esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Radio resource allocation schemes are critical to enhance user experience and spectrum efficiency. In the context of fifth-generation (5G) and future networks, co-construction and sharing among multiple telecom operators, which effectively mitigate challenges stemming from resource scarcity, energy consumption, and network construction costs, also attract wide attention. Therefore, optimal resource allocation techniques in sharing networks should be explored. Current resource allocation schemes primarily optimize for load balancing, single-user throughput, and fairness of multi-user whole network throughput, with minimal consideration for network-level user experience. Moreover, existing approaches predominantly concentrate on specific resource domains, seldom considering holistic collaboration across all domains, which limits the user experience of the whole network. This paper introduces an innovative resource allocation method grounded in the Shannon theorem, incorporating time-frequency-spatial domain multi-dimensional collaboration. More importantly, by constructing an optimization model, we strive to attain optimal network-level user experience. Furthermore, we provide a smart grid technology based on the Artificial Intelligence (AI) method to predict inter-frequency information, including Received Signal Reference Power (RSRP), beam ID, and spectral efficiency, which are modeled as air interface utilization, channel bandwidth, and signal-to-noise ratio, respectively, providing input for the optimization algorithm, which seeks to achieve the optimal time-frequency-space resource allocation scheme. Extensive experimentation validates the effectiveness and superiority of our proposed methodology.  
network-level user experience; co-construction and sharing; resource allocation; multi-dimensional collaboration; 5G and future network +
Footnote †: journal:  
Article
## 5 Results of the Study  
The values in the tables above are listed from top to bottom according to their success status for each model. It can be seen that the successful models are successful in all three RMSE, MAE, and R\({}^{2}\) metrics. However, in a few studies, it can be seen that one of the upper and lower trials was successful in one metric and the other in another metric. Therefore, for convenience, the R\({}^{2}\) metric was used as a criterion both in the listing and in the graphs. The background of values with an R\({}^{2}\) value of 0.99 and above is painted green, and the background of values between 0.98 and 0.99 is painted pink. The background of values with an R\({}^{2}\) value of 0 or negative is colored brown. In most of the studies in the literature, Adam was used as the optimizer, and ReLU was used as the activation function. In this study, methods that can compete with Adam in terms of performance and achieve higher success in some trials have been noted. According to the study results, Nadam obtained the highest values in six groups of studies, Adamax in three groups of studies, and RMSprop in one group of studies. In other groups, the Adam method reached the highest values. In this respect, Nadam and Adamax, which are not prevalent in the literature, attract attention. It is observed that the majority of the study results tested with the FTRL, Adadelta, and SGDfalse methods are painted with a brown background and were unsuccessful. It can be seen that the FTRL method obtained positive values only in five trials and reached an R\({}^{2}\) value of 0.91 in only one trial. It can be seen that the Adadelta method obtained positive values only in six studies and reached an R\({}^{2}\) value of 0.76 in one study. It can be seen that the SGDfalse method obtained a positive value in only one trial and reached an R\({}^{2}\) value of 0.95. Therefore, these three methods are not taken into account in graphical drawings. Since lower values were obtained in the experiments conducted with the Adagrad method compared with other methods, the results of this method have also been removed from some graphs. In a continuation of the fifth section, the titles in the fourth section were analyzed and the findings obtained were evaluated. Therefore, the work continues without changing the titles. However, in order to better compare successful models with each other, models with low performance in some graphs have been removed from the graph. We have also used some methods to solve the overfitting problem in our models. Cross-validation and sliding window splitting are two of these methods. In cross-validation, the
## 2 Materials and Methods
### The Feature Extraction Network of CSPD-YOLO Model
The feature extraction network of the CSPD-YOLO model is shown in Table 2, which can be divided into six parts, as follows: (1) in the first part, the input feature layer was 3 \(\times\) 416 \(\times\) 416, Conv 32 \(\times\) 3 \(\times\) 3 was used for feature extraction, and the output feature layer 32 \(\times\) 416 \(\times\) 416 was generated after the convolution operation; (2) one residual unit and three convolutional layers were employed in the second part--the input feature layer was 32 \(\times\) 416 \(\times\) 416, Conv 64 \(\times\) 3 \(\times\) 3/2, Conv 32 \(\times\) 1 \(\times\) 1, and Conv 64 \(\times\) 3 \(\times\) 3 were used to extract features, and the output feature layer 64 \(\times\) 208 \(\times\) 208 was obtained after the convolution operation; (3) two residual units and five convolutional layers were used in the third part--the input feature layer was 64 \(\times\) 208 \(\times\) 208, Conv 128 \(\times\) 3 \(\times\) 3/2, Conv 64 \(\times\) 1 \(\times\) 1, and Conv 128 \(\times\) 3 \(\times\) 3 were employed for feature extraction, and the output feature layer 128 \(\times\) 104 \(\times\) 104 was generated after the convolution operation; (4) 19 convolutional layers, four residual units, and four dense units were employed in the fourth part--the input feature layer was 128 \(\times\) 104 \(\times\) 104, Conv 256 \(\times\) 3 \(\times\) 3/2, Conv 128 \(\times\) 1 \(\times\) 1, Conv 32 \(\times\) 3 \(\times\) 3, and Conv 256 \(\times\) 3 \(\times\) 3 were used for feature extraction, and the output feature layer 256 \(\times\) 52 \(\times\) 52 was obtained after the convolution operation; (5) 19 convolutional layers and eight dense units were employed in the fifth part--the input feature layer was 256 \(\times\) 52 \(\times\) 52, Conv 512 \(\times\) 3 \(\times\) 3/2, Conv 256 \(\times\) 1 \(\times\) 1, Conv 32 \(\times\) 1 \(\times\) 1, Conv 32 \(\times\) 3 \(\times\) 3, and Conv 512 \(\times\) 3 \(\times\) 3 were used to extract features, and the output feature layer 512 \(\times\) 26 \(\times\) 26 was obtained after the convolution operation; (6) 9 convolutional layers and four residual units were used in the last part--the input feature layer was 512 \(\times\) 26 \(\times\) 26, Conv 1024 \(\times\) 3 \(\times\) 3/2, Conv 512 \(\times\) 1 \(\times\) 1, and Conv 1024 \(\times\) 3 \(\times\) 3 were employed for feature extraction, and the output feature layer 1024 \(\times\) 13 \(\times\) 13 was generated after the convolution operation. Figure 4: The structure of Cross Stage Partial Dense (CSPD) blocks.
## 3 Results
### UAE Values and Their Correspondance to the Seizure Onset Zone  
We generated the TF images of 1928 iEEG signals (mean: 96 signals per patient) and compared their 13 features of visual complexity between images generated from inside the SOZ and those generated from outside the SOZ. Figure 4 shows two iEEG signals from a patient, where the green signal is recorded from a contact located inside the EZ (or resection) and SOZ, while the red signal is recorded from healthy brain tissue (non-SOZ contact). The TF image of the EZ signal shows a strong wideband frequency component above 10 Hz, which is often characteristic of the presence of an epileptiform sharp discharge in the signal and makes the TF image of \"easy visual interpretation\" (and low complexity). For the other contact, the TF spectrum is concentrated in the lower frequency bands, while  
Figure 3: Identifying points of interest (POIs) on the patient’s MRI.  
the rest of the image (frequencies above 6 Hz) presents very subtle differences, which make it difficult to determine each frequency contribution, thereby leading to a \"harder visual interpretation\" and thus higher visual complexity, reflected in the higher UAE.  
When performing statistical analysis across the whole cohort of patients between contacts inside and outside the SOZ, this difference in UAE values was confirmed, as shown in Figure 5. We found that the mean UAE values inside the SOZ were lower than outside the SOZ when looking at various layers (\(p<0.05\) in layers 1 and 11; \(p<0.01\) in layer 7; and \(p<0.0001\) in layers 2, 6, 7, 10, 12, and 13). The largest difference was found in layer 12 (\(p=0.00016\), Cohens D = 0.5, C.I.: 1.18-0.136), followed by layer 10 (\(p=0.00029\), Cohens D = 0.41, C.I. = 0.97-0.11). The other layers, namely layers 2, 6, and 13, showed Cohens D values of 0.30 (C.I. = 0.79-0.01, \(p=0.00097\)), 0.43 (C.I. = 1.085-0.04, \(p=0.00046\)), and 0.35 (C.I. = 0.98-0.06, \(p=0.00097\)), respectively. Upon correcting for multiple comparisons using Bonferroni correction, statistically significant differences are still seen in layers 2 and 13 (\(p<0.05\)) and layers 6, 10, and 12 (\(p<0.01\)).  
Figure 4: iEEG signals from a patient with seizures originating in the frontal lobe. Two representative signals, one from the frontal lobe (inside EZ) and another from the parietal lobe (outside EZ), and their corresponding TF image.  
Figure 5: UAE values inside the SOZ vs. outside the SOZ across the various layers of the VGGnet. The whiskers of the boxplots extend to the most extreme data points not considered outliers (product of 1.5 and the inter-quartile range), while the outliers are plotted individually using the red ‘+’ marker symbol.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
###### Abstract  
Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.  
S 20242465744/10/399/c24206574  
29 March 2024221 May 202414 June 202414 June 202412 October 2024
## 4 Quality Assessment Methodology
### Validation Methods
#### 4.2.1 Validation Metrics  
The definitions of the completeness, precision, uncertainty and accuracy (Table 8), which are applicable to SA validation, are drawn from the experimental recommendations of the Joint Committee for Guides in Metrology (JCGM) regarding the expression of uncertainty in measurement [68] and from the GCOS [64].  
Completeness is the proportion of valid retrievals over an observation domain at any given time. It is, therefore, mandatory to document the completeness of the product (i.e., the distribution in space and time of missing data).  
Two aspects of the precision, which is also called the repeatability, are evaluated: The intra-annual and inter-annual precision. The intra-annual precision (smoothness or \(\delta\) function) corresponds to the temporal noise assumed to have no serial correlation within a season. The \(\delta\) function is computed for each triplet of consecutive observations [69; 70] as the absolute value of the difference between the center and the corresponding linear interpolation between the two extremes, and the median of the \(\delta\) values is provided as an indicator of the intra-annual precision of satellite albedo products [21]. The inter-annual precision (i.e., the dispersion of the albedo values from year to year) was assessed over the 20 desert calibration sites between two consecutive years, and the median absolute deviation is provided as an indicator of the inter-annual precision [21].  
Accuracy is the degree of \"closeness of the agreement between the result of a measurement and a true value of the measurand\" [68]. Commonly, accuracy is used to describe systematic errors and measure statistical bias, but the best practice is to provide the median error as an indicator of accuracy [21].  
Uncertainty is a \"parameter, associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand\" [68]. Uncertainty includes the bias and precision errors and can be estimated by the Root Mean Square Deviation (RMSD). Additionally, the linear model fits are used to quantify the goodness of fit. For this purpose, Major Axis Regressions (MARs) were computed instead of Ordinary Least Squares (OLS) because it is specifically formulated to handle errors in both the x and y variables [71]. Other metrics are used, such as the number of samples (N), which is indicative of the power of the validation; or the correlation coefficient (R, estimated as the Pearson coefficient), which indicates the descriptive power of the linear accuracy test. The percentage of pixels within the predefined uncertainty levels (Table 7, Figure 3) is also quantified.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Software Vulnerabilities
Tools like Ghidra, IDA Pro, or a hexadecimal editor allow the attacker to dissect this code, tracing its instructions to map the device's functionality, from signal handling to security checks [86]. Sophisticated decompilation can even convert this binary into editable C code, opening the door to tailored modifications. With this insight, the attacker uncovers everything, including operational weaknesses, hidden features, or behaviors detrimental to the device's owner, for example, a tendency to broadcast identifiable data in the clear. To mitigate firmware extraction and reverse engineering, manufacturers should implement firmware encryption and readout protection mechanisms, preventing unauthorized access to microcontroller memory. Secure boot processes should be enforced, ensuring that only authenticated and untampered firmware can be executed on the device. Additionally, code obfuscation techniques can make reverse engineering significantly more challenging by disguising critical logic and security mechanisms. Notably, the physical possession of the target device is not always necessary. IoT devices, often affordable and partially open source, invite indirect exploitation. An attacker could acquire an identical unit, extract its hexadecimal data, and reverse-engineer the firmware to mirror the target's behavior, While this may not reveal user-specific tweaks, for example, custom encryption keys, it exposes the core software architecture. Beyond mere understanding, this access enables the attacker to craft malicious alterations, such as embedding backdoors to monitor activity, adding logic for remote control, or masking their presence with stealth routines. These changes, often requiring just a handful of code lines, can be re-uploaded swiftly, turning the device into a compromised asset. To prevent malicious firmware modifications, secure firmware update mechanisms should be employed, requiring updates to be cryptographically signed and verified before installation. Rollback protection should also be in place to prevent attackers from downgrading devices to older, vulnerable firmware versions. Additionally, behavioral anomaly detection in IoT systems can help identify when a device begins exhibiting unexpected activity indicative of tampering. Other vulnerabilities, like code injection, for example, via malformed inputs to a web interface, or privilege escalation, where an attacker exploits flawed permission checks, further broaden the attack surface, allowing attackers to escalate from limited access to full domination. In each case, the software's inherent programmability--its greatest strength--becomes the linchpin of its undoing, exposing IoT ecosystems to a multifaceted array of threats.
## 4 Results and Discussion
### Comparative Performance Between Datasets
#### 4.2.5 The Summary of the Main Findings  
By analyzing the results obtained by the three types of meters, Figure 16 shows how the metrics are affected by the type of meter and the number of applications supported in each dataset for each appliance.  
The results indicate that the oZm v1 has the best F1-score, excelling in detecting the vacuum cleaner (0.82) and the freezer (0.58). The OMPM also performs well in the incandescent lamp (0.67) and fan (0.59). In contrast, the oZm v3 shows lower values in the F1-score, especially in the vacuum cleaner (0.19) and freezer (0.29), because it worked with  
Figure 16: Metrics by meter and appliance. (**a**) F1-score. (**b**) EAE. (**c**) MNEAP. (**d**) RMSE.  
Figure 15: Mean RMSE by appliance and meter.  
a more complex dataset that included twice as many devices as the other models. In terms of the EAE, all three models show an excellent accuracy. The oZm v1 and OMPM have extremely low, near-zero errors on most devices. Although the oZm v3 worked with more devices, it maintains an excellent value, with an error of 0.044 in the vacuum cleaner and 0.016 in the lamp, indicating that its estimates were quite accurate. This result highlights that the model performs well even in a more complex environment.  
Regarding the MNEAP, the values for the oZm v3 in the vacuum cleaner (2.34) and freezer (2.35) are higher than the other models, but not excessively high. These values can be explained by the difficulty of estimating several devices simultaneously. The OMPM and oZm v1 have fewer errors in this indicator, although it is essential to note that they worked with fewer devices, facilitating a better relative accuracy. Finally, the oZm v3 shows the highest values in the RMSE, especially in the lamp (329.98) and the vacuum cleaner (321.83), indicating a more significant variability in predicting these devices. This increased variability is expected due to the larger number of devices in the dataset. If the objective is to indicate a more significant number of devices, the oZm v3 presents the most realistic model. Conversely, if minimizing errors in a simpler environment is prioritized, the oZm v1 and OMPM may be more suitable options.
## 4 Discussion
### Analysis of Results Under Different Confidence Levels  
Different confidence levels represent the wind/storage system's tolerance for extreme scenarios. A higher confidence level requires the system to maintain relatively high revenue even in worst-case conditions, while a lower confidence level allows for higher expected returns at the cost of increased risk exposure. By comparing scheduling results under different confidence levels, the model's performance in scenario-based risk management can be comprehensively evaluated.  
Figure 8: \(SOC\) variation curve with initial SOC set to 0.9.  
Figure 7: \(SOC\) variation curve with initial SOC set to 0.5.  
As shown in Table 8, when the confidence level is relatively low (0.8 and 0.85), the expected revenues are higher--CNY 16,453.56 and CNY 16,447.05, respectively. However, the corresponding minimum scenario revenues are relatively low, at CNY 15,136.98 and CNY 15,163.38. When the confidence level increases to 0.9 and 0.95, the expected revenues decrease to CNY 16,233.78 and CNY 15,772.14, respectively, but the minimum scenario revenues rise significantly to CNY 15,532.48 and CNY 15,736.71. Meanwhile, the CVaR values also decline as the confidence level increases, dropping from CNY 16,166.63 at 0.8 to CNY 15,676.59 at 0.95. These results indicate that higher confidence levels lead to more conservative scheduling strategies, which can effectively mitigate significant revenue drops under extreme scenarios. Therefore, selecting an appropriate confidence level is essential for achieving a balance between risk control and revenue maximization.  
Based on the above analysis, the confidence level should be determined according to the operator's risk preference and scheduling objectives. A higher confidence level, such as 0.9 or 0.95, is appropriate when the focus is on reducing the impact of extreme scenarios and ensuring more stable returns. Conversely, when improving expected revenue is prioritized and the system can tolerate certain deviations, a lower level, such as 0.8 or 0.85, may be considered. This allows for flexible adjustment of the scheduling strategy in line with different operational requirements.
# Research and Development of Automatic Monitoring System for Livestock Farms
## 2 Overview of the System Architecture
### Experimental Hardware Architecture  
The hardware architecture in this study is shown as follows: personal computers, programmable logic controller, \"left-hand side\" communication modules, human machine touch screen interface, radio frequency communication module, etc. were installed in the office. A detailed list of hardware manufacturers and product name is shown in Table 1. On the other hand, there were 4 sets of temperature and humidity sensors, 4 sets of luminance sensors, 4 sets of wind speed sensors, programmable logic controller, \"left-hand side\" communication modules, radio frequency communication module, power distribution box, fans, water control valves, etc. installed in the dairy barn of the livestock farm. The installation layout of dairy barn is shown in Figure 2, and a detailed list of hardware manufacturers and product name is shown in Table 2. The design of hardware architecture in the pig farm was roughly the same as that in the dairy farm. The differences on the hardware architecture between pig and dairy barns were the sensor types. The sensors were configured into temperature, humidity, carbon dioxide, and ammonia sensors according to the environmental monitoring requirements. Besides, a water curtain system was also installed in the pig barn. The overall flow chart of experimental hardware architecture is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device name (product model)** & **Supplier** \\ \hline PLC 1 (FX3U-32 MR) & Mitsubishi Electric CO., Tokyo Japan \\ Left communication module (FX3U-485 ADP-MB) & Mitsubishi Electric CO., Tokyo Japan \\ HMI 2 (EC210-CT1H) & Shihlin Electric CO., Taipei Taiwan \\ Wireless modem (RFU-400) & ICP DAS CO., Hinshu Taiwan \\ PC 3 (DESKTOP - R8C395T) & ASUS CO., Taipei Taiwan \\ \hline \hline \end{tabular}  
\end{table}
Table 1: Experimental hardware device in office listed.  
Figure 1: (**a**) The aerial photo of livestock farm; (**b**) The floor plan of dairy farm.