esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement Chain  
The measurement chain under analysis consists of a pancake-type load cell (Flintec model Q50-0.5T, Flintec Inc., Hudson, MA, USA) and a data acquisition and processing system comprising te National Instruments module model NI 9237 (National Instruments Corporation, Austin, TX, USA) (used with load cells utilizing strain gauge as the measurement method); the National Instruments chassis model NI cDAQ-9174; and a proprietary program developed in the LabVIEW 2022 environment. Figure 4 illustrates the measurement chain at the Propulsion Jets Testing Laboratory of the Brazilian Army Technological Center, and it is possible to observe the key subsystems involved in the static rocket motor test: the rocket motor mounting fixture, which securely holds the motor in place, ensuring no movement occurs during testing; the load cell, which measures the thrust generated by the motor by converting the force into an electrical signal; and the data acquisition system, responsible for collecting and processing the data from the load cell and other sensors in real time, enabling the precise performance evaluation of the rocket motor.  
To clarify the operation of a load cell, consider that the model in question is a 2 mV/V load cell, producing an output of 2 millivolts per volt of excitation for its rated load of 5000 N. The output voltage is directly proportional to both the excitation voltage and the applied force, and this specification is used to determine compatibility with data acquisition systems.  
In the case of this measurement chain, raw data represented in electrical voltage are converted into engineering units (such as force in \(N\)) through a linear equation. The coefficients of this linear equation are determined by comparing the electrical output of the  
Figure 4: Measurement chain.  
load cell with a standard load cell or with the value of a standard weight, allowing for the precise calibration and conversion of measured values into the desired engineering units.  
Two load cells (one standard and one under test) are coupled via an adapter on a tensile and compression machine. Once installed, the load cells undergo mechanical compression testing. The standard load cell serves as a reference to indicate the load to which the evaluated load cell is subjected during the test.  
In this way, the system monitors the applied force value. The standard load cell provides accurate and reliable data on the applied force, ensuring that the load cell under test is calibrated correctly. This ensures that the evaluated load cell operates according to desired standards, providing precise and reliable measurements for future tests.
## 5 Results and Discussion
### Results on Best Performing Kinematic Parameters on the Multi-Classification Problem  
This subsection presents the results, based on a confusion matrix, when the proposed HLSTM-CNN model is evaluated on the top four performing kinematic parameters based on their classification G-Mean score:  
1. Hip Sagittal Angle
2. Knee Sagittal Angle
3. Knee Front Angle
4. FPA Front Angle  
For clarity, the diagonal and off-diagonal blocks of a confusion matrix indicate the correct and incorrect predictions of the true values, respectively. Figures 11 and 12 show the resulting confusion matrices of the sagittal angles of hip and knee, respectively. Both of these kinematic parameters registered a more than 97% G-Mean score on the HOA classification problem with no misclassification of healthy gaits as HOA, and the opposite was kept at a low value as well. Moreover, the bulk of the misclassification is on the THA gait being misclassified as either healthy or HOA (third row). In addition, both healthy and HOA gaits are misclassified as THA gaits with high incidence as well. Based on these results, it is evident that there is a clear distinction between the features of healthy and HOA gaits for these kinematic parameters. Common features may be present on the THA gait that are seen on either healthy or HOA gaits.  
Similar observations can be realized on the confusion matrices of the front angles of FPA and knee as shown in Figures 13 and 14, respectively. Misclassification between healthy and HOA gaits is kept to minimum values, and the bulk of the misclassification is on THA gait prediction (third row and column). In particular, the front angle of the FPA has a significant misclassification of healthy and HOA gaits as THA, thus pulling its performance down.  
Figure 11: Confusion matrix of the hip sagittal angle.  
Figure 12: Confusion matrix of the knee sagittal angle.  
Figure 13: Confusion matrix of the knee front angle.  
Further investigation is needed into the THA misclassifications. It could still provide information on patient satisfaction with the operation. Explicitly, if a THA kinematic parameter is misclassified as healthy, this could be indicative of functional improvements and gait returning to normal, while, if it is predicted as HOA, then gait impairments are still present. With more discriminating kinematic parameters, as predicted by the DL model, agreeing on the results, higher confidence can be attributed to the model's results. This premise is extensively explored in the next section. Finally, regardless of classification outcomes, if a patient reports pain relief, improved mobility, and functionality, the THA could still be considered successful. In the future, we will include patient satisfaction as another THA verification parameter. Surgeons already use longitudinal surveys to track patient satisfaction and THA success. Time periods usually start just after the surgery, then three months and six months, and they continue for years. Our method could be one additional monitoring tool that could be used by surgeons and clinicians to track and improve THA success.
## 3 The Framework of the Proposed Method  
Figure 3 displays the framework of the proposed method, which can be introduced in detail as:  
Figure 3: The framework of the proposed method.  
(1) Decompose the signal collected by the sensors mounted on the rotor by the ISSD method to obtain the decomposed SSCs.  
(2) Perform Hilbert transform on the SSCs to derive the ISSD-HT TFS plane.  
(3) Divide the ISSD-HT TFS plane into eight frequency bands, namely, [0.1X-0.9X], [0.6X-1.4X], [1.1X-1.9X], [1.6X-2.4X], [2.1X-2.9X], [2.6X-3.4X], [3.1X-3.9X], [3.6X-4.4X], and the characteristic frequency band energy entropy of each band is calculated with the method mentioned above.  
(4) Identify the rotor fault types with the SVM classification method.
## 7 Performance Evaluation
### Communication Overhead
optimal packet size of each scheme is identical under bad link quality. MBSA and BFBSA has lower communication overhead than Secure Deluge by approximately 36.1% and 36.5%. In this case, the communication overhead of MBSA becomes larger than BFBSA because the amount of MAC overhead becomes larger as the packet size becomes small whereas the Bloom filter size is fixed regardless of the packet size. Finally, the total communication overhead, summing communication overhead in all three hops, is shown in Figure 16. As you can see, our schemes have less communication overhead than Secure Deluge regardless of the size of the code image. Among our three schemes, MBSA and BFBSA outperform SPA since they can support fully variable packet size. MBSA has lower communication overhead than BFBSA since BFBSA needs a large Bloom filter to reduce the false positive probability.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 3 Analysis of IoT Traffic
### Regular Packet Rate  
The packet rate can also represent the characteristics of an IoT device. Figures 1 and 2 illustrate the packet rate of SmartThings Hub and NetGear Station. From Figures 1 and 2, we can see that in a certain period, the packet rate fluctuates steadily most of the time, and the number of data packets increases several times in a few seconds.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Device Name** & **\# of IPs** & **Server Ports** \\ \hline
1 & SmartThings Hub & 12 & TCP 433 \\
2 & NetGear Station & 21 & UDP 10001, TCP 80 \\
3 & WoShida Cam & 4 & TCP 44730 \\
4 & Xiaobai Cam & 6 & UDP 32100, UDP \\
5 & Philips Hue & 4 & TCP 80, TCP 443 \\ \hline \end{tabular}
\end{table}
Table 1: The network behavior analysis results.  
In summary, the IP addresses and ports that the IoT devices communicate with show prominent statistical characteristics manifested by the limited number of communication endpoints and specific frequently used ports. Most of our sample devices demonstrate a periodical transmission of DNS and ICMP packets. When we analyze the data packets per unit of time, we can also observe the steady flow pattern of each device. Such features are usually caused by periodical upload operations or the use of regular operation settings by the users.
# Automated Optical Inspection System for O-Ring Based on Photometric Stereo and Machine Vision
## 3 O-Ring Database
### Database for Defect Detection
#### 3.1.2 DK (Database of Raw Data with Profilometer)  
There were 160 defects and 160 non-defects included. The defect samples included 60 samples with flow marks and 100 samples with non-fills and indentations. Each sample was reconstructed using optical profilometer and the reconstruction raw data were acquired. Detailed specification of K optical profilometer is shown in Table 2.  
In this experiment, we establish the database with reconstruction raw data in 2.5D format and binary the 2.5D model to get binary image before defect detection. Then, we execute the defect detection we proposed (Figure 5 yellow region). We set the defect depth was 0.08 mm to detect surface defect, which depth is based on ISO standard. The other defect detection condition is same as in the DPS database. The reconstruction time consuming is about 1 min. The defect detection time consuming is about 30 s (Figure 20).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Specification and Parameter** \\ \hline Trademark and model & VR-3100 \\ Magnification & 12\(\times\) \\ FOV(field of view) & 24 \(\times\) 18 mm\({}^{2}\) \\ Resolution & 1024 \(\times\) 768 \\ Working distance & 75 mm \\ Pixel size & 23.4375 \(\times\) 23.4375 m\({}^{2}\) \\ Repeatability (Vertical) & 0.5 μm \\ Repeatability (horizontal) & 1 μm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of optical profilometer VR-3100.  
Figure 20: Defect detection processing with optical profilometer. (**a**) Model of optical profilometer reconstructed (Gray scale); (**b**) Smooth surface model \(M_{\text{S}}\) (Gray scale), this model is the smooth version of (**a**), create by the method in this paper Section 2.2; (**c**) The deviation model \(M_{\text{D}}\), this model created by subtracting (**a,b**) (jet color map and mapped into range 0–500 μm); (**d**) Candidate area (Binary images), which is produced by sliding windows with (**c**).
# Advanced DSP for Coherent Optical Fiber Communication
## 5 AI for Optical Fiber Communication
Similar behaviors can be found in both scenarios. The N-DNN shows a performance close to that of the hard-decision detection, as each input cell is independently processed. Although there are sufficient neurons and hidden layers to improve the training accuracy, the independent processing architecture results in similar performance as the hard decision. The P-DNN provide a better performance since two input cells are connected and jointly trained, where more accurate interference features can be extracted from the network. The H1-DNN network shows further improved performance since the middle input cells are connected to neighboring input cells and therefore accurate interference emulation for the middle cells can be realized. As an evolved version, H2-DNN slightly outperforms the H1-DNN, where the interference to the edge input cell can be properly modelled through the neighboring neuron connection. The F-DNN provides the best performance the since the complete interference to each  
Figure 18: Neural network structures for inter-carrier interference (ICI) suppression in spectrally efficient frequency division multiplexing (SEFDM) systems. sub-carrier is estimated with additional neuron connections. Therefore, the complete interference information can be extracted from the SEFDM signal and an accurate model can be trained.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Space Segment  
_The INSPIRE-SAT 7 CubeSat Payload_
## 2 Related Study
### Vibration Signal Data Acquisition  
(1) Device Sensors  
One study collected real-time vibration data from equipment in an actual factory using an Advantech WISE-2410 LoRaWAN wireless sensor that integrated an ARM(tm) Cortex-M4 processor, LoRa transceiver, three-axis accelerometer, and temperature sensor [20]. It operated within a temperature range of \(-20\)\({}^{\circ}\)C to \(85\)\({}^{\circ}\)C and was powered via USB.  
(2) Vibration Data Feature Transformation  
The raw vibration signals received from the sensors undergo feature transformation. The transformed features are critical for determining the vibration state and mainly include values such as velocity root mean square (RMS), acceleration RMS, acceleration peak, displacement kurtosis, displacement crest factor, displacement skewness, displacement peak to peak, and displacement deviation.
# Sensitivity Analysis of PROMETHEE II for the Evaluation of Environmental Websites
## 2 Materials and Methods
### Protected Areas in Italy and Their Websites  
The creation of the PAs was based on national and European legislation and aimed at protecting the natural and cultural heritage of the country. In Italy, the categories of PAs are as follows: 25 national parks, 147 state-owned natural reserves, 30 state-owned marine reserves, 151 regional natural parks, 419 regional natural reserves, and 576 other protected areas.  
The situation of NPs in Italy has never been fully satisfactory, despite the fact that Italy was one of the first countries in Europe to establish a NP (the Gran Paradiso National Park in 1922). Today, Italian NPs cover about 5% of the country's land (Figure 1). PAs are managed by the Ministry of Environment and there are a total of 3496 PAs in Italy, of which 2621 are Natura 2000 sites and 875 sites are designated under national law.  
Since 1997, Italy has implemented the Habitats Directive and designated Natura 2000 sites in the country [30]. Natura 2000 sites are sources or ecosystem services that can prevent the loss of biodiversity [31]. Furthermore, economic development of the areas that support sustainability may also contribute to their preservation. Indeed, as [PERSON] et al. [32] point out, besides conservation of biodiversity, PAs provides several social and economic advantages that can contribute to the economic development of an area (CEETO, 2018). [PERSON] et al. [33], argue that PAs are places where the conservation of nature should not only focus on the protection of biodiversity and the environment but should also take recreational co-benefits into account. Moreover, [PERSON] et al. [34], and [PERSON] et al. [35], report that PAs are responsible not only for protecting the environment but for constituting the area a tourist destination. In this way, they can contribute in the protection of the environment and help the economic empowerment of local communities through the frame of sustainable development. Empirical studies have demonstratedthat PAs represent an efficient mechanism for combining sustainable land use with socio-economic development [36; 37; 38]. Because of its high naturalness and biodiversity, PAs are becoming increasingly important for recreational opportunity, providing benefits in terms of physical and mental health through outdoor experiences [35; 39; 40]. However, as highlighted by [PERSON] [41], in the case of a NP, economic development is mostly supported through tourism. Tourism can contribute effectively to the local and national economies.  
The importance of the websites for promoting ecotourism and improving the electronic presence of national parks and protected areas through the internet is indisputable [9; 28]. A website may provide a virtual perception of the areas that can expedite the decision to visit an area easier [42]. As a result, Italian NPs were collected (Table 1) and they were evaluated regarding their electronic presence.  
The websites of PAMPBs are considered to be the alternatives in our decision making problem.
# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 2 Related Work
### Machine Learning  
Over the last few decades, machine learning has attracted numerous researchers because of its powerful scalability and excellent performance on high-dimensional data. As a branch of artificial intelligence, the basic idea of machine learning is to allow established models to learn from given data to improve their performance. Machine learning can be divided into supervised learning and unsupervised learning. Supervised learning requires that each sample should contain special markers in addition to the feature values. It predicts the markers from the feature values and then compares the actual markers to calculate the error and uses a recursive algorithm to correct the model based on the error. The most common tasks in supervised learning are classification and regression. Unsupervised learning does not require labeling. It explores the degree of similarity between instances or examines the value relationships between features based on specific metrics and methods. The prediction of breast cancer can be seen as a classification problem in supervised learning. A number of machine learning methods have been proposed in much of the literature to help diagnose breast cancer. Dr. [PERSON] [7] proposed a training model of an artificial neural network (ANN) algorithm using decision tree (DT) algorithm C4.5 by extracting features of various diseases from a routine model. It was found to have high accuracy and also a strong generalization ability. Dr. [PERSON] [2] combined a support vector machine (SVM) with ultrasound texture analysis to classify breast cancer ultrasound images. In 2007, Dr. [PERSON] [8] proposed a cancer prediction model based on the SVM algorithm, which effectively solves the problem of small sample learning and limits overfitting. Dr. [PERSON] [9] proposed a three-stage breast cancer diagnosis method with an optimized SVM classifier for classification, and the accuracy of the image dataset could reach 96.6%. In 2017, Dr. [PERSON] [10] used a particle swarm algorithm to select features of high-dimensional mass spectral data, analyzed and compared the results of extreme learning machine (ELM), k-nearest neighbor (KNN), artificial neural network, SVM, and random forest (RF), and, as a result, verified the feasibility of ELM in cancer diagnosis [11]. In 2019, Dr. [PERSON] [12] proposed a machine learning training method based on the spark model and RF with high fault tolerance, fast training speed, and 99.01% accuracy.
## 4 Evaluation Results and Discussion
### Performance across Video Codecs  
In this subsection, a performance evaluation of the models is done separately for H.264 and HEVC. For this, we divide the _W-SQoE-IV (full)_ database into two sets. The first set (denoted _H.264-encoded_) consists of three H.264-encoded sub-databases of _W-SQoE-IV_pH264_, _W-SQoE-IV_fH264_, and _W-SQoE-IV_uH264_. The second set (denoted _HEVC-encoded_) is comprised of three HEVC-encoded sub-databases of _W-SQoE-IV_pHEVC_, _W-SQoE-IV_fHEVC_, and _W-SQoE-IV_uHEVC_. As the two sets are from the same database of _W-SQoE-IV (full)_, they share the same settings except the codec. This helps eliminate the possible bias in the evaluation process.  
Figure 2 illustrates the average performance of individual models on the two database sets. The error bars show the standard deviation values. The trend is that the models generally yield better performance on the _H.264-encoded_ set. In particular, the _KSQI_ model, which achieves the highest performance for both of the sets, has quite high performance for the _H.264-encoded_ set (i.e., PCC = 0.87, SROCC = 0.87, and RMSE = 0.36) but much lower performance for the _HEVC-encoded_ set (i.e., PCC = 0.70, SROCC = 0.73, and RMSE = 0.59). This result shows that all the considered models, even the ones taking into account HEVC characteristics such as _P.1203_ and _KSQI_, are not very effective for HEVC-encoded sessions. This could be because these models are trained first with H.264-encoded videos and then additionally extended to support HEVC-encoded videos. As the performances on HEVC-encoded sessions are low, hereafter only the databases using H.264 will be discussed in the rest of the paper. This helps maintain a reliable analysis as well as reduce the complexity of the evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Database** & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _[PERSON]’s_ & _LSTM_ & _ATLAS_ & _P.1203_ & _CQM_ & _biQPS_ & _SQ_ & _KSQI_ \\ \hline _W-SQoE-I_ & 0.93 & 0.73 & 0.93 & 0.93 & 0.93 & 0.74 & 0.79 & 0.38 & **
## 2 Materials and Methods
### Sample Characterization  
All samples (original samples and samples originating from the experiments) were characterized chemically and mineralogically and the leaching behavior was determined.  
Prior to chemical characterization, the samples were crushed (jaw, crusher Retsch BB 200), ground (ball mill, Retsch S 1000), dried at \(105\pm 3\)\({}^{\circ}\)C (drying oven, according to DIN EN 14346 [24]), and digested in a microwave with a mixture of HF, HNO\({}_{3}\), and HCl, according to ONORM EN 13656 [25]. Leaching tests were performed in overhead shaker experiments with a grain size <1 mm, according to ONORM EN 14429 [26]. For the samples derived from the laboratory scale experiments, only leachates with distilled water (liquid:solid ratio = 10, 48 h) were prepared (two leachates per sample). For the samples from the pilot scale experiments, nine leachates (one with distilled water and eight with either HNO\({}_{3}\) or NaOH addition) were prepared to investigate the pH-dependent leaching behavior. The required amount of HNO\({}_{3}\) or NaOH was determined prior to the experiments via the acid and base neutralization capacity. The final pH values after 48 h ranged from pH 2 to 13. The pH was measured after 4 h, 44 h, and 48 h and the maximum pH change between 44 h and 48 h did not exceed 0.3 pH units. The digested samples and the leachates were analyzed at the Chair of Waste ProcessingTechnology and Waste Management of the Montanuniversitaet Leoben by using inductively coupled plasma mass spectrometry (ICP-MS, Agilent Type 7500), according to ONORM EN ISO 17294-2 [27].  
The mineralogical bulk composition was analyzed by x-ray diffraction (XRD, PANalytical X'Pert Pro instrument) at the Institute of Applied Geosciences of the Graz University of Technology. The samples were rotated and data were collected for a 20 angle between 4\({}^{\circ}\) and 85\({}^{\circ}\) with a step size of 0.008\({}^{\circ}\), using Co-K\(\alpha\) radiation at 40 mA and 45 kV.  
Electron microprobe (EMP) analyses were performed with a Superprobe JEOL JXA 8200 at the Chair of Resource Mineralogy of the Montanuniversitaet Leoben. Quantitative element analyses and element mappings were recorded in wavelength dispersive spectrometer (WDS) mode using the K\(\alpha\) lines of Ca, Mg, Mn, Si, Al, Fe, V, Cr, Mo, and F. The following spectrometer crystal arrangement was selected: TAP for Mg, Al, and F; PETJ for Si, Ca, and Mo; and LIFH for V, Cr, Mn, and Fe. The instrument was operated with an acceleration voltage of 15 kV and a beam current of 10 nA and calibrated with the following standards: chromite, chromium (III) oxide, wollastonite, diopside, and kaersutite. The detection limits are given separately for all measured elements and samples in Tables S2-S21.