esa-sceva/satcom-chunk-collection huggingface dataset:
# A Communication Framework for Image Transmission through LPWAN Technology
## 3 Proposed Framework
### Framework Interconnection  
#### 3.5.1 LoRa PHY Modulation  
LoRa PHY is based on a Chip Spread Spectrum (CSS) modulation. CSS is a subcategory of DSSS that takes advantage of the controlled frequency diversity to recover data from weak signals, even near the noise level. In CSS, the SF is the number of transmitted bits in a symbol by considering the number of symbols in chips of \(2^{SF}\). The SF is defined in (3), which is related with spread bandwidth \(BW\), the symbol rate \(R_{s}\), and the chirp duration \(T=\frac{1}{R_{s}}\) through Equation (4).  
\[SF=\frac{chip\ rate}{symbol\ rate} \tag{3}\]  
\[2^{SF}=\frac{BW}{R_{s}}=BW\,T \tag{4}\]  
The basic element of LoRa-CSS modulation is the chirp, and its waveform is written in (5).  
\[S_{css}(t)=\begin{cases}e^{j\phi(t)},&\text{if }-\frac{T}{2}\leq t\leq\frac{T}{2}\\ 0,&\text{otherwise}\end{cases} \tag{5}\]  
where, \(\phi(t)\) is a chirp phase. From [13; 50], by taking the relationship of the instantaneous frequency with the phase \(f(t)=\frac{1}{2\pi}\frac{d\phi(t)}{dt}\) and combining with the definition in (4), the LoRa raw chirp signal of (5), in one chirp duration \(T\) it is reduced to (6).  
\[S_{css}(t)=e^{j\left(2\pi\ \frac{BW}{T}\ \frac{t^{2}}{2}\right)} \tag{6}\]  
The chirp may code up to \(SF=12\) bits during a chirp period by shifting the frequency increasing ramp based on the \(2^{SF}\) possible chip values. Thus, each chip code is obtained by a cyclic shift of the chirp reference. A coded chirp signal is expressed in (7), where \(k\) is the number of shifted chips [13].  
\[S_{css}(t)=e^{j\left(2\pi\ \frac{BW}{T}\ \frac{(t-k/BW)^{2}}{2}\right)} \tag{7}\]  
At the receiver, the signal is processed through a matched filter technique by correlating a known raw down-chirp signal with the unknown coded up-chirp signal in order to detect the presence of the shift. In other words, the received coded up-chirp is convoluted with a conjugate time-reversed version of the raw signal. There are two time periods where a frequency shift of value \(BW\) occurs at the time index \(k\), corresponding to the value of the coded symbol. Then, down-converting by sampling the signal at the chip rate \(BW\), the instantaneous frequency becomes continuous over the whole chirp and, after subtracting the carrier, the instantaneous frequency is proportional to the shift \(k\). The FFT of the sampled signal shows a flat response with a peak shifted by the coded symbol value.
# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results  
The experimental protocol considered five datasets, three of which are texture image collections, and two are composed of medical images. The KTH-TIPS dataset contains a collection of 810 color texture images of 200 \(\times\) 200 pixel of resolution. The images were captured at nine scales, under three different illumination directions, and in three different poses, with 81 images per class. Seventy percent of images are used for training, while the remaining 30% are used for testing. The Outex TC 00011-r [24] has a total of 960 (24 \(\times\) 20 \(\times\) 2) images of the illuminant Inca. The training set consists of 480 (24 \(\times\) 20) images and the test set of 480 (24 \(\times\) 20) images. The Amsterdam Library of Textures (ALOT) is a color image collection of 250 (classes) rough textures. The authors systematically varied the viewing angle, illumination angle, and illumination color for each material to capture the sensory variation in object recordings. CRC is a dataset of colorectal cancer histopathology images of 5000 \(\times\) 5000 pixels that were patched into 150 \(\times\) 150 pixels images and labeled according to eight types of structures. The total number of images is 625 per structure type, resulting in 5000 images. Finally, the BreakHis dataset comprises 9109 microscopic images of breast tumor tissue collected from 82 patients using different magnification factors (40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\)). Currently, it contains 2480 benign and 5429 malignant samples (700 \(\times\) 460 pixels, three-channel RGB, eight-bit depth in each channel, PNG format). Figure 3 presents samples of images extracted from the KTH-TIPS, Outex, ALOT, CRC, and BreakHis datasets.  
We employed two experimentation strategies: a train-test split where 70% of data were reserved for training and hyperparameter tuning, and 30% for testing; (ii) a 10-fold cross-validation. The use of two strategies allowed a better assessment of the performance of the proposed approach and a fair comparison with the state of the art. In the experiments, the full feature vector generated by concatenating all descriptors (TiO) was used for training a monolithic classifier using the LDA algorithm and ensembles using HistoB, LightB, and CatBoost algorithms.
# Recent Advances in Bio-Based Wood Protective Systems: A Comprehensive Review
## 4 Bio-Based Fillers  
The renewed attraction towards wood as a building material is driven by growing concerns about sustainability and evolving aesthetic preferences. Nevertheless, due to its organic nature, wood is vulnerable to changes in humidity and exposure to UV radiation. These factors instigate the creation of unstable molecules and lead to the breakdown of its lignin and cellulose constituents [9]. When used outdoors, wooden structures can experience issues like expansion, susceptibility to mould and fungi, changes in colour, yellowing, and a decline in both gloss and structural integrity [107; 108]. As a result, the industrial field is progressively focusing on utilizing novel bio-derived additives in wood coatings. These fillers have the potential to enhance the component's ability to withstand weathering, serve as strengthening agents, or afford properties, such as antibacterial, antifungal, or flame retardant capabilities, as summarized in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Preservative/Impregnator** & **Properties** & **Results** & **Ref** \\ \hline Tannin extract & Reduced ecotoxicity & IC\({}_{20}\) and IC\({}_{50}\) of 22 and 145 mg/L, respectively & [91] \\  & & mass loss (\%) reduced below 25 after 16 weeks of & \\ Stilbenes extract & Improved fungal resistance & incubation with _C. puteanna_, _G. rrabeum_, and _R. placenta_ & [92] \\ Tannin extract & Improved fungal resistance & Antifungal activity comparable to that of & [96] \\  & & chromate copper borate preservative & \\  & & IC\({}_{50}\) values against _T. hirsuta_, _S. commune_, and _P._ & \\ Monoterpene carvacrol & Improved fungal resistance & _sanguineus_ of 87.6, 53.6, and & [99] \\  & & 71.7 mg mL\({}^{-1}\), respectively & \\ Propolis extract & Improved fungal resistance & Mass loss (\%) reduced from 48.8 to 2.7 after & \\  & & incubation with _C. puteanna_ & \\ _Lippia organoids_ extract & Improved fungal resistance & Inhibition index of 100 even at low & \\  & & concentrations against _G. rrabeum_ & [103] \\ Coffee silverskin & Improved fungal resistance & Inhibition coefficient of about 70\% against & \\  & & _T. versicolor_, _G. rrabeum_, and _R. placenta_ & [106] \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the main insights regarding the use of bio-based preservatives and impregnators in the formulation of wood coatings.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 3 System Model
### Modeling of Radar Unmanned Aerial Vehicle (UAV) Swarm Networking
Since the two radar UAV swarms need to transmit data back to the base station for comprehensive analysis, after receiving the target echo the two radar UAV swarms need to convert part of the received signal within the passband into the communication UAV using the frequency difference of \(\Delta f\). This process is called forwarding link of radar UAV swarm. The reason for frequency conversion is to reduce the interference between transmission link and forwarding link. After frequency conversion, the signal still has two main parameters: \(f_{c}^{*}\) and \(b^{*}\). \(f_{c}^{*}\) is the carrier frequency after frequency conversion, and \(b^{*}\) remains unchanged. Frequency division multiplexing mode is still used for both signals. The reason for setting up communication UAV is that due to the energy constraints of UAV, the two radar UAV swarms need to allocate most of the power on the transmission link and minimize the energy cost of the forwarding link. The forwarding power cannot help send data directly back to a distant base station. Therefore, the communication UAV is responsible for collecting data from two signals and forwarding them to the base station. In this paper, the transmission link from communication UAV to base station is not considered. In order to avoid jamming better, the position of communication UAV is tunable. System parameters are shown in Table 1.  
Figure 1: (**a**) Framework of radar swarm networking system; (**b**) internal structure of radar unmanned aerial vehicle (UAV) swarm.
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 3 Single Visual Object Tracking Pipeline for UAV
### Tracking Cycle and Occlusion Handling
Lastly, the 3D coordinates \((x_{w}^{u},y_{w}^{u},z_{w}^{u})_{t}\) of the cluster, modeling the object in the scene space, are reprojected in the image as \((x_{i}^{u},y_{i}^{u})_{t}\). After particles are resampled, the framework clusters them based on their position in the image space. We identify the cluster describing the object, by comparing the position \((x_{w}^{c},y_{w}^{t},z_{w}^{c})\) for every cluster \(c\) against the predicted position of the object \((x_{w}^{p},y_{w}^{p},z_{w}^{p})_{t}\). The closest cluster to the predicted position is considered to describe the object state. Thus, the new/updated position of the object is based on this identified cluster as \((x_{w}^{u},y_{w}^{u},z_{w}^{u})_{t}\). The coordinates in the scene space are then projected in the image space as \((x_{i}^{n},y_{i}^{n})_{t}\) and used for updating the appearance model of the _visual tracker_ component. This avoids adding incorrect training samples, i.e., distractors.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology
### Tensor Construction
#### 4.2.1 Tensor Rank Estimation  
In this paper, CORCONDIA is applied as the tensor rank estimation method using the AutoTen algorithm [38]. It is essential to mention that tensor rank estimation methods are used to get recommendations more than the rank's exact actual value. The algorithm was run five times on randomly chosen tensor \(\mathcal{T}^{(i)}\), and the average estimated rank resulted in a value of \(R=10\), which is the rank used for the experiments.
## 4 Overview of GNSSs
### Galileo  
The European Union, in close cooperation with the European Space Agency, has developed its own system that meets the criteria of accuracy, reliability, and safety called Galileo. The system will be controlled by civilian authorities and will cooperate with GPS and GLONASS. It will provide real-time positioning and timing services at various levels of accuracy, integrity, and availability. Other than existing satellite navigation systems, Galileo is a suitable system for safety-critical applications such as landing aircraft, guiding cars, tracking hazardous materials, and controlling rail traffic [78,82].
# A Novel Real-Time Match-Moving Method with HoloLens
## 4 Experimental Results
### Pre-Visualization  
The performance of the pre-visualization system was compared with that of Spectator View based on the number of polygons, as shown in Figure 12. As mentioned before, HoloLens cannot handle a larger number of polygons (>100,000) due to the lag it causes in frame rate and network speed. In Figure 12a, when the number of polygons was increased to more than 65,000, the frame rate dropped from 60 fps to 30 fps. Additionally, the network speed dropped from 1000 KB/s to 768 KB/s, as shown in Figure 12b. This problem was caused by the HoloLens's so-called \"holographic processing unit\", which can handle only a limited number of polygons at one time, which greatly reduces the frame rate and network speed of the HoloLens.  
Figure 11: Comparison of different images in match-moving with motion blur.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Method** & **Number of Unmatched Pixels** & **Specified Rate Resolution** & **Match Rate (\%)** \\ \hline
15 frame & GT-BM & 2289 & 60,000 & 96.2 \\  & GT-PM & 1263 & 60,000 & 97.9 \\
353 frame & GT-BM & 1475 & 60,000 & 97.5 \\  & GT-PM & 3079 & 60,000 & 94.9 \\
702 frame & GT-BM & 32,383 & 60,000 & 46.4 \\  & GT-PM & 1213 & 60,000 & 98.0 \\
975 frame & GT-BM & 28,549 & 60,000 & 52.4 \\  & GT-PM & 2379 & 60,000 & 96.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Match rate of different images in match-moving with motion blur.  
On the other hand, the frame rate and network speed remained stable for the proposed method. This is because the proposed system does not require graphics information to be transmitted to HoloLens. In general, since high resolution 3D models are used in the film and entertainment industry, real-time virtual-real image synthesis cannot be obtained by conventional methods such as Spectator View. Consequently, it is difficult to obtain the accurate position information required for pre-visualization. Therefore, we employed pre-visualization used in the proposed system, which can obtain accurate position information from HoloLens in real time.
## 2 Related Work
Initiatives such as the family of ISO 10303-STEP (Standard for the Exchange of Product Model Data) standards, e.g., ISO 10303-243:2021 [27] \"Industrial automation systems and integration--Product data representation and exchange\", or the Open Services for Life-cycle Collaboration (OASIS OSLC) specifications, are focused on promoting the implementation of technical engineering processes through an interoperable framework. These initiatives promote the creation of federated environments of services (tools) by defining collaborative engineering ecosystems through data shapes or schemes that serve as contractual agreements for accessing information resources. Both the ISO 10303-STEP and OASIS OSLC families utilise the Representational State Transfer (REST) software versionarchitecture style to manage publicly represented information resources, which can be exchanged in formats such as JSON, XML, or even the Resource Description Framework (RDF). For instance, the specification of the OSLC Knowledge Management (KM) domain represents both the data and metadata of a system artefact [28]. Similarly, the latest version of the SysMLV2 API (Systems Modelling Application Programming Interface and Services specifications) follows this approach by defining a REST API for consuming SysML models as key-value documents. Although these approaches have made great efforts to define an engineering environment as a set of standardised federated set of services, they are mainly focused on metadata exchange, and have only partially addressed the challenges around enabling reuse of system artefacts. In the case of OSLC, the implementations may offer a kind of interface to perform SPARQL-like queries over the indexed metadata without the possibility of properly processing the internal entities and relationships. Other efforts, such as the Open Model-Based Engineering Environment (OpenMBEE) and the Model Management System (MMS) tool, do offer the possibility of accessing specific elements. However, designing the required SPARQL-like queries to gather the proper information resources and deciphering their internal representation are challenging tasks. In summary, the state of the art highlights the challenges and advancements in the field of semantic retrieval of physical models and information representation techniques for building retrieval systems; while various works have focused on system artefact representation, the integration of more advanced artificial intelligence techniques, such as deep learning models for text representations and word embeddings, remains a challenge. Word embeddings offer a method of encoding texts as dense vectors to capture the semantic and syntactic relationships between words, and have found applications in text classification, sentiment analysis, and machine translation. However, existing approaches may not comprehensively consider the similarity with input text patterns.
# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 2 Related Work
### Microblog Influence Analysis
A simple view is to measure the influence of microblogs by calculating the sum of the number of forwards and comments of microblogs in a certain period. For example, the hot posts on Sina Weibo, which is the biggest microblogging platform in China, adopt this calculation method [6]. In order to measure and calculate the influence of microblog contents, [PERSON] et al. [11] defined the influence of each user's microblog according to the number of fans, the number of forwards, and the number of comments. They believed that the number of fans was the main factor in evaluating the influence of the microblog content, and both forwards and comments reflected the topic and influence caused by the microblogs. However, this method might be affected by robot users on microblogging platforms.
## 3 QoS Self-Provisioning and Interference Management Concept
### Step 2--Uplink Parameter Verification
Knowing the other to own macrocell interference ratio, the thermal noise power and the allowed noise rise for macrocells, \(P_{HNB-Iother}^{RX}\) is calculated as  
\[P_{HNB-Iother}^{RX}=\frac{P_{Noise}}{L_{p}}\bigg{(}(NR_{macro}-1)\cdot\frac{iF }{iF+1}\bigg{)} \tag{6}\]  
In addition, the total received power at the HNB, \(P_{HNB-TOTAL}^{RX}\), is a sum of all observed interferer and noise powers, _i.e._,  
\[P_{HNB-TOTAL}^{RX}=P_{Noise}+P_{HNB-MUE}^{RX}+P_{HNB-Iother}^{RX}. \tag{7}\]  
Knowing the total received interference and noise power at the HNB it is possible to calculate the required transmission power of the HUE, \(P_{HUE}^{TX}\), to satisfy the required uplink data rate at the cell border:  
\[P_{HUE}^{TX}=\text{sinr}\big{(}\text{Req}\text{Thp}_{UL}\big{)}\cdot PL_{fmmo} \cdot P_{HNB-TOTAL}^{RX} \tag{8}\]  
where sinr(...) is a mapping function from required uplink data rate to required SINR. Next, the NR generated by the HUE towards the nearest MNB is checked. Based on the assumption that the path loss from the MNB to any location inside the femtocell equals the path loss from MNB to HNB, the HUE generated noise rise, \(NR_{MNB-HUE}\), is calculated as:  
\[P_{MNB-HUE}^{RX}=\frac{P_{HUE}^{TX}\cdot RSCP_{CPICH}^{macro}}{P_{CPCH}\_{ macro}}\ \ NR_{MNB-HUE}=\frac{P_{MNB-HUE}^{RX}+P_{Noise}}{P_{Noise}} \tag{9}\]  
where \(P_{MNB-HUE}^{RX}\) is the received power from HUEs at the strongest MNB and \(NR_{MNB-HUE}\) is the NR contribution from HUEs transmitting with required minimum uplink data rate. Moreover, it is checked whether the generated NR is within the allowed target \[NR_{\textit{MNB-HUE}}<NR_{\textit{HUE}}. \tag{10}\]  
If the NR from HUEs exceeds the NR target, the femtocell range is reduced according to the following iterative expression  
\[PL_{\textit{femuo}}=\frac{PL*_{\textit{femuo}}}{\Delta_{\textit{step}}} \tag{11}\]  
where \(\Delta_{\textit{step}}\) is the reduction of the desired femto path loss and \(PL*_{\textit{femuo}}\) is the previously assumed value of the femto path loss. After reduction of the desired femto path loss according to Equation (11), the calculations from Equation (1) and onwards are repeated. Finally, when Equation (10) is fulfilled it is ensured that the computed HUE transmission power does not exceed the HUE power capping constraint  
\[P_{MNB-MUE}^{RX}=P_{Noise}\cdot\left(\frac{NR_{macro}-1}{1+iF}\right) \tag{3}\]0  
If the HUE power capped constraint is violated, the desired femto path loss has to be reduced according to Equation (11) and the algorithm has to be re-calculated from Equation (1). If both the NR and HUE power capping constraints are fulfilled the next step is the HS-DSCH power calculation.
## 3 Proposed System Concept Architecture
### Turbulence Compensation
It provides flexibility for future extensions of the system, allowing adaptation to different turbulence conditions where higher-order corrections may be required. Moreover, having the capability to achieve high spatial resolution is inherently beneficial, as it allows optimization of the number of pixels according to specific system requirements rather than being constrained by hardware limitations. This setup also includes a lens for aperture averaging, which is particularly beneficial for point receivers. Aperture averaging softens small fluctuations and ensures robust and accurate AoA estimation. As shown in Figure 3 the incoming signal is split into two paths. One path is directed to the OPA system, while a portion of the received beam is used for feedback. This feedback beam is captured by a Shack-Hartmann wavefront sensor, which detects wavefront distortions. A Shack-Hartmann wavefront sensor is an element that is frequently used in adaptive optics systems for detecting wavefront distortions [45]. The detected wavefront is then decomposed into Zernike polynomials to estimate the aberrations. The controller uses this information to generate a control signal that is sent to the LC-SLM. The LC-SLM consists of a liquid crystal layer whose refractive index can be adjusted by applying varying electrical voltages. By controlling the voltages across the array of liquid crystal pixels, the phase of the incoming beam is modulated to counteract the effects of turbulence. Through this feedback loop, the LC-SLM adjusts its phase pattern to correct for the observed distortions. Figure 3: System concept architecture of our proposed coherent UOWC system with cascaded aperture averaging (converging lens) and an adaptive optics system (including LC-SLM as the wavefront corrector, splitter, Shack–Hartmann wavefront sensor, and the feedback control loop) for turbulence compensation and OPA antennas for PAT. Note that the dimensions of the elements have been adjusted for clarity and are not to scale. The method of adaptive optics and aperture averaging proposed for the system is explained in more detail below.