esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 3 Results and Discussion  
In this section, the hosting limit was obtained probabilistically by applying the proposed method to each case. The cases include the base case, keeping the synchronous generator as a synchronous condenser case, concentrating the DER input only in a specific area case, reinforced with a P2P case, and an overlay case. The simulations were conducted using Power System Simulator for Engineering (PSS(r)E). In all cases, the assumed order in which the generator is turned off was that the generator at bus 30 is turned off first, then that at bus 32, then bus 33... and finally, at bus 39; the slack bus was selected as the generator at bus 31. In the implementation process, when the line overload reached 100% or the voltage violation rate exceeded 10%, the point was determined as the limit.  
As the purpose of the simulation was to determine the distribution of the hosting capacity limit, the number of iterations was determined 1000 times for each case. For the reinforced case, 3000 times were performed to better present the results. Although more precise results can be obtained above this number of iterations, no significant change in the distribution or probability of the capacity were recorded; therefore, this number of iterations was sufficient to obtain the desired limiting capacity range. In this simulation, whenever the unit input capacity of the DERs proceeds, it was divided for the capacity proportional to the short circuit level (SCL) of each bus, and the power generation for this capacity was applied based on the probabilistic DER modeling in Section 2.1. This method of the DER integration logic is based on the integrated process considering short circuit ratio (SCR) when linking DC facilities because renewable energy uses an interface based on power electronics. Concerning the reinforcement case, the lack of reactive power supply acts as a large acceptance limit factor, hence the P2P case and the abovementioned  
Figure 4: Modified IEEE 39 bus test system.  
three-terminal MTDC case show similar hosting capacity limit values. Therefore, to show the effect of location selection using the proposed method, the comparison of the line utilization rate when the hosting capacity is reached in each case is also shown. From this result, it is suggested that as the number of stations increases, the degree of freedom in the power flow control selection increases, and thus, the effect of increasing the line utilization rate can be increased. Therefore, the overall flexibility of the system is increased.
# A Review of Transformer-Based Approaches for Image Captioning
## 6 Discussion and Analysis
### Training and Model Parameters
MTL and CL are particularly useful during the pre-training stages, where large-scale text-image-pair datasets are employed. Cross-entropy loss (XE) and CIDEr optimization (SCST) are then used in a similar manner to the non-VLP methods. The effectiveness of the VLP methods is made manifest in the fact that some of the models ([103; 110]) do not use SCST optimization and yet they achieve comparable performance to the non-VLP methods that use SCST. VIVO [40] uses the Hungarian matching loss [121] to address the unordered nature of image tags. Some of the models ([103; 106; 111; 111]) stand out for their simplicity in that they use the same objective for pre-training and fine-tuning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**Objective (Loss)**} & \multirow{2}{*}{**Optimizer**} & \multicolumn{2}{c}{**Transformer Details**} \\  & & **Pre-training** & & **Fine-tuning** & & **N** & **d** & **h** \\ \hline OSCAR [38] & Faster RCNN & MTL-b, CL2 & MTL-s, SCST & AdamW & 24 & 1024 & 16 \\ Unified VLP [109] & Faster RCNN & MTL-bs & MTL-s, SCST & Adam & 12 & 768 & 12 \\ VinVL [39] & ResNeXt-152 C4 & MTL-b, CL3 & MTL-s, SCST & AdamW & 24 & 1024 & 16 \\ VIVO [40] & Faster RCNN & MTL-b, Hungarian & MTL-s, SCST & Adam & 24 & 1024 & 16 \\ LEMON [52] & Faster RCNN & MTL-s & MTL-s, SCST & AdamW & 32 & 1280 & 16 \\ SimVLM [103] & ViT; CoAttNet & PXLM & PXLM & AdamW & 32 & 1280 & 16 \\ OFA [106] & CoAtNet & XE & XE, SCST & AdamW & 24 & 1280 & 16 \\ CoCa [110] & ViT & CL, XE & XE & Adafactor & 36 & 1408 & 16 \\ GIT [111] & CoSwin [107] & CL, XE & CL, XE, SCST & AdamW & 6 & 768 & 12 \\ GIT2 [111] & DaViT [118] & UniCL [109], XE & CL, XE, SCST & AdamW & 24 & 1024 & 16 \\ Universal [112] & CLIP-ViT [53] & Unidirectional LM & Prompt LM, SCST & LAMB [119], Adam & 24 & 1024 & 16 \\ PaLI [55] & ViT-e & XE & XE & Adafactor & 56 & 1792 & 16 \\ BLIP-2 [113] & CLIP-ViT & CL, MTL-s, PXLM & CL, MTL-s, PXLM & AdamW & 12 & 768 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training and model details for VLP models. MTL-b: bidirectional Masked Token Loss, MTL-s: seq2 seq Masked Token Loss, XE: Cross-Entropy loss, CL: Contrastive Loss, SCST: Self-Critical Sequence Training, PxLM: Prefix Language Modeling.
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 1 Introduction  
### Background  
Beer is one of the most widely consumed beverages globally; world brewers produced 1.82 trillion hectoliters in 2020 [1]. However, the substantial water requirements for production and cleaning result in significant waste generation compared to product yield and present the industry with sustainability challenges. The primary waste stream in brewing, aside from water, is brewer's spent grain (BSG), which refers to the husk, pericarp, and seed layers of barley that remain after the extraction of soluble sugars from the grain. This fibrous byproduct accounts for 85% of the total waste from beer production, with the remaining portions consisting of trub (hop solids, clarifying agents, yeast nutrient, and other ingredients added to the boiling stage) and yeast solids. In 2020, the yield of BSG as a result of beer produced reached 36.4 million metric tons [1].  
The Food and Agriculture Organization of the United Nations (FAO) introduced Sustainable Development Goal (SDG) 12.3, which calls for action to reduce food loss throughout the food supply chain. In line with this goal, Champions 12.3, a collaborative group of executives working on SDG 12.3, conducted a study that presented the optimal hierarchy of solutions for valorizing food waste [2]. The top three solutions include: (1) prevention and redistribution to people; (2) diversion to animal feed; and (3) biomaterial/processingof waste. Notably, landfilling is considered \"no valorization\" and is an undesirable method of waste disposal.  
Traditionally, the primary use for BSG has been animal feed (70%), while the rest is landfilled (20%) or used for biogas production (10%) [3]. While using BSG as animal feed is recommended by the FAO, it is not feasible for breweries located far from agricultural communities due to its high water content, excessive weight for transportation, and microbial instability. With the growth of microbreweries in urban areas, this challenge of transportation continues to amplify. Therefore, the brewing industry seeks alternatives to animal feed and landfilling to meet sustainability goals. It is crucial to determine whether specific upcycling techniques for valorizing BSG impose a higher or lower environmental burden than other destination options for BSG. Multiple studies have reviewed the process of converting BSG and other highly fibrous biomasses into biochar [4; 5; 6]. Other studies have reviewed the greenhouse gas emissions associated with producing biochar [7], applying biochar to soil [8; 9; 10; 11; 12], and using biochar for water treatment [13]; however, there are no current studies that analyze the environmental impact of a small-scale biochar production process from BSG to determine if it emits less CO2e than landfilling. In this study, we produce a novel carbon footprint assessment (CFA) of converting BSG to biochar and compare these results to disposing BSG in landfills.
## 2 Materials and Methods
### Data Sources  
**Forest type reference data.** The forest type reference data used in this study were obtained from the Huize County Forestry and Grassland Bureau, specifically from the Forest Management Inventory. The data contained over seventy attributes, such as survey time, management type, origin, dominant species, group area, and tree structure. These data provide an objective overview of the forest resources in the study area and assist in identifying forest resources. We obtained data for 2010 and 2020. As the vegetation growth is affected by seasonal changes, the satellite image also changes, so we further corrected the forest boundary of the selected satellite imagery through visual interpretation.  
**GF satellite imagery.** The spatial resolution of GF-1 Wide Field View (WFV) data was 16 m. Two images provided complete coverage of the study area. The images of GF-1 WFV in the study area can only be cropped to approximately 400 images of 256 \(\times\) 256, among which the class-balanced images can only filter out less than 20, so higher-resolution image of GF-2 is used for training. The selected GF-2 data were imaged on 28 July 2020, with  
Figure 1: Study area. Notes: (**a**) Chinese region, (**b**) Yunnan Province, (**c**) Huize County, Qujing City. The selected area is the GF-2 image area, and the full Huize County area is the GF-1 and Landsat TM/OLI image area. The data information is shown in Table 1.  
a spatial resolution of 1 m. To ensure time consistency and data comparability, we used 2 views of GF-1 WFV image data, both imaged on 27 August 2020. As of now, GF-2 has not fully covered the study area, so GF-1 WFV is the major data.  
**Landsat satellite imagery.** To achieve long-time monitoring, images from Landsat satellite were used for the period before 2013. To ensure time consistency and result comparability, Landsat images were used for the years after 2013 as a reference. Landsat Operational Land Imager (OLI) images were utilized for 2020, acquired on 29 July 2020. Initially, they had a spatial resolution of 30 m in the Red, Green, Blue, and NIR bands. Subsequent fusion of the PAN band was completed to scale up to a spatial resolution of 15 m. Landsat Thematic Mapper (TM) images from 19 May 2006 were chosen to validate the accuracy of the forest/non-forest and needleleaf/broadleaf forest segmentation results. The selected Landsat images in the study area had a Path of 129 and Rows of 41 and 42, which were later mosaicked with ENVI's seamless mosaic tool, and color-corrected using histogram matching.  
**Digital Elevation Model (DEM) data.** An ALOS PALSAR DEM with a spatial resolution of 12.5 m was selected as the morphological reference. Advanced Land Observing Satellite (ALOS) aimed to contribute to the fields of mapping, precise regional land cover observation, disaster monitoring, and resource surveys, with 12.5 m DEM data as one of its products. From 2006 to 2011, it provided comprehensive day-and-night and all-weather measurements. The data information is shown in Table 1.
## 7 Discussion and Open Questions
### Risk Analysis in Digital Twin-Based Validation Frameworks
#### 7.4.2 Traceability and Requirement Coverage Risks  
The complexity of requirements mapping across SiL, HiL, and DT environments poses risks related to incomplete or ambiguous test coverage. Insufficient traceability may lead to omission of critical functional or safety requirements during simulation, validation activities performed on outdated or unverified specifications, and misalignment between virtual test results and physical system behavior [45].  
Examples include the following:  
* Suppose a safety requirement mandates automatic dimming of high beams when oncoming traffic is detected at night. If this requirement is not properly linked to test cases in the HiL validation phase, the software controlling this function might be insufficiently tested, leading to failures not caught before vehicle release;
* If regulatory requirements for glare limits change late in the project, and the updated requirements are not propagated to all simulation environments, the test results may reflect outdated compliance criteria, risking certification delays or recalls.  
This risk emphasizes the need for robust requirements management frameworks that maintain end-to-end traceability [46], ensuring that all requirements are systematically addressed through corresponding test cases at appropriate abstraction levels.
## 4 Discussion  
This study first depicted the altered muscle activation, including the kinetic and kinematic characteristics, in repaired legs with an Achilles tendon rupture. Secondly, it confirmed our hypothesis regarding differences in neuromuscular recruitment strategies in the ankle joint between the involved and non-involved legs when performing the OLHR test in participants who are still recovering from an Achilles tendon rupture. The correlation results demonstrated that the muscle activations in the medial gastrocnemius and soleus muscles were respectively associated with the total ankle angle in the concentric phase and the negative work in the eccentric phase of the OLHR test. Furthermore, the kinematic and kinetic characteristics in the eccentric phase were correlated with the jumping performance. These observations indicated that the soleus muscle is involved in the compromised muscle force production and the storage of elastic potential energy and may affect the jumping performance in patients who have undergone a repair surgery within the past year. These observations suggest the advisability of supplementary muscle-specific rehabilitation for an Achilles tendon rupture in order to promote functional recovery. The comparisons between the repaired and non-injured leg groups showed that the repaired legs exhibited augmented muscle activation in the soleus and tibialis anterior muscles during the concentric and eccentric contractions (Table 2). This high antagonist EMG activity may result from alterations of the neuromotor links between joint mechanoreceptors and antagonists [22; 23]. Furthermore, reductions in the mechanical properties of the medial gastrocnemius muscle with shortened fascicles, as well as the lengthening of the Achilles tendon with low stiffness [6; 18], may also trigger the greater recruitment of the soleus muscle found in this study, which in turn augments the tibialis anterior muscle coactivation, in the legs with an Achilles rupture [24]. This augmented co-activation may result in decreases in kinematic and kinetic performance and characteristics (Figure 2a-d) in injured legs, including in the net joint force or GRF, during the OLHR test, although it \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Kinematic and Kinetic Data**} & **Spearman Rank Correlation Coefficients** \\ \hline Ankle joint (\({}^{\circ}\)) & \(-0.192\) (\(0.293\)) \\ Joint angular velocity (rad/s) & \(0.575\) (\(0.001\)) \({}^{\star}\) \\ Ground reaction force Fz (\(n\)) & \(-0.077\) (\(0.675\)) \\ Mechanical work (J) & \(-0.471\) (\(0.007\)) \({}^{\star}\) \\ \hline \hline \end{tabular} The Spearman correlation coefficients are presented as r\({}_{\text{s}}\) values, with the associated \(p\) values in parentheses. \({}^{\star}\) means significant.  
\end{table} 
Table 4: Correlations between the kinematic and kinetic characteristics (in the eccentric phase of the one-legged heel-raise exercise) and normalized one-legged hopping distance.
## 3 Results and Discussion
### Effect of Infill Pattern on Flexural Properties
#### 3.2.1 In-Plane Testing  
Results for five infill patterns, i.e., line, grid, quarter-cubic, triangular, and tri-hexagon, have been given in Figure 10 and Table 4. The results indicate that, in terms of maximum flexural load, all the infill patterns showed comparable values but the tri-hexagon pattern showed the highest among the tested i.e., 69.0 \(\pm\) 2.77 N; whereas, when the value for the flexural modulus is compared, it can again be observed that although the values are comparable, the triangular pattern has the highest value for the flexural modulus. A comparison based on the percentage difference between these two patterns indicates that a difference of 7% and 2% existed between these two patterns for flexural modulus and load, respectively. Moreover, the tri-hexagon pattern also showed superior strength under flexural loading, i.e., 36.7 \(\pm\) 1.22 MPa. Similarly, a difference of only 8.6% and 13% exists between the tri-hexagon and quarter-cubic pattern in terms of flexural load and modulus, respectively. However, it is worth noting that the performance of the infill pattern is highly subjected to the nature of the loading conditions, i.e., whether it is tensile, compressive, or  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Raster Angle** **(\(\mathbf{\circ}\))**} & **Max. Flexural** & **Flexural** & **Flexural** & \multirow{2}{*}{**Fracture Strain** **(\%)**} \\  & **Load** & **Modulus** & **Strength** & & **(\%)** \\  & **(N)** & **(GPa)** & **(MPa)** & & **(\%)** \\ \hline
0 & 73.2 \(\pm\) 1.36 & 1.61 \(\pm\) 0.01 & 51.9 \(\pm\) 0.97 & Fracture did not occur \\
90 & 67.1 \(\pm\) 2.37 & 1.51 \(\pm\) 0.02 & 47.6 \(\pm\) 1.68 & 5.8 \(\pm\) 0.18 \\
0/90 & 70.9 \(\pm\) 1.64 & 1.53 \(\pm\) 0.04 & 50.2 \(\pm\) 1.16 & 7.6 \(\pm\) 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for edgewise flexural properties.  
Figure 9: (**a**) Edgewise load extension and (**b**) edgewise stress–strain behavior for different raster angles; (**c**) comparison of edgewise flexural strength for different raster angles.  
bending. The pattern performing well for one loading condition may not give an adequate performance for another type of loading. Therefore, it can be safely said that the different infill patterns performed differently under distinct loading conditions.
## 4 Conclusions  
The paper highlighted the potential threats suffered as a result of drone misuse by civil society and why drone detection became essential. In an attempt to implement the DVB-S-based passive FSR system, this paper described the challenges derailing the use of a DVB-S satellite as an illuminator of opportunity for passive application, especially for an airborne target. Based on the FSR capability of detecting a very small signal variation, the passive FSR system was used and detected the quadcopter drone. After a pervasive summary of some implemented DVB-S-based radars, the Measat3 signal waveform was captured and be used for this work. Based on the two scenarios considered, we may be able to conclude that the drone was successfully detected. To further extract other features that help in identifying the drone, we employed EMD and decomposed the detected signal into an IMF for further feature extractions. Based on the extracted feature vectors, we may conclude that the DVB-S-based passive FSR system was successfully implemented and used to detect a low-profile, airborne target, i.e., a quadcopter drone. For now, the system achieved a detection altitude of up to 80 m above sea level, but can be improved by increasing the height of the receiving station. Future studies may include how these extracted vectors will be used for the classification of quadcopter drones from other types of drones and natural targets like birds.  
**Author Contributions:** [PERSON] created the main ideas, analyzed the results, and served as an advisor, [PERSON] performed the simulation and experimental work. [PERSON] and [PERSON] analyzed and validated the idea, concept, and results. [PERSON] and [PERSON] provide feedback on the content of the papers and supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Universiti Putra Malaysia Impactful Grant, grant number (UPM/700-2/1/GPB/2018/9598800). The APC was funded by Universit  
Figure 17: Satellite-based forward-scattering radar potential applications.
## 5 Smart Radio Environment (SRE) with IRS
### Achieving a SRE through IRS
#### 5.1.1 Use Cases of IRS for Future SRE  
Three main use cases of the IRS for future SRE are discussed below:  
* Coverage enhancement: By deploying an IRS in a wireless network, a virtual link can be created to overcome NLOS conditions to deliver better connectivity to the desired user.
* Interference suppression: An IRS can enhance the SINR by suppressing unwanted signals that may interfere with users in a communication network.
* Enhanced security: An IRS can create destructive interference at eavesdroppers or steer the signals to directions not occupied by unintended users and as a result, enhance the security.
* Indoor localization: An IRS can improve estimation of the location of mobile terminals and devices to achieve reliable radio localization and mapping.
* Information and power transfer: Can be achieved by using an IRS to get ambient EM waves and concentrating them toward low-power IoT devices and sensors to simultaneously transfer wireless information and recharge low power sensors and IoT devices [36].
## 4 Results and Discussion
### Economic Aspects of Energy Saving
Makkah, which has a hot arid desert climate, and Istanbul, with a temperate Mediterranean climate, have moderate cost savings of 6.46% and 5.09%, respectively, using HST0.75 + PP0.3 fibre-reinforced LECA-LWAC. Putrajaya, with its tropical hot-humid climate, has the least cost savings of 4.04% using HST0.75 + PP0.1 fibre-reinforced LECA-LWAC.
# Effect of Human Hair Fibers on the Performance of Concrete Incorporating High Dosage of Silica Fume
## 3 Results and Discussion
### SEM of Human Hair Fiber and Silica Fume  
The bulk density of SF varies between 130-430 Kg/m\({}^{3}\). When coal is burned at a temperature of 1600 \({}^{\circ}\)C (2912 \({}^{\circ}\)F), SF is produced. It's divided into two classes by ASTM [29]. SF, Class F, is a siliceous substance with pozzolanic properties that are widely utilized. To further understand the microstructure of the HHFC, a scanning electronic microscopy (SEM) examination was performed, as shown in Figures 10 and 11. It aided in  
Figure 10: SEM of human hair fiber concrete.  
Figure 9: (a) 3D surface diagram for split tensile strength; (b) 2D contour for split tensile strength.  
the comprehension of C-S-H formation mechanisms as well as the identification of flaws in the interfacial transition zone (ITZ), voids, and internal micro porosity. Both with HHFC and SF augmented concrete, SEM data in Figure 10 shows evidence of C-S-H formation, indicating satisfactory hydration. However, the composition in Figure 11 appears to be denser, which could be owing to increased C-S-H formation. Concrete performance was improved by both cement-based C-S-H and SF-based C-S-H forms. The internal spaces formed by free voids of unhydrated cement particles were filled with the human hair fiber, which improved the durability of the concrete matrix. Cement and SF-based C-S-H assist in the densification of concrete mortar, while HHF aids in the filling of voids, improving the overall durability of concrete. To avoid agglomeration, choosing the right fiber ratio is crucial. A ratio of 2% human hair fiber and 15% SF by weight is recommended based on the test results of the current study.