esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 2 Methodology
### Data Preprocessing  
First, data is prepossessed to improve the overall analysis results. The images are loaded from the original resolution of the public and local datasets and resized to a standard size of 224 \(\times\) 224 pixels using bilinear interpolation. No noise filtering and reduction operations are applied to the images. The data augmentation procedure involves various data augmentations through the Keras ImageDataGenerator. During the data augmentation process, all images are randomly rotated at 15%, width and height are shifted at 10%, zoom at 20%, flipped vertically or horizontally. Image enhancement is carried out to adjust the image brightness within the 80% to 120% range. Then, using the Pillow (PIL) library, each image is converted to a PIL image object and then normalized by dividing them by 255.0 to scale the pixel values between 0 and 1. After the data augmentation process, images are converted to RGB format and then reshaped into arrays to be generated iteratively. An example of the applied data normalization and enhancement is shown in Figure 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **T1-w** & **T2-w** & **FLAIR** \\ \hline Repetition time (TR) [ms] & 580 & 4432 & 10,000 \\ Echo time (TE) [ms] & 15 & 100 & 115 \\ Inversion time (TI) [ms] & - & - & 2700 \\ Matrix size & \(80\times 80\) & \(80\times 80\) & \(80\times 80\) \\ Field-of-view (FOV) [mm\({}^{2}\)] & \(250\times 170\) & \(250\times 170\) & \(250\times 170\) \\ Slice thickness [mm] & 5 & 5 & 5 \\ Contrast agent flow rate [mL/s] & 2 & - & - \\ Maximum dose [ml] & 10 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: DICOM header information for the MRI sequences of the local dataset.  
Figure 2: Data augmentation process applied to the preprocessed images from left to right, horizontal and vertical flip, rotate, shear, scale, sigmoid contrast, gamma contrast.  
Then, the augmented enhanced images are down-sampled using average pooling. Average pooling retains a lot of feature composition compared with max pooling, which discards a significant portion of the feature composition [31]. It is defined as:  
\[y_{i,j,c}=\frac{1}{k_{h}k_{w}}\sum_{n=0}^{k_{h}-1}\sum_{n=0}^{k_{w}-1}x_{(i\times s _{h}+m),(j\times s_{w}+n),c} \tag{1}\]  
where \(y(i,j,c),k_{h}\) and \(k_{w}\), and \(1/k_{h}(k_{w})\) represent the output value at pixel \((i,j)\) of the \(c\)th channel, windows of values in the input feature map, and the normalization factor, respectively. The stride values are determined by \(s_{h}\) and \(s_{w}\), and \(x(a,b,c)\) represents the input value at position \((a=i\times s_{h}+m),(b=j\times s_{w}+n)\) in the input feature map \(c\).
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 1 Introduction
### Stack Based Buffer Overflow  
An attack using a stack overflow is the most common type of BOF, which corrupts the function stack frame (FSF) or function activation record. A clear understanding of stack-based buffer overflows requires clarification of the basics of process address space and the layout of a stack as they relate to stack buffers.
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [59] have proposed a reinforcement routing protocol (RRPV) in VANETs. This method integrates model-based reinforcement learning and fuzzy logic. RRPV utilizes a multi-agent model-based reinforcement learning technique called DynaQ, which has a high convergence speed. Model learning and reinforcement learning are two main components in DynaQ architecture. This routing approach applies the fuzzy logic-based system to build the learning model, and the Markov decision framework is also used for the second component. This fuzzy logic-based system has two input parameters, namely link stability and connection quality. Link stability indicates the connection time, which is determined based on the Euclidean distance, relative velocity, and movement direction. Furthermore, the connection quality is also evaluated based on the packet reception ratio. After determining the link quality using the fuzzy system, its results are considered as the state transition probability from the former state to the latter state in MDP. In the reinforcement learning process, any vehicle that has a data packet for sending is considered as an agent. In this case, each vehicle (agent) has two states, including \(F\) (i.e., the node has a packet for sending) and \(D\) (i.e., the node has delivered its packet to a neighbor). The action set represents the transmission of a hello message from the current vehicle to the neighboring vehicle. Moreover, the reward function is determined with regard to the link quality and the Euclidean distance between the two neighboring vehicles. The learning structure of RRPV is represented in Figure 14. MDP generates Q-values, which are used to choose relay nodes. Each vehicle stores a routing table having its Q-values and the V-values of neighboring vehicles. These values are periodically updated, meaning that the routing paths are gradually changed.
## 4 Experiments and Results  
In this section, simulation experiments are executed to compare the different distributed strategies. We first propose the evaluation protocol and then thoroughly compare the performance of the strategies in realizing the swarm consistency and avoiding swarm splitting. Validation experiments are executed to verify the effectiveness of the optimization proposed before. The scalability performance, the tracking performance for a given motion trajectory, and the ability to deal with the sudden change of communication topology are also evaluated.
## 4 The Energy Balance Problem
### EBP: The Energy Balance Protocol  
We assume that each event is sensed by only one sensor. This assumption is not restrictive since we may consider multiple sensing and propagation of an event by various sensors as sensing and propagation of many different events, assuming methods to resolve symmetry (such as clustering, leader election) in cases like when sensors are acoustic and the event is a thunderclap. A sensor sensing an event generates then a data message which should be eventually delivered to the sink. On each ring sector, \(T_{i}\), a number of events occur and a corresponding number of messages (one for each event) is generated.  
Randomization is used to achieve some \"load balancing\" by evenly spreading the \"load\" (energy dissipation). In particular, on ring sector \(T_{i}\) each event is propagated to \(T_{i-1}\) (i.e. the \"_next_\" sector towards the sink) with probability \(p_{i}\), while with probability \(1-p_{i}\) it is propagated directly to the sink S. Each message in \(T_{i}\) is handled stochastically independently of the other events' messages.  
The choice of probability \(p_{i}\) for \(T_{i}\) is made so as the average energy consumption per area unit (and thus per sensor) is the same for the whole network. There is a trade-off from choosing \(p_{i}\): if \(p_{i}\) increases then transmissions tend to happen locally, thus energy consumption is low, however sensors closer to the sink tend to be overused since all data passes through them. On the other hand, if \(p_{i}\) decreases, there are distant transmissions (thus a lot of energy is consumed) however closer to sink particles are bypassed. Calculating the appropriate probability \(p_{i}\) for each \(T_{i}\) and solving the problem of energy balance is very important since it combines efficient data propagation with increased network's lifetime.  
By using an underlying subprotocol [8; 25] we can guarantee that only one \"next hop\" sensor receives the transmitted message. Note also that data messages are of fixed size i.e. no further info is added to a message on its route towards the sink.  
Our protocol is (a) _distributed_, since each sensor chooses propagation probability independently of other sensors, (b) it uses only _local_ information, in the sense that \(p_{i}\) depends only on \(i\), i.e. a parameter related to the distance from the sink. Note that distance from the sink info for each sensor can be easily obtained i.e. during a set-up phase where the sink broadcasts control messages to the network. Several techniques (including signal attenuation evaluation) can be used to estimate each sensor's distance for the sink. (c) The protocol is very _simple_, since it just uses a random choice based only on parameter \(i\).
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 3 System Model
### Sensing Model  
Assume that the received signal at the \(i\)th receiver of local sensing node can be formulated as:  
\[r(n)=h(n)s(n)+x(n) \tag{1}\]  
where \(s(n)\) denotes the primary signal, \(x(n)\) denotes the background noise with Gaussian distribution and \(h(n)\) represents the Rayleigh channel. The sampling frequency is \(f_{s}\) and the sensing duration is \(\tau\).  
The energy value of the \(i\)th local sensing node can be written as shown in (2) and (3):  
\[E_{r}^{i}(n)=\sqrt{\left|r_{1}(n)\right|^{2}+\left|r_{2}(n)\right|^{2}}, \tag{2}\]  
\[r(n)=r_{1}(n)+jr_{2}(n), \tag{3}\]  
where \(r_{1}(n)\) denotes the real part of \(r(n)\) while \(r_{2}(n)\) denotes its imaginary part.  
Then, the energy vector can be obtained:  
\[E_{r}^{i}=\big{[}\begin{array}{cc}E_{r}^{i}(1),&\cdots,&E_{r}^{i}(N)\end{array} \big{]}, \tag{4}\]  
\[N=f_{s}\tau, \tag{5}\]  
Figure 1: The flowchart of centralized CSS (cooperative spectrum sensing).  
where \(N\) denotes the sampling point of the \(i\)th local sensing node.  
After the reporting channel, the energy vector can be updated as:  
\[E_{r1}^{i}=[\begin{array}{ccc}E_{r1}^{i}(1),&\cdots,&E_{r1}^{i}(N)\end{array}], \tag{6}\]  
\[E_{r1}^{i}(n)=h_{1}(n)E_{r}^{i}(n)+x_{1}(n),\;n=1,\cdots,N, \tag{7}\]  
where \(h_{1}(n)\) denotes the reporting channel with the Rayleigh distribution and \(x_{1}(n)\) denotes the background noise.  
At the FC, the two-dimensional matrix is obtained based on each local energy vector:  
\[M=\left[\begin{array}{ccc}E_{r1}^{1}(1)&\cdots&E_{r1}^{1}(N)\\ \vdots&\ddots&\vdots\\ E_{r1}^{k}(1)&\ldots&E_{r1}^{k}(N)\end{array}\right]. \tag{8}\]  
Based on (8), the covariance matrix of \(M\) can be denoted as:  
\[R_{M}=\left[\begin{array}{ccc}R_{M}(1,1)&\cdots&R_{M}(N,1)\\ \vdots&\ddots&\vdots\\ R_{M}(1,N)&\ldots&R_{M}(N,N)\end{array}\right]. \tag{9}\]  
The updated covariance matrix with the mean value removed is shown as:  
\[R_{M1}=\left[\begin{array}{ccc}R_{M1}(1,1)&\cdots&R_{M1}(N,1)\\ \vdots&\ddots&\vdots\\ R_{M1}(1,N)&\ldots&R_{M1}(N,N)\end{array}\right]. \tag{10}\]  
Finally, \(R_{M1}\) is input to the CNN module for the final decision and the decision result can be described as:  
\[D=\psi(R_{M1}), \tag{11}\]  
where \(\psi(\cdot)\) denotes the CNN operations.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 3 Materials and Methods
### OLSR Improvement Selection  
In this section, we propose an algorithm to change the node willingness based on the HELLO messages received after an elapsed period using a game approach before the MPR selection.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
### State of the Art
PreFer, a service developed by Ecodevelopment, produces prescription maps, which, together with a variety of spatial layers (including soil properties, agronomic information, crop indices, statistical and predictive climatic parameters, and yield records), become available to the farmers on a regular basis at 5 m spatial resolution. The prescription maps are created within a GIS, where big data are analyzed using machine learning methodologies [12].
# Optical Interference Suppression Based on LCD-Filtering
###### Abstract  
Using light emitting diodes (LED) for the purpose of simultaneous communication and illumination is known as visible light communication (VLC). Interference by ambient light sources is among the most critical challenges. Owing to the wideband VLC spectrum, the efficiency of wavelength-dependent optical filtering is limited, especially in the presence of sunlight. Multi-user VLC causes additional interference, since LEDs are characterized by a wide viewing angle. Although algorithm-based interference suppression is a feasible method, receiver saturation and especially noise enhancement are two challenges that can only by addressed effectively by filtering in the optical domain prior to the photodetector. In this publication, we propose the use of a liquid-crystal display (LCD) as receiver-side filter unit. The main advantage of this technology is the possibility to focus the field-of-view of the receiver on a specific light source and thereby suppress interference. Interference by ambient light, modulated interference and multi-aperture interference are introduced and signal-to-interference ratio improvements are derived using experimental results for a given LCD characteristic. By deriving the bit error rate for MIMO communications, the potential of the proposed interference reduction method is demonstrated.  
optical wireless communication; visible light communication; free-space communication; liquid-crystal display; optical filtering; interference reduction; ambient light suppression; MIMO +
Footnote †: journal: Applied Physics Letters
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Resource Accumulation
#### 4.1.3 Common Voice  
Common Voice is a free and open crowdsourced data collection project initiated by Mozilla and other companies and research institutes [102]. Volunteers upload speech to the cloud server, and then, anyone can perform annotation via the same platform. In 2019, Common Voice 1.0 was released. It contained 1000 h of speech from 19 languages. At present, Common Voice has been updated to Version 11.0 (21 September 2022), covering 100 languages and containing 16,000 h of speech. In the new release, there are 119 h of Uyghur speech, 2 h of Kazakh speech, and 47 h of Kyrgyz speech. Common Voice is the first multilingual data resource constructed by crowdsourcing, and it is continually accumulating data. For now, the Kyrgyz data in Common Voice is the standard data for Kyrgyz research.
# Effects of the Fruit Harvest Date and Shelf-Life Nexus of Apples on Different Quality Perspectives
## 2 Materials and Methods  
### Apple Varieties and Experimental Design  
The research was conducted in 2024, collecting fruit samples of the two apple varieties, Braeburn and Golden Delicious, from the two commercial apple orchards, \"Mala Ivanca\" (Belgrade, Serbia), located on Avala mountain (Braeburn), and \"Zlatni Jazak\" (Belgrade, Serbia), located on Fruska Gora mountain (Golden Delicious). These orchards were established in 2013, with the slender spindle training system at a planting distance of 3.2 \(\times\) 0.8 m. As a measure for yield control, winter pruning and fruit hand thinning were carried out to achieve a crop load of 110-120 fruits per tree. Drip irrigation was carried out via pipes with emitters at a distance of 60 cm and a flow rate of 1.6 L/h. The amount of water delivered per tree was determined using the reference evapotranspiration and crop coefficient. Fertilization was applied regularly at annual rates of 90 kg N, 150 kg K, and 15 kg P via the irrigation system. The plants were in a good state of health during the trial period.  
The fruits of the varieties that were evaluated were harvested on three harvest dates. The optimal harvest date (R--Ripe) was the middle day of the projected harvest window for long-term apple storage. An unripe harvest date (UR--Unripe) occurs ten days before the optimal harvest date; an overripe harvest date (OR--Overripe) occurs ten days after the optimal harvest date. The optimal moment for harvesting was identified by using the Streif Index (SI) [34], which is commonly used in the apple fruit industry. For the Braeburn variety, the value of SI for ripe fruit was 0.12; for Golden Delicious, the value was 0.11. The apple samples were harvested, transferred to the faculty laboratory within two hours, and stored there at room temperature (up to 22 \({}^{\circ}\)C).  
The fruit was examined for physical and sensory characteristics on four testing days within a 15-day shelf life period: day '0', which was within 24 h of harvesting; day '5', which was 5 days from harvesting; day '10', which was 10 days from harvesting; and day '15', which was 15 days from harvesting.  
The experimental setup of this study is presented in Figure 1.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis  
In order to save time and cost, many products use a random truncation test method for reliability testing. The characteristics of fault data under a random truncation test can be represented in Figure 1.  
Regarding highly reliable and long-life products, most products do not fail at the end of the experiment, resulting in a large amount of truncated data and a small amount of fault data. The existence of truncated data directly affects the fault rank. Therefore, fault rank corrections are required. Meanwhile, considering the impact of empirical distribution functions on parameter estimations, the minimum average square distance is used to select the optimal empirical distribution function. The average square distance does not depend on the value of the fault samples and the optimal empirical distribution function can be selected from the perspective of the overall average. Under the assumption of the Weibull distribution model, the total least squares estimation method is introduced for parameter estimation. Then, linear correlation coefficient and d-test method are used for model hypothesis testing. If two or more distribution models pass the linear correlation coefficient test and the d-test simultaneously, the root mean square error and relative root mean square error are applied to determine the optimal reliability model. A flow chart of the reliability analysis of random truncation small-sample fault data is shown in Figure 2.
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
## 4 Simulation Results and Analysis  
This section discusses the performance analysis between the PID, MFAC, MFLAC, and MFA-SMC schemes.  
Figure 17 shows the step response of the BLDC motor. Table 2 summarizes the performance analysis of the different control algorithms. Table 2 shows that Conventional PID has a Maximum Overshoot of 3.26, a settling time of 1.251, and a rise time of 0.021. Similarly, MFAC has a Maximum Overshoot of 1.85, a settling time of 0.822, and a rise time of 0.134, and it is better than conventional PID. In addition, MFLAC has a Maximum Overshoot of 1.46, a settling time of 0.691, and a rise time of 0.137. The MFLAC result is much better than PID and MFAC. However, the proposed MFA-based SMC control is greatly improved, with a Maximum Overshoot of 1.21, a settling time of 0.492, and a rise time of 0.146. Although there is only a slight improvement in the rise time, the overshoot and settling time is greatly improved and is better than PID, MFAC, and MFLAC.  
Figure 16: Step response of BLDC Motor using Model Free Adaptive and Sliding Mode Control.  
SMC has great significance in the theoretical research field. Nowadays, it is becoming a serious source of solutions to industrial problems. Its potential is proscribed simply by the artistic ability of the individuals working in process control. Thus, the expectation for SMC's application in the processing industries is huge. The primary benefits of SMC are its invariance and quality properties, and that it can deal with bounded modelling uncertainties and bounded disturbances both in linear and nonlinear systems.  
Model-Based SMC requires a modelling process, which is expensive and time consuming. To eliminate the modelling process, data-driven SMC is proposed with application to the position control of a three-phase BLDC motor that only depends on the input/output information of the system. The simulation result shows that the overall performance of the three-phase BLDC motor is significantly improved. Moreover, the step response analysis shows that by using MFA-SMC, the maximum overshoot and settling time has greatly reduced, from 3.26 to 1.20 and 1.251 to 0.492, respectively. The proposed MFA-SMC algorithm can achieve better position control and stability over the traditional PID, MFA, and MFLAC methods.
## 3 Methodology and Materials
### Dataset Used
#### 3.3.2 The AID Dataset  
The second dataset employed in our study is the AID dataset [29], a valuable resource that significantly contributes to the diversification of scene recognition tasks. The AID dataset is characterized by its expansive collection, encompassing 30 distinct scene classes, each encapsulating the unique characteristics of various aerial landscapes. Within each of these scene classes, the dataset includes a substantial number of samples, ranging from 200 to 400 images, all uniformly sized at 600 \(\times\) 600 pixels. To ensure methodical experimentation and robust model development, we strategically worked with a subset of 200 images from each of the 30 scene classes within the AID dataset. This subset facilitated both model training and evaluation processes, with an 80% allocation for training and a 20% allocation for testing, adhering to best practices in dataset partitioning.  
What sets the AID dataset apart is its origin--comprising sample images meticulously collected from Google Earth imagery. While these images undergo post-processing through RGB renderings derived from the original optical aerial images, it has been extensively validated that there is no significant disparity between Google Earth images and genuine optical aerial images. This validation extends even to pixel-level land use and cover mapping, emphasizing the suitability of Google Earth imagery for evaluating scene classification algorithms. The AID dataset encompasses an extensive array of aerial scene types, including but not limited to airports, bridges, forests, meadows, schools, stadiums, and more. Notably, each image within the dataset has been expertly annotated by specialists in the field of remote sensing image interpretation, ensuring the precision and reliability of scene labels. These labels play a crucial role in training and evaluating scene recognition models, contributing to the dataset's overall utility.  
One of the dataset's distinguishing features is its multisource nature, as it amalgamates images from various remote imaging sensors present in Google Earth. This characteristic presents additional challenges for scene classification compared to datasets containing single-source images. Moreover, the AID dataset's inclusivity extends across different countries and regions globally, with a diverse collection of images extracted under varying timeframes, seasons, and imaging conditions. This diversity enhances the dataset's intrinsic intra-class variations, further enriching the data's representativeness and applicability in scene recognition research. Figure 4 shows samples of images from this dataset.  
Figure 4: Samples of images from AID dataset [29].  
Figure 3: Samples of images from scene classification dataset [28].