esa-sceva/satcom-chunk-collection huggingface dataset:
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
###### Abstract  
Low Power Wide Area Networks (LPWAN) are expected to enable the massive connectivity of small and constrained devices to the Internet of Things. Due to the restricted nature of both end devices and network links, LPWAN technologies employ network stacks where there is no interoperable network layer as a general case; instead, application data are usually placed directly into technology-specific two-layer frames. Besides not being able to run standard IP-based protocols at the end device, the lack of an IP layer also causes LPWAN segments to operate in an isolated manner, requiring middleboxes to interface non-IP LPWAN technologies with the IP world. The IETF has standardized a compression and fragmentation scheme, called Static Context Header Compression and Fragmentation (SCHC), which can compress and fragment IPv6 and UDP headers for LPWAN in a way that enables IP-based communications on the constrained end device. This article presents a model to determine the channel occupation efficiency based on the transmission times of SCHC messages in the upstream channel of a LoRaWAN(tm) link using the ACK-on-Error mode of standard SCHC. The model is compared against experimental data obtained from the transmission of packets that are fragmented using a SCHC over LoRaWAN implementation. This modeling provides a relationship between the channel occupancy efficiency, the spreading factor of LoRa(tm), and the probability of an error of a SCHC message. The results show that the model correctly predicts the efficiency in channel occupation for all spreading factors. Furthermore, the SCHC ACK-on-Error mode implementation for the upstream channel has been made fully available for further use by the research community.  
fragmentation; IPv6; LoRaWAN; LPWAN; SCHC
## 2 Methodology
### System Requirements  
The operability of the infrastructure is ensured by means of a system requirements analysis that indicates in detail the expected characteristics:  
1. Communication protocols used: * MQTT for environmental sensors; * HTTP for environmental sensors and video streams; * Modbus for energy meters; * OPC UA as an alternative to the previous points [38].
2. Communication networks: * IP over Wi-Fi (WLAN) for environmental sensors and actuators; * IP over LAN for electricity meters and weather controllers.
3. Data centralizers: * Mosquito MQTT Broker Service; * Schneider EcoStruxture for power-meter.
4. Data platform: * NodeRed; * data processing using custom software ML techniques (Python 3.13, JavaScript ES 15); * Power Bl dashboard for integrated data presentation based on functional requirements; * 3D model visualization with metadata.
5. Database (long-term storage of 3 years or 1 Tb of data): * PostgreSQL; * InfluxDB; * SQLite.
6. Linux data server and local processing and virtualization with GPU video processing capability: * Multicore CPU with at least 8 cores, 16 threads, 2.2 ghz;* 1 Tb storage;
* CUDA hardware GPU with 12 gb RAM for AI algorithm processing and video stream display;
* 64 gb RAM;
* PSU with uninterruptible power supply.
* Machine learning systems based on open source and commercial libraries with potential use of dedicated data processing software (e.g., KNIME 4.18);
* Sensor accuracy based on devices on the market for home automation;
* Multi-user access from the DT platform based on concepts inherited from ISO 27000 [39] in order to guarantee at least one user and one administrator level.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 3 Results and Discussion  
### Spectral Properties  
The variation of the average reflectance spectra in the spectral range of 400-1000 nm with their individual standard deviation (SD) is illustrated in Figure 1. It could be noticed that there were similar profiles of spectral curves between the two Chinese walnut varieties, but spectral intensity differed especially in rang of 800-1000 nm. The general similar spectral curves should be derived from their similar tissue composition, structure, and color presentation in the sample surface. The intensity differences were mainly due to the small differences in contents of surface chemical compositions (such as chlorophyll content, water content, etc.). However, spectral reflectivity was overlapped between different groups, so further data-driven analysis should be conducted to make a clear classification.  
In detail, the reflectance valley at 416 nm was associated with the Soret absorption band, which was closely related to the contents of porphyrins in chlorophyll [38]. The clear downwards wavelength at 980 nm corresponded to the 2 nd O-H stretching overtone of water [18,39]. Another weak valley at 676 nm was responsible for chlorophyll-a [40]. As shown in sub-windows in Figure 1, differences at these three wavelengths could be further applied to be a basis for classifying samples of different varieties.
# An IoT-Based Fog Computing Model
## 6 Experiment and Discussion of Results
### Simulation of the Fog Layer  
Experimental simulations were done by extending CloudSim [2] to simulate the fog layer model proposed in this paper. CloudSim is based on the existing Java based discrete event simulation package in Grid Sim, and is developed on the existing architecture of Grid Sim. CloudSim can run on multiple platforms such as Windows and Linux. With respect to the simulation environment used in this paper, a system running Windows 7 OS with the following hardware configuration was used: CPU-Intel Pentium Dual core P6000, clocked at 1.87 GHz, 8.0 GB of memory and 1 TB of hard disk storage. In this study, eight fog nodes were used in CloudSim environment, similar to that used in [27]. Performance configuration and computing power parameters were adapted from CloudSim and are shown in Table 2.  
In the simulator, the application task parameters include task ID, task length and coordinates, in which task length used millions of instruction (MI) as a unit. Task length means the number of basic instructions of task scheduling requests. For this work, task lengths were set to 1000, which was adapted from the work of [28]. We simulated the coordinates of \(FNs\) in an area, such as a city or a university, so we limited the range of \(FN\) in 0-100, and randomly generated the coordinates of 100 fog nodes, which are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Fog Nodes** & **Node1** & **Node2** & **Node3** & **Node4** & **Node5** & **Node6** & **Node7** & **Node8** \\ \hline Pes & 2 & 4 & 2 & 4 & 2 & 2 & 2 & 2 \\ Mips & 550 & 300 & 650 & 350 & 750 & 800 & 850 & 900 \\ Energy Cost & 10 & 12 & 14 & 15 & 16 & 18 & 20 & 22 \\ Coordinates & \{10,10\} & \{10,40\} & \{10,70\} & \{40,10\} & \{70,10\} & \{70,40\} & \{70,70\} & \{60,80\} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance configuration and computing power parameters (adapted from CloudSim).  
Figure 7: Proposed terminal layer model.  
Genetic algorithm operational parameters similar to those used in (27; 29; 30) were also used in this paper. The parameters used were as follows: a population size of 100; mutation probability of 0.01; maximum iteration number of the algorithm was set to 1000; weighting factors set as: \(\alpha=\beta=\gamma=1/3\); delay limitation, energy limitation and distance were respectively set to 50, 2000 and 5000.  
In this section, we show the simulation experiment results of fog computing layer in: delay (makespan), sum of distance, sum of energy consumption. Then we proved the effectiveness of our proposed GA optimized IoT-FCM model by comparing it with traditional max-min algorithm and improved fog-oriented max-min algorithm. In task scheduling problem, the traditional max-min algorithm usually select the makespan as the main parameter to achieve the relative optimal solution. The fog-oriented max-min algorithm as used in this paper considers multiple parameters (including delay, distance and energy consumption) to calculate the relative optimal solution.
## 4 SSB Signal Generation and SSBN Mitigation Techniques
### Carrier and SSB Signal-Generation Techniques
#### 4.1.1 Bias-Induced Carrier Generation Scheme
\begin{split} E_{\textit{out}}(t)&=\frac{E_{\textit{ in}}(t)}{2}\bigg{[}\left\{1-\frac{1}{2}\bigg{(}\frac{\pi}{V_{\pi}}V_{I} \bigg{)}^{2}\right\}\cos\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}}\pi\bigg{)} -\bigg{(}\frac{\pi}{V_{\pi}}V_{I}\bigg{)}\sin\bigg{(}\frac{V_{\textit{bias}}} {V_{\pi}}\pi\bigg{)}\\ &+j\bigg{\{}\left(1-\frac{1}{2}\bigg{(}\frac{\pi}{V_{\pi}}V_{Q} \bigg{)}^{2}\right)\cos\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}}\pi\bigg{)} \ -\bigg{(}\frac{\pi}{V_{\pi}}V_{Q}\bigg{)}\sin\bigg{(}\frac{V_{\textit{bias}}} {V_{\pi}}\pi\bigg{)}\bigg{\}}\bigg{]}\end{split}  \tag{5}\]  
Furthermore, Equation (5) can be further simplified and rewritten as:  
\[ \begin{split} E_{\text{out}}(t)&=\frac{E_{\textit{ in}}(t)}{2}\bigg{[}(1+j)\cos\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}}\pi\bigg{)}\ -\frac{\pi}{V_{\pi}}(V_{I}+jV_{Q})\sin\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi} }\pi\bigg{)}\\ &\quad-\frac{1}{2}\bigg{(}\frac{\pi}{V_{\pi}}\bigg{)}^{2}(V_{I} ^{2}+jV_{Q}^{2})\cos\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}}\pi\bigg{)} \bigg{]}.\end{split}  \tag{6}\]  
In general, the CSPR can be expressed as:  
\[CSPR=\ 10\log_{10}\frac{P_{\textit{carrier}}}{P_{signal}} \tag{7}\]  
Based on Equation (7), the CSPR of SSB subcarrier modulation with the IQM can be deduced from Equation (6) as [55]:  
\[ \begin{split} CSPR_{\textit{SSB-SCM}}^{\textit{ IQM}}=&\ 10\log_{10}\frac{\bigg{|}(1+j)\cos\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}} \pi\bigg{)}\bigg{|}^{2}}{\big{|}\big{|}\big{|}\big{|}\big{|}\big{|}\big{|}^{2} \big{|}\big{|}\big{|}\big{|}^{2}}\\ =&\ 10\log_{10}\frac{2}{\bigg{(}\frac{\pi}{V_{\pi}} \bigg{)}^{2}\tan^{2}\bigg{(}\frac{V_{\textit{bias}}}{V_{\pi}}\pi\bigg{)} \big{|}\big{|}\big{|}^{2}}\end{split}  \tag{8}\]  
Although this method is the simplest solution, it requires a sharp optical filter to remove one of the two sidebands of the DSB signal. Therefore, apart from the extra expense that has to be incurred on the optical filter, when the spectrum gap between the SSB signal and carrier is narrow, the sideband that is meant to be eliminated will present some residuals, owing to the optical filter nonideal characteristics [53; 55; 56]. A guard band between the signal and the optical carrier is desirable to eliminate one of the sidebands. Furthermore, since the spectral gap bandwidth is the same as that of the signal, half of the electrical spectrum efficiency (ESE) is sacrificed in the process [40; 56; 107]. Based on this, one of the main weaknesses of this method is the additional bandwidth being occupied by the gap interval that would have been employed for data transmission. The additional bandwidth has a considerable effect on the required bandwidth of the ADC and DAC. On the other hand, it is also possible to employ converters with only a fourth of the bandwidth; however, this is at the expense of extra components [108].
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
## 3 Results and Discussion
### Determination of Selected Organic Acids
The concentrations of selected organic acids in red wines are shown in Table 6. During fermentation, the contents of tartaric and malic acids in all samples decreased significantly (\(p\leq 0.05\)) compared to the grape pulp. In the fermentation process, the contents of malic and tartaric acids decreased irrespective of the grapevine used. According to the literature data [82], the content of tartaric acid in wines ranges from 1.5 to 4 g/L, malic acid to 4 g/L, lactic acid from 0.1 to 3 g/L, acetic acid below 0.2 g/L, and citric acid to 0.5 g/L. Among the tested organic acids, the amount of malic and tartaric acids decreased during fermentation. The tartaric acid in wines regulates their acidity. It comes directly from the raw material, (grapes), in which it occurs in the stem, skins, and flesh of berries. In white wines produced with the use of non-commercial yeast strains, more tartaric acid remains than in wines from commercial strains, which positively affects the taste of the wines (a feeling of freshness, refreshment, higher acidity). tartaric acid undergoes degradation by LAB to lactic and acetic acids, with a concomitant increase in volatile acidity [83]. Malic acid is usually converted to ethanol and/or lactic acid during malolactic fermentation. Its content in wines is therefore usually low, and the sensory qualities of the wine are improved due to the milder lactic acid produced. The lactic acid in wines can then be oxidized to acetic acid (acetic acid fermentation by bacteria of the genera _Acetobacter_ and/or _Glucobacter_). Acetic acid in wines is present at a low level, but can be formed very quickly under aerobic conditions. Citric acid is sometimes added to wines to increase acidity and improve the aroma. In red wines produced with the use of non-commercial yeast, an increased content of fumaric acid was observed (the highest in wine with the yeast strain 8_NCS-6.38 \(\pm\) 0.02 mg/L. Apart from the acids analyzed in our research, another compound important from the point of view of flavor is succinic acid, which forms aromatic esters during the aging process of wines.
###### Abstract  
Assessment of a corroded pipe is crucial to determine when it must be repaired or replaced. However, the conventional corrosion assessment codes for the failure pressure predictions of corroded pipes with circumferentially aligned interacting defects are conservative (underestimations of more than 40%), resulting in premature repair or replacements of pipelines. Alternatively, numerical approaches may be used, but they are time consuming and computationally expensive. In this study, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with circumferentially aligned interacting corrosion defects subjected to combined loadings is proposed. An artificial neural network trained with failure pressure obtained from the finite element analysis of the three pipe grades for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of these parameters on the failure pressure of a corroded pipe with circumferential-interacting defects was conducted using the equation to determine the correlation between the defect geometries and failure pressure of the pipe. The new equations predicted failure pressures for these pipe grades with an R\({}^{2}\) value of 0.99 and an error range of \(-9.92\%\) to 0.98% for normalised defect spacings of 0.00 to 3.00, normalised effective defect lengths of 0.00 to 2.95, normalised effective defect depths of 0.00 to 0.80, and normalised axial compressive stress of 0.00 to 0.60.  
artificial neural network; finite element analysis; corrosion assessment method; interacting defects; combined loading
## 3 Results
### Improving GPP Estimation Using SIF and PRI by Considering the CI  
Finally, we investigated the accuracy of GPP estimation based on \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) when the effects of the CI were considered. A total of 70% of the tower-based observations was used to train the GPP estimation models, and the remaining 30% of the dataset was used to validate the models (Table 2). The empirical statistical equations presented in Figure 6 were utilized to determine the g(CI) functions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Crops**} & \multirow{2}{*}{**Timescales**} & \multirow{2}{*}{
\begin{tabular}{c} **Pearson’s** \\ **Coefficient of** \\ **Correlation** \\ \end{tabular} } & \multicolumn{6}{c}{**Partial Correlation Coefficient**} \\ \cline{3-8}  & & & \multicolumn{2}{c}{**Structure**} & \multicolumn{2}{c}{**Environment**} \\ \cline{3-8}  & & & **(NIRv)** & **(NDVI)** & **(PAR)** & **(Ta)** & **(VPD)** \\ \hline \multirow{2}{*}{Soybean} & Half-hourly & \(-\)0.10 ** & \(-\)0.16 ** & \(-\)0.17 ** & \(-\)0.11 ** & \(-\)0.08 ** & \(-\)0.18 ** \\ \cline{2-8}  & Daily & \(-\)0.38 ** & \(-\)0.55 ** & \(-\)0.50 ** & \(-\)0.47 ** & \(-\)0.36 ** & \(-\)0.07 \\ \hline \end{tabular}  
* stands for the significance level of 0.01.  
\end{table}
Table 1: Correlation coefficients for the links between CI and the ratio of \(GPP_{\text{EC}}\) to \(GPP_{\text{TOT\_APRI}}\).NIRv refers to the near-infrared reflectance (NIR) of the vegetation portion, which is expressed as NIR multiplied by NDVI. The control variables are included in parenthesis, and the Pearson and partial correlation coefficients are provided.  
Figure 7: Validation of the effects of CI on the half-hourly GPP estimates using the RF method. The \(\text{GPP}_{\text{RF}}\) represents the GPP predicted by \(\text{GPP}_{\text{TOT\_APRI}}\) and environmental variables (Ta, VPD) without considering CI (**a**). The \(\text{GPP}_{\text{RF\_CI}}\) represents the GPP predicted by \(\text{GPP}_{\text{TOT\_APRI}}\) and environmental variables (CI, Ta, VPD), considering the CI (**b**). The CI value is represented by the color scale. The best-fit line is shown by the solid red line, while the short, dashed, black line is the 1:1 line.  
It is evident that taking the CI into account can improve GPP estimation based on \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). For the half-hourly data, the R\({}^{2}\) values improved from 0.64 to 0.71 and the RMSE values decreased from 8.28 \(\upmu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\) to 7.42 \(\upmu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\); for the daily data, the R\({}^{2}\) values improved from 0.71 to 0.81 and the RMSE values decreased from 6.69 \(\upmu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\) to 5.34 \(\upmu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\). The remaining 30% of the dataset validated these findings (Figure 8). GPP estimation based on \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) could be improved when the influence of the CI was taken into account.
## 3 Materials and Methods
### Development Environment and Tools
#### 3.3.1 Software Frameworks and Programming Environments
Additionally, it facilitates the integration of connected medical devices, with automated alert mechanisms notifying healthcare professionals of critical updates, such as abnormal test results or significant changes in a patient's status. * _Secure Data Handling_--The framework incorporates advanced security mechanisms, including built-in protection against SQL injection, cross-site scripting, and cross-site request forgery attacks, ensuring the integrity and confidentiality of medical data.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 2 \(\Phi\)-OTDR System Principle
### Data Flow Analysis  
As illustrated in Figure 3, the complete \(\Phi\)-OTDR system consists of a data generation unit, a data acquisition unit and a data processing unit. Firstly, the modulated pulse which is launched into the \(\Phi\)-OTDR sensing system continuously generates RBS curves, then the optical signal is detected by PD and converted to an electronic signal. Secondly, the electric signal is acquired by an analog-digital converter (ADC) with a 50 MSa/s sample rate, then stored in the on-chip RAM of FPGA. Owing to a large amount of RBS data collected by the \(\Phi\)-OTDR sensing system, as well the insufficient internal storage space of the FPGA, this will result in data overflows and even the collapse of the whole system if the data is not sent to the host computer in time. Therefore, FPGA must complete the old data which are stored in the on-chip RAM, preprocessing and transmitting them into the CPU before the arrival of new data. The FPGA module is connected to the CPU via a USB transmission interface, achieving  
Figure 2: The diagram of experiment setup.  
data transmission quickly. Eventually, when the amount of data reaches a certain level, the data can be transferred from the CPU to the GPU, in order to realize fast algorithmic processing. Afterwards, the GPU sends data back to the CPU after dealing with data through multi-threads, then the results can be displayed on the screen.
## 1 Introduction
In a nutshell, the algorithm proposed in this study differs from the traditional spectral-recognition algorithms such as PLS-DA, SIMCA, SVM, and ANN. To the best of our knowledge, none of the current spectral-recognition methods use the proposed framework, which combines a sparse representation of the samples with compressed sensing for linear regression in spectral recognition and classification.
## 2 Related Works
### Model Compression and Acceleration
#### 2.1.4 Network Distillation  
Network distillation is another method for compressing deep neural network models. The approach is inspired by the concept of knowledge transfer [54], which trains a compressed model to mimic large and complex ensemble models. [PERSON] et al. [55] introduced knowledge distillation, which extends this idea by transferring knowledge from a bigger model (called the teacher) to a smaller model (called the student). This transfer is accomplished by softening the softmax probability distribution of the teacher model. This allows the student to learn correct classification and the relative similarities among the classes, as classified by the teacher.  
FitNet uses both the softmax output probability and intermediate representations as hints for the student network [56]. The resulting thin and deeper network generalizes well [56] and is computationally less intense than the teacher network.  
Born-Again Networks (BANs) use identical parameterization for the student and teacher networks and transfer knowledge from the teacher network to the student network with similar capacity. BANs show that student networks can outperform teacher networks in terms of accuracy [57].  
Teacher Assistant Knowledge Distillation (TAKD) shows that the size of the models and the gap between the teacher and student model sizes play a significant role in training a better student model. If the gap between the student and teacher model is significant, the student model's performance can be significantly lower than that of its teacher. To address a model or chain of models called teacher assistants is introduced to bridge the gap between the teacher and the student and build a better student model [58]. This can extend the ultimate compression method we are proposing for further compression.  
Collaborative Learning for Deep Neural Networks (CoLeDNN) is a collaborative approach where multiple student networks learn from each other while also learning from the teacher network. CoLeDNN improves the student network's performance by jointly optimizing the loss of all student networks and the teacher network [59].  
Relational Knowledge Distillation (RKD) focuses on transferring the relationship information between the input features to the student network. RKD uses a similarity matrix to measure the pairwise similarities between the input features and transfers this relational knowledge to the student network. This method has shown improved performance in object recognition tasks [60].