esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of Head Position Control System Considering Side-Slipping
The input \(\mathbf{u}\) that quasi-minimizes the quadratic cost function is given by utilizing the estimated state of the observer as follows,  
\[\mathbf{u}=-\mathbf{R}(\hat{x})^{-1}\mathbf{B}(\hat{\mathbf{x}})^{T}\mathbf{P}(\hat{\mathbf{x}})\hat{ \mathbf{x}},\]  
where \(\mathbf{P}(\hat{\mathbf{x}})\) is a symmetric positive definite matrix of the following state-dependent Riccati equation,  
\[\bar{A}(\hat{\mathbf{x}})^{T}\mathbf{P}(\hat{\mathbf{x}})+\mathbf{P}(\hat{\mathbf{x}})\bar{A}(\hat {\mathbf{x}})+\mathbf{Q}(\hat{x})-\mathbf{P}(\hat{\mathbf{x}})\bar{B}(\hat{\mathbf{x}})\mathbf{R}(\hat {\mathbf{x}})^{-1}\bar{B}(\hat{\mathbf{x}})^{T}\mathbf{P}(\hat{\mathbf{x}})=0. \tag{13}\]Last, the tuning algorithm to immediately converge the side-slipping is constructed. An idea to achieve this is to change the weight for the velocity in the axial direction, with respect to each sampling period. In particular, the velocity in the axial direction is incorporated into the weighting matrix. We set the weighting matrices \(\mathbf{Q}_{o}(\mathbf{\xi}_{e})\) and \(\mathbf{R}_{o}(\mathbf{\xi}_{e})\) as follows,  
\[\mathbf{Q} =\text{diag}(Q_{xh},Q_{yh},Q_{s2},\,\cdots,\,Q_{s5},Q_{\omega 1},Q_{vx1 },Q_{\omega j2},\,\cdots,\,Q_{\omega j5},Q_{xy1},\,\cdots,\,Q_{xy5}),\] \[\mathbf{R} =\text{diag}(R_{1},R_{2},R_{3},R_{4}).\]  
where each of the elements are as follows,  
\[Q_{xh},Q_{yh} =1,\] \[Q_{s2},\,\cdots,\,Q_{s5},Q_{\omega 1},Q_{vx1},Q_{\omega j2},\, \cdots,\,Q_{\omega j5} =0.1,\] \[R_{1},R_{2},R_{3},R_{4} =0.1.\]  
The elements for the velocity in the axial direction are set, below, according to the idea:  
\[q_{vyi} =1+\sigma[n]|\phi_{yi}|, (|\phi_{yi}|>1\times 10^{-5}),\] \[\sigma[n] =k\sigma[n-1], \sigma[0]=1000,\,\,k=1.2.\]  
By setting the elements for the velocity in the axial direction, as above, the weights for both a link which is slipping fast and a link which continues slipping are tuned greater than others. Thus, the control system converges the side-slipping aroitly and locomotes the system effectively.
## 2 Background  
In this study, we will focus on NSCLC diagnosis and staging. NSCLC includes three main subtypes namely adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Despite the subtypes differences regarding their onset cell sites, they are grouped together due to their similarity in terms of treatment and prognosis. Figure 1 illustrates the manifestations of the three NSCLC subtypes on CT scans. The characteristics of these subtypes on CT scans [10] can be summarized as follows:  
* Adenocarcinoma (ADC) early manifestation is in mucous cells and shows as rounded or irregular lung nodules of higher attenuation. These nodules are usually present at the outer parts of the lung.
* Squamous Cell Carcinoma (SCC) appears first in squamous cells at the inside lining of the lung airways. These tumors are often white in color. Typically, SCC spread centrally in the hilar cavity but can extend peripherally to the chest walls, as indicated in Figure 1.
* Large cell (undifferentiated) carcinoma (LCC) has the tendency to grow rapidly anywhere in the lung, which makes its survival rate low. Additionally, it lacks the presence of distinctive features except for large nuclei with modest amount of cytoplasm microscopically.  
Within each carcinoma subtype, staging is crucial for effective treatment and follow up planning. The International Association for the Study of Lung Cancer (IASLC) recommends a standard procedure for cancer stage classification based on TNM staging [11]. The assigned cancer category relies on three aspects: the size of the primary tumor (T), the number and location of regional lymph nodes (N), and the presence or absence of metastasis (M). The proposed classification system aims to provide a uniform system for consistent reproducible stage, which is necessary for optimizing treatment. According to the 8 th Edition TNM staging update [11], there are 13 stages depending on different combinations of T, N and M variations. In this study, we will focus on four broad stage categorizations, namely stage I, stage II, stage IIIA and stage IIIB. Detailed definition of the stages can be found in the 8 th Edition TNM staging update [11]. These stages represent localised and regional cancer spread with higher expected survival rates [12]. Hence, timely accurate staging is of immense importance to help optimize the therapeutic outcome.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 5 Termination Condition: Completeness Evaluation
### Distribution-Based Evaluation  
The density-based evaluation can be treated as a local completeness evaluation. Since there could be a situation that, during the evaluation, the robot platform is moving within a certain space only, the density of that space becomes dense enough to satisfy the verification, while the keyframes in other space are still spatially. Therefore, after the coarse evaluation is met and the number of vertexes is large enough, distribution-based evaluation is executed for evaluating globally.  
Distribution-based evaluation, we terminate the exploration according to the Gaussian distribution of all the keyframes within the whole explored space.  
Firstly, the explored space is grided evenly in a cellular decomposition method. An example is illustrated in Figure 8, where the completeness of mapping can be evaluated by the value of each grid. Regarding the fault prevention of localization, the more complete the mapping is, the more data association can be built. The resolution of gridding is determined by the requirement of mapping completeness [24], and it is given manually here. Then, the variance and mean of poses of the keyframes within a grid \(i\) can be calculated, represented as \(v_{i}\) and \(m_{i}\). For an ideal exploration of a grid, the distribution of keyframe poses can be regarded as a Uniform Distribution \(\mathbf{U}(-\pi,\pi)\), where the ideal variance and mean are known, written as \(v_{ideal}\) and \(m_{ideal}\). Therefore, the explored score \(s_{i}\) of a grid \(i\) can be defined as Equation (3):  
\[s_{i}=1/\sqrt{(v_{i}-v_{ideal})^{2}+(m_{i}-m_{ideal})^{2}}. \tag{3}\]  
For a practical problem, it is impossible to reach an ideal uniform distribution where an unlimited sampling is required; in addition, there is no need to build such a distribution since one frame can cover a certain range. By sampling with a given interval, the parameter of sub-ideal distribution can be seen in Table 2. The distribution with a 20-degree interval is used in our experiment.  
For all the grids, we can get the scores of them, written as \(\boldsymbol{s^{grid}}\), and the Gaussian expression of \(\boldsymbol{s^{grid}}\) can be computed, written as \(\boldsymbol{N}(n^{grid},\sigma^{grid})\). The termination condition is satisfied when Equation (4) is met:  
\[\forall s_{i}\in\boldsymbol{s^{grid}},\left|n^{grid}-s_{i}\right|\leq\sigma^{ grid}. \tag{4}\]
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 4 Experiments, Results and Discussion  
### The Dataset and Preprocessing  
Figure 6 highlights the flowchart of methodologies used in this paper. In order to evaluate the performance of the proposed models, we use publicly available Massachusetts Roads dataset [44] for the experiments. The dataset consists of 1171 aerial images covering over 2600 square kilometers of urban, suburban, and rural areas of the state of Massachusetts, USA. The dataset is randomly split into 1108 images for training, 14 images for validation, and 49 images for testing. The spatial resolution of all images is 1500 \(\times\) 1500 pixels covering an area of 2.25 square kilometers per image. The dataset is considered one of the largest and challenging road detection datasets available.  
Before training the model, we preprocess the data as follows. As the spatial size of images is too large for training, we crop images randomly to \(256\times 256\). Then, we apply randomly one of the horizontal flip, vertical flip, or \(90^{\circ}\) rotation augmentation techniques to introduce some randomness into the dataset. Finally, we normalize the images with the mean and standard deviation of the entire dataset. During the inference time, the image is resized to \(1504\times 1504\) as the models accept only images with the size divisible by 16. After that, it is normalized with the mean and standard deviation of the training set. The additional padded two pixels on each side of the image are discarded during the evaluation.
# A Manycore Vision Processor for Real-Time Smart Cameras +
## 1 Introduction  
The emergence of new trends in technology, such as the Internet of Things and Industry 4.0, pulled out several applications based on image processing and computer vision (IP/CV) techniques. Cyber-physical systems, augmented reality, and autonomous machines, among others, all have applications supported by the extensive use of cameras. Most of the conventional cameras are designed for the acquisition and transmission of images and videos. These cameras are not able to support complete applications running under real-time constraints. For these reasons, there is a need for devices capable of acquiring and processing images and videos efficiently and in real-time.  
Images are inherently parallel, and the literature shows that for the IP/CV domain, the efficient parallelism exploration is the key for performance improvement. The underlying hardware/software architecture, as well as the programming model offered to the users, must be designed under application-specific constraints to reach higher performance [1, 2, 3]. To explore the parallelism massively, our approach was to parallelize the processing right after image capture by the pixel sensor. Figure 1 shows the concept used, in which the image is divided into parallel processing units [4].  
Hardware architectures span a broad diversity: ASICs, direct field-programmable gate array (FPGA) implementations, application-specific instruction set processors (ASIPs), very-large instruction word (VLIW) processors, digital signal processors (DSPs), graphics processing units (GPUs), and multi-processor System-on-Chip (MPSoCs), etc. Each approach is an attempt to explore data parallelism, higher processing frequencies, pipelines, or complex instructions. Considering the characteristics of pixel parallelism, depicted in Figure 1, as well as in previous findings from the literature, we propose an IP/CV application-specific architecture: a manycore vision processor system. All hardware/software design decisions are based on IP/CV application-specific needs, from the processing elements microarchitecture to the Network-on-Chip (NoC) interconnects. A general-purpose processor (GPP) and a direct memory access (DMA) scheme complement the manycore to build a novel heterogeneous smart camera architecture.  
The rest of this work is organized as follows: Section 2 depicts a literature review of the main challenges in the area, the state-of-art solutions, as well as the gaps we propose to cover with this work. In Section 3, we explain the HW/SW organization, the main design decisions, and implementation details of each hardware block. Section 4 explains the application used to characterize the HW/SW architecture. Section 5 presents and discusses the results. Finally, in Section 6, we present the conclusion and future works.
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 2 Materials and Methods
### Digitization, Image Processing and Measurement of Bracket Placement Accuracy  
#### 2.5.1 Intraoral Scanning  
SMtest models were covered with a thin layer of scanning spray (BlueSpray, Dreve Dentamid, Unna, Germany). The as-prepared models were digitized using an intraoral scanner (Trios 3, 3 Shape, Copenhagen, Denmark; setting: typical clinical setting, full arch scanning mode). Image registration of the SMtest-IO with SMref was achieved in two steps: first, a reference-point based pre-alignment was performed in MeshLab (MeshLab v1.3.3, Visual Computing Lab, ISTI, CNR). Fine registration was realized using the local best fit algorithm implemented in GOM Inspect 2018 software (GOM, Braunschweig, Germany). The fine-registration procedure was repeated until convergence as described earlier [26].  
To assess the 3D accuracy of bracket placement, surface comparison was utilized. In case intraorally scanned brackets showed scanning errors (Figure 4B,C), the most likely correct areas were identified by a trained observer, which led to exclusion of the hooks at all brackets and tubes (Figure 4A). The minimum, maximum, mean and standard deviation values between SMtest-IO and SMref were recorded for each bracket and tube.
## 5 Results and Analysis
### Ablation Studies  
To deeply understand the contribution of each key component, we conducted a series of ablation experiments, primarily evaluating the change in macro F1 score on the FOMC dataset.  
Table 5 shows that replacing Delta-LoRA with standard LoRA (rank r = 16) results in a 1.7 pp drop in macro F1. This indicates that Delta-LoRA, through its mechanism of updating pretrained weights \(W\), indeed adapts more effectively to the downstream task, enhancing the model's expressive power. Regarding the rank (r) in Delta-LoRA, increasing it from 8 to 16 brings a 0.5 pp improvement, but further increasing it from 16 to 32 yields a very minor gain (0.1 pp). Considering the increase in parameters and computational cost with r, r = 16 appears to be a favorable trade-off point.  
Table 6 shows that defining the interest rate change label based on the 7-day window following the statement release yields the best model performance. A window that is too short (3 days) might capture excessive market noise, while a window that is too long (14 days) might extend beyond the immediate impact scope of the text or be confounded by subsequent events.  
The contributions of domain adaptation (DANN) and multi-task learning (MTL) are analyzed in detail in the following subsections, but briefly, ablation studies show that removing DANN leads to a decrease of approximately 1.8 pp in cross-domain (FOMC \(\rightarrow\) ECB) macro F1, and removing MTL (the hawkish/dovish auxiliary task) results in a decrease of about 1.2 pp in the main task (FOMC nowcasting) Macro F1. These ablation results clearly demonstrate the positive contribution of Delta-LoRA, DANN, and MTL to the overall model performance.
# Trends and Challenges in Network Covert Channels Countermeasures
## 5 Covert Channel Limitation, Elimination and Prevention
### Elimination and Limitation
To illustrate this concept, we consider the case of IPSec [58], which can be natively endowed with padding for preventing covert channels modulating the packet size, and techniques for adding random errors for disrupting mechanisms embedding data by artificially manipulating packet losses. The limit of such an approach is the need of updating implementations and perform such optimizations on a per-protocol basis. Instead, it could be beneficial centralizing such workarounds in a unique network entity. Timing channels appears to be more easy to limit or prevent, at least those manipulating inter packet statistics. In this case, buffering and adding random delays can be sufficient. The drawback of this class of countermeasures is rooted in the indiscriminate penalization of all traffic flows. When in the presence of traffic with real-time constraints, buffering can not be too aggressive, thus leaving the attacker with chances of creating a channel. In this case, the goal is to reduce its steganographic bandwidth in order to render the channel unsuitable for the attack. Literature offers different techniques for timing channels. We mention the use entropy-based schemes to make the detection more robust to prevent the attacker from limiting his/her rate to evade the detection [59], and addressing in a more detail timing channels mimicking realistic patterns [60].
## 2 Materials and Methods  
### Welding Procedures  
The material used in this paper was 1.45 mm thick AA6063-T6 extrusion (nominal compositions in Table 1, measured by Foundry-Master Pro2 optical emission spectrometer), which was machined into 150 mm x 55 mm coupons. A 6 kW diode laser (LaserLine GmbH, Mulheim-Karlich, Germany) with a beam parameter product of 6 mm\(\cdot\)mrad was used. The laser beam was delivered through an optical fibre of 150 um diameter and coupled with the WeldMaster remote welding head (YW52 Precitec GmbH, Gaggenau, Germany), which has the collimating length of 150 mm and focal length of 300 mm. The resulting Rayleigh length is 2.8 mm.  
upper part), which defines the central position of oscillation pattern in the y direction; focal point position offset, \(A_{Z}\), which corresponds the distance along the beam axis between the focused position and the top surface of upper part; laser power, \(P\), which is modulated transversally to the welding direction on four points: \(P_{A}\) to \(P_{D}\), corresponding to the laser power on the upper part (\(t=0\)), reference point (\(t=1/(4f)\)), lower part (\(t=1/(2f)\)) and reference point (\(t=3/(4f)\)). No shielding gas nor filler wire was used throughout the experiments. Samples were wiped with acetone before welding to remove surface contaminations.  
Full matrix of process parameters used in this paper is shown in Table 2. Three individual processing windows were studied in this paper which are defined by: (i) welding speed, \(S_{x}\) ranging from 2 m/min to 6 m/min; and (ii) the beam oscillation frequency, \(f\) which increases accordingly with \(S_{x}\) from 50 Hz to 150 Hz to maintain a constant cycle number over unit weld length. Selection of welding parameters at different welding speeds is based on the premise of good bonding between two parts, guided by previous research [12]. For all welding trials, a constant beam oscillation amplitude, \(A_{y}\) of 1.5 mm and focal offset, \(A_{Z}\) of 2.0 mm were employed. \(P_{D}\) was set to be same as \(P_{B}\) and omitted in the table. The line energy along the x direction, \(E_{L}\) is defined in Equation (1) using the average power across positions from A to D::  
\[E_{L}=(P_{A}+P_{B}+P_{C}+P_{D})/(4*S_{x}),\ [\mathrm{J/mm}] \tag{1}\]  
Figure 2 shows representative weld cross-sections obtained in each processing window and \(P_{B}\) was then further adjusted to extend the processing window.  
Figure 1: Illustration of key welding parameters. (**a**) Front view and top view showing the laser beam path; and (**b**) representation of beam oscillation and power modulation as function of time.
## 2 Nutritional Quality
### Antioxidant Compounds
#### 2.1.2 Lipo-Soluble Antioxidants  
Despite the low incidence of lipo-soluble antioxidants on the TAC of _Brassica_, several studies confirm the high content of lipo-soluble antioxidant in kale and broccoli, moderate in Brussels sprouts, and low amount in cauliflower and cabbage [33]. Among lipo-soluble antioxidants, carotenoids and vitamin E are the most important found in _Brassica_ vegetables.  
* Carotenoids are responsible for the orange, yellow and red pigmentation of several fruits and vegetables, mainly carotenes and xanthophylls. The most represented carotenoids in _Brassica_ vegetables are \(\beta\)-carotene, which the organism transforms to vitamin A, and lutein and zeaxanthin [14]. \(\beta\)-carotene prevents the insurgence of cancer and cardiovascular diseases, and decreases the risk of myocardial infarction, of immune dysfunction and age-related macular degeneration among smokers [33; 40]. [PERSON] [41] analysed the total carotenoid content of several _Brassica_ species and reported them in decreasing order: Brussel sprouts (6.1 mg/100 g), broccoli (1.6 mg/100 g), red cabbage (0.43 mg/100 g) and finally white cabbage (0.26 mg/100 g). In the _Brassica oleracea_ genus, kale possesses the highest content of carotenoids with over 10 mg/100 g of the edible portion [41].  
The _Brassica_ vegetable with the highest content of lutein and zeaxanthin is kale (3.04-39.55 mg/100 g); interesting contents were also found in broccoli and Brussels sprouts [14]. In _B. rapa_ species, 16 carotenoids were identified by [PERSON] and [PERSON] [42]; in _B. chinensis, parachinensis_ and _pekinensis_, lutein and \(\beta\)-carotene are the most abundant carotenoids [15].  
Vitamin E is formed by groups of compounds known as tocopherols and toctorriends; in detail, \(\alpha\)-tocopherol is the main compound found in _Brassica_ vegetables, with the exception of cauliflower, that contains mainly \(\gamma\)-tocopherol [14; 43]. Vitamin E performs a protective activity against coronary heart disease through the inhibition of LDL oxidation [44]. A high intake of vitamin E helps in the prevention of cancers, cardiovascular diseases, neurological disorders, and inflammatory diseases [33]. The content of vitamin E in _Brassica_ species has been studied in the literature, as reported here in decreasing order: broccoli (0.82 mg/100 g), Brussels sprouts (0.40 mg/100 g), cauliflower (0.35 mg/100 g),Chinese cabbage (0.24 mg/100 g), Red cabbage (0.05 mg/100 g), and white cabbage (0.04 mg/100 g) [43].
## 2 Optimization Model
### Thermal Model
#### 2.3.1 Humidity Variation Model  
(1) Calculation of Spectator Stands Humidity  
Air humidity is influenced by the ventilation system through indoor and outdoor air exchange, air infiltration, and audience respiration. Additionally, the operation of dehumidifiers can remove part of the humidity. The specific humidity variation model is given as follows:  
\[w_{z}(t)=w_{z}(t-1)+w_{ven}(t)+w_{leak}(t)+w_{aud}(t)-w_{dh}(t) \tag{21}\]  
where \(w_{z}(t-1)\) is air humidity in the ice sports venue at time \(t-1\) (kgH2O/kgair). \(w_{ven}(t)\) is the impact of the ventilation system on the humidity of the spectator stands (kgH2O/kgair). \(w_{leak}(t)\) is the effect of air infiltration on the humidity of the spectator stands (kgH2O/kgair). \(w_{aud}(t)\) is the moisture generated by audience respiration (kgH2O/kgair). \(w_{dh}(t)\) is the effect of dehumidifier operation on the humidity of the spectator stands (kgH2O/kgair). The impact of the ventilation system on humidity is calculated as follows:  
\[w_{ven}(t)=S_{ven}(t)\tau\frac{\dot{V}\times(w_{ex}(t)-w_{z}(t-1))}{V_{z}} \tag{22}\]  
where \(\dot{V}\) is the airflow rate of the ventilation system (m\({}^{3}\)/h). \(w_{ex}(t)\) is the humidity of the incoming air, assuming the humidity of the air entering the ventilation system is the same as that of the outside air (kgH2O/kgair). \(V_{z}\) is the volume of air in the studied area (m\({}^{3}\)). \(w_{ex}(t)\) is given as follows:  
\[w_{ex}(t)=\frac{\mathcal{O}_{ex}(t)*0.622*ex_{s(t)}}{P_{atm}-\mathcal{O}_{ex} (t)*ex_{s(t)}} \tag{23}\]  
where \(\mathcal{O}_{ex}(t)\) is the relative humidity of the outside air at time \(t\) (%). \(ex_{s(t)}\) is the intermediate coefficient of conversion of temperature, relative humidity, and specific humidity outside. \(ex_{s(t)}\) is given as follows:  
\[ex_{s(t)}=6.112*e^{\frac{[\tau\beta\tau(\theta_{ex}(t)-2\tau\lambda 15)]}{\theta_{ex}(t)}} \tag{24}\]  
where \(\theta_{ex}(t)\) is the temperature of the outside air at time \(t\) (K). The effect of air infiltration on the humidity is given as follows:  
\[w_{leak}(t)=\tau\frac{V_{z}^{leak}\times(w_{ex}(t)-w_{z}(t-1))}{V_{z}} \tag{25}\]  
where \(V_{z}^{leak}\) is air leakage (m\({}^{3}\)/h). \(w_{aud}(t)\) is given as follows:  
\[w_{aud}(t)=\frac{N_{s}(t)\times N\varphi g\times\tau}{V_{z}\rho} \tag{26}\]  
where \(N_{s}(t)\) is the occupancy rate of the venue (%). \(N\) is the total capacity of the ice sports venue (persons). \(\varphi\) is the clustering coefficient. \(g\) is the hourly moisture emission per person (kg/h). \(\rho\) is the air density (kgair/m\({}^{3}\)). \(w_{dh}(t)\) is given as follows:  
\[w_{dh}(t)=\frac{v_{z}(t)S_{dh}(t)\tau}{V_{z}\rho} \tag{27}\]  
where \(v_{Z}(t)\) is the impact of dehumidifier operation on the moisture content of the air in the studied area (kg/h). \(w_{icc}(t)\) is given as follows: