esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Seismic Response Spectra in Mexico
### Site-Specific Spectrum According to MDOC Sismo CFE 2015
The maximum values are defined according to the geometric mean response spectrum graph shown in Figure 14 (represented in red), where the structural periods of the peak acceleration are \(T_{a}=0.154\) s and \(T_{b}=0.368\) s. Thus, the maximum spectral acceleration coefficient values \(a_{0}\) and \(c\) correspond to \(a_{0}=0.226\) g \(=222\)cm/s\({}^{2}\) and \(c=0.854\) g \(=838\)cm/s\({}^{2}\). Since the soil properties data obtained previously in step 2 can have different uncertainties, all spectral accelerations and the space between \(T_{a}\) and \(T_{b}\) should be increased to consider these variations, with a minimum value of 15%. Therefore, the previous variables are modified as \(T_{a}=(0.85)(0.154\,\mathrm{s})=0.131\) s, \(T_{b}=(1.15)(0.368\,\mathrm{s})=0.423\) s, \(a_{0}=(1.15)(0.226\,\mathrm{g})=0.260\,\mathrm{g}=255\,\mathrm{cm/s^{2}}\), and \(c=(1.15)(0.854\,\mathrm{g})=0.982\,\mathrm{g}=963\) cm/s\({}^{2}\). If an accurate precision is required, the Monte Carlo simulation technique should be performed, using the values \(v_{s}\), \(\rho_{s}\), and \(\xi_{s}\) as random variables with a log-normal distribution process [9]. Step 5: Obtain the site-specific elastic design spectrum. With the values of \(a_{0}\), \(c\), \(T_{a}\), and \(T_{b}\) available from step 4 and the soil type III classification from step 2, the seismic elastic design spectrum can be obtained by applying Equation (13). Figure 14 shows the results of the site-specific elastic design spectrum with a blue line. Finally, all the spectral acceleration values, \(S_{a}\), must be multiplied by the structural importance factor, \(F_{IE}\), previously determined in Figure 6:5
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 2 Materials and Methods
### Measurement of Soil Temperature and Soil Water Content  
The soil temperature (TS) and soil water content (SWC) were measured simultaneously with carbon and water fluxes. TS was measured at depths of 5 cm, 10 cm, and 20 cm using a TZS-5X digital thermometer; SWC was recorded at the same depths using a TDR-300 Moisture Meter (Spectrum Technologies Inc., Aurora, IL, USA). Three random readings were recorded in each experimental plot for both TS and SWC, and the average values were calculated.
# Experimental Research on Braking Feedback and Taxing Feedback System of New Energy Vehicles
## 2 New Energy Vehicle Feedback-Related Technology Research  
### Research on the Braking Feedback of New Energy Vehicles  
According to current research, one of the most extensive and in-depth studies in the field of braking energy recovery is the energy management strategy. The goal is to improve the overall vehicle energy economy and to fully exploit the braking energy recovery potential while ensuring braking stability. For brake energy recovery, brake energy management companies want to use as much electric power as possible, but also need to consider the limitations of regulations, motor external characteristics, battery SoC and other factors in the strategy design.
## 3 Evaluating the Accuracy of Determining the Position Coordinates Using Dead Reckoning
\right]\]  
\[\sigma_{x}{}^{2}=\Delta t^{2}\cdot\left(SOG\left(k\right)\cdot\sigma_{COG} \cdot\text{cosCOG}\left(k\right)\right)^{2}+\Delta t^{2}\cdot\left(\sigma_{SOG} \cdot\text{sinCOG}\left(k\right)\right)^{2}\]  
\[\sigma_{y}{}^{2}=\Delta t^{2}\cdot\left(SOG\left(k\right)\cdot\sigma_{COG} \cdot\text{sinCOG}\left(k\right)\right)^{2}+\Delta t^{2}\cdot\left(\sigma_{SOG} \cdot\text{cosCOG}\left(k\right)\right)^{2}\]  
\[\sigma_{xy}=\sigma_{yx}=\Delta t^{2}\cdot\text{sin}\left(2\cdot\text{COG} \left(k\right)\right)\cdot\left(\sigma_{SOG}{}^{2}-\left(SOG\cdot\sigma_{COG} \right)^{2}\right)/2\]  
Using this matrix, in turn, the mean error of coordinates can easily be calculated  
\[M_{xy}=\sqrt{\sigma_{x}{}^{2}+\sigma_{y}{}^{2}} \tag{11}\]and the parameters of the mean error ellipse:  
\[a =\sqrt{\frac{1}{2}\cdot\left({\sigma_{x}}^{2}+{\sigma_{y}}^{2}+\sqrt{p} \right)} \tag{12}\] \[b =\sqrt{\frac{1}{2}\cdot\left({\sigma_{x}}^{2}+{\sigma_{y}}^{2}- \sqrt{p}\right)}\] (13) \[\tau =\frac{1}{2}\text{ arct }\text{tg}\frac{2{\sigma_{xy}}}{{ \sigma_{x}}^{2}-{\sigma_{y}}^{2}} \tag{14}\]  
where  
\[p=\left({\sigma_{x}}^{2}-{\sigma_{y}}^{2}\right)^{2}+4\cdot{\sigma_{xy}}^{2}\]  
\(\tau\)--direction angle of ellipse. Relying on the information provided by the manufacturer of log \"ALIZE\" that \(\sigma_{\text{SOG}}=0.5\) kn and determined arbitrarily \(\sigma_{\text{COG}}=3^{\circ}\) for INS \"VN-200\", using Equations (10) and (11), a mean error graph of position coordinates determined using dead reckoning was prepared (Figure 5). Figure 5 shows that the mean error of position coordinates increases linearly as a function of distance. Every hundred meters it increases constantly at 25.54 m, reaching a value of 127.7 m after traveling 500 m.  
Figure 6 shows the mean error ellipses of position coordinates determined using the dead reckoning method after traveling 200 and 500 m (their dependencies No. 12-13 have been calculated). Figure 6 shows that the largest coordinate error occurs in the direction according to COG. The length of the axis b is equal to the length of the mean error ellipse of coordinates determined using USBL system (Figure 4). This is due to the fact that in both methods of determining the position coordinates the positioning line is a result of the direction measurement that is burdened with the same error value \(\sigma_{\text{COG}}=\sigma_{\alpha}\). Figure 5: Mean error of coordinates as a function of distance covered (adopted \(\textbf{P}\) (\(k=0\)) = \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\), SOG = 1 m/s, COG = 90\({}^{\circ}\)).
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 2 Related Works
### Cloud Detection with CNN-Based  
Thanks to the excellent performance of convolution in computer vision [15], the deep learning method has been widely used in remote sensing. For example, [PERSON] et al. used deep learning methods to extract features from remote-sensing images and perform classification [16], and their experiments achieved good results. [PERSON] et al. designed a cloud detection method named RS-Net [17], which is a framework based on U-net. RS-Net shows SOTA performance, especially on smaller satellites with limited multi-spectral capabilities. Meanwhile, [PERSON] et al. proposed a MF-CNN [18] model based on multi-scale feature extraction. This method takes the combined spectral information as input of MF-CNN to enhance the model's ability to detect thin clouds. [PERSON] et al. introduced a weakly supervised deep-learning-based cloud detection method abbreviation WDCD [9]. WDCD uses the block-level labels to indicate whether there is cloud of the image block, thus reducing the workload of image annotation.  
[PERSON] et al. proposed an End-to-End Algorithm Cloud-Net [19] that consists of a fully convolutional network. This method not only achieves good precision performance, but also seldom requires complex data preprocessing. [PERSON] et al. designed a method based on Fully Convolutional Neural Networks named CM-CNN for FY-3D MERSI [20]. This model has good performance while only using mid-infrared and long-infrared band data. [PERSON] et al. proposed an Encoder-Decoder Deep Convolutional Neural Network [21]. This network is used to perform cloud and snow segmentation.  
However, most of these methods do not pay attention to the feature extraction of small clouds. This will lead to frequent omissions when a large number of broken clouds appear in remote-sensing images.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 4 Discussion
### Anthropogenic- versus Tectonic-Induced Subsidence
This same study [41] along the Chaman Fault also used InSAR velocity-distance profiles and found dominant subsidence caused by water withdrawal further north in the city of Qalat. Both the Qalat study and this study of Quetta Valley show the dominance of anthropogenic subsidence, which is masking the tectonic deformation.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Comparison among Proposed TinyML Algorithms  
The following Table 9 provides a comparative overview of the five proposed algorithms: TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ. The table outlines key features such as federated learning, anomaly detection, data compression, caching strategy, and data quality assessment. This comparison helps in understanding the unique capabilities and functionalities that each algorithm brings to IoT Big Data management.  
```
1:procedureTinyHybridSenseQ (sensorData)
2:Step1:InitializeDataQualityAssessmentModel
3:DeployTinyML models \(\{M_{1},M_{2},\dots,M_{n}\}\) for data quality assessment
4:Train each model \(M_{i}\) to identify anomalies and inconsistencies
5:Step2:HybridStorageStrategyInitialization
6:Define data storage strategies \(\mathcal{S}=\{S_{1},S_{2},\dots,S_{n}\}\) based on type and priority
7:Initialize storage resources \(R_{\text{local}}\) and \(R_{\text{cloud}}\)
8:for each \(dataPacket\in sensorData\)do
9:Step3:AssessDataQuality
10:Quality score \(Q\gets f_{\text{quality}}(dataPacket,M_{i})\)
11:CategorizedataPacket as high-quality or low-quality based on \(Q\)
12:Step4:DataCategorization andPrioritization
13:CategorizedataPacket into type \(T\)
14:PrioritizedataPacket for storage based on \(Q\) and \(T\)
15:Step5:EfficientDataTransfer andStorage
16:if\(Q\) is high and \(dataPacket\) is high-priority then
17:Transfer to central database \(DB\)
18:else
19:Store in \(R_{\text{local}}\) or \(R_{\text{cloud}}\) based on \(\mathcal{S}\)
20:endif
21:Step6:ContinuousModelAdaptation and Reporting
22:Update \(M_{i}\) with new \(dataPacket\) for continual learning
23:Generate and store reports on data quality and storage
24:endfor
25:endprocedure
```  
**Algorithm 5** TinyHybridSenseQ: Data-Aware Hybrid Storage and Quality Assessment for IoT Sensors
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 3 GPON and EPON Networks  
The basic information on the transmission rate, used wavelengths, the maximum number of connected users and the actual range of the network is found in Table 1 (see the GPON column). The transfer medium is described in ITU's Telecommunication standardization sector ITU-T recommendation G.652, which describes a single-mode optical fiber. The optical signal is transmitted bidirectionally using the wavelength-division multiplexing (WDM)-based transfer medium via a single fiber or unidirectionally via two fibers. The non-return-to-zero (NRZ) code is used. The transmitter uses multi-longitudinal mode (MLM) and single-longitudinal mode (SLM) lasers. The attenuation classes of the GPON are defined in [22]:  
* Class A: 5-20 dB
* Class B: 10-25 dB
* Class C: 15-30 dB  
These attenuation specifications do assume the worst case scenario regarding losses on connectors, passive optical devices, fiber splices and optical fiber attenuation.  
The GPON contains optical line termination (OLT), the ONU and the ODN. The OLT unit is the central unit and controls all communications on the network. ONU presents the end of the optical part of the ODN. In general, the ODN contains all transmissions between the OLT and ONU. The total minimum optical return loss (ORL) at the reference point R/S(before ONU) in the ODN must be below 32 dB. The maximum differential path losses (the difference between the largest and smallest loss in a single ODN) should be 15 dB [22; 23].  
The basic information on the transmission rate, used wavelengths, the maximum number of connected users and the range of the network can be found in Table 1 (see the EPON column where the point to multipoint (P2 MP) topology is used). In the standard, the physical layer is divided into four sublayers: reconciliation sublayer (RS), physical medium attachment (PMA), physical medium dependent (PMD), medium dependent interface (MDI) and gigabit media independent interface (GMII). Descriptions of these sublayers and a further description of the associated interfaces are in [24]:  
* MDI specifies the physical media and the mechanical and electrical interfaces between the transfer medium and the physical layer devices.
* PMD is responsible for linking to the transfer medium, and it is placed directly over the MDI. EPON uses WDM-based single-mode optical fibers. Two types of interfaces are supported, specifically: 1000 BASE-PX10 with an overlapping distance of 10 km and 1000 BASE-PX20 with an overlapping distance of 20 km.
* PMA provides functions for transmission, clock restart and phase alignment. This sublayer is primarily designed to specify the clock and data recovery (CDR) time interval.
* The physical coding sublayer (PCS) provides functions for link-coding of data (bits) that may be transmitted over the physical medium. EPON uses 8B/10B encoding [25].
* GMI specifies the interface between the media access control (MAC) layer and the physical layer.
* RS provides mapping of signals from GMII for the link layer.
## 1 Introduction  
Chicken meat has a high consumption rate, and ready-to-cook products constitute a significant portion of chicken products due to their widespread use and high acceptance among many consumers. Burgers are a popular meat product due to their convenience in cooking and ease of consumption. Additionally, there is a growing trend in the consumption of seasoned chicken breasts to meet evolving consumer demands.  
As is well-known, the grinding process, which disrupts the muscle structure, renders the food matrix less stable, making it more susceptible to chemical and enzymatic oxidation processes and promoting increased microbial growth [1; 2]. The same is true of the addition of salt, due to its prooxidant effect [3; 4], and the presence of oxygen during the storage contribute to lipid oxidation. Likewise, the composition and fat content of the meat will also influence the oxidative process of the lipids [5].  
In the meat industry, packaging is one of the most commonly employed methods for preservation. Traditional options are air-permeable packaging, vacuum packaging, and modified atmosphere packaging [6]. Moreover, the incorporation of antioxidants can extend the shelf life of meat products by slowing down lipid and protein oxidation. The industry is currently seeking natural antioxidants as an alternative to chemical antioxidants [7]. For instance, the high polyphenol content in plant or fruits has the potential to decrease oxidative reactions. The transformation of grape byproducts into wine pomace products, with a high concentration of bioactive compounds, represents environmentally sustainable and economically feasible methods [8] signifying a new alternative as a source for functional food ingredients with preservative, antimicrobial, flavoring, and potentially healthy properties [9; 10; 11].  
In this context, our research group has developed a seasoning from winemaking byproducts that is high in dietary fiber, minerals, and polyphenols, such as proanthocyanidins, flavonoids, phenolic acids, and stilbenes [12; 13]. [PERSON] et al. [14] studied the effect of red wine pomace as a seasoning in chicken products to improve microbial stability and product quality, which led to a significant change in the color of chicken breasts. For this reason, we hypothesized that white wine pomace, with its high total polyphenol content [15], could be a good alternative that would not change the color of the product so radically. To date, studying the valorization and application of white grape seasoning in meat is novel, as it has barely been investigated compared to the studies conducted on the effect of red grape seasoning in meat products. In this context, this research aim was to evaluate the effect of the addition of 3% of white pomace seasoning (WPS) during refrigerated storage on chicken products (breasts and burgers) packaged in vacuum or atmosphere conditions, focusing on quality parameters (aw, pH, color), lipid oxidation, and antioxidant capacity.
## 5 Results of the TMC Uncertainties Assessments
### TMC Assessment of TBR Uncertainties Using Lithium and Beryllium Random Files
#### 5.1.2 Random Files for Lithium  
The n+\({}^{6}\)Li nuclear data have a crucial effect on the tritium breeding performances of the DEMO blankets, because it acts not only as the breeder material, but also as a neutron moderator. Therefore, the separate investigation was only carried out to assess the effect of the n+\({}^{6}\)Li nuclear data uncertainty for the TBR. To this end, 300 random nuclear data files for n+\({}^{6}\)Li were prepared with BEKED code as described above to launch TMC calculations. The nuclear data for other nuclides in the MCNP input file were taken from the JEFF-3.2 data library. For the comparison of the effect associated with different covariance data involved in the assessment, two cases were explored: original covariance data (TENDL or JEFF data) and the modified one accounting larger \({}^{6}\)Li(n,t) cross section uncertainty as discussed previously.  
The Gaussian fit of the TBR results' distribution for the former case is shown in Figure 7a. The peak TBR was found to be 1.180 and the \(\sigma\) = 0.0006 (0.051%). Due to the very small uncertainties assigned to the \({}^{6}\)Li(n,t) reaction cross section the Gaussian distribution is very tight with the very small peak width. For comparison, the second option of the covariance data (modified in this work) was also studied to obtain the estimated uncertainty of the TBR. The results for this case are presented in Figure 7b. The peak value of the results is TBR = 1.182 and the \(\sigma\) = 0.020, i.e., 1.7%.  
Figure 6: Distribution of the TBR for the HCPB DEMO due to variation of the \({}^{9}\)Be nuclear data.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 4 Key Distribution
### Registration Workflow  
The registration process can take place without any human interaction. The device connects to the registration URL, and after communication has been established, it sends a POST request to send its identity, its public key and any additional information that the specific system implementation requires to know about the device. In return, the response from the server will contain the URL for the MQTT broker and the server's public key. The mechanism to store the server key and URL on the device will naturally be device-specific (e.g., file-system, flash memory, Electrically-Erasable Programmable Read Only Memory (EEPROM)); but on the server-side, it would be expected that this information would be stored on a suitably protected private database.  
Registration requests can be received from any device without prior arrangement. Simply having registered a device on the system does not establish any specific relationship between that device and any server; nor does it assign any trust to the device. As such, an attacker registering one or more devices with the C2 server has very little impact on the operation of the system. The only reason to consider filtering device registration requests might be in the event that an attacker attempts to overwhelm the registration web-server by sending a large number of mendacious device requests in order to achieve a Denial of Service (DoS) attack.  
Mitigation of this attack would be via modern good-practice implementation of web services: to provide load-balancing to the server backend (both at the web-service layer and also the underlying database mechanism used to store the identities). For a well-designed system, for example one exploiting a micro-services-based architecture, even an effective DoS attack against the registration web-service should not have any effect on the ability of the C2 server to continue to operate.
## 5 Experimental Setup
### ML Models for Classification  
To evaluate the proposed features for sparse trajectories (Section 4.1) and the features obtained via the method described in Section 4.2, we employed multiple ML models, including SVC with an RBF kernel, RF, and DNN. The models were implemented and evaluated using scikit-learn version 1.5.2 [49].  
Although the components of the feature vector \(\mathbf{f}^{\text{GMM}}_{k}\), \(\forall k\in K\) mentioned in Section 4 are given by \(f_{i}\in\{x\in\mathbb{R}_{\geq 0}\mid x\leq\mathcal{T}\},\forall i\in \mathbb{N}_{\leq\mathcal{T}}\), for calculation convenience, we use the feature vector to normalize the components into values between 0 and 1, as follows:  
\[\mathbf{f}^{\text{GMM}}_{k}=\frac{1}{\mathcal{T}}\mathbf{f}^{\text{GMM}}_{k}.\]  
Note that \(\sum_{k\in K}\mathbf{f}^{\text{GMM}}_{k}=1\). Similarly, each element of \(\mathbf{f}^{\text{IDNN}}\) is normalized to the range \(\mathbf{f}^{\text{/IDNN}}\in[0,1]^{n}\) via min-max scaling. Because each ML model has its own hyperparameters, a GS is applied to tune them by providing a specified search range for each model.  
The SVC with an RBF kernel has two hyperparameters: the regularization parameter \(C\) and the kernel coefficient \(\gamma\). These parameters are tuned over the following ranges:  
\[C\in\{10^{1},10^{2},10^{3}\},\quad\gamma\in\{10^{-3},10^{-2},10^{-1}\}.\]  
The RF has several hyperparameters, but tuning all of them is impractical. Therefore, we focus on two key hyperparameters: the maximum depth \(d_{\text{max}}\) and the number of trees \(n_{\text{trees}}\). These parameters are tuned over the following ranges:  
\[d_{\text{max}}\in\{4,6,8\},\quad n_{\text{trees}}\in\{100,300,500\}.\]  
The other hyperparameters of the RF are set to the default values provided by scikit-learn (version 1.5.2).  
The DNN also has numerous hyperparameters. To simplify tuning, we select two of them: the hidden layer size \(h\) and the regularization parameter \(\alpha\). These parameters are tuned over the following ranges:  
\[h\in\{(64),(128),(64,64)\},\quad\alpha\in\{10^{-4},10^{-3},10^{-2}\}.\]  
The DNN uses the Adam optimizer for parameter updates and ReLU as the activation function. All other hyperparameters are set to the default values provided by scikit-learn (version 1.5.2).  
Moreover, we conducted fivefold CV for each parameter pair to prevent overfitting. This method is called GS+CV and is visualized in Figure 7.