esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Use of Agro-Wastes in Concrete Production
### Contributions of Palm Oil Fuel and Rice Husk Ash  
A PO fuel Ash and RHA, other AWs used as supplementary cementitious materials in concrete production, can significantly enhance concrete's compressive strength. While studies indicate that calcined PO fuel Ash (heated to 600 \({}^{\circ}\)C for two hours) can significantly improve compressive strength, limited research is available regarding the specific effects of raw PO fuel Ash and raw RHA on compressive strength. However, studies such as [44] have shown that calcination significantly enhances the performance of these materials. This increase is attributed to higher amorphous silica content and reduced loss on ignition (LOI) due to heat treatment [44,45].  
Similarly, using finely ground RHA as a supplementary cementitious material increased the compressive strength of the concrete compared to control concrete. Optimal compressive strength for RHA concrete was observed at a 20% substitution rate, attributed to increased pozzolanic activity and more nucleation sites [46,47]. Higher RHA content, however, resulted in strength reduction due to a dilution effect. Substituting Ordinary Portland cement with 20% PO fuel Ash increased compressive strength by 10.8%, while adding 20% RHA improved strength by 11.9% compared to reference concrete [44,45,46,47].  
Concrete produced using PO shell aggregate is classified as lightweight. After 28 days of air drying, this concrete's density, approximately 1960 kg/m\({}^{3}\), is 18% lighter than normal-weight concrete (2400 kg/m\({}^{3}\)). Additionally, the compressive strength obtained after 28 days exceeds the 17 MPa minimum required for structural lightweight concrete by 35-65% [48].  
In another study, PO shell was investigated as an aggregate for lightweight concrete, with 28-day compressive strength tests conducted with and without limestone powder. Results demonstrated compressive strengths of approximately 43-48 MPa and dry densities of about 1870-1990 kg/m\({}^{3}\). The addition of limestone powder improves particle packing density, reduces porosity, and enhances compressive strength through its filler effect and role as a nucleation site for hydration reactions. These improvements contribute to the observed differences in strength and density between the two cases [49].  
Studies on PO shell, PO shell Ash, and PO clinker in concrete production have shown that green lightweight concrete positively impacts workability, density, compressive strength, split tensile strength, flexural strength, stress-strain curve, modulus of elasticity, water absorption, and shrinkage. These findings suggest that using high-volume PO waste as lightweight aggregates supports environmentally friendly structural lightweight concrete production. This approach can also contribute to sustainable development in the construction and agriculture sectors of PO-producing countries [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61].
# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Speech Emotion Recognition
#### 2.2.1 Corpora  
Since speech emotion recognition is basically a classification problem, emotional speech corpora play an integral part in the performance of such a system. Nevertheless, obtaining such corpora is difficult. Unlike speech recognition, participants have to either mimic an emotion or record an actual one and manually annotate each entry. Hence, there are three types of datasets: simulated, induced, and natural. Simulated emotional speech corpora such as IEMOCAP [17], EMODB [18], Toronto Emotional Speech Set [19], Danish Emotional Speech (DES) [20], and Italian Emotional Speech Database (EMOVO) [21] are created by actors (professional or amateur). Induced ones, such as eNTERFACE'05 Audio-Visual Emotion Database [22] are recorded while emotion is triggered by some external stimulus. Natural emotions corpora (e.g., AFEW-VA Database [23]) are extracted from real-life recordings.  
Our choice for the ODIN112 system is the only available Romanian corpus, EmoIIT [24]. We also trained and tested the developed classifier using the EmoDB [18] database to compare our implemented classifier with other published results.  
EmoIIT [24] is an emotional speech corpus recorded in Romanian. It is similar in structure to EmoDB, and, to our knowledge, the only Romanian emotional speech corpus available. The speakers are amateur actors, ages 20 to 22 years old, mostly students. The dataset contains 523 recordings, split between 7 emotions: anger, boredom, fear, happiness, sadness, disgust, and neutral.  
The Berlin Emotional Speech (EmoDB) [18] dataset is an emotional speech corpus created by the Institute of Communication Sciences, Technical University, Berlin, Germany. It was recorded by ten professional actors-five men and five women, in German. The EmoDB dataset contains a total of 535 records representing 7 emotions: anger, boredom, fear, happiness, sadness, disgust, and neutral. Recordings were made at a sampling rate of 48 kHz and then down-sampled to 16 kHz.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 5 Results and Discussion
### Goodput  
The number of useful data bytes handed over to the application layer by the transport layer in a unit time is called goodput. Goodput is computed by using Equation (5).  
\[\text{Goodput}=(\text{Sent Data}-\text{Retransmitted Data})/\text{Transfer Time} \tag{5}\]  
End-to-end goodputs of the protocols are shown in Table 3. All these three protocols have a common feature that is multistream except TCP New Reno. With this feature, these protocols can handle multiple independent requests of their applications. The multistream feature resolves the issue of head-of-line blocking and provides data to the application during the period of fast retransmit and recovery. TCP and all its variants stop data delivery to the application layer during the period of packet loss until the receipt of the lost packet. MFast TCP and EMFast TCP overcome this limitation of TCP. It is quite clear from Table 3 that MFast TCP and EMFast TCP provide better performance compared to SCTP and TCP New Reno, whereas EMFast TCP due to CCaFR algorithm further improves the performance, as compared to MFast TCP.  
It is necessary to mention that Fast TCP without the multistream feature, extensively utilizes the receiver buffer during the fast-retransmit and -recovery phases. MFast TCP takes significant time to recover its cwnd after the fast-recovery phase whereas the EMFast TCP overcomes this issue through its CCaFR algorithm, and its impact can be seen in the end-to-end goodput.
## 3 Results and Discussion
### Limitations and Future Work  
In urbanized regions, implementing a robust stormwater management system is critical to mitigating hydrological risks. The proposed framework, founded on an optimized multi-criteria decision-making platform, offers a significant advancement by harmonizing economic, reliability, and resilience factors throughout the infrastructure lifecycle. Notably, it addresses uncertainties associated with hydrological resilience, enhancing urban flood resilience evaluation and adaptability to climate change. This framework represents a valuable tool in designing resilient and sustainable urban stormwater management systems.  
However, several limitations warrant acknowledgment. The resilience assessment employed in this study relies on a simplistic exponential distribution function to approximate the failure probability of GREI. While this approach provides a foundational baseline for analysis, it cannot capture the complexity of real-world scenarios involving numerous parameters and dynamic interactions. Similarly, the design storms used in this study ensure consistency and control over variables, but may not reflect the variability of actual rainfall and runoff patterns. Future research should incorporate empirical rainfall and runoff data to improve the accuracy and applicability of the framework under diverse conditions.  
The current framework primarily considers BCs and PPs as representative examples of GI. While suitable for the study context, this limited selection may restrict the framework's generalizability to other urban environments with diverse GI options. Expanding the framework to include additional GI types, tailored to varying local contexts, would enhance its versatility and applicability.  
Furthermore, the optimization process leverages a genetic algorithm with a predominant focus on economic performance. Although effective in balancing costs and resilience, this approach may not fully address multifaceted real-world challenges, such as non-point source pollution or additional environmental and social indicators. Future work should explore more sophisticated optimization algorithms capable of addressing complex multi-objective problems, integrating factors such as water quality, ecosystem services, and community well-being.
## 2 Materials and Methods
### Study Areas and Datasets
#### 2.1.1 Case 1: Anbandegi in Korea  
The first case study area is Anbandegi in Korea, one of the major highland Kimchi cabbage cultivation areas in Korea [7]. The crops cultivated in the study area include highland Kimchi cabbage, cabbage, and potato, and several fallow parcels exist. As depicted in Figure 1a, each crop is cultivated within a separate parcel.  
Figure 1: Datasets used for crop classification in Anbandegi: (**a**) true color unmanned aerial vehicle (UAV) imagery; (**b**) ground truth map; and (**c**) field photos.  
The total area of all crop parcels is approximately 28.7 ha, and crops are cultivated in small size parcels. Considering the small-scale of the study area, unmanned aerial vehicle (UAV) imagery was used as input for crop classification. One preprocessed UAV image with a spatial resolution of 25 cm, acquired on 25 August 2017, was provided by the National Institute of Agricultural Sciences (NAAS). The single UAV imagery obtained when highland Kimchi cabbage, the major crop in the study area, could be well discriminated from other crops was used for classification based on our previous study [7]. The UAV imagery was taken using a fixed-wing eBee unmanned aerial system (senseFly, Cheseaux-sur-Lausanne, Switzerland) equipped with a Canon IXUS/ELPH camera (Canon U.S.A., Inc., Melville, NY, USA) that included Blue (450 nm), Green (520 nm), and Red (660 nm) spectral bands (Table 1). A ground truth map prepared by the NAAS was employed to select training and reference samples and compute the class purity. Non-crop areas, including forests, roads, and facilities, were masked out using a land-cover map from the Ministry of Environment [36].
## 3 Data and Methods
### DEM Generation with the Ames Stereo Pipeline
#### 3.2.3 Example of ASP Processing and Outputs
This results in orthorectified SPOT6 images in a WGS84 decimal degree projection with pixel resolutions of 1.5 m, equivalent to SPOT6 Level 2A data. 8. The map-projected scenes are run pairwise (AC, AB, BC) through parallel_stereo using all tested algorithms and parameters. Each parallel_stereo output is converted to a 3 m DEM (or 6, 12, and 24 m for the BM-ck25 output) with point2 dem. The majority of the processing in Steps 1-7 insure the best possible map-projection of the images. Improved map-projection results in improved disparity searching and best-possible results in the final SPOT6 DEMs, which can be iteratively generated for many correlation algorithms and parameters in Step 8. Given the large size of the SPOT6 scenes (2700 km\({}^{2}\)), all processing was carried out on a Linux server with 80 CPUs and 1 TB of RAM. Typical processing times for our \(\sim\)1.5 GB scenes were 10 to 20 h. Processing can be carried out on less powerful machines, but this requires the user to reduce the ASP tile size and memory limits (the corr-tile-size and corr-memory-limit-mb arguments passed to parallel_stereo) and would lead to longer computation times. In addition to DEMs output by point2 dem, we also retrieve the triangulation error map (a.k.a. the intersection error). The intersection error provides an indirect, relative metric of DEM quality at every pixel, measured as the shortest distance between the rays emanating from the pixel to each camera, with lower values indicating higher quality matching [21; 24]. At any pixel where the intersection error exceeds the input image resolution (1.5 m), we constrain the point2 dem tool to place a no data hole and do not interpolate these holes in our results. This is to avoid any interpolation artifacts and focus our results on the differences in stereo-correlation algorithms and parameters--including the number of holes.
## 1 Introduction
Because software is used in various fields and its structure has become very complex, software failure cases have become more diverse, such as when a software failure is caused by the dependent effects of other failures [16; 17] or the difference between the actual and developed operating environments [18; 19; 20; 21; 22]. Therefore, research on software reliability models that consider various assumptions, such as dependent failures and operating environment uncertainties, has been continuously conducted. Recent studies have assumed that software test environments are not consistent or ideal and include uncertain factors such as test effort, test coverage, and differences in hardware and software configurations [23; 24]. In addition, studies have been conducted to improve reliability through the relationship with the hardware to which it is applied, rather than by applying it in the isolation of software failures [25]. In this study, we extended the NHPP software reliability model to failures arising from complex software, thereby proposing an NHPP software reliability model that considers both operating environment uncertainty and dependent failure occurrence. Previously, proposed software reliability models were based on a single assumption. However, because software failures can be caused by various factors, we propose a software reliability model that includes the assumptions of dependent failure occurrence. This describes when failures affect each other, as well as the operating environment uncertainty caused by the difference between the development and operating environments. In addition, the software reliability was evaluated using various methods to determine the goodness of fit. However, because various criteria have different characteristics, evaluating excellence using only a few values is infeasible [26; 27; 28]. Therefore, we use a multi-criteria decision-making method (\(MCDM\)) for a comprehensive evaluation, demonstrating the effectiveness of the proposed model through the new \(MCDM\), which reflects the size of the criteria for all models to be compared. In summary, the contributions of this study are as follows:  
* This study proposes an NHPP software reliability model that considers both the uncertainty of the operating environment and dependent faults. * This study demonstrates the superiority of the proposed model through \(MCDM\) using the maximum value to approach comprehensive assessment and multi-faceted interpretation. The remainder of this paper is organized as follows: Section 2 introduces the general NHPP software reliability model and the new NHPP software that considers dependent failures and operating environment uncertainties. Section 3 introduces the criteria and \(MCDM\)s used for model comparison and proposes a new \(MCDM\). Section 4 presents the data and fit, along with numerical example results. Finally, Section 5 concludes the study.
## 4 Analytical Prediction of Short- and Long-Term Strengths under Conventional Triaxial Compression
### Analytical Prediction  
Figure 6 shows the analytical prediction of short-term mechanical response under some classical confining pressures on Beishan granite. [PERSON] et al. [49] had presented the detailed analytical prediction of short-term mechanical behaviors of Beishan granite under all different confining pressures. It is observed that model prediction can correctly capture the typical short-term mechanical characteristics such as the material hardening/softening, confining pressure dependency and volumetric dilatancy, etc. More precisely, the residual strength of model simulation is lager than experimental data due to that the model parameter: friction coefficient \(\eta\) keep unchanged in mechanical loading. [PERSON] et al. [37] gave the residual friction coefficient \(\eta_{I}\) to describe the slope of residual strength envelop, from which is considered the degradation of friction coefficient of crack surfaces.  
Before carrying out the analytical solutions of time-dependent tests, the influence of the time-dependent parameters \(\varGamma\) involved in Equation (14) has been discussed. The model parameter \(\varGamma\) has an obvious influence on creep duration time and transitional velocity from the stage I to stage II of relaxation as shown in Figure 7. The tests were carried out in a thermally isolated small room with a constant temperature of \(20\pm 0.5\)\({}^{\circ}\)C maintained during the experiment because the time-dependent tests are highly influenced by the temperature. Therefore, the time-dependent parameter \(\varGamma\) only influenced by microstructure of material, which is related to the loading history [48]. In this paper, different values of \(\varGamma\) are adopted for achieving satisfactory simulations due to the effect of loading history.  
Analytical prediction of compression time-dependent tests are illustrated and compared with experimental data on Beishan granite under confining pressure \(p_{c}=15\) MPa, as shown in Figures 8 and 9. It is can be seen that the analytical prediction is in good agreement with experimental data on creep strain, relaxation stress, its duration time, and long-term strength of creep and relaxation experiments.  
Figure 6: Comparison between test data and analytical simulation of stress–strain curves from conventional triaxial compression tests on Beishan granite under classical confining pressures. (**a**) \(\sigma_{3}=2\) MPa; (**b**) \(\sigma_{3}=5\) MPa; (**c**) \(\sigma_{3}=25\) MPa; (**d**) \(\sigma_{3}=30\) MPa.  
Figure 8: Analytical prediction of creep tests on Beishan granite with different initial deviatoric stress under confining pressure \(p_{c}=15\) MPa. (**a**) \(q=225.7\) MPa; (**b**) \(q=228\) MPa; (**c**) \(q=232.5\) MPa.  
Figure 7: Parametric studies on the influence of the parameters \(\Gamma\). (**a**) Creep test; (**b**) Relaxation test.
# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 3 Proposed Method
### End-to-End Optimization of the Networks  
**Identity classification task**. Through the feature decomposition module, we obtain the identity-specific feature map \(\chi_{id}\). Then, the \(\chi_{id}\) is translated into an identity-specific feature, \(x_{id}\), at the output layer for use as the age-invariant feature in the final cross-age face verification. To enhance the discriminative identity power, CurricularFace loss [11], which has been successfully applied to boost face recognition performance, is used to supervise the learning of \(x_{id}\) and to ensure identity-preserving information. The CurricularFace loss is represented in Equations (4) and (5).  
\[L_{Curr\_face}=-\frac{1}{N}\sum_{i=1}^{N}\log\frac{e^{s(cos(\theta_{gi}+m))}}{ e^{s(cos(\theta_{gi}+m))}+\sum_{j=1,j\
eq yi}^{n}e^{s\mathbb{I}(t,cos\theta_{j})}} \tag{4}\]  
\[\begin{split}\text{I}\big{(}t,cos\theta_{j}\big{)}& =\begin{cases}cos\theta_{j},&cos(\theta_{yi}+m)-cos\theta_{j} \geq 0\\ cos\theta_{j}(t+cos\theta_{j}),&cos(\theta_{yi}+m)-cos\theta_{j}<0\end{cases}\end{split} \tag{5}\]  
Figure 5: The flow chart of the training process. The whole loss is obtained through the identity classifier, age classifier, and direct sum modules.  
where \(N\) is the number of training samples, \(x_{i}\) is the \(i\)th feature vector corresponding to the ground-truth class of \(y_{i}\), \(\theta_{j}\) is the angle between the normalized features of the prototype corresponding to the \(j\)th identity and \(x_{i}\), the hyperparameter \(s\) determines the radius of the mapped hypersphere, and \(m\) controls the cosine margin. The value of \(t\) indicates the model training stage. CurricularFace loss explores the discrepancy between the real identity and the predicted identity from the identity classification task.  
**Age classification task.** Following previous work [14], the faces of different ages are divided into seven groups: ages 0-20, 21-25, 26-30, 31-40, 41-50, 51-60, and 61 or older. We use an auxiliary age discriminator to guide the decomposition procedure and find intrinsic clues for age information. SoftMax loss is widely used in existing face recognition for the ground truth identity. We use a SoftMax function with cross-entropy loss as a loss function in the age classification task to guide the predicted age approach to the actual age, which is represented in Equation (6).  
\[L_{age}=-\frac{1}{N}\sum_{i=1}^{N}logp_{i} \tag{6}\]  
where \(p_{i}\) is the predicted probability that input \(x_{i}\) belongs to the correct age group.  
**Complete loss and training algorithm.** These three losses (direct sum, CurricularFace, and age) are combined into a multi-task loss for joint optimization, as given in Equation (7).  
\[L_{total}=L_{Curr\_Face}+\lambda_{1}L_{direct\_sum}+\lambda_{2}L_{age} \tag{7}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are scalar hyperparameters to balance the three losses. Both weights \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.01 after the experimental analysis. The model training process is summarized Figure 5.