taken as lost traffic. In contrast, our proposed learning-based scheduling system can adapt to dynamic link states and make proper path selections and redundancy estimations.  
As for traffic protection, several schemes have been proposed, such as Forward Error Correction (FEC) and Automatic Repeat reQuest (ARQ). FEC can waste bandwidth because of the fixed redundancy rate that does not take into account the dynamism of the network. ARQ, on the other hand, uses retransmissions to compensate for lost traffic but can cause network congestion [11; 12] and adversely affect multimedia quality [13]. Thus, it is not preferred for real-time traffic, especially in satellite transmission, which is characterized by long delays. In such scenarios, FEC could provide a solution, but it introduces a computational load on constrained devices such as UAVs. Our learning-based model, on the other hand, provides traffic protection by using the required redundancy, depending on the dynamic network conditions, while avoiding excessive overhead.
# Evaluation of Disassembling Process Inference Based on Positional Relations Matrix
## 2 Disassembling Process Inference Based on Positional Relations Matrix
### Disassembling Process Inference  
#### 2.2.1 Preparation of Disassembly-Move Sets and Disassembly-Part Sets  
We refer to a pair \((j,k)\) that consists of part \(j\) and disassembly direction \(k\) as a disassembly move. On the basis of the positional relations matrix, we can obtain the set of immediate disassembly moves; that is, the set of pairs of parts and disassembly direction that are immediately available, as follows:  
\[\mathcal{A}_{1}=\{(j,k)\mid M_{ijk}=0\text{ for all }i\} \tag{1}\]  
where \(M_{ijk}\) denotes the \(k\)th bit value of \(M_{ij}\). Equation (1) expresses that \(\mathcal{A}_{1}\) consists of disassembly moves \((j,k)\), such that part \(j\) can be immediately disassembled in direction \(k\), since there are no impeding parts \(i\) in the direction such that \(M_{ijk}=1\). Set \(\mathcal{A}_{1}\) gives the following set of immediate disassembly parts:  
\[\mathcal{P}_{1}=\{j\mid(j,k)\in\mathcal{A}_{1}\text{ for some }k\} \tag{2}\]  
The set of two-stage disassembly moves is then obtained as  
\[\mathcal{A}_{2}=\{(j,k)\mid(j,k)\
otin\mathcal{A}_{1}\text{ and }\mathcal{P}_{1} \
i i\text{ such that }M_{ijk}=1\} \tag{3}\]  
Equation (3) denotes that a disassembly move \((j,k)\) in \(\mathcal{A}_{2}\) is not immediately available but can be available after all of the parts in \(\mathcal{P}_{1}\) are removed, since all the impeding parts \(i\) for  
Figure 1: Example combination of parts to be disassembled.  
the move \((j,k)\), such that \(M_{ijk}=1\) are in the set of immediate disassembly parts \(\mathcal{P}_{1}\). Set \(\mathcal{A}_{2}\) accordingly gives the set of two-stage disassembly parts:  
\[\mathcal{P}_{2}=\{j\mid(j,k)\in\mathcal{A}_{2}\text{ for some }k\} \tag{4}\]  
Accordingly, we obtain the set of \(n\)-stage disassembly moves in the same manner as  
\[\mathcal{A}_{n}=\{(j,k)\mid(j,k)\
otin\mathcal{A}_{1\sim(n-1)}\text{ and }\mathcal{P}_{1\sim(n-1)}\
i i\text{ for }M_{ijk}=1\} \tag{5}\]  
where \(\mathcal{A}_{1\sim n}=\mathcal{A}_{1}\cup\cdots\cup\mathcal{A}_{n}\) is the set of disassembly moves to be available by \(n\) stages and \(\mathcal{P}_{1\sim n}=\mathcal{P}_{1}\cup\cdots\cup\mathcal{P}_{n}\) is the set of parts to be disassembled by \(n\) stages. The set of \(n\)-stage disassembly parts is obtained as well.  
\[\mathcal{P}_{n}=\{j\mid(j,k)\in\mathcal{A}_{n}\text{ for some }k\} \tag{6}\]  
This preparation process continues until the \(R\)th stage, where all the combinations of disassembly moves \((j,k)\) are included in the unified disassembly-move set \(\mathcal{A}_{1\sim R}\). At the end of this preparation process, we obtain the disassembly-move sets \(\mathcal{A}_{1},\ \cdots,\mathcal{A}_{R}\) as well as the corresponding disassembly-part sets \(\mathcal{P}_{1},\ \cdots,\mathcal{P}_{R}\).
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 1 Introduction
Literature [16] initially evaluates the network RTK service effect under the combination of B1C+B2a using three reference stations with an average baseline length of 30 km. The rover stations measured in the network combined with the VRS achieved a static three-dimensional (3D) positioning accuracy better than 3 cm. The above studies about the BDS-3 system mainly focus on the aspects of signal quality, orbit, clock error, PPP positioning performance, and RTK service performance. Network RTK technology is an important real-time regional high-precision GNSS positioning technology, but there are few related studies based on the BDS-3 new signal system. In the practical network RTK application scenario, the performance comparison of the BDS-3 new signal combination (B1C+B2a) and transition signal combination (B1I+B3I), the positioning performance evaluation of the single BDS-3 constellation for network RTK service, the positioning performance comparison of BDS-3, BDS-2, and GPS, and the positioning performance of BDS-2/3 constellation joint combination should be further studied. In this paper, five actual reference stations and one test station of a provincial CORS network in central China were verified. Firstly, the service effects of two conventional BDS-3 signal combinations (B1C+B2a) and (B1I+B3I) were compared from two aspects of atmospheric correction accuracy and regional modeling accuracy. Then, taking the GPS system as reference, five experimental schemes of GPS, BDS-2, BDS-3 [B1I+B3I], BDS-3 [B1C+B2a], BDS-2/3 are designed to evaluate the positioning performance of BDS-3 network RTK and compare its service capability with other systems. In addition, different from the conventional plane atmospheric modeling methods, algorithms of this paper partially referenced Flachenkorrekturparameter (FKP) horizontal gradient factor solution strategy, which is described in the positioning algorithm section. The above work is expected to provide some reference for the processing of a new constellation and new signal system of BDS-3 in the practical application scenarios of network RTK. It should be pointed out that VRS of Trimble is a branch of network RTK technology, and its main advantage is that the server does not need to send the physical coordinates of the reference station, so it has good confidentiality. In addition, this technology is compatible with conventional RTK algorithms and is convenient for market promotion. Therefore, it is also the most widely used technology [17; 18]. The research of this paper is mainly based on this technology model.
## Literature Review  
### The Operations in a Seaport Container Terminal  
Figure 1 illustrates two ships berthing alongside the quay of a container terminal. The container terminal is separated into the following three areas: seaside, yard and landslide [3].  
The quay in the container terminal can be configured as either a discrete or continuous quay. With a discrete configuration, the quay is separated into several fixed sections, and each section is allowed to accommodate one ship at a time. With a continuous configuration, the entire quay is used as a continuous line that can accommodate as many ships as possible at one time. Along the quayside, rail-mounted quay cranes are used to load/unload containers to/from trucks. The trucks will transport containers between the berthed ships and the storage areas in the yard area.  
The yard area serves as a buffer between the sea and land sides, which is an area of temporary storage for containers. The yard area is divided into many blocks, and each block is composed of several rows for stacking containers. For each block, there is a yard crane used to load and unload containers on or off the trucks. The containers in the yard area will be transshipped to other ships or transported to customers via road or rail. Figure 2a shows the flows of intra-terminal transshipment between ship 1 and ship 2.  
The land side contains operations consisting of transporting containers to and from the container terminal, by trucks, road or railway. Figure 2b shows two types of intermodal transportation: ship-to-road and ship-to-rail. In this yard area, both intra-terminal transshipment and intermodal transshipments operate simultaneously.  
Figure 1: An example of a seaport container terminal.  
In a container terminal, there are different operational planning problems. These can be listed as follows:  
* Allocation of berths for calling ships (BAP).
* Assigning of QCs for berthed ships for loading and unloading containers (QCAP).
* Scheduling of QCs (QCSP).
* Assigning of yard trucks.
* Scheduling of yard trucks.  
The BAP, QCAP and QCSP are widely recognized seaside operational problems. In this research, we will focus on the BAP of the continuous quay configuration. Relevant CBAP studies are reviewed in the next section.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Fungi as Food Spoilage Agents
#### 2.3.4 Mechanisms of Food Spoilage  
The mechanisms underlying fungal-mediated food spoilage are multifaceted and involve enzymatic degradation, metabolic activity, and the production of secondary metabolites. Proteolytic enzymes produced by spoilage fungi hydrolyze the proteins in food, leading to the production of peptides and amino acids that contribute to off-flavors and odors. Lipolytic enzymes degrade lipids, causing rancidity and off-flavors in fatty foods. Moreover, the formulation of organic acids and volatile compounds by spoilage fungi contributes to the souring and fermentation of food products. Additionally, some fungal species produce mycotoxins. These are secondary metabolites with toxic effects on humans and animals [91].
# Direct Measurement of Aerosol Liquid Water Content: A Case Study in Summer in Nanjing, China
## 3 Results and Discussion
### ALWC Results and Correlations with Other Variables
In Table 1, we compare a few key studies regarding the ALWC in the Yangtze River Delta (YRD) region. Note that all these ALWC values are estimated from thermodynamic models. [PERSON] et al. [20] calculated an average ALWC of 5.33 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) in Nanjing during the summer of 2021. Considering the higher RH (69.05% vs. 63.42%) and similar PM concentration (18.88 vs. 19.50 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\)) in their study, a higher ALWC is reasonable and vice versa. Their work validates the measured values in this study. The ALWC levels in Shanghai, Jiaxing, and Huangshan areas were all higher than the values in this work, due to both higher PM\({}_{2.5}\) concentrations and higher RH values. Of course, other meteorological factors and more importantly aerosol compositions can affect the ALWC too [4].
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 4 Boundary Function of the Surrounding Rock's Loose Zone
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & \multirow{2}{*}{**Formula**} & \multicolumn{2}{c}{**Undetermined Coefficient**} & \multicolumn{2}{c}{**Correlation Coefficient**} \\ \cline{3-6}  & & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) & \(\mathbf{R}^{2}\) \\ \hline
7 & \(y=ax^{2}+bx+c\). & \(-0.1174\) & \(2.5181\) & \(-0.1086\) & \(0.9988\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results for the fitting curve. Figure 11: The fitting curve of the surrounding rock’s loose zone. (2) The prediction correlations for \(\mathbf{L}\) and \(\mathbf{H}_{1}\) can be deduced from the univariate regression equations as below:  
\[ \begin{array}{l}L=-46.63\times 0.9935^{H}+1.16\times B+80.74\times\lambda-47.38 \times\lambda^{2}+\\ 6.76\times\ln(K+0.2403)+1.99\times\left(\text{GSI}-24.45\right)^{-0.05}+4.91\\ \\ H_{1}=-37.56\times 0.9741^{H}+1.10\times B+64.25\times\lambda-95.58\times\lambda^{2}+ \\ 7.44\times\ln(K-0.1532)+23.73\times\left(\text{GSI}-24.47\right)^{-0.26}-27.07 \\ \end{array}  \tag{9}\]  
\[ \begin{array}{l}H_{1}=-37.56\times 0.9741^{H}+1.10\times B+64.25\times\lambda-95.5 8\times\lambda^{2}+\\ 7.44\times\ln(K-0.1532)+23.73\times\left(\text{GSI}-24.47\right)^{-0.26}-27.07 \\ \end{array}  \tag{10}\]  
The correctness of Correlation 7 and Correlation 8 are verified by the variance analysis, as shown in Table 9. The \(F\) distribution value is \(\mathbf{F}(6,4)=6.59\) in Correlation 7 and \(\mathbf{F}(6,4)=7.02\) in Correlation 8. Both are greater than \(\mathbf{F}_{0.05}(6,4)=6.16\) at the 0.05 significance level, indicating that the accuracy of the regression equation meets the requirements. It should be noted that the prediction correlations of \(\mathbf{L}\) and \(\mathbf{H}_{1}\) were established with 80 m \(\leq H\leq 110\) m, 20 m \(\leq B\leq 35\) m, 0.6 \(\leq\lambda\leq 1.2\), 0.5 \(\leq K\leq 2.0\), and 25 \(\leq\) GSI \(\leq\) 40. The parameter values must be defined within the domain.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 2 Optical Remote Sensing
### Spectral Indices as Drought Indicators
\begin{table}
\begin{tabular}{l l l l l} \hline
**Index** & **Expression** & **Notes** & **Year Introduced** & **Applications** \\ \hline Normalized Difference Vegetation Index (NDV) & \(\frac{\rho_{\text{NB}}-P_{\text{Bad}}}{P_{\text{NB}}+P_{\text{Bad}}}\) & & 1974 [53] & U.S. [97], Poland [98] \\ \hline Perpendicular Vegetation Index (PVI) & \(\frac{1}{\sqrt{\text{A}R^{2}+1}}\left(\rho_{\text{NB}}-M_{\text{Pred}}-I\right)\) & \(M\) and \(I\) are the slope and intersection of the soil line in the NIR-Red reflectance space & 1977 [73] & \\ \hline Soil Adjusted Vegetation Index (SAVI) & \((1+L)\frac{\rho_{\text{NB}}-P_{\text{Bad}}}{P_{\text{NB}}+P_{\text{Bad}}+T}\) & \(L\) is an empirical coefficient & 1988 [68] & Kenya [99] \\ \hline Moisture Stress Index (MSR) & \(\frac{\rho_{\text{NB}}}{P_{\text{NB}}}\) & & 1989 [69] & Morocco [100], India [101] \\ \hline Vegetation Condition Index (VCI) & \(\frac{100\text{NDV}-\text{NDV}_{\text{NB}}}{\text{NDV}_{\text{NB}}}\) & \(\frac{\text{NDV}_{\text{NB}}}{\text{NDV}_{\text{NB}}-\text{NDV}_{\text{NB}}}\) & \(\frac{\text{NDV}_{\text{NB}}}{\text{NDV}_{\text{NB}}-\text{NDV}_{\text{NB}}}\) & \\ \hline Amrospherically Resistant Vegetation Index (ARVI) & \(\frac{\rho_{\text{NB}}-(1+\gamma)_{\text{Pad}}-7\rho_{\text{NB}}}{P_{\text{NB}} +(1+\gamma)_{\text{Pad}}-7\rho_{\text{NB}}}\) & \(\gamma\) is an empirical coefficient & 1992 [69] & Poland [98] \\ \hline Anomaly Vegetation Index (AVV) & \(\frac{\rho_{\text{NB}}-P_{\text{Bad}}}{P_{\text{NB}}+P_{\text{Bad}}}\) & \(G\), \(G_{\text{L}}\), \(G_{\text{L}}\), \(G_{\text{L}}\) and \(I\) are empirical coefficients & 1995 [70] & East Asia [100] \\ \hline Enhanced Vegetation Index (EVI) & \(\frac{\rho_{\text{NB}}-P_{\text{NB}}}{P_{\text{NB}}+C_{\text{NB}}-C_{\text{ NB}}+L}\) & & 1995 [70] & India [100], Morocco [101] \\ \hline Normalized Difference Water Index (NDVI) & \(\frac{\rho_{\text{NB}}-P_{\text{NB}}}{P_{\text{NB}}}\) & & 1996 [57] & India [100], Morocco [101] \\ \hline Photochemical Reflectance Index (PRI) & \(\frac{\rho_{\text{NB}}-P_{\text{NB}}}{P_{\text{NB}}}\) & There are other wavelength selections & 1997 [58] & Bolivia [100], Spain [108], China [100],101 \\ Simple Ratio Water Index (SBWI) & \(\frac{\rho_{\text{NB}}-P_{\text{NB}}}{P_{\text{NB}}}\) & & 2001 [50] & Brazil [101] \\ \hline Standardized Vegetation Index (SVI) & \(z=\frac{\text{NDVI}-\overline{\text{NDVI}}}{\sigma}\) & \(\sigma\) is the standard deviation of multi-year NDVI & 2002 [63] & U.S. [68], South Korea [103] \\  & \(\overline{\text{SYI}}=\text{P}(Z<z)\) & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of spectral indices suitable for agricultural drought monitoring. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
## 5 Information Processing Module (IPM) Design
### Logistic Regression (LR)
\[CF_{linR}(k_{0},k_{1},\ldots,k_{N})=\frac{1}{m}\sum_{i=1}^{m}\left(\hat{t}_{ lin}[i]-t_{lin}[i]\right)^{2} \tag{9}\]  
In order to address the previous mentioned issue, the loss function, LF, presented below is typically used:  
\[LF(PLR(f[i]),t[i])= \begin{cases}\quad-log(PLR(f[i])),\ if\ t[i]=1\\ -log(1-PLR(f[i])),\ if\ t[i]=0\end{cases}  \tag{10}\]Assuming that sample i corresponds to a positive class (\(t[i]=1\)), if the estimated probability is close to 1 (\(PLR(f[i])\approx 1\)), then the value of the LF will be \(-log(1)=0\), which indicates not penalizing the chosen parameters. If, on the contrary, the estimated probability is close to zero (\(PLR(f[i])\approx 0\)), the LF will be \(-log(0)=+\infty\), which indicates significantly penalizing the choice of parameters and forcing an optimization of the parameters. A similar reasoning applies when the target belongs to the negative class; therefore, assuming that sample i corresponds to \(t[i]=0\), if \(PLR(f[i])\approx 0\), then the value of the LF will be \(-log(1-0)=0\), which indicates not penalizing the chosen parameters. If, on the contrary, \(PLR(f[i])\approx 1\), the LF will be \(-log(1-1)=+\infty\), which indicates significantly penalizing the choice of parameters and forcing an optimization of the parameters. Finally, it is possible to write the cost function for the m samples of the training dataset, \(CF_{LR}\), as follows:  
\[CF_{LR}(k_{0},\ldots,k_{N})=\frac{1}{m}\sum_{i=1}^{m}LF(PLR(f[i]),t[i]) \tag{11}\]  
In turn, (11) can be rewritten in a single simplified equation:  
\[CF_{LR}(k_{0},\ldots,k_{N})=-\frac{1}{m}\sum_{i=1}^{m}(\log(PLR(f[i]))\times t [i]+log(1-PLR(f[i]))\times(1-t[i])) \tag{12}\]  
The subsequent step involves finding the minimum of (12) by applying an optimization solver like the gradient descent algorithm to ensure optimal values for \((k_{0},\ldots,k_{7})\).