esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Outcome Evaluation  
Through the analysis of patients' computerized medical records, demographic data were collected, including information on gender and age at the time of admission, pre-hospitalization living situation, degree of autonomy in walking and activities of daily living (ADL) before the acute event, total hospitalization days, days of hospitalization at the time of PMR evaluation, and rehabilitation days until patient discharge or death. Data on the subjects' remote medical history were also extracted from medical records, with a focus on cardiovascular, respiratory, diabetic, neoplastic, vascular, and neurological comorbidities. In addition, cumulative scores obtained from the Barthel Index administered at admission and discharge were extracted.  
The Barthel Scale is one of the most widely used clinical tools to assess an individual's physical functionality. The analyzed items evaluate the ability to perform activities such as eating, dressing, managing personal hygiene, washing, controlling bladder and bowel function, walking on flat ground and climbing stairs, performing bed-chair and chair-bed transfers, and executing bathroom transfers. Assignable scores are 0, 5, 10, or 15, with a higher score indicating greater autonomy in that specific activity. The maximum score of  
Figure 1: The diagram illustrates the process of patient selection and participation in the study.  
100 indicates complete independence in all described activities, while the minimum score of 0 identifies a condition of total dependence in managing ADL.  
For each patient, the initial PMR assessment during hospitalization was analyzed, collecting data on objective examination findings. Tactile sensitivity, muscle tone, respiratory deficits, and the presence of dysphagia were considered. Segmental muscle strength was assessed using the Medical Research Council (MRC) scale, and the MRC Sum Score was calculated based on the obtained data. The MRC Scale is an extensively employed method for assessing muscle strength. It assigns a numerical grade (ranging from 0 to 5) to the muscle strength of specific muscle groups, with 0 indicating no muscle contraction and 5 indicating normal strength. The MRC Sum Score is a variation on the MRC Scale that considers the measurement of 12 muscle groups, specifically shoulder adductors, elbow flexors, wrist extensors, hip flexors, leg extensors, and dorsiflexors of the foot, for both sides of the body. By assigning the scores described in the MRC Scale and summing each muscle group, an overall score ranging from 0 to 60 is obtained. Cutoffs of <48 and <36 are used to identify significant weakness and severe weakness, respectively.
# A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### Improvement
#### 2.4.2 Neck Layer  
The YOLOv8 neck layer uses the upsample module to upsample low-resolution feature maps to a higher resolution, the C2f module to fuse features, and the Conv module to extract feature information. These modules combine multi-scale features from the backbone for detection by the detection head. However, there is a problem with poor detection performance for smaller-sized targets. RFAConv is a new convolution operation proposed by [PERSON] et al. [19] in 2024, and its specific structure is shown in Figure 7. Although existing spatial attention mechanisms effectively overcome the inherent parameter-sharing limitations of convolutional neural networks, this is limited to acting before the 1 \(\times\) 1 convolution operation. When the spatial attention mechanism acts before the 3 \(\times\) 3 convolution operation, there is an overlap of receptive fields, which leads to the problem of shared attention weights between sliders that cannot effectively resolve parameter-sharing issues. RFAConv performs group convolution (Group Conv) operations and average pooling (AvgPool) operations on the input feature map. The group convolution operation extracts receptive field spatial features from the input feature map, converting the receptive field into a collection of multiple receptive fields, each being an independent unit. The average pooling operation computes the mean value of each pixel within the input feature map, yielding a feature map that encapsulates global data. After average pooling, the global information feature map is divided into three groups, each applied with convolution kernels of different sizes. After normalization with the Softmax function, an attention weight map is obtained. RFAConv re-weights and adjusts the size of the receptive field spatial features and the attention map, and then outputs a feature map through a 3 \(\times\) 3 convolution operation with a stride of 3 to ensure compatibility with subsequent network layers.  
Figure 6: MHSA structure in BoTNet.  
This research substitutes certain C2f modules within the YOLOv8 network's intermediate layer with RFAConv modules, facilitating a more nuanced refinement of the feature extraction across various regions while maintaining a manageable parameter count. This adaptation empowers the model to adaptively configure its receptive fields, capturing more intricate details for petite foreign objects and broader contextual information for larger ones, thereby bolstering its proficiency in detecting a diverse range of foreign matter sizes.
## 4 Motion Planning and Control in ADAS for City Buses  
As previously mentioned, upon approaching the charger, an underlying process ensures the activation of the ADAS motion-planning system without requiring the driver's intervention. The motion generation subsystem, comprising a constrained path planner and a feedback controller, establishes a reference path from the bus's current configuration to the docking station, which is considered the final configuration. Subsequently, the subsystem ensures that adhering to this path minimises errors caused by uncertainties in localisation and any driver-induced errors.  
In order to ensure the feasibility of the generated path, the maximum permissible curvature and rate of curvature are deduced from the kinematic model of the bus [35]. These values are subsequently established as constraints for the planning task.
# SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks
## 5 Results and Discussion
### Assessment of the Classification Performance  
The first assessment of the proposed model is carried out by evaluating its classification performance under the three datasets described in Section 3, namely BoT-IoT [31], TON-IOT [32], and IoTID20 [33]. For the purpose of this assessment, three SEHIDS models have been constructed, each of which has been fed by one of these datasets, the outputs of these models are then collected and analysed using standard statistical methods from which the confusion matrices are given in Figure 3.  
The results shown in Figure 3 demonstrate that SEHIDS is able to accurately classify the different types of attacks in the three datasets. The lowest reported value is 0.99,  
Figure 3: Confusion matrices of (**a**) BoT-IoT, (**b**) IoTID20, and (**c**) TON-IoT.  
while the highest value is 1. This outstanding performance is attributed to the fact that the design philosophies underlying SEHIDS endow it with tremendous capabilities to adapt its architectures in accordance with the traffic it receives. In particular, exploiting the divergence between the errors generated by the model due to the new input and the residual errors previously generated by the model enables SEHIDS to remediate its classification errors instantaneously. More importantly, the use of these constraints facilitates maintaining SEHIDS to its near-minimal architecture, which in turn improves SEHIDS' ability to capture the intrinsic differences between different class types and embed them error-free in the small hypothesis space. Moreover, the ability of SEHIDS to determine the number of neurons in the new layer as the optimal complement of degrees of freedom leads to an acceleration of the classification performance of the model in a single step. Another outstanding design philosophy that contributes to these excellent results is the use of Quick Propagation and Cascade Correlation as training schemes. These schemes allow the SEHIDS to consolidate the knowledge gained during its development and quickly share this knowledge with the newly added neurons without compromising classification performance.  
A further assessment of the overall accuracy of SEHIDS is performed by measuring the normalised error as a function of time. The results of this assessment, as depicted in Figure 4, show that the normalised errors of the three curves drop sharply after a short time and continue in this way until they reach the zero-error regions. These results are not only consistent with the results of the first assessment but also complement them in the sense that SEHIDS does not take too long to reach the zero-classification error, which in turn allows SEHIDS to effectively recognise different types of classes effectively. It is worth noting that the above results are also consistent with the findings of work investigating the behaviour of models that use constructive learning approaches as a framework. These works show that such models are able to approach zero classification error within polynomial time [25; 49; 50; 51].  
Figure 4: \(Cont\).
## 4 Discussion
### Projected LULCs and ESV Changes by 2035 Under Multiple Scenarios
The CA-Markov model's simulation of future LUCs is predicated on the assumption that conditions will remain relatively stable. Yet, the simulation of LUCs under different scenarios entails a degree of uncertainty. For example, unexpected events (such as natural disasters, pandemics, etc.) may affect the reliability of the simulation results, particularly in rapidly changing regions where external factors could cause land use change patterns to deviate from historical trends. Therefore, the results based on a stable assumption carry inherent uncertainty. Additionally, factors such as the model's spatial resolution, time step, and the accuracy of input data may also influence the reliability of the simulation outcomes. When considering land use limiting factors, due to limitations in data acquisition, only restrictions on water bodies and farmland were included. However, the scope of ecosystem protection is not limited to water bodies and farmland but also includes important ecological areas such as forests, grasslands, and wetlands, which play crucial ES functions in LUCs. If the protection of these ecological lands is not considered, it may lead to an overestimation of the protection needs for water bodies and arable land in model predictions, thereby underestimating the protection requirements for other ecological areas. Additionally, the lack of consideration for the protection of important architectural clusters could result in an inaccurate spatial distribution of urban expansion, affecting the real prediction of LUCs. Additionally, since land use simulation and ESV assessment are based on hypothetical LUC scenarios and do not account for all potential limiting factors, this may lead to overestimation or underestimation of ESV. Especially when considering policy interventions, if changes in protected land areas are not fully incorporated into the model, the assessment of ES functions may lack sufficient accuracy and comprehensiveness. Therefore, future ESV assessments should place greater emphasis on the multidimensional constraints of various ecological regions and the impact of policy interventions in order to provide more precise predictive results.
## 5 Experimental Data Analysis Based on Linear Regression
### Data Analysis of Disturbance Force and Lateral-Line-System Data on Sway  
The second set of experiments is to calibrate parameters in Section 3.2, which determines the relationship between the lateral disturbance force and the data of the measuring line system.  
In this group of experiments, the lateral face of the robot is the facing-flow surface, and the bow surface is the non-facing-flow surface.  
\[{p^{\prime}}_{i}=p_{i}-p_{a} \tag{13}\]  
where \(p_{a}\) is the value of the artificial lateral-line node \(p_{0}\) which is located at the nose tip of the robot. \(p_{i}\) is the data of \(p_{L1}\), \(p_{L2}\), \(p_{L3}\), \(p_{L4}\), \(p_{R1}\), \(p_{R2}\), \(p_{R3}\) and \(p_{R4}\).  
The linear relationship between the force of the robot in sway and \(p_{i}\) is proved in Section 3.2. The profile of an underwater vehicle in a lateral direction is a plane surrounded by an Myring curve. The artificial lateral-line system can only obtain pressure data at key points of the surface. That is not a representation of the pressure on the whole surface directly. Therefore, these points need to be interpolated to obtain a set of relatively complete data \(p^{\prime}\). After interpolation, all the data are integrated with the area of the side section to obtain the required disturbance parameters \(A_{left}\) and \(A_{right}\).  
\[A=\int_{0}^{L}r(l)p^{\prime}dl \tag{14}\]  
where \(r(l)\) is the Myring profile curve equation of the underwater robot. \(L\) is for the length of the robot.  
Multiple linear regression is performed using the measured disturbance data \(F_{sawy}\) and the disturbance parameter \(A_{left}\) and \(A_{right}\). The regression equation is obtained [37], and \(a_{2}\), \(a_{2}^{\prime}\) and \(b_{2}\) are the parameters obtained by multiple linear regression.  
\[F_{sawy}=a_{2}A_{left}-a_{2}^{\prime}A_{right}-b_{2} \tag{15}\]
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 2 Quantum Key Distribution Systems and Networks  
Quantum communication is the field of study related to the transmission of quantum states between two or more parties. The most widely accepted applications for quantum communication fall in the field of cryptography, where the laws of quantum mechanics are exploited to secure data, and specifically, to share secret keys between symmetric parties, a technique named quantum key distribution (QKD). The timespan over which humanity has adopted cryptography in some form covers thousands of years. Since the mathematical formalization of cryptosystems, many variants of such systems have been created, all based on difficult mathematical problems, relying on the assumption that nobody could solve them in a realistic amount of time. Such cryptosystems are not really immune from attacks, especially as computation power and theoretical know-how increase, meaning that the probability of being able to decipher encrypted data increases over time. The advent of the new paradigm of quantum computing, where individuals are able to break most of the well-known cyphers, further confirms that just using computationally hard problems to secure data is not enough in the mid-term timescale. More generally, other applications than QKD exist for quantum communication, such as the transfer of quantum states being processed by quantum computers, the transmission of information using techniques such as quantum teleportation, or less known applications such as quantum coin flipping [1], a primitive technique useful in many algorithms. In the following we address the field of secure quantum communications, therefore restricting the paper to the topic of QKD in all its aspects, because it is the technique closest to have an industrial impact.
## 4 PAH Occurrence and Formation in Processed Meat, Edible Oils, and Cereal-Derived Products
### Features Underlying PAH Formation in Edible Oils
Oils with higher smoke points are more suited for processing at high temperatures [7]. For instance, the smoke points of brown rice oil, sesame oil, and perilla oil are 257 \({}^{\circ}\)C, 165 \({}^{\circ}\)C, and 161 \({}^{\circ}\)C, respectively. By altering the proportions of the oil mixtures to include more brown rice oil, a reduction in PAH levels was observed in seasoned-roasted layer (snack food) by up to 32% at 380 \({}^{\circ}\)C [190]. The deep frying of dough sticks in peanut, sunflower, rapeseed, rice bran, soybean, and palm oils revealed a considerable rise in polar compounds and PAH levels with the increase in frying time, further informing on the choice of oil type used for frying [191]. Olive oil was attributed to higher toxic emissions during deep frying compared to soybean and palm oils, which is in line with the recommendations indicated by other reports [2; 6; 7; 192]. To this end, the generation of PAHs in oil during high-temperature cooking may be favored by the aromatization and dehydrocyclization of monounsaturated fatty acids [140]. Moreover, extending the practice of reusing the frying oil leads to the progressive accumulation of PAHs in oil [175; 192; 193]. Consequently, it is crucial to discard spent oil, replace it often, and utilize the right frying conditions (i.e., temperature and duration) and oil type to lower the PAH levels in fried food products [7; 175]. Other physical factors, such as exposure to heat or UV rays, have also been investigated for their exerted effects on PAH occurrence in vegetable oils. [PERSON] and [PERSON] et al. (2016) explored the change in PAH content in rapeseed and sunflower oils under various UV radiation and temperature settings, resulting in a significant PAH decline in both cases that was strongly correlated with the duration of exposure [194]. However, new PAHs (photoinduced) with higher toxicities than the parent compounds may be produced [2].
_Appl. Sci._**2015**, \(5\), 145-156; doi:10.3390/app5020145  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Multicolored Femtosecond Pulse Synthesis Using Coherent Raman Sidebands in a Reflection Scheme**  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\), [PERSON] 3,\({}^{\dagger}\), [PERSON] 2 and [PERSON] 2,\({}^{\ast}\)**  
1 Department of Physics, The University of Michigan, Ann Arbor, Michigan 48109, USA;  
E-Mail: [EMAIL_ADDRESS]  
2 Department of Physics and Astronomy, Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843-4242, USA;  
E-Mails: [EMAIL_ADDRESS] (A.A.Z.); [EMAIL_ADDRESS] (X.H.)  
3 National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899-8543, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-979-845-7733.  
Academic Editor: [PERSON]  
_Received: 22 February 2015 / Accepted: 3 June 2015 / Published: 11 June 2015_  
**Abstract:** Broadband coherent Raman generation emerges as a successful method to produce multicolored femtosecond pulses and time-shaped laser fields. In our study, coherent Raman sidebands are generated in a Raman-active crystal, driven by two-color femtosecond laser pulses. An interferogram of the sidebands based on coherent Raman scattering is produced in a novel reflection scheme. The relative spectral phases of the sidebands are obtained from the interferogram using a numerical simulation. This enables us to retrieve the ultrafast waveform using coherent Raman sidebands.  
**Keywords:** subfemtosecond pulses; coherent Raman scattering; molecular modulation
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 4 Simulation Verification  
In this section, the finite element method is used to build a sparse aperture segmented mirror system consisting of seven sub-apertures to verify the algorithms proposed in Sections 2 and 3. It is also to compare the co-phase maintenance accuracy of two active optical control modes of segmented mirrors and to verify the correction capability of the WH technique for wavefront aberration of segmented mirrors.
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 2 Preliminaries
### LOPUB Scheme
#### 2.5.3 Estimation, LASSO  
Following the encoding and perturbation of their data, users transmit the processed information to the central server. The central server then acquires the distribution of users, incorporating random noise introduced by RR. For every bit \(b\) within each attribute \(j\), the central server counts the number of \(1\)'s \(\hat{s}_{j}^{i}\) as \(\hat{y}_{j}[b]=\sum_{i=1}^{N}\hat{s}_{j}^{i}[b]\). Subsequently, the original count \(y_{j}[b]\) is approximated as  
\[y[b]=\frac{\hat{y}[b]-\frac{fN}{2}}{1-f}, \tag{5}\]  
after calculating the original count, the candidate bit matrix is generated using a set of candidate Bloom filters \(\mathcal{H}\), implemented as  
\[M=\mathcal{H}_{1}(\Omega_{1})\times\mathcal{H}_{2}(\Omega_{2})\times\cdots \times\mathcal{H}_{d}(\Omega_{d}), \tag{6}\]  
where \(d\) represents the number of attributes. The block diagram illustrated in Figure 1 outlines the process. It reviews the steps undertaken by the central server, providing an example with \(k=2\). The aim is to estimate the distribution of attributes \(U_{1}\) and \(U_{2}\) in the subsequent steps. To derive the distribution from the noise data, \(\mathbf{y}=M\beta\), a regression technique is used. The technique employed is Least Absolute Shrinkage and Selection Operator, commonly known as LASSO. LASSO is a linear regression method that incorporates regularization to enhance prediction accuracy, as introduced by [23].
## 5 Results and Analysis
### Loss Function and Evaluation Index  
The loss function is used to quantify the deviation between the predicted values and the ground truth, allowing continuous model optimization towards convergence to minimize the overall loss and achieve optimal prediction results. In order to effectively balance the tomato plant and background contributions to the segmentation model loss, and thereby improve the accuracy of tomato plant segmentation, Dice Loss was used as the model training loss function, denoted by \(D\). Dice Loss is particularly advantageous for its ability to address the class imbalance commonly encountered in semantic segmentation tasks, thereby promoting more accurate segmentation results.  
\[D=1-\frac{2\sum\limits_{i=1}^{N}y_{i}^{*}y_{i}+\varepsilon}{\sum\limits_{i=1}^{N }y_{i}^{*}+\sum\limits_{i=1}^{N}y_{i}+\varepsilon} \tag{12}\]  
In the formula, \(N\) represents the total pixel value in the image, \(y_{i}^{*}\) denotes the true value of the \(i\) pixel, \(y_{i}\) represents the predicted value of the \(i\) pixel, and \(\varepsilon\) is a parameter adjustment value used to prevent division by zero, set to \(10^{-5}\).  
During the training process, in addition to calculating the model loss, it is essential to select appropriate parameters to evaluate the model performance. In this study, _IoU, MIoU, PA,_ and _mPA_ were chosen as metrics to evaluate the segmentation performance of the model. The formulas for these specific metrics are as follows [22; 23]:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{13}\]  
\[PA=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[MIoU=\frac{\sum\limits_{i=1}^{n}\left(IoU_{i}\right)}{n} \tag{15}\]  
\[mPA=\frac{\sum\limits_{i=1}^{n}\left(PA_{i}\right)}{n} \tag{16}\]  
where _TP_ represents cases where a sample actually belongs to the positive class and the model predicts it as such. _FN_ represents cases where a sample actually belongs to the positive class, but is misclassified as negative by the model. _FP_ denotes samples that actually belong to the negative class, but are misclassified as positive by the model. _TN_ denotes samples that actually belong to the negative class and are correctly classified as negative by the model. \(n\) denotes the total number of classes.