esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PLSR Correlations between Chemical Composition and Sensory Perception
#### 3.4.2 Frozen Samples
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [36] identified 2-nonanone as one the volatiles that contributed positively and significantly to the flavour of lamb, while pyrazine volatiles have been associated with roasted meat odour in lamb meat [27]. Browned and livery had high negative loadings along Dimension 1 and were associated with flavour volatiles like heptanal, 2-ethylfuran, pyridine, dimethyl disulphide, dimethyl trisulphide, and 3,5-diethyl-2-methyl pyrazine. Heptanal is of importance in cooked beef flavour and may impart a pleasant fruity flavour contributing towards the perception of browned flavour [12]. Additionally, 2-ethylfuran are formed from the degradation of lipids; although they are sometimes regarded as markers of lipid oxidation, they often contribute towards what is described as burnt, sweet, coffeelike flavour associated with browned flavour [58]. In contrast, dimethyl disulphide and dimethyl trisulphide are often described as sulphuric and unpleasant off flavours [59] which may explain the increased perception of livery flavours in beef [60]. Oxdised had high positive loadings along Dimension 2, and was mainly associated with the presence of amino acids such as threonine, phenylalanine, isoleucine, tyrosine, and methionine. Aromatic amino acids (phenylalanine, tyrosine, tryptophan, and histidine), especially tryptophan, showed high levels of oxidation that can contribute to oxidation flavour in dry-cured hams [61]. In addition, methionine can be easily oxidised to methanol or methanethiol and has been found to be associated with off-odour and oxidised flavours in meat [62].
## 4 Results
### Model Evaluation
#### 4.3.2. LSTM-RNN  
Figure 8 and Table 10 show the outcomes obtained when employing the LSTM-RNN algorithm for forecasting the greenhouse's internal temperature with a one-hour lead time.  
Figure 7: Results for one hour internal temperature prediction using XGBoost. Prediction for fall (**a**), winter (**b**), spring (**c**), and summer (**d**).  
The LSTM-RNN model outperformed XGBoost in all seasons, with the most significant improvement observed during summer (Figure 8d), where XGBoost had the lowest R\({}^{2}\) among all experiments. Additionally, the MAE metric showed a hysteresis below 2 \({}^{\circ}\)C in  
Figure 8: Results in internal temperature prediction using LSTM-RNN. Prediction for fall (**a**), winter (**b**), spring (**c**), and summer (**d**).  
each season. These experiments demonstrate that the LSTM-RNN model is more effective than XGBoost for this particular application.  
Figure 9 compares the metrics of the proposed models with those of the year's seasons, highlighting the differences.
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
###### Abstract  
The waste generated by the brewing industry, particularly brewer's spent grain (BSG) and wastewater, presents challenges for sustainable management practices. While BSG is traditionally utilized as cattle feed, this option is not universally accessible. This study considered the environmental impact of a novel, laboratory-based process for converting BSG into biochar that also utilizes brewing wastewater, as compared to disposing of BSG and cleaning chemical wastewater. The study employed a carbon footprint assessment approach to quantify the greenhouse gas (GHG) emissions associated with each disposal method, using one unprocessed kg of BSG as the functional unit. The results indicated that landfilling BSG generated approximately 3 kg CO\({}_{2}\) equivalent (CO\({}_{2}\)e) per kg of unprocessed BSG, whereas biochar production reduced emissions to 1.18 kg CO\({}_{2}\)e per kg of BSG. The study concluded that diverting BSG from landfills to biochar production presents a viable strategy for minimizing environmental impacts associated with BSG disposal. However, several factors must be considered in the development of a biochar production facility, including biochar transportation. These elements may contribute more GHG emissions than landfilling if not properly designed.  
greenhouse gas assessment; brewer's spent grain (BSG); biochar; circular economy +
Footnote †: journal: Applied Sciences
## 5 Experiment Evaluation
### Extra Bandwidth Consumption  
The peers in the P2P-based VoD system require auxiliary information to prefetch data, and the bandwidth consumed by the auxiliary information is the extra bandwidth. To prefetch data for the peers using the anchor-prefetching strategy or the DSA strategy, two extra fields must be added to the index file: the anchor length and the list of starting segment numbers for each anchor. When a peer begins watching an on-demand video, the index file from the server or peers must first be obtained and locally saved. Then, the peer requests the segments according to the index file and the user's playing request. Therefore, to implement these prefetching strategies, one peer must only transmit two additional fields.  
However, the API strategy prefetches both anchors and popular segments. To achieve this prefetching strategy, adding the two fields of the anchor length and starting segment number for each anchor to the index file is not sufficient, as previously mentioned. To acquire the popular segments of the current system, the server must perceive which segment of the video is currently played by each online peer. To satisfy this requirement, two information fields (the video ID and currently played video segment) should be added to the heartbeat packet, which is periodically sent by peers. The heartbeat packet is a collection of real-time basic information of the peers that must be periodically uploaded to the tracker server (TS) by online peers. The server manages the peers through the heartbeat packet information. A peer sends a heartbeat packet every 30 s by default.  
When an online peer watches a 60 min video, we assume that the user watches the video for a total of 30 min over a series of VCR operations. The extra bandwidth required for the three strategies is shown in Figure 8.  
In the API strategy, the peer's playing information must be added to the periodically sent heartbeat packet, which consumes 20-30 times more bandwidth than the other two strategies. However, the anchor-prefetching strategy and proposed DSA strategy in this paper consume notably little extra bandwidth for each video, which is within the acceptable range.  
When tens of thousands of online peers watch thousands of on-demand videos in the P2P-based VoD system, the extra bandwidth consumed by the API sharply increases compared to the other two strategies. In other words, the implementation of the API causes a significant system burden. Therefore, the API has poor practicality.  
Figure 8: Extra bandwidth consumption of different strategies.
# Seismogram Rearrangement as a Perspective Basis for Defining New Attributes
## 4 Discussion
### Is Seismogram Randomness Suitable for Its Statistics?  
Seismogram is the product of random processes, and as such, it is formally a random variable. If the seismogram's complexity is defined by the number of EFs (one attribute more), it does not always provide sufficient statistical \"mass\" for getting reliable statistical characteristics and conclusions, even with the large number of SFs. Moreover, the waveform complexity is very different in various recordings (acceleration, velocity, displacement) of the same event, even at the same site, and also depends on filtering conditions [7] \"article 1.01, p. 22, Figure 13\". We considered the usual file sizes of earthquakes in databases [33], with mostly 5000-10,000 amplitudes (numerical values), taken at the rate of 0.01 s.  
Although it seems to be a sufficient statistical sample, these 5000 values are not 5000 independent but considerably correlated, yielding effectively a few hundred of practically really independent ones, and, hence, suitable for statistical conclusions.  
Another constraint is that we seek for the attributes to be taken from individual seismograms, not so much from a bunch of them, not to speak of a statistical ensemble of seismograms, since the earthquake is an event per se, expressing \"individuality\", i.e., it is \"one-shot\" event.  
The confirmation of the fact that it is not easy to cluster earthquakes, one can simply find classification schemes in earthquake databases, for example, the PEER database [33]. It is enough to see under which criteria they are grouped, although it is the result of tremendous experience. This issue is considered in more details in [10].  
There is a weak homogeneity in the earthquake population. The seismograms of the same event, of the same component, taken at sites with almost equal distances from the hypocenter, show considerable variations in waveform caused by different propagation conditions between fault-site, so that they arrive at a station carrying different stochastics.  
Simply, seismograms are too non-stationary events, truly transients, to be suitable for tight clustering. Seismic arrays [8] \"Ch. 9\" may effectively bridge such gap, but the fact of inhomogeneity remains in power. In this aspect, see the elaboration about the random bipartite graphs as a means for organizing seismic database [10].
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 6 Results
### Experimental Results
Since the probability of decoding the symbols successfully is above 99.6% for the three distance's ranges, the experiment demonstrated that the modulation scheme is functional, even when only 1.5 light bands are extracted from the frame. Additionally, the BER results for three consecutive frames are comparable to the outcomes presented in [29; 30], which are considered viable modulations schemes. Since each transmitted symbol contains two bits, the throughput of the system is \(f_{\mathrm{CAM}}*2/3=16.67\) bps.
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Baseline Soil Erosion by Land Use and Land Cover  
From the study, croplands which mostly occupy areas with a high mean annual precipitation (central highlands and Lake Victoria parts of the country), had the highest mean erosion rates of 17.86 t ha\({}^{-1}\) yr\({}^{-1}\) (Table 5). This was followed by Barelands and sparsely vegetated lands in the northern ASALs, which had mean erosion rates of 3.39 t ha\({}^{-1}\) yr\({}^{-1}\) and 2.62 t ha\({}^{-1}\) yr\({}^{-1}\), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Erosion Class** & **Area (t ha\({}^{-1}\) yr\({}^{-1}\))** & **Mean Annual Rainfall Erosivity** & **Mean Annual Soil Loss** \\
**(d ha\({}^{-1}\) yr\({}^{-1}\))** & **(10\({}^{4}\) ha)** & **(MARE) (Baseline)** & **Rate (MASLR) (Baseline)** \\  & & & **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline
0–5 & Slight & 4689.2 & 1263.7 & 0.98 \\
5–10 & Moderate & 431.4 & 3472.2 & 7.06 \\
10–20 & High & 301.9 & 4690.9 & 14.08 \\
20–40 & Very High & 194.6 & 6120.1 & 27.97 \\
40–80 & Severe & 130.1 & 7171.9 & 55.83 \\ \(>\)80 & Very Severe & 79.3 & 8850.0 & 127.44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Baseline soil erosion rates per severity classes in Kenya.  
Figure 9: (**a**) Spatial distribution of the mean baseline annual soil erosion rate; (**b**) Baseline erosivity density distribution.
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## Appendix A Anomaly-Based Heterogeneous Intrusion Detection
### Signature-Based Heterogeneous Intrusion Detection  
Signature-based heterogeneous IDS rely on predefined rule sets to identify known attack patterns. By offloading string matching and regular expression operations to ASIC, FPGA, or other hardware platforms, these systems directly filter non-matching traffic, alleviating CPU-processing burdens. [PERSON] et al. (2019) introduced an FPGA-based IDS offloading system that comprehensively delegates NIC operations, rule pattern matching, and traffic classification, enabling dual detection mechanisms (signature-based regular expression processing and anomaly-based traffic classification) for comprehensive attack protection. [PERSON] et al. (2019) proposed \"Gnort\", a GPU + CPU IDS that offloads computationally intensive pattern-matching tasks to underutilized GPU resources, achieving higher system throughput when combined with Snort. [PERSON] et al. (2020) presented an FPGA-prioritized design that centralizes traffic reception, processing, and control in FPGA, achieving 100 Gbps throughput with 10K rules and 100K concurrent flows through flow table reorganization and multi-string matching optimizations.  
As illustrated in Table 2, signature-based heterogeneous intrusion detection systems demonstrate superior performance in detection accuracy and throughput compared to anomaly-based heterogeneous intrusion detection systems. To mitigate their inherent limitation of solely detecting known attack patterns, this deficiency can be effectively compensated through enhanced rule set update frequency and expanded rule set coverage. Consequently, this study adopts a signature-based heterogeneous computing architecture for the intrusion detection system design. At the implementation level, we integrate the header matching and fast-pattern matching methodologies derived from Snort3's design philosophy. By implementing hardware-accelerated deployment on an FPGA (Field-Programmable Gate Array) platform, the proposed architecture achieves significant improvements in system throughput and rule set scalability.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**System Type** & **Advantages** & **Disadvantages** \\ \hline Anomaly-Based & High accuracy, allowing for the detection of unknown attacks; The algorithm design is more flexible and can adapt to different anomaly scenarios. & High false positive rate, The model is solely dependent on the sample database and algorithm design, resulting in significant fluctuations in performance. \\ Signature-Based & High accuracy; Low false detection rate; Large throughput. & Only attacks for which the rule is known can be detected, and performance depends on the size of the rule set and the frequency of updates. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of heterogeneous intrusion detection systems based on anomalies and signatures.
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 3 Materials and Methods
### Viewport Pixel Quadtree Index Structure
#### 3.2.1 Structure Design of VPQ  
Quadtree divides spatial regions from two spatial dimensions into four equal rectangular subregions; then, it divides each subregion into four recursive subregions until the set depth is reached [25]. In order to be compatible with online geographic information applications, we take the Web Mercator projection [26] of global geographic space as the description space of geographic vector data, and perform the recursive decomposition of the spatial range based on quadtree. The projection takes the equator as the central latitude, the prime meridian as the central longitude, and the intersection of the two lines as the origin, and the longitude lines are parallel to each other and equally spaced. The projection does not include part of the north and south poles, and the overall projection area is square to facilitate the use of quadtree organization.  
In the design of index structure, if the spatial range of the index node is aligned with the spatial range represented by the pixel, the spatial range that affects the pixel value can be quickly retrieved and the value generated. Accordingly, the index structure of VPQ is shown in Figure 2. The whole VPQ is composed of isomorphic index nodes. Each index node contains five parts:  
1. _GeoCoding_: The unique identification coding of the node. In order to speed up the spatial intersection calculation efficiency, the node coding adopts Geohash coding.
2. _BBOX_: The spatial range of the node.
3. _Count_: The number of geographical vector features contained in the node.
4. _Cnodes_: Pointers to the four children of the node.
5. _Level_: The depth of the node in VPQ.  
Figure 1: Viewport generalization visualization model of geographic vector data.  
The index range of the root node of VPQ is the global geographic spatial range, and also serves as the general entrance of spatial range queries corresponding to the pixel. For branch nodes, if the four subregions of the node spatial range contain geographic vector features, the left-upper node (LUNode), right-upper node (RUNode), left-bottom node (LBNode), and right-bottom node (RBNode) pointing to the upper left, upper right, lower left, and lower right regions, respectively, of the spatial range of the parent node are created after division, and are arranged in accordance with the z-order spatial filling curve. For leaf nodes, if the node exists, this means that the node contains geographic vector features.
## 3 Used Datasets and Training Tricks
### Gradient Descent Optimization Algorithms  
Because the data are iteratively fed to the algorithm in batches instead of all at once, a gradient optimization method must be employed for the algorithm to be able to learn. Perhaps one of the most popular and simplest algorithms in this category is the Stochastic Gradient Descent, which uses the gradient and a parameter called learning rate to update the weights of the model. The equation of this method is shown below:  
\[\theta_{t}=\theta_{t-1}-\eta\ g_{t}, \tag{1}\]  
Figure 5: Samples from the Mobility Aids dataset [47]. (**a**) Scene from a large room with various actors walking around. (**b**) Scene of a narrow hallway featuring occlusion and people who are easy to misclassify.  
Figure 6: Samples from the MOT17 Det dataset [48; 49; 50]. (**a**) Sample from MOT17-09 that contains images taken from outside a shop alley. (**b**) Sample from MOT17-11 which contains images taken from inside a shopping mall.  
where \(\theta_{t}\) is the weight to be updated, \(\eta\) is the learning rate and \(g_{t}\) is the gradient computed at the current step.  
Further advancements have been made to this algorithm, such as the addition of momentum, which smoothens the gradient when close to a local optimum point, or the Nesterov accelerated gradient, which is a momentum-based gradient method that also considers where the parameter would be after the update and adapts accordingly.  
There are also adaptive gradient methods, a frequently used one being Adam [52]. However, as noted by [PERSON] et al. [53], the weight decay regularization employed is not equivalent to the L2 regularization as many people think, but is modified during the optimization steps. To solve this problem for the adaptive methods, [PERSON] et al. [53] proposed AdamW, in which the weight decay step is decoupled from the optimization step. Algorithm 1 shows the differences between the two methods, whilst Table 1 shows the meaning of the parameters used, as well as the default values they are initialized with.  
```
1. Repeat until stopping criterion is met.
1. Increment step time: \(t\gets t+1\)
2. Update gradients: \(g_{t}\leftarrow\
abla f_{t}(\theta_{t-1})+\lambda\theta_{t-1}\)
3. Compute the first moment vector: \(m_{t}\gets\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}\)
4. Compute the second moment vector: \(v_{t}\gets\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\)
5. Correct first moment bias: \(\hat{m}_{t}\gets m_{t}/(1-\beta_{1}^{t})\)
6. Correct second moment bias: \(\hat{v}_{t}\gets v_{t}/(1-\beta_{2}^{t})\)
7. Update learning rate: \(\eta_{t}\gets SetScheduleMultiplier(t)\)
8. Update parameters: \(\theta_{t}\leftarrow\theta_{t-1}-\eta_{t}\big{(}\alpha\hat{m}_{t}/\big{(} \sqrt{\hat{v}_{t}+\epsilon}\big{)}+\lambda\theta_{t-1}\big{)}\)
```  
**Algorithm 1**Pseudocode Adapted from [1, 54]. The Code Written in Red is Used only by Adam with L2 Regularization, While the One Written in Green Is only for [PERSON].
## 5 Discussions and Future Directions
### Challenges and Future Directions for ANN in CM
#### 5.2.2 Data Availability  
ANN is an effective tool for data mining. However, most ANN research focused on developing and applying ANN models in the CM field but ignored streamlined data collection and processing. According to [PERSON], et al. [45], tremendous time spent on data mining work usually consists in preparing the data. Figure 7 shows the six types of data sources used in existing articles. Historical project files are the most extensive data source, followed by online databases. A careful review of the selected article reveals most articles used one-time data collection. Only a few have long-term database plans in the selected articles. Due to the complexity of CM, it is not unusual that the data collected, processed, and applied inevitably have errors. The problems of null values, misleading values, outliers, and non-standardized values make the application of ANN challenging in CM. Although previous studies have completed data standardization [47], there is no comprehensive data quality analysis of ANN in CM.  
Future research could investigate how to collect efficient and standardized data from historical and new projects. Data standards need to be developed, and automated information collection system should be adopted to ensure sustainable data updating and high data quality. Additionally, in many manufacturing companies, enterprise resource planning (ERP) has become a successful management platform providing decision support for managers [86]. It is necessary for construction companies to learn from ERP to develop an integrated platform. [PERSON] and [PERSON] [1] proposed a business data integration platform framework, including enterprise resource database, construction process database, and enterprise data warehouse to realize the integration and utilization of engineering data and  
Figure 7: Data source of ANN application in CM.  
business data in construction companies. In addition, the industry's professional bodies can also actively hold seminars to encourage enterprises to invest in data storage and processing [87]. The government should strengthen the publicity and guidance of database construction and application as well.
## 8 The IoT Applied to Marine Environmental Monitoring  
IoT-based technologies, as well as wireless sensor networks (WSNs), a subset of IoT, can be applied to the monitoring and protection of marine environments [13]. In particular, monitoring activities employing IoT technology can be used for ocean sensing and monitoring of water quality [134], coral reef protection, offshore and deep-sea fish farms, and wave and current watching [13].  
The development of an adaptive, scalable WSN must foresee such critical properties as autonomy, scalability, adaptability, durability, and simplicity [135]. On the other hand, the design and deployment of a lasting and scalable WSN for marine environment monitoring should consider all of the following peculiar challenges mentioned in the second paragraph. Other issues can concern the devices and sensor nodes, which can be highly reliable because of the difficult deployment and maintenance. In addition, their coverage needs to be carefully evaluated, because their application over large areas far from direct control and with expensive, delicate equipment should be protected against possible acts of vandalism [13; 135].  
Overall, an online marine monitoring system needs (1) sensors adequate to measure seawater features, (2) a controller or processor unit to compute the data from sensors, and (3) communication equipment to send data from the processing unit to the cloud via ground stations. Sending large amounts of data (as large images or videos) to the cloud requires the combination of the IoT and cloud computing, because satellite communications may be expensive, in terms of both money and energy consumed [136; 137] (Figure 6), and sensing stations should be relatively small and light.