esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Robustness Tests  
#### 5.4.1 Robustness Tests of the Baseline Regression  
To ensure the reliability of results, we conduct comprehensive robustness tests in three dimensions (Table 10). First, the temporal heterogeneity test shows that after excluding samples from the 2020 COVID-19 pandemic period, the regression coefficient of government procurement policies (GPP) remains significantly negative, indicating that the pandemic did not systematically bias the baseline results. Second, robustness tests addressing data distribution indicate that after applying a 2% two-tailed winsorization to mitigate outliers, both the significance level (\(p<0.01\)) and the coefficient (\(\beta=-1.312\)) of GPP remain stable, confirming that the results are robust. Third, when the dependent variable is replaced with \"carbon emission intensity per unit of secondary output\", the GPP regression coefficient increases in magnitude (\(\beta=-2.047\)), indicating that GPP exhibits a stronger carbon reduction effect on industrial emissions. All models demonstrate strong explanatory power (R-squared \(>0.6\)), further confirming the reliability of the model specification.
# Evolution and Testing of NB-IoT Solutions
## 2 NB-IoT Background  
In the last few years, the evolution of 3 GPP standards related to IoT has accelerated due to the pressure of cellular stakeholders who require a standard solution to go one step further to provide a real cellular and standard solution for LPWA (Low Power Wide Area) networks. Behind this pressure are the billions of potential new subscribers originated by the IoT use cases.  
IoT covers a wide range of use cases, which can only be partially covered by other previous 3 GPP technologies such as GSM or Machine Type Communication (MTC) introduced in Release 8, and new ones such as eMTC or EC-GSM and unlicensed LPWA such as Lora or Sigfox. In contrast, NB-IoT is a 3 GPP effort to penetrate the ultra-low cost, power and throughput, extended coverage, and delay tolerant IoT marketplace, which, in 5G terminology, is the massive Machine Type Communication (mMTC) use case. This use case contains the greatest number of potential IoT subscribers, on the order of several billion.  
5G NR (New Radio), the term coined for the global 5G standard new radio access technology, covers, in the first version, eMBB (enhanced mobile broadband) and URLLC (ultra-reliable, low-latency communication) use cases. mMTC will not be considered until Release 17. In the meantime, NB-IoT is providing the solutions to cover the 5G mMTC use case. This converts this technology into the foundation for NB-5G.  
The first version of NB-IoT was released in July 2016, as part of Release 13 in TR 45.820 [7]. This version of the NB-IoT standard focused on improving indoor coverage, support for massive numbers of low throughput device sensitivity, ultra-low device cost, low device power consumption, and optimized network architecture. This translates to the comparative numbers in Figure 1.  
The fundamentals of this technology have been analyzed in previous works, as well as its comparison with other LPWA technologies [8, 9]. In this paper, we will update these analyses. As we will discuss, Release 14 and Release 15 introduce new features to cover new use cases. With the features provided in Release 13 (reduced complexity, lower power, deeper coverage and higher density), the use cases covered by NB-IoT are mainly related to sensors, but new features such as positioning and mobility enhancements (NB-IoT Release 13 only supported cell-reselection in the idle state) have extended its applicability to wearables and tracking services.  
NB-IoT should not be confused with the new item introduced in Release 16, \"New Radio Industrial Internet of Things\" (NR-IIoT) [10], which is focused on factory automation use cases. The key technology enablers of NR-IIoT are the NR URLLC (short TTI, reliability) and the Time Sensitive Networking (TSN) (accurate reference time, QoS for wireless Ethernet, Ethernet compression, etc.). 3 GPP Release 17 also introduces a new device with reduced capabilities, called NR-Lite, to cover IoT use cases with higher requirements that can be not be provided by Nb-IoT and LTE-M, such as higher data rate, higher reliability, and lower latency.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 3 The TMMNN
### Recommendation Layer  
As shown in Figure 3, the recommendation layer consists of two parts: matrix factorization and a multi-layer perceptron. Starting from the bottom of the structure in Figure 3, this layer is fed with the user and item embedding vectors. These two types of embedding vectors can be decomposed in the feature cross layer along the left path. They can be directly spliced along the right path as well. Finally, the results of the above two paths are spliced, and the final prediction is obtained after judgment by the activation function.  
In the traditional methods, the model will send embeddings with different dimensions for the matrix factorization and the multi-layer perceptron [20]. However, in the scenario of multi-dimensional embedding, it is difficult for the model to adjust the embedding dimensions for the two components separately, so in this paper, we use two-layer matrix factorization and a multi-layer perceptron to integrate multi-dimensional embedding technology into the model. First, the recommendation layer receives normalized embedding from the user and item embedding layers, labeled \(e^{(u)}\) and \(e^{(i)}\), respectively. Then, our model feeds these two normalized embeddings into matrix factorization to obtain the vector \(e^{\prime}\) according to Equation (3):  
\[e^{\prime}=h^{\mathsf{T}}(e^{(u)}\odot e^{(i)}) \tag{3}\]  
\(h^{\mathsf{T}}\) is the weight of the corresponding position, and \(\odot\) is the bit-wise multiplication. Then, in order to retain the original features of users and items, the embedding \(e^{\prime}\) is spliced with the embedding \(e^{(u)}\) and \(e^{(i)}\) of users and items, and sent to the multi-layer perceptron. The specific process is shown in Equation (4):  
\[\begin{split} e&=e^{\prime}\oplus e^{(u)}\oplus e ^{(i)}\\ h_{1}&=\tanh(W_{1}e)+b_{1}\\ h_{2}&=\tanh(W_{2}h_{1})+b_{2}\\ &\dots\dots\\ h_{m}&=\tanh(W_{m}h_{m-1})+b_{m}\end{split} \tag{4}\]  
\(\oplus\) is the connection operation, \(tanh\) represents the activation function, and \(W_{k}\) and \(b_{k}\) are trainable weight and bias parameters, respectively. The \(h_{m}\) of the last layer will be fed into the activation function, and the activation function will be selected according to different tasks. For the binary classification tasks and multi-classification tasks introduced in the following section, the model applies the Sigmoid function and the Softmax function, respectively.
## 2 Materials and Methods
### Factors and Indicators  
When forming the hierarchy of aspects and indicators, the principle of decomposition is applied. The idea is to form a general hierarchy, while the specificities of the analysed OHS systems may require the introduction of additional indicators to address them. The multi-level structure consists of the OHS objective at the root, aspects at the first level, factors at the second level, and indicators at the leaves of the tree. The structure facilitates the application of AHP and related methods of multi-criteria analysis. The proposed conceptual hierarchical decision-making structure, based on previous research [32; 34; 35], is shown in Figure 2.  
Aspects define a strategic approach to addressing OHS challenges. The primary consideration is the risks that the employees face during daily work activities, evaluated based on the level of danger and the duration of exposure. In addition, the social perception of preventing injuries, fatalities, and occupational diseases as significant losses for both individuals and the community influences the definition of the OHS goals and the initiation of organisational activities. Improving an OHS system also requires financial and material resources, which can be a considerable burden for small companies. At the next level of the hierarchy are the factors. The proposed factor structure and classification of indicators are shown in Table 1. The OHS system does not operate in isolation but is constantly affected by changes that occur in its environment. Improvements in legal regulations provide a significant incentive for OHS system enhancements [36; 37],  
Figure 2: Decision-making hierarchy. along with technological advancements [38], the adoption of new industry standards, and knowledge exchange in communities of practice [39]. Access to external funds can significantly help small companies overcome financial constraints. Technological progress is a major driver of development across various industrial sectors. Technological innovations are increasingly applied to workplace safety, both to monitor production processes and assess working conditions [40]. For such systems, factors like sensitivity in detecting irregularities and reliability are crucial. However, for smaller companies, the costs of purchasing and maintaining this equipment must also be taken into consideration. Training can be more effective if modern technologies are applied [41].
## 3 Results
### Experiment and Analysis
#### 3.2.3 Verify the Anti-Aggression of LFCTEM  
We carried out simulation experiments to verify the anti-aggression of LFCTEM. The ON-OFF attack scenario of malicious edge devices is simulated experimentally. The malicious ON-OFF attack is realized by simulating the positive cooperation and negative states of the edge devices alternately. In this attack scenario, a total of 50 rounds of interaction were simulated. Malicious edge devices first cooperate actively in the first 10 rounds of interaction to obtain a high trust value. In rounds 10 to 20 of the interaction, the malicious edge devices launch the ON-OFF attack, changing from the positive cooperative state to the negative state. The attack will damage the CEEC trusted environment. In  
Figure 10: Comparison of error detection rate of malicious edge devices of LFCTEM, DTEM and FITEEV.  
rounds 20 to 40 of the interaction, the malicious edge devices continue to pretend to be actively cooperating to cheat trust. In rounds 40 to 50 of the interaction, the malicious edge devices again switch to the negative state for the ON-OFF attack. Figure 11 shows the change of the trust value of the edge devices of LFCTEM in the ON-OFF attack scenario.  
As can be seen from Figure 11, in the initial interaction rounds (the first 10 rounds), the malicious edge devices disguise as a positive cooperation state, so their trust value is in the trusted state. In the interaction rounds from 10 to 20, the malicious edge devices start to launch ON-OFF attacks and switch to the negative state. LFCTEM can quickly and accurately reflect the abnormalities of edge devices through interactive data and the trust values of negative edge devices decrease quickly. Moreover, due to the negative behavior of the edge devices, their incentive scores are greatly attenuated, which leads to the decrease in the CMF factor. The decrease in the CMF factor requires the positive cooperation of the edge devices for a period of time to gradually earn the incentive reward scores. Therefore, the recovery of trust values of malicious edge devices requires better cooperative behavior and more time. This means that the proposed LFCTEM can quickly and accurately identify malicious behaviors and effectively resist ON-OFF attack.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 3 Results and Discussion
### Micro-Mechanism Analysis
#### 3.5.4 SEM Analysis  
The microstructural transformations of UHPC before and after modification are illustrated in the figure. Figure 14a,b presents SEM images of the UHPC interface in the absence of modified aggregate. Figure 15a,b depicts the SEM images showcasing the interface changes in UHPC when 20% modified aggregate is incorporated; Figure 15c,d reveals the morphology of hydration products within UHPC containing the modified aggregate.  
As evident from Figure 14a,b, the surface of the UHPC without modified aggregate exhibits a notable number of pores, and some fine, round, and smooth-surfaced fly ash particles remain incompletely reacted. The interface is relatively looser, with a poor connection between the cementitious materials and aggregates, and cracks and gaps are present in the interface transition zone (ITZ). Aggregate is not fully enveloped and bonded by the gel, which compromises the compactness of the UHPC. By comparing Figure 15a,b, it becomes clear that the addition of modified aggregate renders the UHPC interface denser, with a reduction in unreacted fly ash particles. At the same time, the increased production of gel enhances the bonding effect with the aggregate. The aggregate is thoroughly wrapped, and the bonding is more robust, resulting in fewer micro-cracks and an improved interface degree between the substrates. Figure 15c,d reveals the presence of numerous hydration products in the UHPC.  
Figure 14: Interface morphology (**a**,**b**) without modified aggregate.  
SEM images reveal that incorporating 20% modified aggregate significantly enhances the hydration reaction degree of cementitious materials in the UHPC, leading to the formation of a greater quantity of C-S-H gels. At the same time, the ITZ between the aggregate and matrix becomes more tightly bonded, resulting in the improved density of the UHPC, which aligns with the pore test results. Related studies indicate that when concrete aggregates exhibit high hydrophilicity, a water film tends to adsorb on their surface, and during cement hydration, this can promote the growth of loose, large crystals like calcium hydroxide at the interface, thereby compromising interfacial bonding strength [50]. However, the hydrophobic groups introduced after modifying aggregates with the SCA prevent the formation of a continuous water film on the aggregate surface, thus avoiding the growth of loose large crystals at the interface and improving the bonding strength of the ITZ [49]. This also elucidates why adding an appropriate amount of modified aggregate can improve the mechanical properties of UHPC.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 3 Multi-Pedestrian Tracking Method Based on IVDM
### Construction of KC-YOLO Detection Model
#### 3.1.1 Optimal Pedestrian Detection Frame Determination  
In the context of pedestrian detection, YOLOv5 defaults to using k-means clustering to generate anchor frames. However, before performing k-means clustering, it is crucial to initialize \(k\) cluster centers, as the convergence can be significantly affected by uninitialized cluster centers. To address this issue, we employ the k-means++ clustering method [31]. Here is how it works:  
Figure 2: KC-YOLO network model structure (Concat is primarily responsible for combining addition and residual convolution operations; CBL is a convolutional block; Up sample means that usampling operations are performed; CSP2_1 divides the input feature map into two parts).  
* Initially, a random sample point is selected from the dataset as the first initial cluster center.
* Then, the shortest distance between each sample point and the currently existing cluster centers is calculated.
* Finally, each sample point is chosen as the next cluster center with a probability proportional to the shortest distance. The sample point with the highest probability is selected as the next cluster center.  
This approach provides a more reliable initialization method, improving the stability and convergence of the clustering process, which, in turn, optimizes the selection of detection frames. The formula for the calculation is as follows:  
\[P(\mathbf{x})=\frac{D(X_{i})^{1}}{\sum\limits_{i=1}^{n}D(X_{i})^{1}} \tag{1}\]  
where \(C_{i}\) represents the first initial cluster center; \(D(X)\) denotes the shortest distance between each sample point and the currently existing cluster centers; and \(P(X)\) represents the probability of each sample point being selected as the next cluster center.
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 4 Discussion
### Implications for Agro-Climatic Studies  
Accurate reproduction of temperature-based Agcls through RPs provides valuable insights into the complex interactions between temperature and crop. This enhances the ability of assessing the effects of temperature on crops, soil conditions, and overall agricultural productivity. This is crucial for providing informed decision-making, such as planting schedules, crop selection, irrigation, and other agricultural practices. In addition, this will improve the modeling and forecasting of plant growth, disease development, and pest dynamics, and will ultimately lead to improved efficiency and yields of crops.  
An accurate reproduction of Agcls based, solely or partly, on RPs precipitation is challenging due to the dynamic and stochastic nature of precipitation, varying in intensity and frequency. The uncertainties in such precipitation-related AgcIs can influence the assessments of agricultural productivity, water management, and decision-making. For instance, underestimating the frequency of dry days could compromise the accurate characterization of dry/wet spell lengths and drought periods [11; 26]. Additionally, oversetimating precipitation amounts can result in an inaccurate evaluation of water availability, underestimation of irrigation requirements, or exaggeration of potential crop yields. This challenge extends to disease management decisions, as seen in the case of HyI, where such mismanagement can lead to unnecessary or excessive control measures, with potential costs and environmental consequences. All these outcomes can lead to ineffective irrigation scheduling and practices, inadequate drought preparedness, and, ultimately, negative impacts on crop yield and quality.  
In this study the need for critical evaluation in agro-climatic assessments is highlighted. Understanding both the strengths and limitations of RPs holds significant implications for agro-climatic studies. In a changing climate, where the increase in temperatures and shifts in precipitation patterns are anticipated, precision in agriculture assessments is crucial. Relying on inaccuracies could not only amplify uncertainties in climate change assessments but also limit the ability to make the distinction between climate change impacts and data errors. Therefore, a comprehensive understanding of RPs and their limitations is crucial, along with validation against local observations, and cautious interpretation and application. All these elements are essential for informed decision-making in agriculture and climate change adaptation.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 4 Results
### Area Consistency Validation Results  
According to Equation (3) in Section 3.5, the area consistency coefficient (Acc) of green tide extraction results among remote sensing satellite data in Table 3 was calculated, and the results were shown in Table 7. The Acc between Landsat and HY-1C was the best, reaching 96%. The Acc between MODIS, Landsat, and HY-1C was also between 85% and 90%. However, GF-1 and HJ-1 had relatively low Acc with the other three, which was because the other data were on the same day, and the time was relatively closer, so the drift and deformation of the green tide were relatively small, while GF-1 and HJ-1 differed by 3 and 1 days, respectively, resulting in lower Acc of the area. The difference between GF-1 and HJ-1 was 2 days, and the Acc between them reached 88-89%. The consistency validation results show that the green tide extraction results from multi-source RS data were generally consistent, and multi-source RS data could be used to monitor green tide more accurately and comprehensively in real-time.
## 4 Proposed DEIDV-Hop Algorithm
### Process of Flooding  
Flooding, as a classic routing algorithm, is the simplest and most reliable one. Nevertheless, it excessively occupies network resources and generates a large amount of communication overhead, making routing and link resources inefficient.  
The routing algorithms involved in the first and second step of the DV-Hop algorithm in this paper are based on the Flooding, including the original DV-Hop algorithm. To reduce network communication overhead, reduce network energy consumption as well as to extend the network life cycle, the EEDCF routing algorithm [42] is considered and applied.
## 3 Materials and Methods
### Determination of Volatile Organic Compounds  
The volatile organic compounds (VOCs) present at the end of fermentation were measured using headspace-solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) (Agilent 6890N GC system, Beijing, China; coupled to an Agilent 5975B VL mass spectrometer with triple axis detector, Wilmington, DE, USA). Samples were prepared by centrifuging (2000 rpm, 4 \({}^{\circ}\)C for 10 min, Sorvall RT 6000, Wilmington, DE, USA) a sub-sample (35 mL) of the ferment and adding the resulting supernatant (10 mL) to a 20 mL headspace vial (Phenomenex, Auckland, New Zealand) containing 3 g of NaCl along with 20 \(\upmu\)L of the internal standard (3-heptanone (Sigma-Aldrich, Saint Louis, MO, USA) (0.008 mg/mL, prepared with methanol)). The vials were quickly capped with a Teflon-faced septa and stored at 4 \({}^{\circ}\)C in a refrigeration block (Peltier cooling tray) in the autosampler until analysis. Three analytical replicates were prepared from each supernatant to provide a total of six replicates for each fermentation medium (biological replicates: 2 and analytical replicates: 3). Samples were analysed in a randomised order, blocked by replicates. At the beginning of each replicate and at the end of each replicate, two blank samples consisting solely of RO water were analysed.  
Prior to GC-MS analysis, each headspace vial was incubated at 40 \({}^{\circ}\)C for 5 min, after which the SPME fibre (divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), 50/30 \(\upmu\)m Stableflex, Supelco, Bellefonte, PA, USA) was inserted through the septum of the vial and exposed to the headspace for 40 min at 40 \({}^{\circ}\)C. Upon completion of the extraction procedure, for thermal desorption, the fibre was inserted into the front injector port of the GC (set at 230 \({}^{\circ}\)C) for 2 min in splitless mode followed by 3 min in split mode (purge flow: 50 mL/min). Between samples, the SPME fibre was conditioned in the back inlet at 270 \({}^{\circ}\)C with a purge flow of 50 mL/min for 2 min. VOCs were separated through a Zebron ZB-Wax capillary column (60 m \(\times\) 0.32 mm inner diameter \(\times\) 0.5 \(\upmu\)m film thickness: Phenomenex, Torrance, CA, USA) using helium as the carrier gas at a constant flow rate of 1 mL/min. The oven temperature programme was set at 50 \({}^{\circ}\)C for 5 min initially, followed by heating at a rate of 5 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C, followed by 10 \({}^{\circ}\)C/min until 240 \({}^{\circ}\)C, and finally held for 5 min, for a total run time of 45 min. Mass ions were measured between 29 and 300 m/z via electron ionisation (EI mode 70 eV). The ion source temperatures of mass ion traps were sustained at 230 \({}^{\circ}\)C with a quadrupole temperature of 150 \({}^{\circ}\)C.