esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Datasets
### Flux Tower Measurement  
We use the measurements from the FLUXNET2015 Dataset [26] for the verification of the estimations. The six flux tower sites used in this study include four vegetation types: evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), mixed forest (MF), and grassland (GRA). Based on previous versions of the FLUXNET Dataset releases (FLUXNET Marconi Dataset 2007 and FLUXNET LaThuile Dataset 2007), the FLUXNET2015 Dataset includes several improvements in data quality control protocols and the data processing pipeline, including close interaction with tower teams to improve data quality, new methods for uncertainty quantification, and use of reanalysis data to fill long gaps of micrometeorological variable records.  
Eddy covariance systems give the direct measurement of NEE, and the measured GPP is calculated by subtracting the respiration using a community standard method [27; 28]. However, uncertainties remain in the measured GPP values such as underestimation of the total ecosystem respiration (\(R_{eco}\)) at night, uncertainties of the gap-filled algorithm, and partitioning and random errors of flux measurements [29; 30; 31], which may increase the mismatch between measured GPP and simulated GPP. To verify the model results, the six eddy covariance towers including 17 site-years were selected to represent four vegetation types (Figure 1; Table 1). Detailed descriptions of sites information are available at [26].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **Site Code** & **Site Name** & **Latitude (\({}^{\circ}\)N)** & **Longitude (\({}^{\circ}\)E)** & **IGBP \({}^{\circ}\)** & **Years Used** & **Reference** \\ \hline
1 & CN-Din & Dinghushan & 23.1733 & 112.5361 & EBF & 2003–2005 & [PERSON] et al. (2011) [32] \\ \hline
2 & CN-Qia & Qianyanzhou & 26.7414 & 115.0581 & ENF & 2003–2005 & [PERSON] et al. (2013) [33] \\ \hline
3 & CN-Cha & Changbaishan & 42.4025 & 128.0958 & MF & 2003–2005 & [PERSON] et al. (2009) [34] \\ \hline
4 & CN-Dan & Dangxiong & 30.4978 & 91.0664 & GRA & 2004–2005 & [PERSON] et al. (2006) [35] \\ \hline
5 & CN-HaM & Haibei Alpine & 37.3700 & 101.1800 & GRA & 2003–2004 & [PERSON] et al. (2006) [36] \\ \hline
6 & CN-Cng & Changling & 44.5934 & 123.5092 & GRA & 2007–2010 & [PERSON] et al. (2011) [37] \\ \hline \end{tabular}  
* IGBP: vegetation type at site. EBF: evergreen broadleaf forest; ENF: evergreen needleleaf forest; MF: mixed forest;
* GRA: grassland.  
\end{table}
Table 1: Description of flux tower sites used for gross primary productivity (GPP) validation in this study.  
Figure 1: Locations of six flux tower sites used in this study. EBF: evergreen broadleaf forest; ENF: evergreen needleleaf forest; MF: mixed forest; GRA: grassland. The seven geographic regions are: Northwest China (NW), North China (NC), Northeast China (NE), Southwest China (SW), Central China (CC), East China (EC), and South China (SC).
## 2 Materials and Methods
### Covariate Selection Techniques  
The Boruta method was applied to sort out all-relevant covariates for soil PSFs prediction. It is a wrapper method, developed on the basis of random forest (RF) algorithm. Therefore, similarly to RF, it can handle nonlinear as well as linear relationships [21; 29]. To identify the irrelevant variables, the shadow attributes are created, and their importance is used to identify irrelevant variables [29; 49]. Minimal-optimal features were selected to develop parsimonious models for PSF prediction. Instead of exhaustively searching all possible subsets of all relevant sets, which are computationally intensive and impractical, strategic subset selection techniques suggested by [PERSON] et al. [21] were employed. In this study, greedy forward (GF), greedy backward (GB), hill climbing (HC), and simulated annealing (SA) were implemented to compose the minimal-optimal sets [50]. The greedy algorithms, including GF, GB, and HC, search for local optima. The GF algorithm starts evaluating subsets with only one predictor variable. Then, by adding predictor variables one by one, GF finds its best subset. On the contrary, the GB algorithm begins with the whole set and drops predicting variables in turn until it finds its best subset. HC algorithm starts with a subset composed of arbitrarily selected variables. It finds a better subset by incrementally including or eliminating one variable only when a subset has higher predictive power. SA is a heuristic algorithm searching for global optima. It resembles the annealing procedure in metallurgy and starts from a certain higher \"temperature\" (i.e., initial temperature) to find the optima [51; 52]. As the temperature decreases at a certain rate, the optima in the algorithm gradually stabilizes, but this may be a local optimum rather than a global one. To find the global optima, SA introduces a random update to a subset, making it possible to accept a worse prediction to escape local optima and reach the global optima [50]. These four covariate selection algorithms were implemented with Boruta [29], rpart [53], caret [54], and Fselector [55] packages in the R statistical Programming Language.
## 5 Example
The results demonstrate that the algorithm proposed in this study provides accurate predictions of the rocking response excited by random vibration. To evaluate the accuracy and computational efficiency of the proposed method, we conducted 1000 numerical simulations of package rocking dynamics under random loading, using a time step of \(\sigma_{1}\) = 0.01 s. For comparison, the second-, third-, and fourth-order Runge-Kutta methods were also employed, using the time step \(\sigma\) = 0.001 s. Table 2 summarizes the computational runtimes and the mean squared errors (MSEs) relative to the benchmark results obtained via the fourth-order Runge-Kutta method with a refined time step (\(\sigma\) = 0.0001 s). As evidenced in Figure 6b and Table 2, the prediction error of the rocking response decreases with increasing order of the Runge-Kutta method, while the computational runtime exhibits a corresponding increase. Notably, the proposed algorithm achieves comparable accuracy in predicting package rocking responses while significantly reducing the computational time compared to higher-order Runge-Kutta methods. Figure 5: Analysis results of rocking block excited by sinusoidal acceleration: (**a**) acceleration recorded on test table; (**b**) rocking response of block excited by sinusoidal excitation. Figure 6: Block rocking response excited by random excitation: (**a**) random excitation acceleration; (**b**) block rocking response analyzed by proposed algorithm and Runge-Kutta method. Compared with the Runge-Kutta method and the equivalent model method, the freestanding package rocking response analysis method proposed in this study offers several advantages. It enables the determination of the exact time instant at which the package impacts the vehicle and identifies the smooth rocking response intervals. As a result, the analysis efficiency is significantly improved while maintaining good accuracy.
# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 2 Datasets and Methods
### Evaluation Metrics  
We utilize four key indicators including accuracy, precision, recall, and F1-score to comprehensively evaluate the performance of the model in classification prediction tasks. Accuracy is the proportion of correct predictions in the total samples, representing the overall rate of correct predictions of the model, precision is the probability of actual positive samples among predicted positive samples, and recall is the ratio of the number of correctly predicted samples to the number of samples that should be predicted; it focuses on the  
Figure 2: The workflow of LoRA fine-tuning.  
ability of the model to recognize positive samples. F1-score combines the considerations of precision and recall, providing a more comprehensive and balanced evaluation view. The F1-score can reflect the overall performance of the model on the classification prediction task more effectively. The corresponding calculation method of each metric is shown in Equations (5)-(8):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1=\frac{2*Precision*Recall}{Precision+Recall} \tag{8}\]  
where TP represents positive samples predicted to be positive, TN represents negative samples predicted to be negative, FP represents negative samples predicted to be positive, and FN represents positive samples predicted to be negative.
## 3 Characteristics of Roof Fracture Positions in Gob-Side Entry Retaining
### The Influence of Coal Seam Thickness on Main Roof Fracture  
The special characteristics of three-soft coal seams (low coal strength and weak roof and floor) exacerbate the influence of coal seam thickness on the fracture location of the main roof. The influence of coal seam thickness on the main roof fracture location shows the following patterns:  
(1) Thin coal seams (3-4 m)  
The fracture location of the main roof is at the edge of the goaf or slightly toward the solid coal side, forming a \"goaf-side fracture\" pattern (Figure 2a). When the coal seam is thin, the suspended length of the main roof is short. The load is evenly transferred to the goaf gangue through the immediate roof. Fracture is likely to occur at the edge of the goaf, forming a stable articulated structure, which is beneficial for gob-side entry retaining.  
(2) Medium-thick coal seams (5-7 m)  
Compared with thin coal seams, the fracture location significantly migrates toward the solid coal side, changing to a \"solid coal rib-side fracture\" pattern (Figure 2b). The increase in coal seam thickness leads to an increase in the suspended load of the main roof, causing stress concentration on the solid coal side. When the coal seam strength is insufficient to provide support, the fracture location shifts deeper. At this time, after the main roof fractures, it is prone to form a \"cantilever beam\" structure. There is a large deformation of the surrounding rock in the initial stage, and it relies on gangue filling for stabilization in the later stage.  
(3) Critical thickness  
When the coal seam thickness is \(\leq\)4 m, the fracture location is on the goaf side or at its edge, meeting the \"favorable fracture conditions\" for gob-side entry retaining. When the thickness is >4 m, the fracture location moves into the solid coal side, the stability of the surrounding rock decreases significantly, and the difficulty of gob-side entry retaining increases.  
Figure 2: Schematic Diagram of the Fracture Position of the Main Roof. (**a**) Fracture on the goaf side, (**b**) Fracture above the coal wall, (**c**) Fracture above the gob-side roadway and (**d**) Fracture close to the goaf side.
###### Abstract  
Currently, the energy dissipation efficiency of intermediate column dampers is extremely low, and traditional lever amplification damping systems occupy a large space in buildings. Aiming at solving these problems, this paper puts forward a new intermediate column-lever negative stiffness viscous damper (CLNVD), which has the characteristics of small impact on building space and significant amplification of the damper displacement. The CLNVD consists of the following four parts: the viscous damper, the negative stiffness device, the lever, and the intermediate column. This paper introduces the displacement amplification coefficient (\(f_{d}\)) to assess the CLNVD's displacement amplification effect and introduces the energy dissipation coefficient (\(f_{E}\)) to assess the CLNVD's energy dissipation effect. The expressions for \(f_{d}\) and \(f_{E}\) are derived according to the geometric magnification coefficient and effective displacement factor. Moreover, the impacts of multiple factors including the CLNVD's position, the lever's amplification coefficient, the bending line stiffness of beam, the negative stiffness, the damping coefficient, the damping index, and the inter-story displacement on the CLNVD's\(f_{d}\) and \(f_{E}\) are elaborated. The analysis results reveal the following: when the CLNVD is located in the middle of the span, the \(f_{d}\) and \(f_{E}\) of the CLNVD will be maximized, and \(f_{E}\) will increase first and then decrease as the beam's bending line stiffness increases. Meanwhile, the amplification capability of the CLNVD increases as the lever's amplification coefficient \(\chi\) rises. When the negative stiffness does not exist, there exists an optimum lever's amplification coefficient \(\chi\) that maximizes \(f_{E}\). When the combination of damping coefficient \(c\) and index \(\alpha\) satisfies a specific relationship, \(f_{E}\) of the CLNVD reaches its largest value. When the negative stiffness and the loss stiffness of VD are within the region proposed in this paper, the CLNVD will achieve a higher \(f_{d}\) and avoid providing significant negative stiffness. Subsequently, this paper proposes an optimization design method of the CLNVD. Finally, the amplification effect of CLNVD as well as the effectiveness of its optimization design method are verified through examples. In the case study, the CLNVD offers a larger damping ratio under the circumstance of fortification earthquakes. Under fortification and rare earthquakes, the inter-story displacement of Scheme 1 has been decreased by half roughly. According to the above-mentioned results, the CLNVD provides a brand-new approach for designers in the seismic design of buildings. Furthermore, this paper will provide beneficial reference for the damping design of other amplification devices equipped with negative stiffness dampers.  
vibration reduction; intermediate column-lever negative stiffness damper; displacement amplification coefficient; energy dissipation coefficient; optimum control +
Footnote †: journal: Applied Sciences
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Array Eavesdropper
#### 5.2.1 Eavesdropping Probability in IUSNs  
Figure 9 shows the simulation results of eavesdropping probability in IUSNs with an array eavesdropper. There is an excellent agreement of the simulation results with the analytical results, further confirming the accuracy of our model. Similarly, as shown in Figure 9, the eavesdropping probability increases with the node density \(\rho\) and decreases with the increased values of frequency \(f\). Figure 9 also shows that the eavesdropping probability decreases when wind speed \(w\) increases or spreading factor \(k\) increases.  
Figure 8: Eavesdropping probability in AUSNs with an _isotropic_ eavesdropper versus node density \(\rho\) (/km\({}^{2}\)) when \(f=10\) kHz, \(f=30\) kHz and \(f=90\) kHz under different spreading factor \(k\) and wind speed \(w\). (**a**) \(k=1\), \(w=0\,\mathrm{m}/\mathrm{s}\); (**b**) \(k=1\), \(w=10\,\mathrm{m}/\mathrm{s}\); (**c**) \(k=2\), \(w=0\,\mathrm{m}/\mathrm{s}\); (**d**) \(k=2\), \(w=10\,\mathrm{m}/\mathrm{s}\).
## 2 Literature Review
### Mclp  
In the basic formulation of the maximal covering location problem, there is no limitation for the capacity of the located facilities and they are formulated for a single time period. Uncapacitated facility means that service by each facility can be provided limitless as long as the recipients are within the coverage standard. However, in most of the real-world applications of covering problems, considering capacity limitations for facilities is a more realistic assumption. Most facilities have limits on service capabilities due to physical, political, structural, regional, and other reasons [20].  
Another restrictive assumption of basic covering location problems is considering the planning through the time horizon called multi-period or dynamic models. So-called dynamic location models consider a multi-period operating context where the demand varies between different time periods [21]. In multi-period MCLP, decision makers are interested in finding the optimal way of locating a definite number of facilities in different periods. The application of multi-period MCLP can be found in locating emergency service centers in populated regions that on-road accidents may happen and the number of facilities to be located may fluctuate between different periods of time because of daily traffic, weather situation, etc. Moreover, each opened facility at the beginning of a time period can be closed at the end of that time period in a multi-period MCLP [22]. In this regard, [PERSON] et al. [23] addressed a general discrete covering location model in which they considered a finite planning horizon that is partitioned into several time-periods. Because the time periods are not necessarily of the same length and in each period, it is allowed that multiple facilities/equipment can be opened or closed in each location at some cost. Furthermore, [PERSON] et al. [23] assumed that each demand point should be covered by at least a specific number of facilities and coverage lower than the minimum threshold undergoes a time-dependent penalty cost. These features of the studied problem result in a different way of demand point allocations to the facilities in each time period.  
[PERSON] and [PERSON] [24] studied a multi-period maximal covering location problem in which the total number of facilities that have to be opened, is located gradually over time. From this perspective, their model is an implicitly dynamic model. Another characteristic of their developed model is the dynamic capacity for each of the located facilities. As the application of their model is locating ambulances in emergency bases, locating a different number of ambulances in each time period makes it possible to set different levels of capacities in each time period. [PERSON] and [PERSON] [25] developed the multi-period capacitated maximal covering problem considering uncertainties in server availability. Their problem addressed allocating doctors to non-operational primary health centers and as the population that needs to be served by the facilities was changing over time, they studied their problem in different time periods.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 2 Comparison of Wireless Performance in the 915 MHz and 2.45 GHz ISM Bands
### DC Power Consumption
#### 2.2.1 Wireless Protocols
Finally, focusing the attention on coexistence issues with external interfering network(s), most of the WBAN systems work in the 2.45 GHz ISM Band, but it represents an already crowded band because the pervasive presence of devices that operate at the same frequencies using different standards, such as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Operation Frequency Band** & **Maximum Data Rate (kbps)** & **Typical Communication Range (m)** & **Topology** & **Ref.** \\ \hline Bluetooth & & & & & \\ BR/EDR & 2400-2483.5 & 2100 & 10 & P2P & [32] \\ \hline BLE & 2400-2483.5 & 2000 & 10 & P2P; Star; Mesh & [32] \\ \hline ZigBee & 400–470,800–960; 2400–2500 & 250 & 100 & P2P; Star; Mesh & [14] \\ \hline Z-Star & 779–965 & 200 & 100 & P2P; Star; Mesh & [33] \\ \hline WiFi & 2400–2500; 5725–8875 & 54 \(\times 10^{3}\) & 30 & Star & [15; 34] \\ \hline HaLow & 755–928 & 8670 & 100 & Star & [35] \\ \hline ANT/ANT+ & 2400–2457 & 60 & 5 & P2P; Star; Mesh & [36] \\ \hline LoRa & 135–175, 410–525, 779–787, & 300 & 2000–5000 & Star & [37; 38] \\  & 863–869, 902–928 & & & & \\ \hline UHF RFID & 860–960 MHz & 640 & 15 & Star & [39; 40] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Survey of potential Wireless Protocols for next generation Internet of Things. Wi-Fi, Bluetooth, ZigBee and others [24; 44]. This aspect suggests to follow a path toward the use of different radio protocols working at different frequencies in order to reduce the interference as well as to reduce multiple retransmissions and hence the power consumption and to improve the Quality of Service (QoS).
# MAC Layer Protocols for Internet of Things: A Survey
## 3 Long Range MAC Layer Protocols  
Based on their own requirements such as rate, distance coverage, robustness, etc., the existing network protocols need some adaptation to meet the necessary requirements to attend IoT services. In some cases, some protocols were developed to meet IoT applications that demand far-reaching, reliable and robust transmission. Some of the protocols classified as protocols for LP-WANs are able to satisfy the demand for protocols with a large coverage area. LP-WANs protocols can overcome some mobile cellular network failures increasing strong adaptations to meet the IoT requirements.  
LP-WAN are presented as good candidates to support several of the previously mentioned requirements of the IoT structure, and are able to surpass the short-range restriction of the LANs [11]. Among the possible solutions are the proprietary and unlicensed ISM band technologies Sigfox, LoRa/LoRaWAN, against mobile cellular network solutions such as LTE-A (Long Term Evolution--Advanced) and Narrow Band IoT (NB-IoT). Mobile cellular network technologies,  
Figure 15: IEEE 802.11 standards.  
with licensed spectrum or not, can satisfy energy and latency requirements, and it is better to use existing infrastructure [83; 84; 85].  
Communication challenges and the broad set of specifications of M2M communication were added to LTE-based protocols. The development of MTC (Machine Type Communication) resources in the context of LTE (Long Term Evolution) were started in version 10, or Release 10 (R10), of the LTE-A standard [86]. During the development of M2M communication, the 3 rd Generation Partnership Project (3 GPP) committee defined a new profile, called CAT-0, or Category 0, for the operation of the MTC of low-power WAN (Wide Area Network) networks [87]. In release 13 (R13) from 2016, two special categories CAT-M for MTC and CAT-N for Narrowband-IoT (NB-IoT). These categories was included to support the characteristics of M2M communication and IoT technology, respectively [7]. Such categories will be better addressed in the document. In the literature, it is possible to find references to the CAT-N standard as NB-IoT and the CAT-M standard as LTE eMTC, LTE-M2M, LTE-M and CAT-M1. In this document, the notation LTE eMTC and NB-IoT will be used.
## 3 Materials and Methods
### Optimization of the QuEChERS Procedure
#### 3.5.1 Selection of Extraction Solvent  
Generally, the recoveries of analytes are influenced by the extraction solvent [36]. ACN has a superior phase separation ability during liquid-liquid partition and salting-out when used for extracting mycotoxins [12; 37; 38]. The presence of acid in the extraction solvent is crucial for extracting acidic toxins (including OTA), especially formic acid [12]. Therefore, the effect of different extraction solvents, i.e., ACN, ACN/ formic acid (99.9/0.1, \(v/v\)), ACN/ formic acid (99/1, \(v/v\)), and ACN/ formic acid (95/5, \(v/v\)), on the OTA extraction efficiency was compared. The OTA peak areas were determined by adding OTA standard solutions to the blank samples. The OTA-spiked samples were tested in triplicate.  
As shown in Figure 7, OTA was detected after extraction with all four solvents. ACN/ formic acid (95/5, \(v/v\)) yielded the highest OTA peak area, but the obtained extracts exhibited a darker color compared to the other three extracts. Moreover, the use of ACN/ formic acid (95/5, \(v/v\)) resulted in more interference from the coextracted materials during extraction. ACN/ formic acid (99/1, \(v/v\)) yielded the second-highest OTA peak area. Thus, ACN/ formic acid (99/1, \(v/v\)) was used as the extractant in subsequent studies.