esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Performance Evaluation and Comparison
#### 3.6.2 Evaluation Metrics  
Based on the confusion matrix [42], which was created by comparing classification results to test samples parcel by parcel, overall accuracy (OA), precision (P), recall (R), and F1 scores were extracted to evaluate crop classification accuracy. The OA was determined by dividing all correctly classified parcels by the whole validation dataset. Precision and recall were calculated using Precision = TP/(TP + FP), Recall = TP/(TP + FN), where TP, TN, FP, FN represent the number of true positive, true negative, false positive, and false negative parcels, respectively, in the confusion matrix. In addition, F1 = 2 \(\times\) UA \(\times\) PA/(UA + PA), the harmonic mean of the PA and UA, was more meaningful than OA for a special crop type. Greater OA, P, R, and F1 scores indicated superior results, and vice versa.
## 4 Discussion
### Trends of Vegetation Production and Rainfall  
A non-parametric method, the MK test, was applied to understand vegetation trends in the GSDM. There has been an overall decline in vegetation cover in most areas over the last 30 years, indicating that the district is facing degradation. There were negative NDVI trends in most parts of the district (about 45%), although only 0.65% were significant. Vegetation change is considered an indicator of environmental changes that have serious ecological consequences for the district and thus have negative impacts on ecosystem functioning, energy balance, biodiversity and land-atmosphere interactions [54]. A positive NDVI trend indicates that the ecology and ecosystem conditions have improved. The study found that productivity is increasing in other parts of the district (about 53%), but the trends are not significant.  
The district experienced decreasing rainfall over the 30-year period, however these trends were not significant. The Fetakgomo Tubatse local municipality and some parts of Elias Motsoaledi local municipality recorded the greatest decrease in rainfall, while vegetation production showed improvements. This could be due to bush encroachment and alien invader plants observed during field visits that might contribute to the increase in NDVI, in low rainfall areas [13; 55]. Bush encroachment affects agricultural activities and savannah ecosystems and as such it is important to understand the reasons for bush encroachment under decreasing rainfall in these areas to improve vegetation production conditions by identifying appropriate SLM measures [56].  
Figure 7: Spatial NDVI RESTREND pattern.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 2 Related Work  
### Yolox-Tiny Object Detection Algorithm  
Developed by Megvii Technology in 2021, the Yolox-tiny model is a lightweight object detection model that represents an advancement over previous Yolox series models. Fundamentally, the Yolox-tiny is based on a specialized deep-learning algorithm that employs convolutional and residual networks. Its core principle entails utilizing feature maps from shallow network convolutions as both inputs and outputs, subsequently feeding them into deeper layers for enhanced feature extraction. Additionally, the model fuses input feature maps with new ones generated by deeper network convolutions, thereby enhancing the image features. These enhanced feature maps are relayed to classifiers and regressors to ascertain the location and type of objects detected.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 2 Fault Diagnosis of High-Speed Train Bogie System
### Fault Diagnosis of Train Cardan Shaft  
The cardan shaft is the key power transmission component of high-speed trains. It is connected to the drive motor and the gearbox through two universal joints, which transmit the traction force generated by the motor to the gearbox, and then drive the train forward. Additionally, it is a slender mechanical structure with little bending and stiffness, which makes it easy to appear to various degrees of deformation during the train's long run, causing abnormal vibration of other components, thus leading to damage. Since the cardan shaft not only needs to transmit traction power but also needs to coordinate complex motion relationships, its failure causes a major train accident. Their main diagnostic methods are displayed in Figure 4.  
In order to quickly diagnose the misalignment fault of the cardan shaft of trains, [PERSON] et al. [41] presented a method based on dual-tree complex wavelet packet transformation (DTCWPT), which greatly eliminated the interference of other mechanical noises in the vibration signal of the cardan shaft and was verified by train data. By using wavelet packet decomposition, [PERSON] et al. [42] proposed a method based on double decomposition and double reconstruction (DDDR), which can effectively extract the fault features of the universal axis of the train and is better than wavelet decomposition and wavelet singular  
Figure 3: Fault diagnosis method of gearbox: (**a**) improved complementary ensemble empirical mode decomposition (CEEMD) [33]; (**b**) time-frequency statistical feature (TFSF) [34].  
value decomposition. By using the Hamming window, [PERSON] et al. [43] designed a method based on an improved morphological filter that can effectively identify the characteristic frequencies and multiplicative information of train cardan shaft faults.  
Therefore, the fault diagnosis of the train cardan shaft is mainly made by fault feature extraction, and the main applied methods include wavelet packet transform, morphological filter, singular value decomposition, etc.
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 2 DFEC Ranging Principle  
### Overview  
A schematic of the multi-channel dual-frequency signal transmission base station is given in Figure 2. As shown in the figure. First, the time-frequency control module completes the local clock in real time, and outputs stable 1 PPS and accurate reference clock frequency. The reference clock obtains the required dual-frequency signal reference frequency through the clock frequency division module. Under the control of 1 PPS, each frequency baseband signal modulation is completed in the baseband signal generation module, and finally the RF signal combining output is completed by the up-conversion module. Since the signals of different frequencies of multiple channels are controlled by the same clock frequency, Since the carrier waves transmitted from the antennas are all generated by a  
Figure 1: dual-frequency ranging principle.  
single phase-locked loop (PLL), their wavelength and frequency are the same, so all signals have the same time-frequency characteristics. At the same time, for each frequency signal transmitted on the same channel, because the hardware transmission path of each signal is the same, we can consider that the equipment delay between the signal of the same channel is the same, so the measurement distance error is known.
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 7 Conclusions
### Abbreviations  
The following abbreviations are used in this manuscript:  
\(C/N_{0}\) carrier-to-noise density ratio  
\(I/N_{0}\) interference-to-noise density ratio  
AGC automatic gain control  
ANF adaptive notch filtering  
ASIC application-specific integrated circuit  
CDF cumulative distribution function  
CDMA code-division multiple access  
CEP circular error probable  
COTS commercial-off-the-shelf  
CW continuous-wave  
DAC digital-to-analog converter  
DFT discrete Fourier transform  
DSP digital signal processor  
DVB-T digital video broadcasting - terrestrial  
DWT discrete wavelet transform  
FDAF frequency-domain adaptive filtering  
FFT fast Fourier transform  
FMCW frequency-modulated continuous-wave  
FPGA field-programmable gate array  
GNSS global navigation satellite system  
HDDM high-rate DFT-based data manipulator  
HE high-end  
HW hardware  
IDFT inverse discrete Fourier transform  
IFFT inverse fast Fourier transform  
IM interference mitigation  
INR interference-to-noise ratio  
IP intellectual property  
ISR interference-to-signal ratio  
KLT Karhunen-Loeve transform  
LC low-cost  
ML machine learning  
PB pulse blanker  
PSD power spectral density  
PVT position, velocity, and time  
RF radio-frequency  
RFE radio-frequency front-end  
RTL register-transfer level  
SBAS satellite-based augmentation system  
SSC spectral separation coefficient  
SWAP size, weight, and power  
VHDL very-high-speed integrated circuit hardware description language  
Figure 16: Interference #6: Filtered noise with 4 MHz bandwidth with GPS L1/CA.  
Figure A7: Interference #7: Filtered noise with 35 MHz bandwidth with GPS L1/CA.  
Figure 14: Interference #5: Slow frequency hopper with a dwell time of 100 us and a frequency range of 35 MHz with Galileo E1 BC.  
Figure 15: Interference #6: Filtered noise with 4 MHz bandwidth with Galileo E1 BC.  
Figure 16: Interference #7: Filtered noise with 35 MHz bandwidth with Galileo E1 BC.  
Figure 17: Interference #8: Filtered pulsed noise with 35 MHz bandwidth, 50 % duty cycle, and 1 ms pulse width with Galileo E1 BC.  
Figure A22: Interference #4: Fast frequency hopper with a dwell time of 1 us and a frequency range of 35 MHz.  
Figure A23: Interference #5: Slow frequency hopper with a dwell time of 100 us and a frequency range of 35 MHz.
## 2 Methodology
### Overall Architecture  
Given a pair of registered images \(\{T^{1},T^{2}\}\in\mathbb{R}^{C\times H\times W}\), RCCD predicts a change mask, denoted as _Mask_, using only the encoder, decoder, and detector components, excluding the supervisor module during inference. This process can be defined as follows:  
\[Mask=\text{RCCD}(T^{1},T^{2}) \tag{1}\]  
The encoder generates a four-stage multi-scale pyramid of downsampled features, \(\{F_{D1}^{t},F_{D2}^{t},F_{D3}^{t},F_{D4}^{t}\}\), from each input image with a feature downsampling ratio of \(\{4,2,2,2\}\) between consecutive stages:  
\[\{F_{E1}^{t},F_{E2}^{t},F_{E3}^{t},F_{E4}^{t}\}=\text{Encoder}(T^{t}),\;t\in\{1,2\} \tag{2}\]  
The decoder upsamples input features via interpolation and a UDD module. Each level, except the lowest (\(F_{E4}^{1}\) and \(F_{E4}^{2}\)), concatenates its input with the previous level's UDD output before its own UDD processing, as follows:  
\[F_{Dl}=\begin{cases}\text{Decoder}_{(l)}(F_{E1}^{1},F_{EI}^{2},F_{D(l+1)})&l \in\{1,2,3\}\\ \text{Decoder}_{(l)}(F_{EI}^{1},F_{EI}^{2})&l=4\end{cases} \tag{3}\]  
The detector utilizes two UDD modules as foundational building blocks, taking the outputs from each UDD level as input. It then employs a two-channel convolutional layer to generate the final CD prediction \(P\) and binary _Mask_, as follows:  
\[Mask=Binary(P)=\text{Detector}(F_{D1},F_{D2},F_{D3},F_{D4}) \tag{4}\]  
During training, the supervisor employs three loss functions: Cross-Entropy Loss (\(\mathcal{L}_{CE}\)) to evaluate the similarity between the result (\(P\)) and the Label (\(L\)), and two L1 loss  
Figure 2: Illustration of the RCCD framework, including the representation consistency deep supervisor.  
functions to supervise local spatial (LS, Section 2.3) similarity (\(\mathcal{L}_{LS}\)) and global style (GS, Section 2.3.1) consistency (\(\mathcal{L}_{GS}\)), respectively:  
\[\mathcal{L}_{CE}=-\frac{1}{HW}\sum_{n=1}^{HW}[L_{n}\log(P_{n})+(1-L_{n})\log(1-P _{n})], \tag{5}\]  
\[\mathcal{L}_{LS}=\frac{1}{8\cdot\mathcal{C}_{l}H_{l}W_{l}}\sum_{l=1}^{4}\sum_ {n=1}^{\mathcal{C}_{l}H_{l}W_{l}}(||LS(F_{El}^{1},F_{El}^{2})_{n}-L_{n}||_{1}+ ||LS(F_{Dl})_{n}-L_{n}||_{1}), \tag{6}\]  
\[\mathcal{L}_{GS}=\frac{1}{4\cdot\mathcal{C}_{l}^{1}\mathcal{C}_{l}^{2}}\sum_ {l=1}^{4}\sum_{n=1}^{\mathcal{C}_{l}^{1}\mathcal{C}_{l}^{2}}(||GS(F_{El}^{1})_ {n}-GS(F_{El}^{2})_{n}||_{1}), \tag{7}\]  
\[\mathcal{L}=\mathcal{L}_{CE}+\lambda_{1}\mathcal{L}_{LS}+\lambda_{2}\mathcal{ L}_{GS}, \tag{8}\]  
where \(L_{n}\) represents each pixel between \(P\) and \(L\). The \(El\) and \(Dl\) denote the \(l\)-th stage encoder and decoder. The LS and GS module are calculated as in Equations (18) and (20), and the LS module is capable of accepting two forms of input.  
Therefore, the optimization process of RCCD for the parameters \(\theta^{*}\) aims to achieve consistency with the ground truth \(L\), as follows:  
\[\theta^{*}=\arg\min_{\theta}\mathcal{L}(\text{RCCD}((T^{1},T^{2},\theta),\ L) \tag{9}\]
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.1. LCM-Based Methods (DNGM, TLLCM)  
Methods based on single-frame local contrast generally perform better on datasets with clean backgrounds, such as Seq.4 and Seq.5. Although the WTTLCM method achieves relatively high AUC values in Seq.1 and Seq.2, its lower SCRG and BSF values indicate difficulty in effectively detecting targets amidst significant clutter. This issue is further illustrated by the ROC curves in Figure 9, while TLLCM's elevated D-statistic values in Seq.1-Seq.3 (1.65 \(\times\) 10\({}^{-4}\), 9.93 \(\times\) 10\({}^{-5}\), 7.95 \(\times\) 10\({}^{-5}\)) further confirm its inadequate suppression of high-confidence false alarms.
## 2 Materials and Methods
### Determination of Antimicrobial Activity  
Test substances and microorganisms were used for the determination of the minimum inhibitory concentration (MIC) and for biofilm inhibition tests. The tests were performed with the formulated products containing initial concentrations of chitosan hydrochloride of 10 g/L and of the biosurfactants from _C. quinoa_ and _G. max_ of 7 and 8 g/L, respectively. As positive controls, 3 commercial mouthwashes containing different active ingredients and surfactants were used: 1. cetylperidinium chloride (0.5 g/L) and surfactant polysorbate 80 (CMCC); 2. menthol (0.42 g/L) and surfactant poloxamer 407 (CMM); and 3. chlorokinetic digluconate (1.2 g/L) and surfactant polysorbate 20 (CMCD).  
The antimicrobial activity was determined by the resazurin-based turbidometric assay. The buffered resazurin solution was prepared in a dark environment by dissolving 5 mg of powdered resazurin stain (Merck, Darmstadt, Germany), 375 mg of potassium dihydrogen phosphate, and 556 mg of dibasic sodium phosphate in 100 mL of sterile distilled water using a spatula, amber bottle, and magnetic stirrer, sterilized beforehand. For complete solubilization, the dye was stirred for 1 h. Buffered resazurin was used to minimize possible changes in color due to a reaction with any of the components of the mouthwash or due to color changes.  
The MIC values of the isolated substances and formulation products were determined as described by [PERSON] et al. [22]. Briefly, the 96-well flat-bottom microplates were filled with a final volume of 100 uL (mouthwash at different concentrations + culture medium + standardized inoculum). The concentrations of the mouthwashes were standardized with decreasing values following the letters marked on the microplate (A-H), with A being the highest and H being the lowest concentrations tested, respectively. Bacterial suspensions of _Staphylococcus aureus_ (ATCC 6538), _Enterococcus faecalis_ (ATCC 29212), _Escherichia coli_ (ATCC 25922), _Pseudomonas aeruginosa_ (ATCC 9027), and _Streptococcus mutans_ (ATCC 25175) were obtained using brain heart infusion (BHI, Merck, Darmstadt, Germany). Suspension of the _Candida albicans_ (URM 14053) yeast was obtained using Sabouraud dextrose broth (SDB, Merck). The pre-inocula of the target microorganisms were standardized on the 0.5 MacFarland scale, corresponding to 1.5 \(\times\) 10\({}^{8}\) colony-forming units (CFU)/mL. The microplates were incubated at 37 \({}^{\circ}\)C for 24 h for the bacteria and 37 \({}^{\circ}\)C for 48 h for the yeast. After the incubation period, 30 uL of resazurin stain was added to each well for the determination of cell viability, the microplate was incubated again at 37 \({}^{\circ}\)C for 1 h, and then the color was checked. Viable cells reduced resazurin and turned pink/reddish color, while the inactive ones remained blue/purple [24]. The lowest concentration of the mouthwash, at which there was no cell viability, was considered the MIC. The experiments were carried out in triplicate.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 4 Results
### NB-IoT Communication and Sensor Integration  
The test consisted of only one device integrating both the sensors: the GNSS and the NB-IoT module publishing data to the oneM2M platform on the cloud. Regarding Figure 9, the test was essentially conducted by equipping 'Server 4' with the BG95 shield and NB-IoT and GNSS antennas. The ICON oneM2M-compliant TIM cloud platform [37] was used to monitor the data in the cloud. Figure 10 shows the dashboard of the TIM ICON cloud services that are used for this project. Data in the cloud can be viewed by navigating to the resource tree and checking the data in the inbox corresponding to the MSISDN code of the SIM put into the device. Figure 11 shows the CTD's data on the TIM ICON OneM2M compliant platform, hence verifying the NB-IoT connectivity, the sensors and the GNSS operation.  
Figure 9: CTD Proof-of-Concept setup [36].  
Figure 10. Dashboard of the ICON OneM2M compliant platform.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 4 Scheme Description  
In disaster-monitoring systems, the MAC protocol plays the most important role in terms of delay, throughput, and reliability. The distributed polling scheme is an important type of the MAC protocol. In WSNs, polling involves sending a wireless query to each node and waiting for a unique response from the addressed node. This method supports real-time and high-throughput applications, and can automatically adapt to changes in traffic. For a distributed polling scheme in WSNs, a particular token frame is assigned to allocate wireless resources, and this token is transmitted along a virtual logical ring. Although the distributed polling scheme requires nodes of a cluster to be within a one-hop range, it provides high real-time and abnormity-handling power. Abnormity handling guarantees that the failure of any node cannot invalidate the entire network using a distributed polling scheme. This can significantly improve the reliability of WSNs in disaster-monitoring systems.  
The nodes of WSNs are divided into two types for disaster-monitoring applications. One type includes superior nodes, such as seismic monitoring nodes, which have superior data and alert data for transmission. The other type comprises ordinary nodes, such as picture-monitoring nodes, sound-monitoring nodes. Ordinary nodes have ordinary data to transmit. The operation of HT-MAC is divided into rounds, each of which consists of two stages: a set-up stage and a steady-state stage. To optimize energy consumption and maximize network lifetime, these two stages are performed alternately. At the beginning of one round, at the set-up stage, the nodes in this system are clustered according to their relative energy and location. Clustering can significantly improve the energy efficiency  
Figure 1: Network topology diagram of the collaborative disaster-monitoring system.  
of the entire network. At the steady-state stage, the HT-MAC scheme is based on the distributed polling scheme. The timeline of HT-MAC at the steady-state stage is shown in Figure 2.  
In this study, the protocol stack of ordinary and superior nodes includes physical, data link, network, transport, and application layers. Cooperation between layers is very important. The physical layer is responsible for the digital coding, modulation, filtering, and spreading of the signal. The physical layer of the protocol stack utilizes the direct-sequence spread-spectrum technique. The HT-MAC protocol is power-aware and can adjust the transmission power to save energy. The network layer protocol adopts static routing technology, and the nodes in the cluster send information to the cluster head, and then the cluster head converges and forwards the information to the base station. The transport layer is responsible for maintaining the flow of the superior, alert, and ordinary data. Different types of sensing tasks can be built and used in the application layer.  
The operating procedures of the HT-MAC scheme in normal and abnormal situations are as follows.
_Article_  
**COTS-Based Architectural Framework for Reliable Real-Time Control Applications in Manufacturing**  
**[PERSON] 1,*\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\), [PERSON] 3 and [PERSON] 1**  
1 Department of Digital Systems, University of Thessaly, 41500 Larisa, Greece; [EMAIL_ADDRESS]  
2 Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece; [EMAIL_ADDRESS]  
3 General Department, University of Thessaly, 41500 Larisa, Greece; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +30-2410-684596 check for updates  
Received: 26 March 2020; Accepted: 2 May 2020; Published: 6 May 2020  
**Featured Application: The proposed architectural framework may benefit the design and development methodologies of systems based on commercial off-the-shelf (COTS) components, with its enhanced approach in building reliable real-time control systems. Moreover, this architectural framework broadens engineering ideas and improves the solutions of such systems by increasing the trust and reliability of COTS components in manufacturing and reducing engineering costs. The real-time applications that rely on COTS components can be directly benefited from this framework.**  
**Abstract: The challenge of keeping the development and implementation of real-time control systems reliable and efficient and at the same time, low-cost and low-energy, is getting harder. This is because system designers and developers are faced with the dependability, inflexibility and often high-cost of specialized or custom-built hardware and software components. This research attempts to tackle issues such as the reliability and efficiency of real-time control systems and advance further the current state-of-the-art. For this purpose, a strong emphasis is placed on finding novel efficient solutions based on standardized and commercially available off-the-shelf hardware/software components. In this direction, this research applies credible and feasible methodologies (e.g., model-based design, component-based design, formal verification, real-time scheduling, prototyping, and validation) in an innovative enhanced way. As an important outcome, a versatile integrative design approach and architectural framework (VIDAF) is proposed, which supports the development and implementation of reliable real-time control systems and applications using commercial off-the-shelf (COTS) components. The feasibility and applicability of the proposed system's architecture are evaluated and validated through a system application in embedded real-time control in manufacturing. The research outcomes are expected to have a positive impact on emerging areas such as the Industrial Internet of Things (IIoT).**  
**Keywords: control systems; real-time; general-purpose; commercial off-the-shelf; hardware/software components; reliability; availability; efficiency; low-cost**