esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Training Details  
DCE module. The image encoder utilized 32 attention heads, whereas the text encoder employed 16 attention heads. Training was performed with a batch size of eight and a total of 800 epochs. In the training of the CTIMM, the dataset was partitioned into a training set constituting 90% of the data and a validation set representing the remaining 10%. This division was randomly conducted multiple times for robust assessment. The optimization was conducted using an AdamW [34] optimizer with parameters \(\beta_{1}=0.9,\beta_{2}=0.999,weight\_decay=0.25\). The learning rate was set to 0.0001, and the learning rate schedule followed the CosineAnnealingWarmRestarts [35] with \(T\_0=400\), \(T\_mult=1\), \(eta\_min=0\), and \(last\_epoch=-1\). The experiments were conducted on a workstation equipped with a 16-core AMD Ryzen 9 3950X processor, an NVIDIA GeForce RTX 2080 Ti GPU, and 64 GB of DDR4 memory. The CTIMM was implemented using the open-source machine learning framework PyTorch Lightning 2.0.0.  
AAG module. Training was performed with a batch size of eight and a total of 600 epochs. The division of the dataset was the same as that used by the DCE module to obtain the pre-trained text encoder. In the training process of the DSTF-GAN, the Adam [36] optimizer was utilized with \(\beta_{1}=0.5\), \(\beta_{2}=0.999\). The learning rates for the generator and discriminator were set as \(lr_{G}=0.0001\) and \(lr_{D}=0.0003\), respectively. The experiments were conducted on a workstation equipped with a 16-core AMD Ryzen 9 3950X processor, NVIDIA GeForce RTX 2080 Ti, and 64 GB of DDR4 memory. The DSTF-GAN was implemented using PyTorch Lightning. The pre-trained latent stable diffusion model was employed to render the generated conceptual sketches, with \(strength=0.6\sim 0.8\), \(prompt=\text{'}A\text{ high }-\text{rise }building,\text{ }3D^{\prime},\text{ }scale=30,\text{ }ddim\_steps=100\).
## 3 Results and Discussion
### Optimisation of Volatiles Extraction from Pineapple By-Products by HS-SPME
#### 3.2.2 Effect of Independent Variables on Volatiles Extraction
The 3D contour plot portrays an increase in the response when the solution temperature decreases from 60 to 30 \({}^{\circ}\)C then a decrease at high temperatures. At low temperature and low NaCl, the response obtained is high. Figure 2b illustrates the correlation between time and NaCl on the sum of areas of volatiles when the temperature was kept constant at 30 \({}^{\circ}\)C. The 3D contour plot portrays the increase in response when the solution time increases from 30 to 60 min then the decrease to a lower time. At the high time and low NaCl, the response obtained is improved. Finally, Figure 2c illustrates the correlation between temperature and time on the sum of areas of volatiles when NaCl was kept constant at 0 M. The 3D contour plot portrays the increase in the response when the solution temperature decreases from 50 to 30 \({}^{\circ}\)C then the decrease at high temperatures. At low temperature and high time, the response obtained is high. Thus, it could be possible to conclude that NaCl does not enhance the extraction of target volatiles, whereas low temperature and high extraction time benefit the highest response in this study. Similar results have been previously reported for two pineapple varieties (Tainong No. 4 and No. 6) in which 33 characteristic compounds were extracted at 25 \({}^{\circ}\)C for 40 min [29] without the addition of NaCl [6]. In a different study, 44 volatiles from the pulp and core from peeled pineapples were extracted at 25 \({}^{\circ}\)C for 40 min with low content of NaCl (10:2 ratio of weight sample to NaCl. For minimally processed pineapple fruit, the optimal extraction conditions by HS-SPME obtained through a Doehlert design were 60 min at 35 \({}^{\circ}\)C without the addition of NaCl [7]. Figure 2: _Cont_.
## 4 Discussion
### Potential Sources of Uncertainty  
Several factors may affect the accuracy of cropping intensity maps generated from the proposed algorithms. Firstly, we utilized the WorldCover product to derive the cropland basemap of Shaanxi Province. While the extracted cropland areas were largely consistent with the TNLS results, there were still some disparities. For example, the cropland areas appeared slightly smaller in southern Shaanxi (Figure 5b). This could be attributed to the high degree of fragmentation in these regions [51], where individual cropland plots may be smaller than the size of a single pixel, leading to misclassification as other land cover types. Secondly, in some areas of Shaanxi, the two-year-three-crop rotation system is practiced [52]. However, this study did not specifically distinguish this category, but rather broke it down into a single-cropping and a double-cropping classification. While this approach may slightly impact the calculated cropping intensity, its effect was limited due to the relatively small area of these plots.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 2 Impact Simulation Model
### Bit-Rock Interaction Analysis  
The cyclic impact force between the rock and the applied loading is a major excitation source for the vibration of the BHA within the wellbore. During the drilling process, the bit is simultaneously subjected to torque from the drill string, axial impact loads from the impactor, as well as resistance and reaction forces from the formation being drilled [19; 20; 21]. Based on force equilibrium analysis, the expression of the impact force during drilling can be written as:  
\[F=1.762\times m_{E}^{2/3}\times R^{2/5}\times(QH)^{3/5} \tag{6}\]  
\(m_{E}\) is the deformation modulus of the rock, kPa; \(R\) is the equivalent volumetric radius of the falling rock, m; \(Q\) is the mass of the falling rock, t; and \(H\) is the impact height.  
According to the axial impact force equation of the drill bit:  
\[\left\{\begin{array}{l}P_{2}(t)+m_{z}a(t)+F_{z}(t)=0\\ P_{2}(t)=WOB-F_{fz}(L)\end{array}\right. \tag{7}\]where \(m_{b}\) is the mass of the drill bit, \(A_{b}\) is the cross-sectional area of the impact hammer, \(v_{0}\) is the impact velocity, \(u(t)\) is the footage depth of the drill bit, \(\rho\) is the density of the hammer material, and \(c\) is the longitudinal wave velocity of the material, m/s.  
The axial force model of the drill bit is given as follows:  
\[\left\{\begin{array}{l}P_{z}(t)+m_{z}a(t)+F_{z}(t)=0\\ P_{z}(t)=WOB-F_{fz}(L,\mu,\theta)\end{array}\right. \tag{8}\]  
\(P_{z}(t)\) is the drilling pressure from the drill string at time \(t\), kN; \(m_{z}\) is for the mass of the drill bit, kg; \(F_{z}(t)\) is the longitudinal reaction force of the rock to the drill bit at time t, kN; WOB refers to the Weight on Bit; and \(F_{fz}(L,\mu,\theta)\) represents the borehole wall frictional resistance in the z-direction, while fluid resistance is neglected.
## 3 Results and Discussion
### Calibration Results
Overall, the background fluctuations of the spectra were significantly reduced after CWT preprocessing, which is likely the main reason for the improvement in the generalization of the strawberry model to different fruits. Figure 2: NIR spectra of three different fruits preprocessed with CWT **(A)**. Cross-validation results for established PLS model for strawberry **(B)**. Prediction of the strawberry model when applied to different fruits **(C)**. Figure 2B displays the variation of RMSEC and RMSECV with different \(n\)LV. Initially, both RMSEC and RMSECV were observed to be relatively high. However, as the \(n\)LV increased, the RMSEC and RMSECV gradually decreased. The RMSECV reached its minimum value (0.53 \({}^{\circ}\)Brix) at \(n\)LV = 19. The model developed with this optimal LV showed good prediction results for strawberry, as evidenced by the close agreement between the predicted and reference values of both the calibration and prediction sets shown in Figure 2C. However, for grape and apple, the predictions were far worse than those for strawberry, especially for SSC in apple, which were both overpredicted.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Mode Selection
#### 7.5.1 Mode Alterations Volume  
One of the specific challenges in mode selection is the frequencies of the mode alteration that have to be done. Due to the wireless channel random nature, the mode alteration can happen as frequent depending on the mobility of devices and the number of scatterers. Furthermore, most of the papers related to D2D studied a single-cell scenario (D2D pairs and a single BS are attached to each other) to make the analyzing process easy to follow and straightforward. Therefore, there is a need for more research studies that focus on provisioning lasting solutions so that the number of alterations can be minimized in the mode selection process.
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 3 Materials and Methods
### T5 Fine-Tuning  
Pre-trained models like T5 serve as a foundation for various NLP tasks but require fine-tuning to adapt them to specific domains. Fine-tuning involves training the pre-trained T5 model on task-specific data to enhance its performance for the target task [15].  
To begin the fine-tuning process, the pre-trained model, which has learned general language patterns from a large text corpus, undergoes parameter adjustment using a smaller dataset specific to the task. This dataset contains input examples and corresponding outputs relevant to the target task, such as text classification, named entity recognition, or machine translation.  
During fine-tuning, the pre-trained model is exposed to the task-specific dataset, and its parameters are updated using optimization techniques like gradient descent. The goal is to minimize a task-specific loss function that measures the discrepancy between the model's predictions and the true outputs in the dataset. Through fine-tuning, the model learns task-specific patterns and relationships to make accurate predictions or generate appropriate outputs.  
Concept masking is a technique employed during T5 fine-tuning and is particularly useful for tasks involving generating outputs based on specific concepts or entities. Instead of traditional token replacement, concept tokens (e.g., \"CONCEPT-1\", \"CONCEPT-2\", etc.) are used as placeholders for desired concepts or entities. During fine-tuning, these concept tokens are replaced with the corresponding concepts from the training data, enabling the model to learn accurate concept generation based on the given inputs.  
Concept masking empowers the T5 model to be trained to generate outputs conditioned on specific concepts or entities. This technique proves beneficial for tasks like text summarization, question answering, and text generation where precise control over the generated content is essential. By incorporating concept masking during fine-tuning, the model can learn to generate accurate outputs based on the provided concepts or entities.
## 3 Results and Discussion
### Improved Application of Brine
After the application process, the soil samples were left outside for drying for several days. The wind tunnel experiment was performed in the same procedure described in Section 2.1 with two wind velocities: 6.5 and 9.5 m s\({}^{-1}\). The dust-PM emission fluxes were derived from the PM10 concentration measured during the experiment. Then, emission coefficient was calculated as the ratio between the dust fluxes of the Brine sample (different concentration) and the control (no-treatment) sample, in which, the lower value, the higher dust emission suppression. The dependence of PM10 emission in the Brine concentration applied on the soil under two wind velocities, 6.5 and 9.5 m s\({}^{-1}\), is presented in Figure 7. The results indicate that the Brine concentration can be reduced to 60% (use of 0.8 L m\({}^{-2}\) instead 1.5 L m\({}^{-2}\)) and 40% (use of 0.5 L m\({}^{-2}\) instead 1.5 L m\({}^{-2}\)) for winds of 9.5 m s\({}^{-1}\) and 6.5 m s\({}^{-1}\), respectively, and still keep on high efficiency of \(>\)90% in dust emission suppression. Although the high efficiency of the Brine to prevent dust emission by wind in both laboratory (Table 2) and field (Tables 3 and 4) conditions, the exact level of reduced Brine concentration that is recommended to be applied in the field should be determined by a targeted experiment.
# Bounce: A High Performance Satellite-Based Blockchain System
## 2 Related Work
### Blockchain Protocol Landscape
#### 2.1.2 Permissioned Protocols  
Centralized approaches embody the opposite philosophy from permissionless consensus. Such an approach entails a single trusted central entity such as a _bank_[18]. Because this is both simple and efficient, the centralized approach was implicit in the digital cash proposals of the 1980s and 1990s. Unfortunately, the centralized approach entails strong assumptions about the trustworthiness and fault tolerance of the bank. Furthermore, such a bank would yield tremendous power, including the power to censor certain transaction types (or specific transactions) from the network. Such censorship is undesirable in, for example, cryptocurrency applications.  
Semi-centralized approaches fall in between. Instead of depending on a single arbiter of consensus as in the banking setting, they manage consensus by requiring a supermajority of semi-trusted principals. The Hyperledger Framework [19] and the Facebook Diem project (formerly Libra) [20] are the most prominent of such systems. Hyperledger's consensus mechanisms include: (i) Kafka [21], which entails having a single leader order transactions, is vulnerable to fail-traitorous-arbitrary (also known as Byzantine) failures, but is tolerant of fail-crash (also known as fail-stop) failures; (ii) Redundant Byzantine Fault Tolerance [22] allows traitorous nodes as long as they constitute fewer than \(1/3\) of the total nodes participating in the protocol; (iii) and trusted computing environment based systems (e.g., based on Intel's SGX) [23]. By contrast, [PERSON] proposes the use of roughly a dozen semi-trusted nodes which run an original Byzantine fault-tolerant consensus protocol. In summary, any proposal based on semi-trusted entities must either select them very carefully or permit them to be monitored.
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 4 Building Retrofit Measures and Major Affecting Elements in Shanghai
### Key Elements Affecting Building Retrofit Measures in Shanghai
#### 4.2.2 Retrofit Technologies  
These are energy-conserving strategies adopted to stimulate the sustainability and energy efficiency of buildings [16]. They vary from the use of energy-efficient equipment, smart management and control systems and the integration of renewable energy sources to energy consumption changes. Based on their features, it is recommended that retrofit technologies are applied in the following trend: Payback period, efficiency, effectiveness and ease of implementation [81]. However, in line with their applicability, some concerns need to be considered, as an appropriate decision towards the selection of retrofit technologies is subjected to these concerns:  
* _Specific retrofit technology._ Building characteristics were estimated to determine a buildings energy usage by 42% [82]. However, every building is unique and has specific characteristics depending on the building type. Consequently, building retrofit technologies used in one building may not have the same contributive impact on another building.
* _Integration of retrofit technologies._ Second to the definition of building type and the associated energy consumption, it is paramount to define the necessary measures required to conserve energy in these buildings. In most cases, the recommended optimum building retrofit measures are multiple ECMs. However, the benefits obtained from this is not equal to the sum of the individualistic measures [83], the reason being that the performance of multiple ECMs is subject to the integrative nature and interactions between ECMs and the building subsystem [84].
* _Selection of retrofit technologies._ This issue is a multi-objective optimisation problem, which tends to define the synergetic performance of various combinations of ECMs. In the selection process, a variety of proposed measures are assessed based on energy, environmental, social and financial targets to ensure retrofit maximisation [85; 86]. The weighting factor assignments and criteria selection are essential determinants in devising the optimum retrofit package for each building type [16].
* _Intricacies of optimisation problems._ To best define a suitable retrofit package for a building or type of buildings, optimisation of the synergy of various ECMs is necessary. The optimisation of ECMs can be achieved either via a model-free (expert system) or model-based (requiring energy simulation tools) approach. In both approaches, the knowledge database and access to it are essential. Absence of this is likely to introduce significant errors in optimisation estimations [87]. Moreover, the integration of both approaches will promote an adaptive and well-suited model for any given building type [88; 89]. Notable optimisation packages include generic algorithm (GA), simulated annealing (SA) and branch and bound (B&B) [16; 61].
## 2 Properties of Fine Granular Area
### Influences Regarding the Formation Process of FGA
#### Influence of the Loading Frequency  
Many investigations were carried out to clarify the influence of frequency on fatigue behavior and FGA-formation. The results showed no significant influence of the loading frequency for high-strength metallic materials [67; 68; 69; 29; 56; 60]. [PERSON] et al. [70] tested the effects of loading frequency on VMCF behavior for bearing steels with different heat treatments resulting in different strengths. They observed that high-loading frequencies lead to an increased fatigue resistance in low strength states of the bearing steel while no influence of the frequency was observed for high-strength steels. They conclude that the influence of the frequency is related to the dislocation movement. They suggest that the dislocation movement decreases with higher frequency at the same stress amplitude than at lower frequencies and as a result the accumulated damage decreases and the fatigue resistance is increased. Similar results focusing on different ferritic-perlitic microstructures of low alloyed steels are reported by [PERSON] et al. [71]. However, for materials like high-strength steels with a high density of obstacles impeding dislocation movement, this influence is significantly reduced. For an aluminum alloy [PERSON] et al. [72] also observed a 5 to 10 times longer lifetime while testing at 20 kHz compared to tests performed at 75 Hz. The difference of fatigue life between 20 kHz and 75 Hz is attributed to environmental effect of water vapor which was found to reduce fatigue life by increasing the crack growth rates of aluminum alloys. So, depending on the material the influence of testing frequency has to be considered to evaluate the fatigue strength in VMCF-regime. The influence of environment on the fatigue behavior in the VMCF-regime is discussed in more detail in the next chapter.
# Experimental Study on the Workability and Stability of Steel Slag Self-Compacting Concrete
## 3 Experimental Results and Workability Evaluation
### Static Stability of SSCC (Segregation Resistance, SR)  
The sieve analysis test was used to analyze and evaluate the static stability of fresh SSCC. The sieve analysis test results of SSCC with different VPS are listed in Table 13.  
According to Table 13, we can see that with the increase of VPS and paste-aggregate ratio, SR is in a fluctuating trend, the numerical dispersion is large, there is no obvious rule, but the overall trend is increasing. The maximum SR value of 8.7% appears when VPS is 100.0%, the minimum when VPS is 60.0%. According to the analysis of the test phenomenon, the reason for the decrease of VPS = 0% and 20% SR value is that the water demand in the mixture is relatively increased due to the steel slag sand with high water absorption, so the segregation rate of the mixture is relatively reduced. When VPS is 0%, 20%, 40% and 60%, SR value is between 5% and 15%, which belongs to SR2 level, and the flow time T500 of each test group is smaller, which shows that when VPS is 0%, 20%, 40% and 60%, each test group has better liquidity and no segregation. SR value is larger than that of the previous groups when VPS \(\geq\) 80.0%, although S\({}_{80}\)SCC and S\({}_{100}\)SCC have higher viscosity and better segregation resistance, indicating that the probability of segregation risk between coarse aggregate and paste increases.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### Proposed Fingerprinting Strategies
#### 3.2.2 Enhanced Net  
The _Enhanced Net_ strategy shares with the _Net_ strategy the metric defined in (2) and the sorting/grouping procedure described in Section 3.2.1. In addition, for each RP \(i\), the following distance from the TP \(j\) is evaluated:  
\[d_{Ent\_Net}(i,j)=\sqrt{\sum_{l=1}^{L_{i,j}}\Bigl{(}p_{l}^{i}-p_{l}^{j}\Bigr{)}^ {2}}, \tag{5}\]  
where \(p_{l}^{i}\) and \(p_{l}^{j}\) indicate the readings of the selected RF parameter (one among RSSI, RSRP, RSRQ, and SINR) in RP \(i\) and TP \(j\) for the \(l\)-th NPCI in common. RPs within each group are then sorted as a function of increasing \(d_{Enh\_Net}(i,j)\) and in cases where \(N_{g}<k\), the first \(k-N_{g}\) RPs in the sorted list for group \(G_{q+1}\) are selected.
## 5 IETF ROLL Working Group
### Research Challenges
#### 6.1.3 Block Transfer  
In many cases the payloads that CoAP needs to carry is very small (e.g., just a few bytes for temperature sensor, door lock status or toggling a light switch). In these cases the basic CoAP message provides very efficient means of communication and work very well. However in some cases CoAP needs to handle larger payloads (e.g., firmware update). Since CoAP is based on datagram transports such as UDP or DTLS, data fragmentation and reassembly is not offered by these transport protocols. Relying on IP fragmentation is also not very helpful, because fragmentation and reassembly does not perform well in LLN due to memory requirements imposed by route-over routing as described in Section 4. Additionally, IP fragmentation can handle only payloads up to 64 KiB. Thus, providing a mechanism at the application layer that is able of transferring large amounts of data in smaller pieces becomes a necessity. This will not just help avoid the 64 KiB UDP datagram limit, but also will help avoid both IP fragmentation (MTU of 1280 for IPv6) and also adaptation layer fragmentation in LLNs (60-80 bytes for 6 LoWPAN).  
Figure 15: An example of Constrained RESTful Environments (CoRE) resource discovery and Constrained Application Protocol (CoAP) request.  
To overcome the payload size limitation, draft-ietf-core-block defines two CoAP options: Block1 and Block2 [88]. By using this pair of options CoAP becomes capable of transferring a large payload in multiple smaller CoAP messages. Both Block1 and Block2 options can be present both in request and response messages. In either case, the Block1 Option pertains to the request payload, and the Block2 Option pertains to the response payload. An important aspect of this mechanism is that often the server can handle block transfers in a stateless fashion--it does not require connection setup and the server does not need to track each transfer separately and thus conserve memory.