esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fat Content  
The research results suggested that choosing a substrate with the highest fat content does not necessarily lead to the highest fat accumulation in the larvae, in contrast to proteins (Figure 3). We have noticed that larvae reared on different substrates exhibit variations in fat content. Mealworms from group 2L, reared on substrate 2S, had the significantly (\(p<0.01\)) highest fat content at 34.22 \(\pm\) 0.491% compared with larvae from group 3L, reared on substrate 3S, which had the lowest fat content at 21.12%.  
[PERSON] et al. conducted a research study examining the impact of various diets on the lipid and FAs content of mealworms [19]. According to the authors, the fat content in the substrate did not exhibit a statistically significant influence on the fat content observed in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Larvae**} & \multicolumn{6}{c}{**Substrate**} \\ \hline Samples & 1L & 2L & 3L & 4 CL  
the larvae, analogously as in our study. The aforementioned researchers have identified the highest fat content in larvae cultivated on an oat flour substrate (48.31 \(\pm\) 3.81%), whereas in our investigation using wheat by-products (in sample 2L), fat content was 34.22 \(\pm\) 0.491%, showing a difference of 14% [19]. One of the hypotheses for the difference in fat content between our study and the aforementioned study, and what could have influenced it, is that different research methods, larval treatments, and substrates were used.  
Meanwhile, in another study, performed by [PERSON] et al., 11-week-old mealworm larvae grown on experimental diets were examined. When grown on wheat bran, the fat content (26.1 \(\pm\) 0.82%) was 5.6% less than in our 1L sample (31.78 \(\pm\) 0.525%) [16].  
In the aforementioned study by [PERSON] et al., the influence of the substrate on the fat content of mealworm larvae was also determined. The findings indicate that, in the case of using ware potatoes, as opposed to our sprouted potatoes that are no longer suitable for human consumption (1L), the fat content was measured at 31.2 \(\pm\) 0.4 g/100 g, while in our study, it was 31.78 \(\pm\) 0.02% [46]. It is noteworthy that the results exhibit no statistically significant difference. This comparison shows that even with by-products that are no longer fit for human consumption and should be disposed of, mealworms process them into quality fats.  
In the study, we can confirm the conclusions by [PERSON] et al. that the lower the protein content in the diet, the higher the fat content is found in the larvae [16].
## 2 System and Signal Model
### FSO Link  
We consider that the FSO system utilizes the intensity modulation and direct detection (IM/DD) technique and the On-Off keying (OOK) modulation.  
The received signal at \(R_{i}\) can be expressed as  
\[y^{FSO}_{SR_{i}}=\eta h_{SR_{i}}x_{fso}+\eta_{f}, \tag{1}\]  
where \(x_{fso}\) represents the transmitted signal. Since the OOK modulation scheme is used \(x_{fso}\in\left\{0,2P_{f,SR_{i}}\right\}\), \(P_{f,SR_{i}}\) is the average transmitted optical power of the source, \(\eta\) is the detector response, \(h_{SR_{i}}\) indicates the channel coefficient of the first hop, and \(n_{f}\) is the zero-mean additive white Gaussian noise (AWGN) with variance \(\sigma_{f}^{2}\). The instantaneous received SNR at \(R_{i}\) can be expressed as  
\[\gamma^{FSO}_{SR_{i}}=\frac{2P^{2}_{f,SR_{i}}\eta^{2}}{\sigma_{f}^{2}}h^{2}_{ SR_{i}}=\overline{\gamma}^{FSO}_{SR_{i}}h^{2}_{SR_{i}}, \tag{2}\]  
where \(\overline{\gamma}^{FSO}_{SR_{i}}\) is the average SNR for the source-to-relay FSO link.  
Figure 1: The multiple UAV-based aeronautical communication system with hybrid FSO/RF links.  
The relay utilizes the DF mode, meaning that the instantaneous received SNR at \(D\) can be expressed as  
\[\gamma_{R,D}^{FSO}=\frac{2P_{f,R,D}^{2}\eta^{2}}{\sigma_{f}^{2}}h_{R,D}^{2}= \overline{\gamma}_{R,D}^{FSO}h_{R,D}^{2}, \tag{3}\]  
where \(P_{f,R,D}\) is the average transmitted optical power of the relay, \(h_{R,D}\) indicates the channel coefficient of the second hop, and \(\overline{\gamma}_{R,D}^{FSO}\) is the average SNR for the relay-to-destination FSO link.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 3 Experimental Results
### Structural Performance of Shield Tunnel Strengthened by In Situ Spraying Mortar
#### 3.2.4 Performance Comparison between Strengthened Segment and Unstrengthened Segment  
The strengthening effect of the shield tunnel lining structure is mainly reflected in the ultimate bearing capacity. The load-convergence deformations of the strengthened and unstrengthened segment are shown in Figure 17. A is defined as the elastic limit point of the unstrengthened segment, and B is defined as the ultimate bearing capacity point of the unstrengthened segment. D is defined as the elastic limit point of the strengthened segment, and the E is defined as the ultimate bearing capacity point of the strengthened segment. The slope, \(k_{0}\), of OA is the elastic-stage stiffness of the unstrengthened segment, and the slope, \(k_{1}\), of CD is the elastic-stage stiffness of the strengthened segment. A performance comparison between the strengthened and unstrengthened segments is shown in Table 4.  
Figure 16: Stripping values of each point at the crown and bottom of the segment.  
Figure 17: Load–convergence deformation of the strengthened and unstrengthened segments.  
From Table 4, it can be seen that the ultimate bearing capacity of the segment strengthened by in situ spraying mortar is 10% higher than that of the unstrengthened segment.
###### Abstract  
It is well known that the performance of hot mix asphalt (HMA) in service life is closely related to a proper aggregate gradation. A laboratory study was conducted to investigate the effects of nominal maximum aggregate size (NMAS) on the performance of stone matrix asphalt (SMA). The volumetric characteristics and performance properties obtained from wheel tracking tests, permeability test, beam bending test, contabro test are compared for SMA mixes with different NMAS. The results indicated that voids in mineral aggregate (VMA) and voids filled with asphalt (VFA) of SMA mixtures increased with a decrease of aggregate size in aggregate gradation. SMA30 had the lowest optimum asphalt content among all the mixtures. Increase of NMAS contributed to improvement of the rutting resistance of SMA mixtures. However, a decrease of NMAS showed better cracking and traveling resistance. Permeability rate of SMA was primarily affected by the air voids (AV) and break point sieve, but was also sensitive to aggregate gradation to some extent, with reduced NMAS corresponding to less permeability rate. Based on the test results, SMA5 and SMA13 are suggested to be used as a water-proof layer in bridge deck pavement, and SMA20 and SMA30 are suggested to be used as binder course in asphalt pavement, which needs to possess superior rutting resistance at high temperature.  
Keywords:SMA; nominal maximum aggregate size; rutting; cracking; permeability; raveling +
Footnote †: journal: Applied Sciences
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Criminal Justice  
The utilization of XAI has notably showcased its potential to augment transparency, accountability, and equity within the criminal justice system. Extensive research has probed the deployment of XAI tools across diverse domains encompassing risk assessment, bail determinations, and sentencing protocols.  
For instance, [PERSON] and [PERSON] [82] undertook an exploration into XAI's role in predicting recidivism within the criminal justice sphere. Their work accentuated the paramount importance of employing XAI tools judiciously and transparently to ensure fairness in decision-making processes. Counterfactual fairness, a concept introduced by [PERSON] et al. [83], emerged as a pivotal consideration. They proposed a method to integrate this notion into XAI models, thereby mitigating biases that might otherwise perpetuate.  
Moreover, [PERSON] and [PERSON] [84] provided an incisive discourse on the indispensable value of incorporating XAI models across various applications, including the criminal justice domain. Their insights underscored the pivotal role of XAI in averting the potential pitfalls of biased decisions.  
In conclusion, the advent of XAI as a pivotal tool within the criminal justice domain manifests its potency in fostering fair, informed, and unbiased decision-making processes, holding the potential to reshape the landscape of judicial systems for the better.
## 1 Introduction
A total of twenty-two parameters are extracted for the time period of July 2018 to August 2022 that include seven water quality parameters, i.e., total dissolved solids (TDS), pH, electrical conductivity (EC), Secchi disk depth (SDD), dissolved oxygen (DO), turbidity (Tur) and chlorophyll-\(\alpha\) (chl-\(\alpha\)) are acquired from the Sentinel-2 Multispectral Imager (S2-MSI) Level 1C (L1C) satellite; six air pollutants that include carbon monoxide (CO), nitrogen dioxide (\(NO_{2}\)), ozone (\(O_{3}\)), sulphur dioxide (\(SO_{2}\)), formaldehyde (HCHO) and methane (\(CH_{4}\)) are acquired from the Sentinel-5 Precursor Level 2 (SSP-L2) TROPoSpHeric Monitoring Instrument (TROPOMI); three meteorological parameters, namely air temperature, wind speed and total precipitation are taken from the ERA5 Climate Reanalysis Project, (ERA5-CRP); and lastly, six hydrological and topographical parameters that include slope, aspect, soil type, lithology, geology and land use/land cover are acquired from the Digital Elevation Model (DEM) created with Shuttle Radar Topography Mission (SRTM) data. The NSFWQI method that uses the water quality parameters for evaluation is used to compare the quality of the Rawal Stream Network with the new proposed EWQI that is based on the extracted twenty-two parameters. Moreover, using a remote sensing and machine learning approach can help in analyzing the different factors affecting water quality which are applicable on a global scale. This research reveals that the new proposed EWQI is a much more reliable and accurate index compared to the state-of-the-art NSFWQI method as it: (i) operates well with or without missing parameters, (ii) identifies the temporal and seasonal variations, and (iii) considers all other environmental factors while classifying the water body. The major contributions of this study are as follows:  
1. Twenty-two parameters are extracted for the stream network of the Rawal watershed that include seven water quality parameters, six air pollutants and three meteorological and six hydrological/topographical parameters pertaining to the years (2018-2022) for the monsoon months of June to September. 2. A multimodal indexing technique, EWQI, is proposed that involves five steps: parameter selection, sub-index calculation, weight assignment, aggregation of sub-indices and classification using a machine learning approach for weight assignment, sub-index calculation and remote sensing technology for parameter selection to extract twenty-two multimodal parameters. This paper is organized as follows: Section 2 discusses the related work. Section 3 explains the proposed EWQI. Section 4 covers the proposed methodology for the extraction of the twenty-two parameters, and the application of the EWQI method is discussed for the Rawal stream network. The results of the comparison between NSFWQI and EWQI are discussed in Section 5. In Section 6, the conclusion of this research is presented.
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 4 Simulation and Analysis
### The Time Required to Locate Nodes  
To a certain extent, the time required to complete the positioning can reflect the complexity of the positioning algorithm, and reflect how much energy is generated in the positioning process indirectly. The relationship between the number of anchor nodes and the time required for positioning is shown in Table 1, it can be seen that the anchor nodes have a relatively large number of tasks, including collecting hops information and collating and forwarding packets, so the direct impact on time is most obvious. Compared with the standard DV-Hop, DV-Distance increases the process of calculating the signal attenuation value. CDV-Hop increases the coordinate calibration after estimating, the improved algorithm adds the correction and iterative refinement process in the distance estimation and trilateral positioning, so the computational complexity of the three algorithms is increased, and the time required is relatively long. The CDV-Hop and DV-Hop algorithms are relatively simple, so the computational complexity is low, and they can be applied to those occasions that do not require high precision.  
Under normal circumstances, it can be seen from Table 1 that the time required for positioning will increase as the number of nodes participating in the network increases. The increasing number of network nodes means that the consumption time of information transmission and data acquisition also increases correspondingly. At the same time, the increase of data volume also increases the time required to calculate.  
When the topology of the wireless sensor network presents a long band, the connectivity of the nodes is changed to a certain extent, and the time required for different algorithms will be different. The number of nodes in the network and the time required for positioning are shown in Table 2. It can be seen that when the number of nodes participating in the positioning increases from 10 to 50, the range of DV-Distance and proposed algorithm is less than 500 ms\(\sim\)600 ms, while DV-Hop and CDV-Hop are nearly doubled.
## 2 The Components of a Fully Bilingual ASR System
### Lexical Models  
An in-house bilingual grapheme-to-phoneme (G2P) converter has been developed and applied to obtain the phonetic baseforms of words in Basque and Spanish [27; 28]. All words are then gathered in a single lexicon. The G2P converter is based on two pronunciation dictionaries for Basque and Spanish, each including hundreds of thousands of words, with verb inflections, declined words, numbers, acronyms, etc. These dictionaries, which were initialized from Mozilla CommonVoice (cv-corpus-5.1-2020-06-22) [29], Aditu [30] and Albayzin [31], include nominal pronunciations in terms of the reduced set of acoustic units, obtained by applying a different set of pronunciation rules for each language [32; 33]. Dictionaries grow dynamically as new words are found in Basque Parliament sessions: if a known word is found, the G2P converter uses the stored pronunciation; but if an unknown word is found, pronunciation rules are applied to obtain its phonetic baseform, which is stored in the corresponding dictionary. In the case of unknown words or known words existing in both dictionaries, we must decide about the language (Basque or Spanish). This decision is based on the context: the language with more words in a window around the current word is chosen. In fact, a series of window sizes are considered, starting at 1 (one word at each side of the word under analysis) and increasing to 2, 3, etc., (up to the length of the sentence), until a reliable decision can be made (note that some words appear in both dictionaries). This strategy is found to be effective in practice, leading to very few errors. Numbers and ordinals (such as 25, 13.87, 1., etc.)--which are also transcribed either in Basque or in Spanish depending on the context--are assigned their most likely pronunciation, though sometimes it might not match the actual pronunciation. For instance, the Spanish phrase '1.5 millones' ('1.5 millions') is transcribed as 'uno coma cinco millones' ('one point five millions') while the speaker might have actually said 'un million y medio' ('one million and a half'). Finally, acronyms are expected to be written in all-caps and assumed to be spelled, with exceptions being listed in the pronunciation dictionaries. After processing each Basque Parliament session, the pronunciations of new words added to the dictionaries are supervised and validated by a human expert.
## 5 Conclusions  
In this paper, we proposed a multiple instance classification framework for gastric cancer pathological image classification. The proposed framework was composed of ImageNet-pretrained CNNs and GCNs. The former was utilized to extract patch features in the light of its superior ability in tackling diverse datasets and obtaining effective global features. The latter achieved information fusion in both physical space and feature space for all instances in the same bag. The features extracted by ImageNet-pretrained CNNs had better representation and generalization ability compared with traditional operators. The GCNs with hierarchical pooling module can fuse node features hierarchically and implicitly combine the spatial topological information of instances into the global representation of the bag. The framework can effectively integrate the spatial topological relationships between patches, alleviating the problem that some existing multiple instance classification methods cannot take advantage of the structural information between instances. The experimental results tested on two real gastric cancer pathology image datasets showed that graph-based methods achieved superior performances compared with several currently available MIL approaches.  
However, our method had limitations in providing instance-level predictions which is necessary for these classifiers to be translated into clinical practice. We envisage incorporating the instance-level prediction module into the whole framework while maintaining the superior classification performance of the proposed model in the future. Instance-level evaluation could further help specialists locate the areas crucial to final predictions, and enhance the interpretability of models based on deep learning algorithms.  
Conceptualization, X.X.; methodology, X.X.; software, X.X.; validation, X.X.; formal analysis, X.X.; data curation, X.X.; writing--original draft preparation, X.X.; writing--review and editing, X.X. and X.W.; supervision, X.W.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Shuffle** & **Recall (\%)** & **Precision (\%)** & **F1\_Score (\%)** \\ \hline \multirow{3}{*}{SRS} & Before & 90.40 \(\pm\) 0.032 & 91.16 \(\pm\) 0.032 & 90.75 \(\pm\) 0.034 \\  & After & 88.86 \(\pm\) 0.036 & 88.30 \(\pm\) 0.032 & 88.33 \(\pm\) 0.035 \\ \hline \multirow{3}{*}{Mars} & Before & 98.24 \(\pm\) 0.004 & 98.26 \(\pm\) 0.004 & 98.24 \(\pm\) 0.004 \\  & After & 96.54 \(\pm\) 0.005 & 96.59 \(\pm\) 0.005 & 96.51 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation experiments for spatial topological structure information.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 3 Public-Key Cryptography with an Authentication Table Key Management Scheme (PCAT)  
The proposed key management scheme is based on public-key cryptography. Asymmetric encryption is used to protect the privacy of the messages exchanged during the key establishment. Each node holds a pair of public and secret keys. Public-key cryptography provides secure communications without the predistribution of shared secret material. Thus, an opponent that compromises a node will not obtain any useful information about the rest of the network. However, an additional authentication mechanism is required in order to recognize the eligible nodes. In PCAT, each node stores an authentication table composed of a row for every other node in the network. Each row contains the information required to authenticate a node.
# Adaptive Segmentation of Streaming Sensor Data on Edge Devices
## 2 Related Work  
Stream computing requires low-latency real-time algorithms that can process massive amounts of data generated by multiple sources at very high speed [4]. Such algorithms should be able to pre-process and analyze on-the-fly high-velocity streams of data coming from sources such as the Internet of Things (IoT) devices, sensor networks, wearable and mobile devices, market data.  
The key value of data coming from such sources is their \"freshness\", and they should be processed and analyzed as soon as they arrive, which is the key assumption of the big data stream analytics [4]. Such a requirement leads to the need for low-latency real-time algorithms because the batch computing approach, in which data should be stored first before it is processed and analyzed, is not sufficient [4; 5; 6]. In recent years, the research has mainly been focused on algorithms for real-time analysis of big data streams and there was not much research into the noisy or incomplete streaming data pre-processing phase [4].  
Below, the research related to the proposed algorithm characteristic features--the real-time data stream segmentation, smoothing, and compression--is presented. Moreover,  
Figure 1: Conceptual diagram of the considered problem: the streaming preprocessor (segmenter) maps a _stream of data points_ to a _stream of cubic spline segments_, which form a \(C^{2}\)-continuous curve.  
the related research works on splines are mentioned and the selected possible application areas for the proposed algorithm are reviewed.
## 2 Literature Review  
### Sidewalk Autonomous Delivery Robots  
SADRs are typically defined as robots that autonomously navigate sidewalks for a certain distance and adhere to traffic regulations to deliver food or items to consumers [15; 16]. The delivery range of SADRs primarily covers outdoor environments such as parks, campuses, and residential apartments. Equipped with sensors and navigation technology, they generally travel at a walking pace [4]. Their payload capacity typically ranges from 10 to 30 kg, allowing for single-trip deliveries only, thus preventing the possibility of simultaneously serving multiple customers. SADRs belong to the category of non-humanoid robots [17], meaning that they lack a human-like appearance or structures such as limbs and facial features, and are commonly used for specific tasks. To ensure stability during delivery and to accommodate a certain capacity, SADRs are typically designed to resemble a mobile box with wheels.  
SADRs have also gained attention in South Korea. According to data from the first half of 2022, SADRs accounted for 2.8% of the search volume on Naver and Google, highlighting their prominence in the logistics field [18]. Emerging companies such as Neubily, Woowa Brothers, CJ Logistics, and ROBOTICS have designed and manufactured SADRs. Neubie, Dilly Drive, Gaemi, and Mobin are four SADRs currently operating or under trial in South Korea, as shown in Figure 1. The South Korean government has taken measures to support the commercialization of SADRs, formally implementing the \"Amendment to the Act on the Promotion of Development and Distribution of Intelligent Robots\" in November 2023, allowing SADRs to enter public spaces. Given this societal background, there is an urgent need for research on HRI in SADRs.  
Figure 1: Four sidewalk autonomous delivery robots (SADRs) in South Korea: (**a**) Neubie; (**b**) Dilly Drive; (**c**) Gaemi; (**d**) Mobin.