esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Digital Twins and Healthcare Systems  
DTs are virtual replicas or representations of physical objects, systems, or processes [12]. They are created by capturing real-time data from sensors, devices, or other sources connected to the physical object or system. It provides a digital counterpart that mirrors the behavior, characteristics, and performance of their physical counterparts. They enable the real-time monitoring, analysis, and simulation of the physical objects or systems, allowing for predictive maintenance, optimization, and improved decision-making. One can find applications of this technology in various industries such as manufacturing, biomedical, transportation, and energy. Figure 2 illustrates the three fundamental components of a DT as follows:  
1. Industrial Component: This encompasses complex systems that rely on the environment in a virtual sense and on the physical environment. The industrial part represents the connection between the physical world and the DT. 2. Application Component: This is focused on the characteristics and contents related to the virtual representation. It deals with the virtual vision and functionalities of the DT, providing a platform for the monitoring, analysis, and simulation of the physical system. 3. Communication Component: This focuses on establishing cyber-physical connections. It enables the exchange of information and data flow between the physical system and its DT counterpart. Communication plays a crucial role in facilitating real-time interactions and synchronization between the physical and virtual domains. Together, these three components form the foundation of the technology, encompassing the industrial, application, and communication aspects necessary for its operation and integration with the physical world. Since the COVID-19 pandemic, the world has been focusing on digitizing and improving healthcare in many aspects by introducing wearable devices, mobile apps, and other monitoring technologies. With the increasing  
Figure 2: The architecture of the DTs model. digitalization of public health, the handling of information and big data has been focused on and researched by many scientists and researchers. Significant effort has been made in the field of big data, machine learning, and other computational methods to provide an effective method for the monitoring, treatment, and prevention of disease, as shown in Table 1.  
One of the methods that can be used to measure, monitor, diagnose, and manage brain and mental activity is electroencephalography (EEG). Recent developments in wearable EEG technology have made it possible to remotely collect brain activity data in real time, which is a huge step forward in the biomedical field. Therefore, there is a large amount of research on remote monitoring and diagnosis through wearable EEG devices. Integrating new technology with EEG signals improves the ability to monitor and detect difficult
# COTS Battery Charge Equalizer for Small Satellite Applications
###### Abstract  
This paper describes the design and implementation of a battery equalizer circuit for small satellites, developed under the New Space philosophy exclusively using commercial off-the-shelf (COTS) components. The primary objective is to ensure high reliability for mission-critical power systems while adhering to strict cost constraints. In order to achieve this objective, the design incorporates a robust analog control circuit, thereby avoiding the complexities and potential single-point failures associated with digital controllers. A comprehensive study of various cell-balancing topologies was conducted, leading to the selection, hardware implementation, and comparative analysis of the two most suitable candidates. The results of this study provide a validated, cost-effective, and reliable battery equalizer solution for developers of small satellites.  
18650 lithium-ion battery; battery equalizer; commercial off-the-shelf; CubeSat; New Space 2020
## 2 Materials and Methods
### Gathered Data  
A total of 37,283 devices were analyzed during the 12 h period from 12 a.m. to 12 p.m. on the 14 June 2022. The dataset consists of 4,211,336 entries. From the QoE assessment perspective, only the events connected to changing the STB state, event playing, channel profile playback, and stalling occurrences are essential for further processing. Event logs contained incidents related to starting or shutting down the decoder, changing a channel or event, playing a live channel, and using auxiliary functions such as timeshift, catchup, and nPVR. Bitrate changes, frequency, and the playback time of a particular video profile were also taken into account, as were the occurrences of rebufferings and their duration.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 2 Related Work
### FACE: Fast AES-CTR Mode Encryption  
The compact implementation of AES-CTR (i.e., FACE) for Intel processor was presented in CHES'18 [9]. The FACE method took advantage of the fact that small portion of Initial Vector (IV) value is only affected by the change of counter values. By utilizing this case, the FACE stores repeated values in a form of cache table and reused, which reduced the encryption timing. The FACE method largely consists of four steps.  
The first step is called FACE\({}_{rd0}\), which makes a pre-computed table of results of Round 0's Add-RoundKey. In particular, it stores 12 bytes out of 16 bytes in the IV block. In the CTR, only 1 counter is different between the first and the second IV block. The Add-RoundKey operation can be optimized away by pre-computing 12 bytes out of 16 bytes in the IV block. The pre-computed table can be utilized by \(2^{32-1}\) times of encryption operations.  
The second step of FACE is FACE\({}_{rd1}\) for Round 1. After the Mix-Columns operation of Round 1, only the first column of the state is updated. This is impact of the last byte, which is the only distinct byte from previous block of AES-CTR. The remaining columns maintain previous values since they are not affected by the last byte. This pre-computation can be used up-to 256 times, which may update other counter values.  
The third step of FACE is FACE\({}_{rd1+}\). The only difference between first and second blocks is the first column value, which may be updated in the FACE\({}_{rd1}\) step. The others are not changed and can be computed with the cached table. The first column is updated with 1-byte in the Mix-Columns operation.  
The fourth step of FACE is FACE\({}_{rd2}\). The counter value updates the first column of Round 2. In Round 2, counter values are updated. These values update other columns in the Sub-Bytes operation. Afterward, all 16 bytes are updated in the Mix-Columns operation. However, many values can be reused during the Mix-Columns operation.  
However, the original FACE method is efficient for 8-bit counter mode. After the 256-th block, the look-up table is changed. This frequent updates can be used by the attacker as an attack point. In order to avoid this incident, the FACE method should be implemented in a regular pattern.
## 2 Materials and Methods
### Modeling Setup
#### 2.2.5 Model Predictions  
The calibrated model was applied to simulate and predict the following three scenarios:  
* First (initial) prediction scenario. Long-term prediction of changes in groundwater flow, hydrodynamic, and balance conditions and their resources while maintaining the 2022 prevailing conditions;
* Second prediction scenario. Assessment of the Zhem Basin's approved exploitable groundwater resource security (availability). The second prediction scenario was aimed at checking the availability of approved operational groundwater resources per aquifer and water well in the event of their simultaneous operation. Estimation of exploitable resources, in accordance with the existing approved methodology in Kazakhstan, comes down to comparing the calculated groundwater levels drawdown with water withdrawal equal to the approved resource and the maximum permissible drawdown for a given water well. The calculated drawdown must be less than the maximum permissible drawdown. The maximum permissible drawdown is determined based on the pump's technical capabilities, but must not exceed half the thickness of the pumped aquifer;
* Third prediction scenario. Evaluation of the maximal possible water withdrawal at which the predicted groundwater head drawdown will not exceed the permissible values. This calculation was carried out only for aquifers for which the predicted drawdown of the second scenario exceeded the maximum permissible drawdown.  
Predictions were performed for two periods, namely 25 years and 50 years. The beginning of the prediction model corresponded to the end of the calibration model period on 31 December 2022 (5475 days) and ended at 23,000 days. The first prediction scenario evaluated the annual exploitable groundwater withdrawal value for various posts using the 2022 initial parameters. The second prediction scenario assessed the annual groundwater withdrawal-approved exploitable groundwater resource values for 123 aquifer segments. In the third prediction scenario, groundwater withdrawal was the same as in the second scenario, excluding those segments for which the maximum possible water withdrawal was calculated. In all the scenarios, the BCs remained the same as those in 2022.  
The Theis solution [35] was used to adjust the model cell size to the average radius of the reservoir (pumping) area, enabling the computation of cell area transition cuts (corrections) of the real well size and modeling to compare the actual drawdowns.
## 3 Experimental Results and Analyses
### Training Methodology  
We implement YOLOv3-Lite using the Tensorflow 1.7.0 [26] and Python 3.7 running on an NVIDIA Tesla K20 GPU in the programming environment Linux 16.04. In YOLOv3-Lite, batch normalization is used after each convolutional layer, in both the deep separable convolution and in several standard convolution layers, which is used to speed up convergence in the training process. Firstly, we use the mechanism of transfer learning to train the network. For the backbone network, we adopt the parameters pre-trained in ImageNet. In fine-tuning, the initial learning rate is set to \(1e-3\) and is divided by 10 after every epoch. The Adam [27] is employed to update the network parameters, with a batch size of 10 in each iteration. We train the network with 300 epochs in total. Secondly, all layers are unfrozen to train in detail for 50 epochs with a batch size of 4. The initial learning rate is set to \(1e-4\) and the decay rate is the same as the fine-tuning stage. Adam is also used in this phase. The initialization parameters of the proposed model are shown in Tables 2 and 3. Using a pre-training weight training network can greatly reduce training time and experimental resources and can converge faster. Then we can adjust the weights of the whole network, through training,to make the network model more suitable in the context of aircraft structure crack detection.
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 5 Conclusions  
In this paper, we provide a new solution to attenuate the shear wave noise in OBN data. Our method leverages self-supervised deep learning to perform an adaptive subtraction of shear wave noise. Compared with traditional adaptive subtraction, this method utilizes the rich information within the data and the powerful nonlinear fitting capability of DNN. Experiments on synthetic data verify the feasibility and effectiveness of the method. Simultaneously, the application on field data demonstrates that the proposed method effectively removes the shear wave noise while protecting the signal. We also conducted a comprehensive investigation of the denoising order and parameter settings of our method, providing insights into their influence on the denoising results. In conclusion, the proposed method can effectively remove the shear wave noise in the Z-component while preserving the signal, showing potential applications in the processing of data for OBN acquisition.  
Conceptualization, L.C., Z.C. and B.W.; methodology, L.C., Z.C. and B.W.; investigation, L.C. and Z.C.; formal analysis, L.C.; data curation, L.C. and Z.C.; visualization, L.C.; supervision, B.W. and J.G.; funding acquisition, B.W.; writing--original draft, L.C. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Loss function and SNR curves in network training (learning rate = 0.001). (a) Weight decay = \(1\times 10^{-6}\). (b) Weight decay = \(1\times 10^{-8}\).  
Figure 13: Loss function and SNR curves in network training without network parameter regularization. (a) Learning rate = 0.01. (b) Learning rate = 0.0001.  
* This work was supported by the Natural Science Basic Research Program of Shaanxi under Program 2023-JC-YB-269.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
###### Abstract  
This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.  
wheelchair; telepresence; assistive technology; 360-degree video; WebRTC
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 3 System Model
### Reading Model  
The transmitted packets are stored in a read-only buffer at the user side, and subsequently read under a fixed rate. In the context of the classic BT problem, the reading rate of the user is typically set as \(\frac{B}{T}\) packets per time-slot. To avoid any interruption to the reading process at the user, we next develop a continuous reading constraint for the transmission of \(B\) data packets within an arrival period, i.e., \(T\) time-slots. To this end, we first denote by \(m[t]\) the index of the current time-slot over its corresponding arrival period, which is represented by  
\[m[t]=t\mod T. \tag{9}\]Here, we have \(m[t]\in\mathcal{M}=\{0,1,2,\ldots,T-1\}\). As shown in Figure 2, we can avoid the interruption of the data reading process, as long as the amount of remaining unread data at the user side (red line) is no less than the amount of remaining in-transmitted data at the base station side (blue line). This way, we formulate the continuous reading constraint as  
\[\frac{B-q[t]}{m[t]}\geq\frac{B}{T}. \tag{10}\]  
To circumvent the influence of the zero point, we introduce  
\[q[t]T+m[t]B\leq TB. \tag{11}\]  
Subsequently, we find that optimizing the user-perceived capacity directly, under the constraint of average power, is highly challenging. Therefore, we consider optimizing the dual problem of improving the user-perceived capacity, which minimizes the average power while satisfying the user-perceived capacity. Based on this, we formulate an optimization problem to minimize the long-term average transmission power consumption under the continuous reading constraint in Equation (10), i.e.,  
\[\min_{\{s[t]\in\mathcal{S}\}} \lim_{N\rightarrow+\infty}\frac{1}{N}\sum_{t=1}^{N}P[t] \tag{12a}\] \[\text{s.t.} q[t]T+m[t]B\leq TB,\] (12b) \[q[t+1]=\min\{(q[t]-s[t])^{+}+a[t+1],Q\},\] (12c) \[m[t]=t\mod T. \tag{12d}\]  
Before leaving this section, we shall emphasize that the system determines the transmission rate \(s[t]\) by jointly considering the current values of the queue state \(q[t]\), the channel state \(h[t]\), and the time index \(m[t]\). As such, an energy-efficiency scheduling policy is conducted to achieve the user-perceived capacity.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Challenges Necessating Automation
#### 3.1.2 Ineffective Identification of Threats  
The inability of traditional forms of automation (intrusion prevention systems and intrusion detection systems) to detect novel threat variations was cited as the predominant reason for cyberattacks materializing [33, 34]. This appears to be a twofold issue. Traditional automation is largely signature and anomaly-based, and threats are becoming more complex in their execution. Ref. [35] issue remarks that complexity (as seen in advanced persistent threats) results in increased indicators of compromise that security analysts and their tools must now account for, leading to an additional administrative burden. Attributing ineffective detection to poor rule configuration and tools repeatedly issuing unsolicited alerts was also reported [36]. Considering that IDSs were initially proposed in the 1980s, ref. [37] encourage moving beyond passive cybersecurity (one that is static) and into the realm of active defense through the implementation of AI and ML technology. Anomaly-based detection systems generate security alerts because the flagged behavior differs from what is usually observed. However, refs. [38, 39] mention that abnormal behavior is often not related to maliciousness. Cyber situational awareness has been defined as gathering information about a situation and utilizing it to differentiate between the suitability of potential actions [40]. A security analyst's failure to establish situation awareness in a SOC due to the multitude of responsibilities they have (including monitoring, detecting, responding, configuring tools, integrating new tools, team, and AI-agent collaboration) constitutes the failure of effective threat detection according to two papers [2, 41]. These challenges were mentioned in 18/49 (37%) articles analyzed.
## 4 Limitations and Recommendations  
This study is primarily qualitative and exploratory. Due to its preliminary nature, a comprehensive quantitative analysis of detection accuracy and method comparison was not feasible. However, the evaluation of different technologies' applicability provides crucial guidance for future research.  
Future research will quantify the cost-effectiveness and performance limitations of advanced inspection methods in the mining industry. This will involve collecting data on detection probability, sizing accuracy, and false positive/negative rates for Magnetic Flux Leakage and Ultrasonic Guided Wave of In-Line Inspection technologies. A robust comparison of these methods will inform best practices for pipeline integrity management. Each technique has limitations: Guided Wave Ultrasonic Testing is less effective over long distances in buried sections due to signal decay; Metal Magnetic Memory is limited to ferromagnetic materials; and ILI is generally infeasible for low-pressure or irregularly constructed pipelines. In addition, other parameters for performance evaluation could include the operation risk, the accuracy or the reliable of the inspection data [46].  
This study focused on specific water supply pipelines in the mining sector. Future research should expand this scope to include cost-benefit analyses, other pipeline types (e.g., slurry transport), collaborative frameworks and partnerships to develop innovative solutions, and asset management systems integrating inspection data for informed decision-making. Further research could also enhance corrosion mapping capabilities in geodynamically active regions, examine the economic impact of advanced techniques on pipeline longevity, and assess the suitability of specific commercial ILI technologies (e.g., SmartBall(r) [47]) and robotic solutions integrating UT, MFL, and Electromagnetic Acoustic Transducer sensors (e.g., UT-CS HAWK(r) [48], as well as SpirALL(r) MFL/EMAT [49]) for mining operations.
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 4 Data Processing  
### Simulation of Forest NPP in Current Condition  
The NPP modeling strategy described in Section 2 was applied at a spatial resolution of 1 km and a daily temporal scale for the study period (2005-2012) using the inputs described in Section 3. In Equation (4), two GPPs were used: the one estimated by the production efficiency model (GPP\({}_{\text{PEM}}\), Figure 2b) and the one simulated by Biome-BGC (GPP\({}_{\text{BGC}}\)) [19], which also simulated \(R_{\text{G}}\) and \(R_{\text{M}}\). In Equation (6), the GSV map and the data reported in Table 3[46] were used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Forest Type** & \(\rho\) (**Mg m\({}^{-3}\)**) & \(\mathcal{C}\) (m\({}^{3}\) m\({}^{-3}\))** & \(\mathcal{D}\) \\ \hline EBF & 0.70 & 1.45 & 0.47 \\ LDBF & 0.69 & 1.33 & 0.45 \\ HDBF & 0.61 & 1.36 & 0.45 \\ LENF & 0.53 & 1.53 & 0.42 \\ HENF & 0.38 & 1.31 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic density (\(\rho\)), biomass expansion factor (\(C\)), and stem carbon allocation ratio (\(D\)) for the calculation of potential GSV and CAI [46].  
The NPP estimated by the two methods was assessed for the pixels containing the NFI3 plots indicated in Table 1. For each of these pixels, a reference annual NPP was calculated from NFI3 CAI as  
\[\text{NPP}_{\text{NFI3}}=50\text{ CAI }\rho\text{ C/D} \tag{7}\]  
\(D\) being the stem carbon allocation ratio (Table 3), and the 50 factor accounting for both the unit conversion to m\({}^{3}\) ha\({}^{-1}\) and the transformation from carbon mass to dry mass (2 kg kg\({}^{-1}\)). Annual NPP was calculated for the same locations following the methodology reported in Section 2, but GSV from NFI3 was used instead of the GSV map to test the modeling strategy in the best possible conditions. The calculated NPP was compared to NPP\({}_{\text{NFI3}}\) by means of the following statistics calculated by forest type: coefficient of determination (\(R^{2}\)), mean bias error (MBE), and root mean square error (RMSE).