esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this study, an attempt was made to estimate live animal weight by using stereo vision and semantic segmentation methods in the literature. Within the scope of the study, a stereo vision device was prepared, and stereo images of 85 cattle whose weights were known beforehand were obtained with this setup. Segmentation maps of the animals in these images were created with the Deeplab v3+ deep learning model, which is one of the semantic segmentation models.  
Using the segmentation maps, the number of pixels covered by each animal in the image and their distance to the camera plane were calculated using the stereo distance calculation technique. A dataset was created by combining these obtained data. The dataset was created from the data obtained from photographs of animals taken from two different angles, from the side, and from the back.  
Using this dataset, three different artificial neural networks, which are architecturally similar to each other, are trained. When the trained neural networks were compared, it was seen that the third neural network trained with the whole dataset was significantly more successful than the first two neural networks. At this point, it is clear that neural networks to be trained with datasets created with images taken from more angles will be more successful. For example, top images of cattle contain important information about the animal's body structure. It can be said that networks trained with a dataset that includes top-shot data, if possible, will be more successful.  
In addition, it is possible to say that neural networks will make more successful predictions if the quality and quantity of the dataset are increased. In the resulting estimations, although rare, dramatically incorrect estimations were observed. Weight estimations of animals that were limited in number in the dataset, that were light in weight, and whose stereo distance was very different from the rest of the dataset were found to be relatively unsuccessful. Therefore, it is clear that creating a more comprehensive and homogeneously distributed dataset will significantly increase the performance of the models.  
Moreover, characteristics such as race and gender of animals directly affect their weight. For example, if the body sizes of two animals of different breeds are assumed to be exactly the same, it will be seen that the weights of these two animals are different from each other. At this point, in the study, a deep learning method that recognizes the breed and gender of the animal can be developed and the performance in weight estimation can be increased with a separate training model for each breed.
## 5 Experiments
### Harmful Traffic Classes Detection
In BoT, attacks specifically exploit IoT devices, using compromised IoT devices (botnets) to generate high volumes of traffic aimed at overloading the network or devices. In ToN, attacks target IoT and traditional systems, reflecting complex, mixed environments, including cloud and edge infrastructure. It is worth noting that the detection of DoS and DDoS attacks is based on a filtered set of traffic features. This ultimate set does not include the source IP address. In many attacks, the huge repeatability of the IP source is the principal indicator of this type of attack. However, hopefully not the only one, other determining factors are usually present in higher layers of the protocol, which influence other traffic features in our datasets. The last two co-occurring classes are _Backdoor_ and _Reconnaissance_. Both are present in just two sets and are much less frequent than _Benign_, _DoS_, and _DDoS_. In each of the two classes, analyzing the metrics and visualizations (Figure 4) and applying the same train of thought, one quickly notices that, although samples from different sets are assigned to a class of the same name, the internal characteristics of features exhibits significant differences between features of traffic samples of the same class originated from different datasets. The main difference between the Backdoor class in ToN and NB15 is the environment being targeted. The ToN focuses on IoT devices and industrial systems, allowing attackers persistent, unauthorized access to critical infrastructure, including smart devices and edge systems. In the NB15, Backdoor attacks target traditional network systems and servers, aiming to maintain covert access to corporate or personal computing environments. Similarly, the difference between _Reconnaissance_ class in BoT and NB15 is the type of targets. The BoT focuses on probing and scanning IoT devices to find vulnerabilities specific to resource-constrained, internet-connected devices. In contrast, in NB15, the traditional network infrastructure is scanned, gathering information such as open ports or system configurations in more conventional IT environments. Apart from models trained on particular datasets, the models trained on a superposition of datasets were also investigated. The training set was created by merging all four component datasets into a single superset of 4 million samples. The performance of such a classifier was measured in the same way as in individual cases--the resulting measures are shown in the last section of each table (the train set name is 'All' in this case). Contrary to models trained on individual sets, the model trained on superset performs well on all  
Figure 3: The t-SNE visualization of _DoS_ class (**left**) and DDoS class (**right**) with labeled original sets. test sets. The reason for such good results is the increase of intra-class variability achieved thanks to merging. This observation also indicates that extending the training set during the use of the model, followed by re-training, allows for adaptation of the NIDS to the real working environment.
# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 3 Proposed Method
### Model Structure  
In terms of the model structure, YOLOv7-3D can be divided into three parts: the backbone feature extraction network, the multi-scale feature pyramid, and the multi-branch decoupled detection head. Due to the significant scale differences among various traffic objects in the Rope3D dataset, this paper proposes a 5-layer FPN structure and a multi-scale spatial attention mechanism (MSSA) to improve feature saliency and detection accuracy for different-scale objects. The overall network architecture of the model is shown in Figure 1.  
Figure 1: The network architecture diagram depicts, from left to right, the specific structures of the main feature extraction network, the Feature Pyramid Network (FPN), and the five multi-branch decoupled detection heads.  
In this work, the model does not directly predict the seven degrees of freedom of the targets \((x,y,z,l,w,h,\theta)\). Instead, it predicts nine degrees of freedom for the targets \((x_{c},y_{c},\varphi_{z},\sigma_{z},l,w,h,\sin(\theta),\cos(\theta))\). Here, \(x_{c}\) and \(y_{c}\) represent the projected points of the center of the 3D bounding box on the 2D image. \(\varphi_{z}\) and \(\sigma_{z}\) represent the depth value and the logarithmic variance of the depth value, respectively. The introduction of these parameters aims to quantify the uncertainty in the predictions by considering the variance. \(l\), \(w\), and \(h\) represent the length, width, and height of the 3D bounding box, respectively, while \(\sin(\theta)\) and \(\cos(\theta)\) are values required to compute the heading angle. The schematic diagram of degrees of freedom is shown in Figure 2.
## 2 Background and the Related Work
### Related Works  
Researchers have proposed a variety of solutions, ranging from advanced encryption techniques, security policy frameworks, and the use of genetic algorithms for cryptographic innovations. This section explores these works, focusing on their methodologies, strengths, and limitations. The study of related works is organized into three segments: (a) encryption and network security in SDN, (b) security policy deployment in SDN, and (c) the GA-based encryption approach.
## 2 Theoretical Model
### Distribution of the Temperature of a Jet
#### 2.4.1 Adiabatic Temperature Rise  
The deformation stress of most metallic materials depends primarily on its strain history, deformation strain rate, and deformation temperature [23]. In other words, the equivalent stress of a material can be simply expressed as follows:  
\[\sigma=f\big{(}\epsilon,\dot{\epsilon},T\big{)}. \tag{9}\]  
Equation (9) is referred to as the constitutive equation of material deformation. Due to the differences in the material's properties as well as research methods and perspectives, Equation (9) has many functional forms. The Johnson-Cook (J-C) [24] constitutive equation, as shown in Equation (10), is used in this study to calculate the flow stress of the SCJ.  
\[\sigma=(A+B\epsilon_{p}^{n})(1+Cln\dot{\epsilon}^{*})(1-T^{*m}), \tag{10}\]  
where \(A\), \(B\), \(n\), \(C\), and \(m\) are the constitutive parameters of the material to be determined, \(\dot{\epsilon}^{*}=\frac{\dot{\epsilon}}{\dot{\epsilon}_{0}}\)(where \(\dot{\epsilon}_{0}\) is the reference strain rate), and \(T^{*}=\frac{(T-T_{r})}{(T-T_{m})}\) (where \(T_{r}\) is the reference temperature and \(T_{m}\) is the melting point of the material). The rise in the material's temperature due to the work done by PD can be calculated using the following equation [25]:  
\[\Delta T(\epsilon)=\frac{\beta}{\rho C_{v}}\int_{0}^{\epsilon}\sigma d\epsilon, \tag{11}\]  
where \(\rho\) is the density of the material, \(C_{v}\) is the specific heat of the material, and \(\beta\) is the thermal conversion coefficient, that is, the ratio at which plastic work is converted into heat (\(\beta\) is set to 0.9 in the calculations in this study). Substitution of Equation (10) into Equation (11) provides the following equation:  
\[\int_{T_{0}^{*}}^{T_{1}}\frac{dT^{*}}{1-T^{*m}}=\frac{0.9\big{(}1+Cln\dot{ \epsilon}/\dot{\epsilon}_{0}\big{)}}{\rho C_{V}}\int_{0}^{\epsilon}\big{(} \sigma+B\epsilon^{n}\big{)}d\epsilon. \tag{12}\]  
A numerical calculation based on Equation (12) yields the material temperature rise due to PD, \(\Delta T_{1}\).
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 1 Introduction
Simulations are conducted with Dassault Simulia CST Studio Suite [55] using an abdominal tissue layer model as well as the torso anatomical voxel model's torso from the abdominal area. The power flow analysis is conducted to evaluate propagation depth at different frequencies in the abdominal tissues. Specific absorption ratio (SAR) values, total antenna efficiency, and impact of the bending is studies, while simulation and measured results are also compared. This is the first flexible on-body antenna that has been presented for implant communications and which covers such a wide frequency band. Besides, compared to the recently published antenna structures, the proposed monopole antenna is a very simple, more compact in size, and represents good peak efficiency. This paper is organized as follows. Section 2 presents the antenna model and prototype. Section 3 presents the methodology, including a description of the layer model and the voxel model used in this study. This section also discusses pros and cons of these simulation models. Additionally, a description of measurements is given. Section 4 presents the simulation and measurement results. Section 4 presents a discussion, including future perspectives.
# Surface Defect Detection Methods for Industrial Products: A Review
## 2 Traditional Feature-Based Machine Vision Algorithm for Surface Defect Detection
### Color Feature-Based Method
#### 2.2.4 Other Color Features  
In addition to color histograms, color moments, and color aggregation vectors, color features commonly used for surface defect detection of industrial products include color sets and color correlation maps. Among them, the color set is also a kind of global color features and matching method; it is an approximation to the color histogram, which is expressed as a binary feature vector; by constructing a binary search tree, the retrieval speed can be speeded up. The color correlation graph describes the proportion (probability) of the number of pixels of a certain color in the whole image, which can reflect the spatial correlation between different color pairs; usually, it needs high hardware conditions.
## 3 Results  
From February 2019 to December 2019, 119 patients expressed interest in the study (Figure 2); after a phone consultation 32 patients appeared eligible for the study and were invited to undergo a chairside consultation.  
Out of 32 patients, 18 matched the inclusion and exclusion criteria, and were included in the trial. Five of these 18 patients were indefinitely delayed due to personal circumstances. Between March 2019 and October 2019, 13 pairs of implants were placed in 13 patients successfully (mean age: 61 years; range: 29 to 91; five males and eight females, non-smokers) (Table 1).  
Fourteen implants were placed in mandibular molar sites, four in maxillary molar sites, five in mandibular premolars sites and three in maxillary premolar sites. Patient #13, who had a history of treated localised stage III grade B periodontal disease, was excluded despite successful osseointegration as he was unable to complete the implant restorative process due to a combination of personal circumstance and COVID-19 disruptions. Patient #12 was excluded from the final analysis as a mechanical failure was detected at the 12-month review (4.6 \(\times\) 7.5 mm control implant with a >1 crown to implant ratio, located in the upper left first molar site). Hence, 11 pairs of implants were included in the final analysis.  
Implants were restored with screw retained single crowns, manufactured from monolithic zirconia with the exception of patient #2 who received two porcelain-fused metal crowns.  
As these numbers fell short of the requirement for statistical power as calculated, this study is treated as a \"pilot study\".
# Influence of Drift on Robot Repeatability and Its Compensation
## 3 Data Acquisition
### Warming Up Robot Joints and the Repeatability Measurement  
The first step is to obtain the relationship between the drift and the robot joint's temperature in time. The robot trajectory is defined to reach the highest possible temperature. Since the robot temperature gradually increases during recurring trajectory executions, the resulting dataset will contain data for the entire temperature range. The trajectory is defined in such a way that all joints of the robot simultaneously move between their limit positions with a defined speed. It represents the worst possible case because all the joints are moved throughout their entire working range.  
A similar real-time thermal error compensation method was presented by [PERSON] et al. [13]. The approach utilizes the temperature of the joints; however, in contrast to our work, the researchers warmed up the joints one by one. Based on preliminary tests and measurement data (to be presented later in the text), we concluded that this solution inappropriately simplifies the result due to the fact that each joint thermally interact with other joints and are effectively cooled by the motion of the previous joints of the robot serial structure. Figure 5 describes the process of measurement. The robot executes the heating trajectory and, after every cycle, moves to the measuring nest (see Figure 4). After the stabilization delay, the output values are read from all the displacement sensors, and the cycle is repeated. When the relative joint temperatures (relative to ambient temperature) are no longer changing, the operational temperature is reached, and after an additional sixty minutes, the measurement stops.  
With the use of the methodology presented in this article, the robot warm-up procedure is performed. The results of the measurements are shown in Figure 6.  
Figure 5: Flowchart of the control system for the experimental measurements.  
The graph (Figure 6) describes the relation between drift and joint temperatures in time. The six temperature trends displayed on the left Y-axis of the graph visualize the average values for each robot joint (denoted by \(J_{1}\)-\(J_{6}\)). The joint average value is evaluated based on the values obtained from four sensors installed around the corresponding joint. The ambient temperature is subtracted, so the displayed values represent relative joint temperatures. The right Y-axis of the graph describes the trend of repeatability in TCP Z-axis. The lowest measured value is set as reference value in order to be able to compare measurement results between confocal sensor and laser profilers.  
For the measurement depicted in Figure 6, the difference of the repeatability between cold and warmed-up conditions is 210 \(\upmu\)m. For comparison, the robot manufacturer declares 25 \(\upmu\)m as the value of repeatability [26]; this value is, however, valid for constant temperature conditions only.
## 3 Results and Discussion
### Wear Resistance  
Grinding ratio is a measurement index for evaluating the wear resistance and tribological performance of diamond composites [11]. The results for grinding ratio of impregnated diamond samples with different mass percent concentration nano-WC are summarized in bar chart (Figure 7). The grinding ratio increases remarkably after adding WC nanoparticles, proving that nano-WC plays a significant role in wear resistance of impregnated diamond composites. The grinding ratio increases firstly and then decreases with nano-WC content increases and grinding ratio of sample SD3 shows an about 100% increase in comparison with SD0. The wear resistance of impregnated diamond composites is higher than other WC-based, Fe-based, Co-based and Ni-based diamond composites [7; 8; 21; 22; 23].  
The diamond retention capacity largely determines the wear resistance of impregnated diamond composite [3]. As shown in Figure 4, WC nanoparticles are found on fracture surface, and these nanoparticles enhance the internal friction coefficient in the matrix as same as the friction coefficient between diamond grits and matrix [3; 15]. Under complex and alternating cutting force, diamond grits show a tendency to rotate and then pull out of matrix easily. The increase of friction improves retentive capabilities of the matrix and reduces the rotating tendency, which enhances diamond retention in the matrix that is benefited to wear resistance of diamond composites. Hence, the grinding ratio of samples SD2, SD3 and SD4 with nano-WC addition is larger than reference one without nano-WC addition and SD3 with 2.8 wt% nano-WC has the best value. While concentration of nano-WC exceeds higher than 2.8 wt%, agglomeration phenomenon is harmful to the reinforcement effect.  
The bending strength is also a factor affecting the wear resistance. The theoretical relationship between bending strength and wear resistance has been analyzed in the literatures [9; 10; 11]. A higher bending strength leads to a stronger support for diamond grits, meaning that diamond grits would not be lost permanently by pulling out when facing complex stresses conditions. But it can be found that the bending strength of SD1 is larger than SD0, SD2, SD3 and SD4 but the grinding ratio of SD1 with 2.2 wt% nano-WC is smaller than that of SD2, SD3 and SD4 and similar to SD0. This indicates  
Figure 7: Results of grinding ratio test of impregnated diamond samples.  
that other factors weaken the wear resistance of SD1. As evidenced in Figure 5d or Figure 6b, the low relative density of SD1 means it has high porosity. The high porosity of SD1 implies the matrix wears faster than other samples, which causes diamond grits to pull out of matrix more easily. So the low relative density results in the decrease of grinding ratio of SD1.
## Materials and Methods
### Preparation of Membranes and Permeation Test  
To prepare the graphene-based membranes, concentrations of GO and rGO at 0.01, 0.05, 0.10, and 0.15 wt% were added to the NMP solution and exfoliated by sonication. A polymer concentration of 15 wt% (PSf and PVDF) was then dispersed in the NMP solution and stirred for 12 h at 50 \({}^{\circ}\)C. After sonication for 1 h, the solution was maintained at room temperature overnight without stirring until the solution contained no bubbles. The membranes were produced by using the immersion phase inversion method with a micrometric film applicator. To conduct full liquid-liquid de-mixing, the embryonic membranes were submerged in a water bath for 24 h. The average thickness of the final membrane was 120 \(\upmu\)m.  
Membrane permeation tests were conducted by using a dead-end stirred cell system (MilliporeTM, Burlington, MA, USA) with a capacity of 800 mL (Figure 1). The membrane effective area was 15 cm\({}^{2}\), and the permeation test was conducted at 20 \({}^{\circ}\)C and 0.1 MPa. Prior to the permeation testing, each membrane was first compacted at 0.15 MPa with distilled water for 20 min to obtain a steady flux. The pure water fluxes of the membranes were calculated with Equation (1):  
\[J_{W}=\frac{V}{S\times t} \tag{1}\]  
where \(J_{W}\) is the pure water flux (LMH), \(V\) is the permeate volume (L), \(S\) is the membrane area (m\({}^{2}\)), and \(t\) is time (h). All permeation data, including those from fouling and rejection tests, were obtained three times and represented as the average value.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### Activity Recognition
Activity recognition models may then be implemented on a mobile device to provide near real-time classification. As smartphones have become increasingly powerful, the concept of \"online\" activity recognition has become an important research area, referring to implementing the entire classification process (i.e., data collection, pre-processing, feature extraction, and classification) locally on the device [13; 197].