esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Indicators  
#### 2.3.1 Indicator Construction  
Based on the current technical specifications for pipeline water delivery engineering \"GB/T 20203-2017\", water-saving irrigation engineering standards \"GB/T 50536-2018\", irrigation and drainage engineering design standards \"GB50288-2018\", and micro-irrigation engineering standards \"GB/T 50485-2020\", the four standards identify Water Supply Uniformity C\({}_{\text{u}}\) as a key indicator, with C\({}_{\text{u}}\) serving as the primary metric. Both the first and fourth standards specify the minimum pressure for emitters, and in conjunction with urban pressure reliability, this has been utilized to define the pressure reliability for the irrigation pipeline network. Additionally, in pipeline system design, the concept of economic flow velocity is introduced, with flow velocity deviation employed as an indicator. These core indicators were then used to develop a hydraulic reliability evaluation index system for irrigation pipeline networks.
* Uniformity of water supply. This indicator assesses the uniformity of outflow at each node within a specific research area. A higher value indicates greater uniformity in water supply across the pipeline network. The calculation is performed using the following formula:  
\[C_{u}=1-\frac{{\sum_{i=1}^{n}}{|q_{i}-\overline{q}|}}{n\overline{q}}, \tag{3}\]  
where \(C_{u}\) represents the water supply uniformity, \(\overline{q}\) denotes the average flow rate of the branch valve, and segmented calculations are applied when the lengths (or control areas)of branch pipes differ. In this case, the calculation is performed for sections of equal length (or control area); \(Q_{i}\) represents the water flow rate of the \(i\)-th branch valve.
* Pressure head reliability.  
The ratio of the actual pressure head at each node in the pipeline network to the minimum required pressure head is calculated using the following formula:  
\[H_{k}=\frac{H_{i}}{H_{\text{min}}}, \tag{4}\]  
where \(H_{k}\) represents the pressure head reliability and \(H_{min}\)[31] denotes the minimum node head that the pipeline network must maintain. Referring to the minimum pressure head requirements for urban water supply pipelines, \(H_{\text{min}}\) is calculated as the sum of the design working pressure head of the emitters, the maximum head difference in the community (\(\Delta h\)), and the safety factor. Specifically, the design working pressure head of the emitters is 10 m, \(\Delta h\) is 7.5 m, and the safety factor is set to 5 m, in accordance with the Irrigation and Drainage Engineering Design Standards \"GB50288-2018\".  
* Flow velocity deviation.  
This indicator evaluates whether the flow velocity in each section of the pipeline network falls within the appropriate range. The calculation is performed using the following formula:  
\[|v|=\frac{|v-v_{i}|}{v_{i}}, \tag{5}\]  
where \(|v|\) represents the flow velocity deviation, \(v\) denotes the flow velocity of the \(i\)-th branch pipe, and \(v_{i}\) is the economic flow velocity of the \(i\)-th branch pipe.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 4 Mathematical Justification
### Federated Aggregation Robustness Under Drift-Aware Weighting  
In Phase 6, we propose a drift-aware FL mechanism. The core idea is to assign dynamic weights  
\[w_{k}^{r}=\frac{|D_{k}^{r}|}{\sum_{j=1}^{k}\left|D_{j}^{r}\right|}\cdot f(\delta _{k}^{r}), \tag{24}\]  
where \(\delta_{k}^{r}\) is the drift score (combining short- and long-term indicators) for machine \(k\) at round \(r\), and \(f(\cdot)\) is a decreasing function, for example \(f(x)=exp(-\lambda x)\). Then we update the global parameter:  
\[\theta_{global}^{r+1}=\sum_{k=1}^{K}w_{k}^{r}\theta_{k}^{r}. \tag{25}\]  
This ensures that heavily drifting machines receive smaller weights (or are dropped altogether in extreme cases) to protect the global model from corruption [30; 31].  
For simplicity, assume each local loss \(L_{k}(\theta)\) is \(\mu\)-strongly convex and \(L\)-smooth (Lipschitz). Then, we can derive the following theorem on convergence.  
**Theorem 3**: (Convergence under Drift-Aware FL)_. If \(\delta_{k}^{r}\in[0,\Delta_{\max}]\) is bounded and \(f(\delta_{k}^{r})\geq c>0\) for all \(k,r\) (e.g., \(c=exp(-\lambda\Delta_{\max})\)), then with a suitably small learning rate \(\eta\), the iterative updates,_  
\[\theta_{global}^{r+1}=\sum_{k=1}^{K}w_{k}^{r}\theta_{k}^{r},\ \theta_{k}^{r}= \theta_{global}^{r+1}-\eta\
abla L_{k}\left(\theta_{global}^{r}\right), \tag{26}\]  
_converge to a neighborhood of the global optimum \(\theta^{*}\), where the neighborhood size depends sublinearly on the level of drift \(\delta_{k}^{r}\)._  
Proof.:
1. Let \(\overline{w}=\underset{k,r}{\min}w_{k}^{r}>0\). Then, for any round \(r\), \[\left\|\theta_{global}^{r+1}-\theta^{*}\right\|\leq\sum_{k=1}^{K}w_{k}^{r}\| \theta_{k}^{r}-\theta^{*}\|.\] (27)
2. Using \(\mu\)-strong convexity and Lipschitz assumptions, we derive a Polyak-Lojasiewicz (PL)-type inequality [32], ensuring that the loss decreases monotonically with the number of rounds.
3. If \(\delta_{k}^{r}\) becomes too large for a machine \(k\), the drop strategy can be employed to exclude that machine from aggregation in the current round. Because only a small fraction of machines is typically dropped, the overall convergence is not compromised.
4. Combining these results, under a sufficiently small \(\eta\) and a strict positive lower bound \(c\), \(\{\theta_{global}^{(r)}\}\) converges to a neighborhood of \(\theta^{*}\).
# Metadata Quality for Biobanks
## 3 State of the Art
The proposed set of metrics characterize  
1. the availability of publicly available machine-readable metadata and the metadata longevity,
2. the support for identifier management and the possibility for the public registration of the identifier schemes for the metadata,
3. the existence of authorization procedures and secure access protocols,
4. the degree of support for the knowledge representation languages,
5. the availability of the licensing process,
6. the provenance specifications,
7. the evidence of ability to find the digital resource in search results,
8. the linking to other resources and the degree of support of the same FAIR principles for the linked resources,
9. the degree of meeting community standards. [PERSON] et al. [34] aim at making it possible to create metadata complying with FAIR principles for a specific domain. To support this, they evaluate the existing metadata against the following criteria: richness (similar to the completeness, supporting usefulness), consensus (coherence from the point of view of the source, level of agreement in the community), accessibility and transparency (ease of use for different stakeholders, supporting usefulness and findability via providing different levels of granularity, supporting open standards, etc. ), providing linked metadata (supporting compatibility and interoperability), functional implementation (including the security of use via authorization and authentication). The problem with all the above papers, and, in particular [20, 21, 22], is that they target metadata repositories in general, ignoring the specifics of the biobank domain. Up to now, the problem of establishing the framework for collecting and assessing biobank metadata quality was not the target of the state-of-the-art research. This is unfortunate, as the general treatment of quality in metadata repositories is not always applicable to biobank metadata quality, for example, it does not reflect the specifics of biobanks as data brokers, the specific goals of biobank metadata, and the specific structure of its quality. Furthermore, no paper addresses the exact set of metadata quality characteristics and metrics applicable to the biobank domain; some described characteristics may not be applicable to this domain, whereas some biobank-specific characteristics can be missing. Our paper addresses this void by proposing a comprehensive framework specifically targeting biobank metadata quality, taking into account the specific goals of biobank metadata, its structure, and the structure of its quality. Furthermore, we describe an exact set of quality characteristics and metrics specifically applicable to the biobank domain.
## 2 Laboratory Setup
### Computer System with Power Electronics  
A computer system with power electronics is both the brain and the heart of our application. A modern digital signal controller (DSC), together with a current sensor, are used as a computer system, while a motor driver and power supply unit (PSU) serve as power electronics. The former collects and processes necessary information, and enables execution of control algorithms. Actually, it is a hardware into which the (1+1)-DES is embedded. The latter delivers information received as a commands from the computer system to the DC-motor.  
The following computer configuration is employed in our study:  
* digital signal controller Texas Instruments TMS320F28377S Delfino,
* current sensor Allegro ACS712,
* motor driver STM L298N,
* power supply unit InterTech CombatPower CP-650W Plus.  
The DSC architecture consists of two computer components: the central processing unit (CPU) and control law accelerator (CLA). These can both run on clock frequency 200 MHz in hardware parallelism and execute 800 million instructions per second (MIPS), which provides the means for the online execution of OAC-DES.  
The control program is thus divided into two parts: the two linear controllers which run on the CLA accelerator (coprocessor), and the main management program, together with OAC-DES and ANN, which run on the CPU. Table 1 specifies the most important properties of TMS320F28377S [32].  
Figure 4: Non-linear characteristic \(\omega_{a}=f(i_{r})\) of the robotic mechanism.  
The current sensor operates on the Hall Effect. Its input is connected to the DC-motor, while the output goes to the DSC. It is then incorporated into the CPU using an A/D conversion. If the current sensor weakens the power of the DC-motor to receive usable information, then the motor driver does the opposite, i.e., strengthens the information to power the DC-motor. The PSU guarantees that the computer system and motor driver are well supplied.
# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
###### Abstract  
In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system's ability to defend against malicious user attacks was validated. The experimental results demonstrate the method's effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission.  
crowd sensing; federated learning; contribution level; gradient similarity +
Footnote †: journal: Medical Imaging  
1
These papers are related to various satellite data products, such as incident solar radiation [38; 39; 40], chlorophyll fluorescence [41], surface directional reflectance [42; 43; 44], aerosol optical depth [45], albedo [46; 47], land surface temperature [48; 49; 50], upward longwave radiation [51], leaf area index [52; 53; 54; 55], fractional vegetation cover [56], forest biomass [57], precipitation [58], evapotranspiration [59; 60; 61], freeze/thaw [62], snow cover [63], vegetation productivity [64; 65; 66; 67; 68], phenology [69; 70], biodiversity indicators [71], drought monitoring [72], forest disturbance [55], air-quality monitoring [73], sensor design [74], and sampling strategy [75] for validation with in situ measurements. Most of these papers are based on optical-thermal remotely-sensed observations, but a few papers are also based on microwave [62; 63] and Lidar [54; 76] data. Although these 40 papers do not represent a large sample, they demonstrate that few studies have been undertaken on physical modeling for understanding remotely-sensed signals and use of Chinese satellite data in their analysis. This latter shortcoming calls for the further improvement of Chinese satellite data quality. This work was supported in part by the National Key Research and Development Program of China (No. 2016 YFA0600101) and the National Natural Science Foundation of China (No. 41331173). We would like to thank the members of the Scientific Steering Committee and the Organizing Committee of the Third National Forum on Quantitative Remote Sensing at Beijing Normal University for their great contributions. The authors declare no conflicts of interest
## 5 Conclusions and Future Works
**Nomenclature**  
_Sets and Indices_  
\(j\in S_{l_{DC}}\)  
Root nodes of the distribution network  
\(i\)/\(i^{\prime}\in S_{IB}/S_{TB}\)  
Nodes of distribution network  
\(l\in S_{L}\)  
Branches of distribution network  
\(g\in S_{DG}\)  
Distributed generation units  
\(e\in S_{EVCS}\)  
Fleet charging stations  
\(f\in S_{FV}\)  
Fleet vehicles  
\(m\in S_{M}\)  
Microgrids in the distribution network  
\(s\in S_{S}\)  
Scanarios in the second stage  
\(K_{ii}^{+}\)  
Set of arcs in TSN starting from m.  
\(K_{m}^{-}\)  
Set of arcs in TSN ending at m.  
_Parameters_  
\(C_{L}^{R}\)  
Cost of hardening line \(l\) (S)  
\(C_{e}^{E}\)  
Cost of FEVCS installation at node \(e\) (S)  
\(N_{H}\)  
Average number of storms in a year  
\(\pi_{s}\)  
Probability distribution of each scenario  
\(B\)  
Budget for infrastructure planning (S)  
\(VoLL_{i}\)  
Value of lost load at node \(i\) (S)  
\(LH_{l,s}\)  
Status of line health  
\(BH_{i,s}\)  
Status of bus health  
\(M\)  
A large number  
\(r_{ij}\)  
Resistance of branch \(ij\) (ohms)  
\(x_{ij}\)  
Reactance of branch \(ij\) (ohms)  
\(p_{S}^{DC,max}/Q_{S}^{DC,max}\)  
Active Power Output Limit of DG \(g\) (MW). \(Q_{S}^{DC,max}\)  
Reactive Power Output Limit of DG \(g\) (Mvar). \(S_{S}^{Q}\)  
Apparent Power Capacity of Limit of DG \(g\) (MVA). \(G_{f,e}^{0}\)  
Initial position of fleet vehicle \(f\) is at node \(e\). \(D_{j,f}^{P}/D_{j,t}^{Q}\)  
Active/Reactive power load at node \(j\) of the ADN at time \(t\) (MW/MVAr)  
\(V_{i,min}\)  
Lower limit of voltage at node \(i\) (V)  
\(V_{i,max}\)  
Upper limit of voltage at node \(i\) (V)  
\(p_{e}^{EVCS,max}\)  
Maximum charging power of EVCS \(e\) (kW)  
\(S_{f}^{EVCS,max}\)  
Maximum charging capacity of EVCS \(e\) (kVA)  
\(\eta^{dch}\)  
Efficiency of EVCS \(e\) (%)  
\(SOC_{Initial}^{P}\)  
Initial SOC of the fleet vehicle (MWh)  
\(SOC_{f}^{min}\)  
Maximum SOC of the fleet vehicle \(f\) (MWh)  
\(SOC_{f}^{min}\)  
Minimum SOC of the fleet vehicle \(f\) (Mvar)  
_Variables_  
\(R_{l}\)  
Line \(l\) was hardened during planning  
\(E_{e}\)  
EVCS is installed at node \(e\). \(s_{j,t}^{d}\)  
Active power load shedding at node \(j\) of the ADN at time \(t\) (MW/MVAr)  
\(Ar_{i,s}\)  
Status of node \(i\) in scenario \(s\)  
\(A_{i,m,s}\)  
Bus i belongs to microgrid \(m\) in scenario \(s\)
# Research and Development of Automatic Monitoring System for Livestock Farms
###### Abstract  
In this study we have developed an automatic monitoring system based on wireless communication networks in both dairy and pig farms to replace traditional manual data collection of the environmental conditions and manual controls of fans and water control valves in livestock farms to solve the man-power shortage problem for livestock farming. Firstly, sensors for detecting temperature, humidity, illumination, wind speed and the control circuit and communication system were installed. The monitoring programs were subsequently designed to transmit the data back to the user interface display of the office through RFU-400 wireless communication modules, and the data collected from the farm environment have been stored in a database for data analysis. Finally, the fans and water spray valves have been automatically activated duly to improve the temperature and humidity of the livestock farms. We analyzed the data collected from the sensors with regard to the lactation yields for dairy cows, and suggested optimized environmental parameters for dairy cows to increase their appetite and lactation yield, or increase the feed conversion rate of the pigs. We expect the process and results of this study can result in helpful reference to livestock farming, and help to achieve the best economic benefits in raising cattle, pigs and so forth.  
envinmental monitoring; wireless communication; dairy cows; heat stress; apparent temperature; human machine interface +
Footnote †: journal: Applied Sciences
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 2 Methodology  
### Observation Model for BDS-5G PPP  
#### 2.1.1 Observation Model for BDS PPP  
The BDS PPP observation model is shown in Equations (1) to (2):  
\[P_{j}^{(s)}=\rho^{(s)}-c\Big{(}\delta_{tr}\ -\delta_{ls}^{(s)}\Big{)}+\delta_{ ion}(j)+\delta_{trop}+\delta_{tide}+\delta_{rel}+\varepsilon_{p}, \tag{1}\]  
\[\lambda_{j}\Phi_{j}^{(s)}=\rho^{(s)}-c\Big{(}\delta_{tr}\ -\delta_{ts}^{(s)} \Big{)}+\lambda_{j}N_{j}^{(s)}-\delta_{ion}(j)+\delta_{trop}+\delta_{tide}+ \delta_{rel}+\varepsilon_{\varphi}, \tag{2}\]  
where \(j\) is the subscript of frequency \(f\); superscript \(s\) is the satellite number; \(P\) is the pseudorange observation; \(\Phi\) is the carrier phase observation; \(\rho\) is the geometric distance from the receiver to the satellite; \(\delta_{tr}\) represents the receiver's clock difference; \(\delta_{ts}\) is the satellite clock difference; \(c\) is the speed of light; \(\lambda\) is the wavelength; \(N\) is the ambiguity; \(\delta_{ion}\), \(\delta_{trop}\), \(\delta_{tide}\) and \(\delta_{rel}\) represent ionospheric delay, tropospheric delay, tidal and relativistic effects, respectively; \(\varepsilon_{p}\) and \(\varepsilon_{\varphi}\) represent other measurement noise.
## 2 Background and Related Work
### Related Work
#### 2.3.3 SAST-DAST Comparisons Studies  
The study of [PERSON] and [PERSON] [10], compares SAST vs DAST tools against web services benchmarks and show that the SAST tools usually obtain better true positive ratios and worse false positive ratios than DAST tools. Additionally, these works confirm that SAST and DAST can find different types of vulnerabilities.
## 6 MATLAB Simulation  
This section demonstrates the effectiveness of our multi-agent guidance law through MATLAB simulations. The sample interval is \(T=0.5\) s. The safety distance \(\beta\) is set as \(100\) m.  
In Equation (12), the measurement noise \(\mathbf{v}_{k}\) is generated with \(\mathbf{v}_{k}\sim\mathcal{N}(\mathbf{0},\mathbf{R}_{k})\), where \(\mathbf{R}_{k}\) is the identity matrix such that every diagonal element is 1. This implies that the standard deviation of measurement noise is 1 meter.  
Considering the process noise, the motion model of the \(i\)-th drone (\(i\in\{1,2,\ldots,M\}\)) is  
\[\mathbf{r}_{i,k+1}=\mathbf{r}_{i,k}+T*v_{i,k}*\mathbf{u}_{i,k}+\mathbf{n}_{i}. \tag{47}\]  
Here, \(\mathbf{n}_{i}\) indicates the process noise of the \(i\)-th drone, such that each term in \(\mathbf{n}_{i}\) has a Gaussian distribution with a mean of 0 and a standard deviation of 1 meter. Because Equation (47) contains the process noise term \(\mathbf{n}_{i}\), Equation (47) is distinct from Equation (5).  
At sample-index 0, the target is at (0, 10,000, 5000) in meters, and the virtual agent is at the origin. The ship is located at (0,0,0) at sample-index 0. Furthermore, the maximum speed of a drone is \(v_{max}=20\) meters per second. At every sample-index \(k\), the target's speed, \(v_{k}^{t}\), is 200 meters per second. See that the target moves much faster than a drone.  
We say that a target is _captured_ when the distance between a drone and the target is less than \(\Delta=10\) meters. Moreover, a target is captured at sample-index \(k\) when \(l(\mathbf{r}_{k}^{t},\mathbf{r}_{k-1}^{t})\) crosses the grid formation between sample-index \(k-1\) and \(k\). Because \(s^{u}=\Delta\), the target is considered to be captured between sample-index \(k-1\) and \(k\).  
Furthermore, a simulation ends when the distance between the ship and the target is less than \(\Delta\). This implies that the ship is hit by the target.