esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Data Acquisition  
#### 2.1.1 Study Area and Assessment of Disease Severity  
Anhui Province is located in southeastern China, in the lower reaches of both the Yangtze River and the Huaihe River. It is located in the transitional climate zone between the warm temperate zone and subtropical zone [21]. Because the average temperature ranges from 14 \({}^{\circ}\)C to 17 \({}^{\circ}\)C and the annual rainfall ranges from 770 mm to 1700 mm [21], FHB occurs frequently [1]. According to the Anhui Meteorological Administration, in the middle of April 2018, excessive precipitation and high temperatures occurred in the south of Hefei. The winter wheat, which was in the susceptible infectious growth period (flowering stage) [5], was conducive to the FHB epidemic [3]. Three experimental fields were selected: Guohe Town (31\({}^{\circ}\)29\({}^{\prime}\) N, 117\({}^{\circ}\)13\({}^{\prime}\) E) and Baihu Town (31\({}^{\circ}\)14\({}^{\prime}\) N, 117\({}^{\circ}\)27\({}^{\prime}\) E), Lujiang County; Hefei City, Shucheng County (31\({}^{\circ}\)32\({}^{\prime}\) N, 116\({}^{\circ}\)59\({}^{\prime}\) E); and Lu'an City (Figure 1). To ensure the quality of the experimental data, the period from May 8 to May 13, 2018 (the period of the disease detection of wheat, i.e., grain filling and maturity stages), was selected as the ideal time to obtain valid and typical hyperspectral data.  
The occurrence of FHB is completely natural in experimental fields in the winter wheat growing season, and survey data for a total of 89 ears were obtained through the visual method. The disease severity for each ear was visually determined by an experienced plant pathologist according to Rules for Monitoring and Forecast of the Wheat Head Blight (Fusarium graminearum Schw./Gibberellae (Schw.) Petch), issued in 2011 (GB/T 15796-2011). At the time of the survey, threshing occurred first. Then, the ratio of the number of diseased kernels to the total number of kernels of the ears was used as the severity of the individual ear (Figure 2). The grading standards of wheat FHB occurrence severity are shown in Table 1. In order to reduce the difficulty of identifying the severity of the disease, the severity of FHB was further reclassified into three classes: healthy (<1/5), mild (1/5\(\leq\) the ratio of diseased kernels <1/2), and severe (the ratio of diseased kernels \(\geq\)1/2).  
Figure 1: Location of the study area.  
\begin{table}
\begin{tabular}{c c} \hline
**Level** & **Description of Occurrence Condition** \\ \hline
0 & The ratio of diseased kernels = 0 \\ \hline
1 & The ratio of diseased kernels \textless{} 1/4 \\ \hline
2 & 1/4 \(\leq\) the ratio of diseased kernels \textless{} 1/2 \\ \hline
3 & 1/2 \(\leq\) the ratio of diseased kernels \textless{} 3/4 \\ \hline \end{tabular}
\end{table}
Table 1: The grading standards of wheat Fusarium head blight (FHB) occurrence severity.  
Figure 2: FHB-infected ear of winter wheat at different stages of severity.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### DCC-EL Forecasting Framework  
A new prediction framework DCC-EL is proposed based on the combination of dilated causal convolutional neural network and ensemble learning. The framework structure is shown in Figure 3.
## 4 Results and Discussion
### Model Performance Evaluation
The area under the curve (AUC) for the model is 0.958, which indicates excellent classification performance with the capacity to identify stuck events while maintaining a low false positive rate. This high AUC score emphasizes the model's robustness and practical applicability in industrial operations, where effective anomaly detection is essential. To assess the feasibility of real-time application, the model's inference time was evaluated. On a standard workstation (Intel i7 CPU, 16 GB RAM), the average prediction time per sequence was approximately 14 milliseconds. This enabled rapid anomaly classification with minimal latency. Again, this low computational cost confirms the suitability of the  
Figure 10: ROC curve. Figure 9: Confusion matrix results. LSTM autoencoder for deployment in time-sensitive drilling operations where prompt detection is critical for operational safety and efficiency. As shown in Table 3, traditional supervised machine learning models demonstrate reasonable performance in stuck pipe detection tasks, with accuracy values ranging from 84.1% to 90.6% and F1-scores between 82.6% and 89.3%. Among them, Random Forest achieved the highest performance with an accuracy of 90.6%, precision of 91.5%, and recall of 87.3%. However, the proposed LSTM autoencoder outperformed all these methods, achieving an accuracy of 99.06%, precision of 97.12%, recall of 91.34%, and a F1-score of 94.15%. These results highlight the strength of the unsupervised approach in capturing complex operational patterns from normal data alone.
# Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers
###### Abstract  
Mobile internet technology (MIT) is considered a significant advancement in information and communication technology (ICT), due to its crucial impact on the financial system and social life. In addition, it is an essential technology to overcome the digital divide between urban and rural areas. In terms of agricultural advancement, MIT can play a key role in data collection and the implementation of smart agricultural technologies. The main objectives of this study were to (i) investigate MIT adoption and use in sustainable agriculture development among selected wheat farmers of Pakistan and (ii) examine the crucial factors influencing MIT adoption. This study selected 628 wheat farmers from four districts of Khyber Pakhtunkhwa Province (KPK), Pakistan, for sampling. This study used a bivariate probt method for sampling wheat farmers. The analysis of wheat farmer's data showed farmer's age, farm size, farm location, and knowledge about Internet technology (IT) are strongly correlated with MIT adoption in sustainable agriculture development. Results showed on average, 65% of wheat farmers have mobile devices supporting these Internet technologies, and 55% use MIT in agricultural environments. Since the extant research on MIT adoption for agriculture production in Pakistan is sparse, this study helps advance MIT adoption-based studies. These outcomes may draw the attention of decision-makers dealing with IT infrastructure and agricultural equipment who can support farmers adopting MIT.  
sustainable; agriculture; rural; mobile internet technology; bivariate probt model; agricultural modernization; Pakistan; wheat; smart Article  
Mobile internet technology; Agriculture; Urban
## 2 Methodology
### Mathematical Model of PMSM
The constraints are as follows:  
\[ \begin{array}{l}\sqrt{\left(i_{d}^{2}+i_{q}^{2}\right)}\leq\sqrt{2}I_{N}\\ \sqrt{\left(u_{d}^{2}+u_{q}^{2}\right)}\leq\frac{U_{k_{d}}}{\sqrt{3}}\end{array}  \tag{7}\]  
As this is a real physical system, constraints on the states and inputs have to be considered during the controller design given. In the wheel/rail transmission, the premise for obtaining different traction forces is not to destroy the adhesion moment between the wheel/rail. In this paper, the calculation formula of the train wheel/rail adhesion coefficient \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Motors x (1, 2, 3, 4)** & **Motors y (1, 2)** \\ \hline bus voltage \(U_{dc}\) & 380 V & 380 V \\ stator rated current \(I_{N}\) & 63 A & 20 A \\ stator inductance \(L_{d}=L_{d}\) & 0.0083 H & 0.0024 H \\ stator resistance \(R\) & 0.376 \(\Omega\) & 0.369 \(\Omega\) \\ permanent magnet flux \(\psi_{f}\) & 0.9720 Wb & 0.8815 Wb \\ the rotor moment of inertia \(J\) & 0.2026 kg:m\({}^{2}\) & 0.0182 kg:m\({}^{2}\) \\ the number of pole pairs \(P_{n}\) & 1 & 1 \\ viscous damping \(B_{u}\) & 0.00464 N:m-(s:rad\({}^{-1}\)) & 0.003 N:m-(s:rad\({}^{-1}\)) \\ the rated speed \(n\) & 2500 rpm & 2500 rpm \\ The rated torque \(T_{e}\) & 123 N:m & 37.4 N:m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Permanent-magnet synchronous motors parameters table. \(u_{j}\) is used to calculate the maximum traction torque. When the train speed \(v<50\) km/h, the \(u_{j}\) is as follows [26]:  
\[\mu_{j}=0.24+\frac{12}{100+8v_{max}} \tag{8}\]  
Then, the maximum traction torque is  
\[T_{j}=\frac{\mu_{j}mgr_{w}}{r_{g}q_{motor}} \tag{9}\]  
The maximum accelerations \(a_{ux}^{max}\) and \(a_{uy}^{max}\) can be calculated from the maximum output torque \(T_{out}^{max}\):  
\[T_{out}^{max}=min\left\{\text{rated torque }T_{e},\text{ maximum traction torque }T_{j}\right\} \tag{10}\]  
The maximum accelerations of the crane and trolley are \(a_{ux}^{max}=1.558\) m/s\({}^{2}\) and \(a_{uy}^{max}=2.608\) m/s\({}^{2}\), respectively. Additionally, this system adopts 17-bit incremental rotary encoders to measure the angular velocity and rotor-position of the motors. The measurement error of the motor rotor-position is \(\pm 0.001^{\circ}\), and the measurement error of the motor angular velocity is \(\pm 0.2\) rad/s. Rotor-position measurement noise adds Gaussian white noise to the \(\theta_{e}\), the amplitude is 0.001\({}^{\circ}\), and the sampling period is 1/10,000 s. Angular velocity measurement noise adds Gaussian white noise to the angular velocity feedback \(\omega_{m}\), the amplitude is 0.2 rad/s, and the sampling period is 1/10,000 s. The current measurement noise also adds Gaussian white noise to the feedback loop of \(i_{d}\) and \(i_{q}\), the noise amplitude is 0.2 A, and the sampling period is 1/10,000 s. The discrete signals \(\theta_{e}\), \(i_{d}\), and \(i_{q}\) are delayed by one sampling period. Then, the signals are passed to the PI controllers or \(dq/\alpha\beta\) converters.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Slotted-ALOHA (S-ALOHA)
#### (xiii) Buffering_Slotted_ALOHA  
Buffering_Slotted_ALOHA discussed and analyzed in [44] is a new protocol for dealing with typical difficulties at UWSNs [124]. It is employed to reduce the migration of nodes from one group to another. This protocol divides the accessible network into discrete parts called closed groups. Every closed group within the accessible network has a tiny pool with a predetermined number of nodes for lowering traffic within the group, avoiding collisions, and thus reducing sending time. Another important feature of the closed group is the presence of an underwater sink (UW-Sink) node, which functions as a leader and interacts with a small number of communicating nodes within the closed group. Outside the closed groups, the Underwater Main Sink (UW-Main Sink) is responsible for data arriving from UW-Sink or normal nodes within the closed groups. As illustrated in Figure 5. The main purpose of a time slot is to allow each communicating node in a closed group to deal with the UW-Sink as a default choice if it is available, or with the UW-Main Sink as a backup option if the default choice is not available [125]. Furthermore, until the sending data packet is validated, it is retained in a buffer in each node. Checking the time-slots cases before sending a data packet is a key goal for speeding up the communicating process. The topology of this network is also shown in Figure 5. It is found out that this protocol can boost network throughput while lowering the average end-to-end delay factor and energy consumption ratio. Different types of ALOHA protocols are compared in Figures 8-11 based on energy consumption, average propagation latency, throughput, and dropped nodes.
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 4 Types of Persisters
### Type III or Specialized Persisters  
Specialized persisters do not rely on slow growth or reduced metabolic rates to survive antibiotic exposure. Instead, they exhibit persistence mechanisms specific to particular antibiotics. These cells can arise spontaneously, as observed in _Mycobacteria_, where stochastically low levels of the enzyme catalase-peroxidase--responsible for activating isoniaizd--enable persistence [17]. Alternatively, they can be induced via stress signals, such as ciprofloxacin persisters, which are triggered when _E. coli_ is exposed to this antibiotic [18; 20].
## 3 Results  
### Baseline Demographics and Clinical Characteristics  
After applying the inclusion criteria implemented by Flatiron Health, 278 patients with la/mUC were selected, 214 of whom met the inclusion criteria for this study, which were established to accurately identify patients who were eligible for and received avelumab 1 LM (Figure 2). Mean age was 69.0 years (Std Dev: 9.2). Most patients were male (76.6%) and White (66.4%), with an ECOG PS of 0 (32.2%) or 1 (44.9%) (Table 1). Similar proportions were seen for the body mass index (BMI) categories of normal BMI (32.2%), overweight BMI (27.6%), and obese BMI (28.0%). The majority (73.8%) of primary tumors occurred in the bladder. The most common sites of metastases were distant lymph nodes (57.0%), bone (29.4%), and lungs (27.1%). \begin{table}
\begin{tabular}{l l} \hline \hline
**Characteristic** & **Avelumab 1 LM (n = 214)** \\ \hline \hline
**Age at la/mUC diagnosis, years** \\ \hline \hline Mean (Std Dev) & 69.0 (9.2) \\ \hline Median (Q1, Q3) & 70.0 (64.0, 76.0) \\ \hline
**Year of index date, n (\%)** \\ \hline
2020 & 43 (20.1) \\ \hline
2021 & 75 (35.0) \\ \hline
2022 & 96 (44.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline demographics and clinical characteristics. \begin{table}
\begin{tabular}{l c} \hline
**Characteristic** & **Avelumab 1 LM (n = 214)** \\ \hline
**Sex, n (\%)** & \\ \hline Male & 164 (76.6) \\ \hline Female & 50 (23.4) \\ \hline
**Race, n (\%)** & \\ \hline White & 142 (66.4) \\ \hline Other & 31 (14.5) \\ \hline Black or African American & 6 (2.8) \\ \hline Asian & 2 (0.9) \\ \hline Unknown & 33 (15.4) \\ \hline
**Region of residence, n (\%)** & \\ \hline South & 99 (46.3) \\ \hline Northeast & 37 (17.3) \\ \hline Midwest & 30 (14.0) \\ \hline West & 23 (10.7) \\ \hline Other & 1 (0.5) \\ \hline Unknown & 24 (11.2) \\ \hline
**Practice type, n (\%)** & \\ \hline Community & 191 (89.3) \\ \hline Academic & 18 (8.4) \\ \hline Both & 5 (2.3) \\ \hline
**Site of disease, n (\%)** & \\ \hline Bladder & 158 (73.8) \\ \hline Renal pelvis & 30 (14.0) \\ \hline Ureter & 24 (11.2) \\ \hline Urethra & 2 (0.9) \\ \hline
**Disease grade, n (\%)** & \\ \hline High grade (G2/G3/G4) & 174 (81.3) \\ \hline Low grade (G1) & 12 (5.6) \\ \hline Unknown/not documented & 28 (13.1) \\ \hline
**Stage at initial diagnosis, n (\%)** & \\ \hline
0 & 2 (0.9) \\ \hline I & 3 (1.4) \\ \hline II & 9 (4.2) \\ \hline III & 7 (3.3) \\ \hline IV & 99 (46.3) \\ \hline Unknown/not documented & 94 (43.9) \\ \hline
**ECOG PS at la/mUC diagnosis date, n (\%)** & \\ \hline
0 & 69 (32.2) \\ \hline
1 & 96 (44.9) \\ \hline
2+ & 21 (9.8) \\ \hline Unknown/not documented & 28 (13.1) \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 2 System Model
### Channel Model  
In the proposed cell-free massive MIMO system, the downlink wireless channel between APs and UEs is modeled as a Rayleigh fading channel. Each AP is equipped with a single antenna, and all APs are coordinated by a CPU responsible for power allocation. The small-scale fading component of the AP-UE channel follows a Rayleigh distribution, which is commonly used in rich scattering environments. The complex baseband channel coefficient between AP \(i\) and UE \(j\) is modeled as follows:  
\[h_{i,j}\sim\mathcal{CN}(0,\beta_{i,j}) \tag{1}\]  
where \(\beta_{i,j}\) is the large-scale fading coefficient that accounts for path loss and shadowing. This coefficient is defined using a distance-dependent path loss model, as follows:  
\[\beta_{i,j}=\frac{1}{1+d_{i,j}^{\alpha}} \tag{2}\]  
Figure 1: A cell-free massive MIMO downlink architecture in which a CPU coordinates power allocation across distributed APs.  
where \(d_{i,j}\) is the distance between AP \(i\) and UE \(j\) and where \(\alpha\) is the path loss exponent. Each AP transmits with an individual power level, and each UE receives the superposition of signals from multiple APs. The received signal at UE \(j\) can be expressed as follows:  
\[y_{j}=\sum_{i=1}^{M}h_{i,j}\sqrt{p_{i,j}}x_{i}+n_{j} \tag{3}\]  
where \(p_{i,j}\) denotes the transmit power from AP \(i\) to UE \(j\), \(x_{i}\) is the signal transmitted by AP \(i\), and \(n_{j}\) represents additive white Gaussian noise (AWGN) at UE \(j\), modeled as \(n_{j}\sim\mathcal{CN}(0,\sigma^{2})\). The downlink SINR at UE \(j\) is provided by [3]  
\[\text{SINR}_{j}=\frac{\sum_{i=1}^{M}|h_{i,j}|^{2}p_{i,j}}{\sum_{k\
eq j}^{K} \sum_{i=1}^{M}|h_{i,k}|^{2}p_{i,k}+\sigma^{2}}. \tag{4}\]  
Using the SINR, the achievable rate for UE \(j\) is calculated based on the Shannon capacity formula as follows [17]:  
\[R_{j}=B\log_{2}(1+\text{SINR}_{j}) \tag{5}\]  
where \(B\) denotes the available system bandwidth. As a system-level performance indicator, the average downlink SE is obtained by averaging the achievable rates across all UEs as follows:  
\[R_{\text{avg}}=\frac{1}{K}\sum_{j=1}^{K}R_{j}. \tag{6}\]  
While cellular networks can improve the summed SE by allocating more power to users with favorable channel conditions, this often results in unfair resource distribution and degraded performance for users at the cell edges. In contrast, cell-free massive MIMO systems aim to provide uniformly good service to all users through cooperative transmission and centralized power coordination. Therefore, the average SE serves as a more appropriate and fair metric for evaluating the overall performance of cell-free architectures.
## 6 Simulation Experiments and Performance Analysis
### The Trade-Off between Radar Jamming and Communication Performance
Under specific conductions, such as \(\eta=1:3\), \(\eta=1:7\), and \(\eta=1:31\), the CFAR processing results align consistently with those presented in Figure 14a-c. Combining the various simulation results reveals that different interleaving scenarios exert distinct effects on radar jamming and communication. In communication, when \(N_{com}\) is greater than \(N_{jam}\), the improvement in the communication rate is counterbalanced by a deterioration in BER performance. Conversely, when \(N_{com}\) is less than \(N_{jam}\), the BER performance remains unaffected by channel equalization, yet the communication rate continuously decreases. In jamming, the increase in the number of frequency-domain points for the joint signal, regardless of whether \(N_{com}\) is greater than \(N_{jam}\) or \(N_{com}\) is less than \(N_{jam}\), leads to the dispersion of jamming energy in the time domain, thereby diminishing the jamming effect. Therefore, the ratio of interleaving between radar jamming and communication emerges as a performance trade-off. Based on the analyses and simulations presented, it can be concluded that under the condition of \(\eta=1\), denoting equal interleaving of communication and jamming, the overall performance of the joint signal is relatively better.
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 4 Case Study  
### Data and Experimental Settings  
#### 4.1.1 Data  
The proposed method is validated in Haikou City. Haikou is in the northern part of Hainan Island, at the northern edge of the low-latitude tropical zone. As of 2017, the city had a permanent population of approximately 2.27 million. The proposed method uses multi-source urban data, including Didi OD data, road network data, and POI data from May 2017. The Didi data include a total of 1,987,642 records, containing information such as the starting and ending points, departure time, arrival time, and travel distance, as shown in Table 1. The spatial distribution of the start and destination of Didi orders is shown in Figure 4. The road network data come from OSM and are used for dividing Haikou into districts. The POI data are sourced from Amap and include 14 types such as shopping, food and beverages, and daily life services, with a total of 128,505 POI points, as shown in Table 2.  
Figure 3: Regional functional distribution learning.  
Haikou City is divided into four districts, including Xiuying District, Longhua District, Qiongshan District, and Meilan District. Among these, Longhua District houses core commercial areas such as the Guomao Business District, Jiefangxi Business District, and Mingzhu Plaza. It exhibits the highest consumption level in the city, with daily foot traffic surpassing the combined total of the other three districts, thereby positioning it as the central urban area of Haikou. Since most of the city's population activities are concentrated north of the Haikou Ring Expressway, we selected the area north of the Haikou Ring Expressway as the study area. Based on road network data, the study area is divided into 503 regions, as shown in Figure 5. The Didi data are spatially mapped to obtain the origin and destination regions. The 503 regions are treated as the nodes \(V\) of the network, the OD flow is treated as the edges \(E\), and the inflow and outflow volumes during different periods for each region are used as node attributes \(A\), constructing the mobility network \(G~{}=~{}(V,~{}E,A)\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **POI Type** & **Number** & **ID** & **POI Type** & **Number** \\ \hline
1 & accommodation service & 6723 & 8 & shopping & 34,850 \\
2 & commercial house & 4552 & 9 & finance and insurance service & 1551 \\
3 & medical service & 5310 & 10 & tourist attraction & 667 \\
4 & daily life service & 18,067 & 11 & public facility & 743 \\
5 & sports and recreation & 3793 & 12 & enterprises & 13,409 \\
6 & government organization & 4131 & 13 & food and beverages & 22,490 \\
7 & education service & 5059 & 14 & transportation service & 8160 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of POI data.  
Figure 4: Heat map.
## 3 Case Study
### Optimization Results of a Typical Summer Day  
The optimization results for a typical summer day are depicted in Figures 7-9. By considering the optimization outcomes from both the typical spring day and typical summer day, it can be inferred that the proposed model is not limited by seasonality and maintains both economic efficiency and robustness throughout different seasons.  
From Figures 7 and 8, it can be seen that on a typical summer day both the approach proposed in this paper and the robust optimization are to purchase electricity from the power grid during the valley and flat periods for its power shortage and ES charging. The electricity purchased by robust optimization is significantly higher than that of the approach in this paper and the economy is reduced. From Figure 8, it can be seen that  
Figure 5: ES optimization results for industrial parks on a typical spring day.  
Figure 6: Optimization results for exchanged power on a typical spring day.  
The combined analysis of the spring and summer simulations demonstrates that the proposed model can effectively address the uncertainty of PV and load, ensuring the economic optimization of the industrial park while maintaining robustness in the face of uncertainties. This indicates that the model's performance is not restricted to specific seasons and can be reliably applied throughout the year, providing a comprehensive solution for industrial park operations. At same time, interconnected microgrids can exchange power with each other and reduce dependence on the grid, thereby reducing the cost of microgrids and improving the economy.  
By considering the optimization results from both typical spring and summer days, it is evident that the proposed model is capable of ensuring both economic efficiency and robustness, thereby demonstrating its effectiveness and applicability across different seasons.