esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Chemotherapy Dosing for Obese Patients  
Obesity among patients living with cancer is becoming increasingly common, with one in four UK adults classed as obese [55; 56]. It has been linked to an increased risk of developing cancer, as well as outcomes in patients with cancer. For example, obesity is not only a risk factor for developing hormone receptor-positive breast cancer [57], but it has been shown that obese patients have worse overall survival than non-obese patients [58; 59; 60; 61]. One possible explanation for this overall decrease in survival is that oncologists intentionally reduce cytotoxic chemotherapy dosages for obese patients according to their 'ideal bodyweight' or at a \"capped\" BSA of 2 m\({}^{2}\)[14; 57]. This practice of 'dose capping' is performed to avoid the dose-limiting toxicity effects of chemotherapy treatment, including febrile neutropenia, which are thought to be caused by administering drug dosages that are calculated using actual bodyweight [62; 63].  
The effect of obesity on treatment-related toxicity remains debated in the literature. For example, [PERSON] el. al. demonstrated that obese patients receiving uncapped chemotherapy do not experience increased rates of febrile neutropenia or toxicity compared with overweight or normal weight patients with early breast cancer [64; 65], while [PERSON] et al. showed increased toxicity rates in obese patients [66]. [PERSON] et al. in their meta-analysis suggests dose capping might in fact contribute to the reduced disease-free and overall survival outcomes for these patients [60; 61; 67; 68]. These and other similar findings investigating other tumour groups [53; 69; 70] have contributed to the American Society of Clinical Oncology (ASCO) recently publishing guidelines that suggest that obese cancer patients should be treated using standard BSA-based chemotherapy dose prescriptions according to their actual bodyweight and should not be capped [14]. Further research to assess the appropriate dosing strategies for people with elevated body weight that includes pharmacokinetic and outcome data in those who are not dose-capped is needed to provide practitioners with evidence-based guidance for determining chemotherapy dosages for obese patients.
## 2 Related Work
### Relevant Research on Chinese Address Parsing
#### 2.1.4 Deep Learning-Based Address Parsing  
The sequence-labeling model is a typically used deep learning Chinese address-parsing method, where the address text is regarded as a sequence whose characters at each position are labeled using the address components to which they correspond.  
Many Chinese address-parsing methods based on deep learning have been proposed. Certain research results have been obtained using these methods [32; 33]. For example, [PERSON] et al. [34] proposed a Chinese address-parsing method using the RoBERTa-BiLSTM-CRF deep learning model to alleviate the heavy reliance on word segmentation dictionaries and the inability to effectively recognize address elements and their types when using existing address-matching methods. Parsed addresses were standardized, and their composition was analyzed to improve the address-matching results. [PERSON] [35] improved the accuracy of Chinese address parsing by constructing an address-parsing model based on BERT-BiLSTM-CRF. [PERSON] et al. [36] proposed a Chinese address-parsing method that integrated neural networks and spatial relationships by targeting the word segmentation and randomness, diversity, and ambiguity of Chinese address elements. The address model that adopted spatial relationships could inherit the address elements of the hierarchical relationship model and accurately locate the address according to the spatial relationships among its elements. [PERSON] et al. [9] devised a labeling system for segmenting Chinese addresses. They utilized BiLSTM networks to capture contextual features and integrated CRF to ascertain the optimal labeling sequence. This approach led to an improved identification of placename entities.  
Although Chinese address-parsing methods based on deep learning have achieved good results, most deep learning models often ignore the fusion of local and global features during feature extraction from address elements. This results in the loss of semantic feature information, thereby reducing the accuracy of address element recognition. Finally, the error is propagated in the next processing link of address parsing. Therefore, in this study, a method for fusing local and global features was introduced into the deep learning model to improve the accuracy of Chinese address parsing.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Datasets  
The NTU RGB+D 60 dataset is one of the most widely used indoor benchmarks for skeleton-based human action recognition. It contains a total of 56,880 action sequences across 60 action categories, collected using Kinect v2 depth sensors (Microsoft Inc., Redmond, WA, USA). These sequences were performed by 40 volunteers, where the last 10 action categories involve interactions between two subjects, while the remaining are single-person actions. Actions were captured simultaneously from three different viewpoints using three Kinect v2 sensors, enabling multi-view analysis of each action sequence. To evaluate the generalization capability of algorithms under various conditions, the dataset authors provide two standard evaluation benchmarks:  
* **X-Sub:** The 40 subjects are split into two distinct groups, used respectively as training and testing sets. This setting primarily assesses the generalization capability across different subjects.
* **X-View:** Approximately 37,920 sequences captured from cameras 2 and 3 are used for training, and around 18,960 sequences captured by camera 1 are reserved for testing, thereby assessing model robustness under viewpoint variations.  
The NTU RGB+D 120 dataset is an extended version of NTU RGB+D 60 and currently represents the largest indoor skeleton-based action recognition dataset. It includes 120 action categories and comprises a total of 114,480 action sequences, recorded by 106 subjects in diverse scenarios. Like the original dataset, actions are synchronously captured using three Kinect v2 sensors, but NTU RGB+D 120 significantly improves upon NTU RGB+D 60 in terms of quantity and action diversity, including a broader variety of everyday activities and two-person interactions.  
Similar to NTU RGB+D 60, when utilizing this dataset, specific abnormal samples are excluded, and sequences with insufficient frames are either zero-padded or looped to reach the target length. Additionally, if the second subject is missing from a sequence, zero-padding is applied accordingly.
## 2 Background  
### SCADA Alarms  
Alarm systems [21] play an important role in process monitoring. Due to advanced technologies, modern wind turbines use hundreds of sensors and actuators as parts of their many control loops. This situation can result in a large number of measured variables and their corresponding configured alarms. Thus, alarms can be generated at a high rate. A wind turbine SCADA system is integrated with an alarm function, which monitors the condition of wind turbines and their subcomponents.  
Alarms are stored in the SCADA database. A list of the alarms used in this paper is shown in Table 1. They are recorded in chronological order. There are three types of alarms: information alarms, warning alarms, and fault alarms. Information alarms are generally used to communicate changes in certain operating conditions, e.g., wind turbine is reset, or a manual switch is engaged. Warning alarms are generated when the monitored variables come close to exceeding thresholds. Fault alarms are generated when these thresholds are exceeded. The attribute 'code' is the unique code of an alarm. The attribute 'flag' represents the start and the end of each alarm. Hence, each alarm has two records.
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 9 Applications of Derived Analytic Expression of MSE of the MUSIC Algorithm
### Evaluating the Performance of Ultrasonic Imaging  
In [30], the MUSIC algorithm for DOA estimation is considered. Basically, the MUSIC algorithm is super resolution algorithm in that it can resolve closely spaced signals, Super resolution in the ultrasonic imaging is very important: Whether two closely located objects can be resolved or not is highly dependent on the resolution of the adopted imaging algorithm. Therefore, the MUSIC algorithm can be a good candidate for ultrasonic imaging. In, the authors derived the expression of the resolution, which is a measure of resolving capability of two closely located objects. Although, in [30], the explicit expression of the resolution is derived, the explicit expression of the estimation accuracy is not presented, and the derived explicit expression of the MSE of the MUSIC algorithm can be used to evaluate the MSE of the MUSIC-based estimates. Therefore, the difference between [30] and this paper is that the resolution is evaluated in [30] and the MSE is evaluated in this paper. In imaging, usually, resolving two closely-spaced objects is important, which is why many studies on derivation of the expression of the resolution of the MUSIC algorithm have been conducted. Absolute peak location can also be important in some ultrasonic imaging when the estimation of the exact location of the object is important. In that case, the MSE should be obtained to quantify the accuracy of the peak location in the MUSIC-based imaging.  
The peak location of the MUSIC algorithm-based imaging corresponds to the location of an object, and the MSE implies how close the estimated location is to the true location of the object.  
Evaluation of Ranging Accuracy in the MUSIC-Based Laser Ranging Algorithm in Terms of the Mean Square Error  
In [31], the authors proposed to apply the MUSIC algorithm in range estimation for use with frequency-modulated continuous wave (FMCW) laser. Traditionally, the fast Fourier transform (FFT) is for ranging. For improvement of ranging accuracy via the super resolution property of the MUSIC algorithm, the MUSIC algorithm can be employed for frequency estimation. The ranging accuracy in terms of the mean square error can be obtained from (40) of this paper if the laser measurement noise can be modelled as an additive Gaussian noise.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results  
### Intensive Small Area Mapping by UAV  
A time series of UAV images was used to assess the possibility of detecting weed change with time. The capture areas are shown in Figure 1. The capture dates and times are shown in Table 2.  
Throughout the study, WiField loggers collected water depth and temperature data, which is shown in Figure 4. Evaporation and leaking/seepage is indicated by smoothly declining water levels, as seen in the chemical treatment channels (C1 and C2). These channels were 'locked-up' after chemical application with no water allowed back in during the treatment period. The untreated channel (U) was maintained at a constant water level by the automated channel control system, except towards the end of the study where it was drained and then refilled. The mechanical treatment channel (M) had low water levels while the de-silting was occurring, and was  
Figure 3: Method of generating a classified image from a satellite image. The land and selected channel geometries are constant across images, so a new classifier is generated for each image without a user needing to manually define new water, land and weed polygons.  
The first image in the time series (2 July) from the C1 channel was used to assess class reflectances, and classifier accuracy with different bands, resolutions and classification algorithms. The selected parameters were then used on a time series of images from the U (untreated) channel to validate the classification method over numerous image dates. The validated method was then used over all the channels (U, M, C1 and C2) over the whole time series to assess changes in the weed area to compare the different weed control methods.
# Recent Lipid Membrane-Based Biosensing Platforms
## 5 Practical Applications of Lipid Membrane Devices in Food, Environmental, and Clinical Analysis  
### Applications of Lipid Film Sensors Based on Glass Microfilter  
An atrazine lipid film biosensor was reported in the literature, which was based on microfiber glass filters between two Saran-WrapTM partitions in which the lipid was deposited [79]. A transient ion current signal (duration of seconds) was the result of interactions of atrazine with the lipid membranes and appeared within 1 min following the injection of atrazine into the bulk electrolyte solution. The introduction of an anionic lipid (35% DPPA) in membranes and of calcium ions in the electrolyte solution increased the sensitivity of the technique. Similarly, the use of platelet-activating factor (PAF; an ether analog of PC) in membranes increased greatly the sensitivity.  
A Flow Injection Analysis (FIA) technique of mixtures of the triazine herbicides simazine, atrazine, and proparazine, using a mixture of PC (Phosphatidyl Choline)/ Dipalmitoylphosphatidic Acid (DPPA) in glass fiber filter-supported lipid films, was reported in the literature [12]. When a sample containing a mixture of these herbicides was injected into the flowing carrier KCl electrolyte solution, a transient ion current signal (duration of seconds) appeared in less than 2 min following the injection. The peak heights of these signals were linearly correlated to the herbicide concentration, \(\mu\)M detection limits. The signal did not decrease but rather remained practically constant, even when repetitive cycles of injections were performed. The time of appearance of the transient signal varied on the hydrophobicity of each herbicide and increased to the order of simazine, atrazine, and proparazine, thus allowing the simultaneous determination and analysis of these triazines in mixtures.  
Figure 7: A simple diagram of the device design (from [75] with permission).
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 3 Methodology
### Data Diversification and Validation for Generalization and Robustness  
To ensure the system's adaptability and robustness across a wide range of scenarios, we emphasize methods to diversify and validate the training data. This is essential for developing models that not only perform well on the data they were trained on but also generalize effectively to new, unseen situations. One key approach is multi-source data integration, which combines diverse data sources to create a comprehensive training dataset. We integrate historical data, such as financial records and resource allocations, with real-time inputs, including citizen reports and event management metrics. Historical data provide context by capturing trends, decisions, and behaviors over time, which is crucial for understanding long-term patterns and making accurate predictions. In contrast, real-time data ensure the model remains responsive to current conditions, allowing it to adapt dynamically to the latest information, such as live feedback from citizens or immediate event metrics. By fusing these data sources, the model gains the ability to manage evolving scenarios and shifting governmental requirements, learning from both static past conditions and the dynamic nature of real-world events.  
To further enhance the model's reliability, we implement cross-validation techniques, specifically K-fold cross-validation, to assess consistency across different data subsets. In this method, the data are divided into K parts, where the model is trained on K-1 folds and assessed on the remaining fold, repeating the process K times. This ensures that the model's performance is evaluated on all available data, providing a robust estimate of how well it will perform in unseen cases. The iterative nature of K-fold cross-validation mitigates overfitting, a common issue where models become overly specialized to specific data points and struggle to generalize. By validating across multiple folds, the model becomes more dependable and robust, ensuring it performs consistently across diverse administrative conditions and adapts effectively to real-world challenges.
# Designing an Adaptive Underwater Visible Light Communication System +
## 8 Results
### Manual Angular Displacement Results  
To make the system adaptive, initial trials were conducted by manually changing the position of the Li-Fi without using any motor. These trials aimed to evaluate the effect of line-of-sight and non-line-of-sight displacement on the system's performance.  
The reference angular positions of RX with respect to TX are shown in Figures 14 and 15.  
when the receiver is positioned perpendicular to the transmitter. Additionally, 8-PPM experiences a higher BER at the same angular positions compared to 4-PPM.  
The power of the signal in Figure 17 is lowest when the receiver is not aligned with the transmitter and reaches its maximum when they are perfectly aligned at 0\({}^{\circ}\). Additionally, the graph indicates that 4-PPM has higher power than 8-PPM because 4-PPM transmits once every four slots while 8-PPM transmits once every eight slots, making 4-PPM more efficient in terms of power.  
The experiment for non-line-of-sight (NLOS) communication was conducted in a similar manner to LOS communication. In this case, the transmitter was positioned at a 20\({}^{\circ}\) angle, as shown in Figure 15, relative to the LOS distance between the transmitter and the receiver, while the receiver's position was adjusted as shown in Figure 15.  
Figure 16: BER analysis for LOS communication.  
Figure 17: Power analysis for LOS communication.  
Figure 18 demonstrates that the BER increases when the transmitter's angle of incidence moves out of the receiver's field of view (FOV). However, once the beam re-enters the receiver's FOV, the BER begins to decrease.  
In Figure 19, the power for NLOS communication is at its minimum when the incident beam is outside the receiver's FOV. However, as the transmitter and receiver align, the power increases, leading to improved signal reception.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 3 Hardware-Based Mitigation Techniques
### NX Bit  
Von Neumann architecture, a design used in all mainstream microprocessors, enables the same memory space to store both code and data. This can be accomplished using paging, which does not permit the user to set read, write, and execute permissions independently on a specific memory region. The following three options are available for setting the access permissions for a specific region: non-accessible, readable-executable (RX), and readable-writeable-executable (RWX). So, if the read bit is set, the page will also be executable, and this is the main reason that makes code-injection attacks possible [22]. It is proposed that the NX bit be introduced to the hardware in order to remove the execution permissions from memory regions containing data. Through the support of the NX bit, operating systems can mark certain memory areas (heap, stack) as non-executable.  
Different architectures use different terms with similar features for NX bit. AMD first introduced NX bit in AMD64 processors (Athlon64, Opteron) in x86 architecture. It allows the controlling execution per page rather than the whole segment by adding a new page table entry [23]. Intel included a similar feature as XD (execute disable) in x86 Pentium 4 processors [24]. A new page table entry (PTE) format was introduced in ARMv6 that included XN (execute never) bit. [PERSON] first introduced NX bit in Linux Kernel 2.6.8 in 2004 for 64-bit CPUs [25]. NX support is present in Ubuntu 9.10 and all later versions. The mac OS X 10.4.4 onwards supports NX bit on all Apple-supported processors. In Windows operating system, it was first implemented in Windows XP Service Pack 2 and Windows Server 2003. The NX version of Windows is called Data Execution Prevention (DEP) [26].  
Operating system support is required for getting benefits from hardware supported NX bit as page table is an Operating system entity. NX bit refers to bit number 63 (starting from zero), which is the most significant bit in the page table. The code can be executed if the NX bit is set to zero (0) for the particular page; if it is set to one (1), it is a non-executable page containing data only. The NX (no-execute) feature is only available with the 64-bit mode. An important thing about the NX feature is its run-time strategy as there is no need for recompilation for getting benefit from this feature. Operating systems mark the stack/ heap memory as non-executable by taking advantage of NX bit, thus preventing a considerable portion of code injection attacks that exploit the Buffer overflow. Figure 7 shows the illustration of NX bit entry in the x86-64 page table entry [27].
## 3 Experimental Results
### Principal Component Analysis (PCA) Method  
Principal Component Analysis (PCA) [29] stands as a key technique in the field of signal analysis, especially when confronted with multi-dimensional datasets such as neural recordings. The fundamental goal of PCA is to reduce the complexity of high-dimensional data by transforming them into a lower-dimensional space while retaining the maximum variability present in the original data. In the context of spike sorting from biosignals, PCA serves as a powerful tool to extract the most informative features from the recorded waveforms. By identifying the principal components orthogonal axes along which data vary the most, PCA effectively reorients the data, allowing for a more intuitive representation that often captures the underlying patterns and relationships among different spikes. This dimensionality reduction not only aids in visualization but also facilitates subsequent analysis techniques, such as clustering algorithms, by reducing noise and improving the separability of distinct spike shapes. In this regard, PCA improves the precision and efficiency of spike-sorting procedures and provides more insightful interpretations drawn from neural recordings.  
Figure 8 provides insight into the data distribution following dimensionality reduction to three principal components. The centroids of the groups obtained via K-means are prominently displayed. Furthermore, a 3D representation of the formed groups is depicted in Figure 9. Notably, this representation illustrates the segregation of three distinct spike groups for the human pancreatic signal instead of the two initially found by the K-means without PCA (Table 6), which struggled to achieve such differentiation. Moreover, PCA exhibits the capability to identify an additional group within the macaque monkey signal, a spike group that was initially misconstrued as noise by the K-means algorithm. This misclassification stemmed from the fact that the spike and the noise signal shared identical amplitudes as can be observed in Figure A5. The affirmation of a shape's correspondence to a spike depicted in Figure A5a stems from a distinct classification criterion. Specifically, a total of 30 samples were grouped in this particular spike configuration. This contrasts with the shape depicted in Figure A5b, where only a single signal was identified. This discrepancy hints at a potential distinction, the former shape aligning with a grouped spike, possibly originating from a nearby neuron, while the latter's sole detection might suggest a spike emitted by a more distant neuron [30]. The discernible difference in amplitude between the two shapes contributes to this analysis, corroborating their distinct neural origins. These results probe how PCA, by extracting salient features, contributes to the improved effectiveness of the K-means clustering method.