esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
In this paper, we present a novel scheme employing machine learning methods for cross-technology interference classification in IWSAN. The proposed method employs a three-step classifier composed of a lightweight feature-extraction process, a preliminary classification stage employing four SVMs and a final decay, allowing for classification among interference from IEEE 802.11 networks, microwave ovens, as well as the presence of interference-free channels. The tests conducted in industrial environments, including a wide range of interference scenarios, show an average classification accuracy of 84% and up to 98% for IEEE 802.11 active networks, with a channel sensing time of 300 ms. The memory footprint of the samples collected in this sensing time remains below 600 bytes per channel thanks to the limited sampling frequency. The extremely short time required for sensing renders the developed solution a promising candidate for the adoption in superframe-based TSCH networks by means of spectrum-sensing-reserved timeslots. In this paper, we have also highlighted the fundamental influence of device calibration on the performance of spectrum-sensing-based methods using COTS WSN hardware, which is a matter often overlooked in related literature. In particular, it is shown that the classification accuracy of the proposed solution is significantly influenced by the intrinsic hardware variations.  
Figure 16: Operation delays in \(\upmu\)s for the channel-sensing task implemented in TelosB motes.  
We leave to future works further investigations on the potentialities of SVM-based-methods for interference classification in IWSAN. Other notable aspects of interest are the inclusion of the channel-sensing and classification mechanism in a TSCH network and a run-time assessment of the solution, as well as the development of interference mitigation strategies.  
The authors would like to thank Imerys Mineral AB for the access to their production plant in Sundsvall and [PERSON] from Mid Sweden University for the helpful feedback.  
The authors declare no conflict of interest.
## 4 Experiment and Result
### Experimental Settings  
HRNet-CFR: The network gradient optimization function was the Stochastic Gradient Descent (SGD) algorithm, the basic learning rate was \(1\times 10^{-2}\), the momentum was 0.9, the weight attenuation coefficient was \(5\times 10^{-4}\), and the iteration round was 60 epochs. During the training process, the learning rate decreased linearly. The image input size was designed to be 1024 \(\times\) 1024 \(\times\) 4, and the batch size of each GPU was 1.  
Deeplabv3+: In the training process, the initial learning rate was \(1\times 10^{-4}\), the decay rate was \(1\times 10^{-5}\), the expansion rate list = (6, 12, 18), and iterative rounds were 60 epochs.  
UPerNet: In the training process, the initial learning rates of the Encoder and Decoder were both \(2\times 10^{-3}\), the decay rate was \(1\times 10^{-4}\), the weight of deep supervision loss was 0.4, the power in poly to drop the learning rate was 0.9, and iterative rounds were 60 epochs.  
The system of this experiment was Ubuntu 18.04, and the processor was Intel(r) Core(tm). The i7-6850k graphics processor (GPU) was the Nvidia GeForce Titan X GPU with 12 GB memory.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Main Features of the Model  
The object detection task for various mining equipment was carried out using the state-of-the-art YOLO11 model (Jocher and Qiu, 2024) [74]. This model is well regarded for its efficiency in real-time applications due to its advanced convolutional neural network (CNN) architecture, which optimizes the trade-off between accuracy and computational cost.
###### Abstract  
This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-parts, namely, distributed room-level units as well as a centralized main-part AI controller for maximizing efficient HVAC management in single-family residences as well as small-sized buildings. The system utilizes an LSTM model for forecasting temperature trends as well as an optimized control action using an envisaged DQN with predicted states, sensors, as well as user preferences. InfluxDB is utilized for gathering real-time environmental data such as temperature and humidity, as well as consumed power, and storing it. The AI controller processes these data to infer control commands for energy efficiency as well as thermal comfort. Experimentation on an NVIDIA Jetson Orin Nano as well as on a Raspberry Pi 4 proved the efficacy of the system, utilizing 8761 data points gathered hourly over 2023 in Cheonan, Korea. An added hysteresis-based mechanism for controlling power was incorporated to limit device wear resulting from repeated switching. Results indicate that the AI-based control system closely maintains target temperature setpoints with negligible deviations, affirming that it is a scalable, cost-efficient solution for intelligent climate management in buildings.  
predictive optimal control; deep reinforcement learning; HVAC; Modbus TCP; smart building; climate management Article
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.1 Electrocardiography (ECG)  
An ECG is a waveform that shows the transmission of electric potentials via heart muscles in the context of time. Overall, an ECG waveform represents a non-invasive way of the viewing heart function. A standard ECG output is obtained through 12 leads, however a wireless sensor uses less number of leads. From Table 1[30], considering a sampling rate of 250 Hz and sampling resolution of 16 bits, we have sample interval time (SIT) = 1/250 = 4 ms and maximum possible data size (MPDS) = sampling rate \(\times\) resolution = 250 \(\times\) 16 = 4 Kbps = 500 bytes/s. Thus, the data need to send (DNS) in 4 ms can be computed as:  
\[\text{DNS}\ =\frac{\text{MPDS}\times 4}{1000}=2\text{ bytes} \tag{1}\]  
In this paper, we consider the minimum MAC 9 bytes overhead. Equation (2) shows a complete packet size which is sent in the ECG node case:  
DNS after 4 ms including minimum MAC and physical (PHY)header  
\[\begin{array}{l}=2\text{ (data)}+9\text{ (MAC header)}+6\text{ (physical layer header)}\\ =17\text{ bytes}=136\text{ bits}\end{array} \tag{2}\]  
Equation (3) computes the required data rate:  
\[\text{Total data rate required by ECG packet}=\text{Sampling rate (250)}\times\text{packet size (136)}=34\text{ Kbps} \tag{3}\]  
Equations (1)-(3) show that after each 4 ms, a specific amount of the data will be ready for transmission. In the slotted CSMA/CA case, whenever this gets the channel and only transmits the single packet if the remaining CAP is enough to transmit that packet.
# Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
## 3 Results of Uncertainty Modelling  
Table 2 shows a comparison between the results obtained by the methods presented above. This includes results obtained for the statistical parameters by MLM accounting for run-outs. Furthermore, a characteristic, 5% quantile is estimated using the MLM estimates resulting in \(\log k\ =\ 18.77\), which is larger than the characteristic value equal to 17.054 specified in the Eurocodes (see [38; 39], and Table 2).  
The Markov Chain Monte Carlo simulation results in Figure 5a show that \(\log\ k\) is mostly in the interval 18-19, and in Figure 5b, \(m\) is close to 5, which is in agreement with the fixed value used for MLM. It should be noted that \(m\) is assumed fixed in the reliability section. The Posterior marginal density function is also shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Mean by** & **Mean by** & **Standard** & **Standard** & & & \\  & **MLM** & **Bayesian** & **Deviation** & **by Bayesian** & **Distribution** & **Remark** \\  & & **Approach** & **by MLM** & **Approach** & & & \\ \hline \(\varepsilon\) & 0 & 0 & — & — & Normal & Error term \\ \(\sigma_{\varepsilon}\) & 0.39 & 0.21 & 0.06 & 0.04 & Normal & Standard deviation of error term \\ \(\log k\) & 18.77 & 18.72 & 0.07 & 0.05 & Normal & Location parameter in Wöhler curve \\ \(m\) & Fixed to 5 & 5.03 & — & 0.02 & Fixed/Deterministic & Slope of Wöhler curve \\  & & & 0.06 & 0.03 & Deterministic & & Correlation coefficient between location and standard deviation of error \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results.  
Figure 4: Correlation between \(k\) and \(m\)  
Figure 5: Markov Chain Simulation for: (**a**) \(k\); (**b**) \(m\).
## 4 Experiments
### Datasets  
* CK+ The CK+ dataset contains 593 video sequences from 123 objects. The duration of the sequences varies from 10 to 20 frames and they show the transition from neutral to peak expressions (Figure 3). Out of these sequences, the last three frames of 327 video sequences were selected, totaling 981 images.
* Fer2013 The Fer2013 dataset was generated from the International Conference On Machine Learning (ICML) 2013 challenge. The images in the dataset are all grayscale images of size 48 \(\times\) 48, with seven types of expressions corresponding to the numbers 0-6, namely, anger, disgust, fear, happiness, sadness, surprise, and neutrality, respectively (Figure 4). The training set is 28,709 images and the test set is 3589 images. It should be noted that there are various influences on these images such as occlusion, age, pose, etc. and there are also some labeling errors in the test set of this dataset, which leads to a low test accuracy on this dataset. In addition, the recognition accuracy of the naked eye on this dataset is only about 65%.  
Figure 3: CK+ dataset sample.  
* JAFFE The JAFFE dataset collects seven categories of facial expressions from 10 Japanese women, which contain six basic expressions and one neutral expression (Figure 5). There are 213 images in total, each with a resolution of 48 \(\times\) 48, and the number of images for each expression category is 30 on average. In this experiment, the training set totaled 174 images and the test set was 39 images.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 2 Surveying TDMA-Based V2V Communications  
MAC protocols can be broadly categorized into two categories, **contention-based** and **contention-free** (also known as uncoordinated and coordinated, respectively). The contention-based medium access mechanisms require no synchronization among the vehicles, thus being generic and simple to use, at the cost of inferior efficiency in the channel utilization under high network load. Oneof such mechanisms, CSMA/CA, is used by the most established standard for V2V communication, IEEE 802.11p/DSRC, that in turn is the technical foundation of two regional protocol stacks, namely the wireless access in vehicular environment (WAVE) [8] stack in the USA, and the ETSI ITS-G5 [9] in Europe. In CSMA/CA, a random backoff time drawn from an exponential distribution is enforced to mitigate collisions; however, due to the stochastic nature of the process, CSMA/CA fails to provide communication with bounded delays and may suffer from profound service degradation in dense traffic scenarios.  
Time-division multiple access (TDMA) protocols address the shortcomings of CSMA/CA by assigning concrete slots of time to each node, thus implementing a contention-free mechanism. TDMA protocols are well suited for time-bounded communication in platoon scenarios, given that platoons are groups of multiple agents working towards a common goal, thus making it easier to synchronize their communications. TDMA protocols can be broadly categorized into distributed and centralized, with the former further dividing into direct implementations and overlay protocols, and the latter into cluster-based and road-side unit (RSU)-managed (that we will not address as it departs from our V2V scenario). Cluster-based TDMA MAC protocols map naturally onto the platooning application, where the leader can serve as the local network coordinator for each group likewise a leader vehicle in a platoon. Overlay protocols offer exclusive communication time slots on top of an existing MAC protocol. In vehicular scenarios, the base protocol is typically IEEE 802.11p, making the overlay protocols easy to implement and deploy as well as tolerant to contending traffic (handled by the CSMA/CA layer underneath).  
Figure 1 shows a comprehensive taxonomy overview of the various families of MAC protocols in VANETs. In the remainder of this section, we review these families presenting representative examples of MAC protocols available in the literature. Finally, we also review existing models of network performance under specific MAC protocols. Most models are analytical and focus on the performance of CSMA/CA, as it is the MAC protocol implemented by IEEE 802.11p.
## 2 Materials and Methods  
### Synthesis of Al-BiSn Composites  
In this study, commercial aluminum (Al) powders (size \(\sim\)45 \(\upmu\)m and purity \(>\) 99.7%) were employed as the matrix material. Bi60 Sn40 (BiSn) alloy particles (average particle size ranging from \(\sim\)10-20 \(\upmu\)m and purity \(>\) 99%, supplied by Phoenix Scientific Industries, UK) was added as a reinforcement particle to develop Al-BiSn composites. The scanning electron microscopy (SEM) images and energy dispersive spectroscopy (EDS) spectra of as-received Al and BiSn powder are presented in Figure 1.  
Figure 1: SEM images and EDS spectra of (**a**,**b**) Al and (**c**,**d**) BiSn particles.  
A schematic of the experimental process to synthesize Al-BiSn composites is depicted in Figure 2. Pure Al and (5, 10, and 15 wt.%) BiSn-reinforced aluminum composites were synthesized using the PM technique assisted by HMWS. Required amounts of pure Al powder and BiSn particles were carefully weighed and blended for 1 h at 200 rpm in a RETSCH PM-400 mechanical alloying machine (Haan, Germany). No balls or process control agent were used during the blending step, and the blending process was carried out without any protective atmosphere. The blended powder mixtures were then uniaxially cold-compacted at a pressure of 1000 psi to obtain billets of 40 mm in length and 36 mm in diameter. The same parameters were used to compact the pure aluminium without blending. Hybrid microwave-assisted two-directional sintering technology was used to sinter the cold-compacted billets. The billets were heated to 550 \({}^{\circ}\)C in a 900 W, 2.45 GHz Sharp microwave oven with SiC as the microwave susceptor material in the absence of an inert atmosphere. The sintering temperature was determined using dummy aluminium blocks and a K-type thermometer in the microwave sintering furnace. A calibration chart (time versus temperature) was made. The compacted billet was sintered in the microwave oven for a precalibrated duration corresponding to a temperature of 550 \({}^{\circ}\)C. Following the predetermined time in the microwave, the billet was allowed to cool to room temperature without any holding time under ambient atmospheric conditions. The microwaves were absorbed internally by the billet, and the SiC susceptor heated up to provide radiant heat to the billet externally. This helped in delivering bidirectional heating to the billet as compared to conventional sintering or sintering with microwaves only [40; 41; 42; 43]. The as-sintered billet was then further processed using hot extrusion.
# Optimizing the Spectral Characterisation of a CMYK Primer with Embedded CMY Printer Modelling
## 2 Methodology
### CYNSN-Based Backward Modelling  
As mentioned above, backward modelling is the reverse process of forward modelling, which can be expressed as follows,  
\[\underset{\delta}{minRMSE}(R_{t},F(\delta)), \tag{8}\]  
where \(\delta\) denotes the control values of the printer, \(R_{t}\) is the target spectral reflectance to be reproduced, \(F\) represents the forward model and \(RMSE\) is the root-mean-square error [39] between the predicted and target spectral reflectance. For colorimetric colour reproduction, the RMSE could be substituted by a colour difference metric, such as the CIEDE2000 [31].  
Up to now, many algorithms have been proved to be effective in inverting the YNSN model, for instance, the work of [PERSON] [40], [PERSON] and [PERSON] [14], [PERSON] et al. [27], etc. However, since in the CYNSN model the colour separation process eventually takes place in a certain cell, the optimal cell must be targeted before separation. To solve this, one approach is to try every cell to find the best one, which is definitely impractical due to the large number of cells. In 2010, [PERSON] et al. proposed a method which first chose several candidates among all the cells based on the colour difference between the target spectral reflectance and the reflectance of the imaginary cell centre, and then targeted the optimal cell according to the backward modelling accuracy [21]. In our recent work, such a method was optimised by a gamut judging method [19]. In that study, we first divided the printer into several larger subdomains, named supercells, and then adopted that gamut judging method to decide whether or not the target colour was located inside the gamut of those supercells. After that, the candidates containing the target colour were further divided into small cells and the optimal small cell was eventually selected by a similar method of [PERSON] et al., with the only exception that the imaginary middle point reflectance of each cell was predicted by a BPn-CYNSN model, instead of the point-averaging method of [PERSON] et al.
## 3 Results and Discussion
### Morphological Observation  
#### 3.6.1 Scanning Electron Microscopy  
As presented in Figure 7, the morphological structure of chitosan was studied by SEM; the surface morphologies of CSC and CSS are obviously different. The surface of CS is clean and lamellar, which is the typical surface morphology of chitosan [16]. CSC has round pores and ultrafine fibers, showing irregular multilayer conformation. CSS consists of round pores, microfibers and microcrystals, with a large number of prominent microfibers, and the particles in the shape are uneven and fragmented. [PERSON] et al. [48] observed the fragmentation with fibrillar structure in the SEM of chitosan. The diverse morphology of chitosan may be due to the different arrangement of hydrogen bonds between the chitosan samples [49].
## 2 Objective  
The objective of this study was to calculate the mortar air void content based on CT scanned samples' volume information and then to calculate the mortar's volumetric properties. Although researchers can assume the mortar's volumetric properties, which makesmanufacturing easier, mortar material with similar volumetric properties as a mortar mixture would be useful when studying interstructure stress and volume change. Scholars have conducted similar studies testing the performance of asphalt mortar and coarse aggregate asphalt separately and then deciding on the optimal gradation composition of the mortar and coarse aggregate asphalt for the design of a mixture [27]. The challenge of mortar theorization and application is that there is no clear definition of the volumetric properties of mortar. When discussing the air void percent, it means the void percent of a mixture. The void percentage in a mortar and in the coarse aggregate will not affect the mixture's air void value, but they may have different mortar air void contents. There are two existing methods to conduct mortar research. Firstly, assuming that the air voids that exist in mortar are similar to those in a mixture, the mortar design is conducted the same as for mixture design. The usage of each part and the volumetric properties can be controlled. Secondly, when studying the quantitative air void properties in mortar through CT, the results are accurate but disposable, which means that the mortar's air void content and the mortar's air void percent in the mixture can only be obtained at high cost. Therefore, this study attempted to combine the accuracy of the CT approach and the certainty of the mixture design approach to develop a general and relatively accurate approach to mortar air void determination.  
To achieve this objective, three types of asphalt mixtures with different nominal maximum particle size (NMPS) were manufactured in the laboratory and scanned using an industrial CT scanner. The scanned images were processed with binarization and threshold segmentation. Then, all of the voids in mixture were identified, numbered, counted and fitted with a statistical curve. The small voids were counted in the mortar, while the large ones were counted in the mixture; then, the identified air voids in the mortar of the mixture could be calculated. In addition, a new method to calculate the mortar air void content was designed and promoted based on the specific surface area method. There were mortars with different NMPS for each mixture; thus, in total, there were 12 types of mortar air voids calculated, and the sensitivity of the mortar air void content to the mixture's air void content and asphalt aggregate ratio was analyzed.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 5 Proposed Method
### Matching Strategy  
In this method, after projection, each model obtained one top view and four side views. The feature vectors of the side views were compared with the pre-value (as show in Figure 8) and the category that best matched was selected as the determination result. If all four side views were non-concave images, the non-concave image closest to the pre-value among the side views was chosen as the representative view of the side view; if not all of them were non-concave images, the view closest to pre-value was chosen as the representative view among the concave side views. This is reasonable because, during the projection process, models with concave structures can still yield non-concave structure views at specific angles, whereas the reverse is not true, and as long as other topologies are found in the views, the model can be determined to have this structure. The specific process is shown in Figure 9. After obtaining representative side views, the QDAH of the top and side view were sequentially combined to obtain the feature vectors of the model. Finally, the similarity was calculated using Euclidean distance and sorted to obtain the final retrieval result.  
Figure 8: Average quotient distance–angle histogram.  
Figure 7: Example of angle distribution: (**a,b**) diagram of the difference in angle distribution, (**c,d**) angle histograms. Due to symmetry, only the histograms of the first 9 intervals have been calculated to display here.