esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Myofibrillar Proteins
#### 3.1.5 Circular Dichroism  
Generally, the secondary structure of MPs is stabilized by intermolecular hydrogen bonds. However, the freezing process destroys the intramolecular hydrogen bindings, resulting in conversion among different types of secondary structures [31]. Therefore, the determination of secondary structural changes in MPs is crucial. Different secondary structures in natural proteins are exhibited under CD analysis. The changes to the secondary structures in the MP samples re shown in Figure 4. The \(\alpha\)-helical structure is an important conformation which is mainly comprised of total myosin protein, which maintains the secondary structure stability of proteins mainly through hydrogen bonds [32]. The \(\alpha\)-helical structure has one positive peak at 190 nm as well as two negative peaks at 204 nm and 221 nm. During frozen storage, the content of the \(\alpha\)-helix of the MP secondary structures gradually decreased. This reduction in the \(\alpha\)-helix is attributed to the changes in myosin during frozen storage [33].  
Figure 3: Fluorescence intensity of myofibrillar proteins treated with different concentrations of OVA/\(\beta\)CD during 90 days of frozen storage at \(-18\)\({}^{\circ}\)C. (**A**) 0% OVA/\(\beta\)CD treatment. (**B**) 2% OVA/\(\beta\)CD treatment. (**C**) 4% OVA/\(\beta\)CD treatment. (**D**) 6% OVA/\(\beta\)CD treatment.  
After being frozen for 90 days, the \(\alpha\)-helix content of all MPs samples decreased, ranging from 29.6 to 12.2% for the control sample, and from 30.1 to 14.7% for 6% the OVA/\(\beta\)CD treated samples. The \(\alpha\)-helix content of the control (0%) decreased, while the \(\alpha\)-helix content of the 6% OVA/\(\beta\)CD treatment was more stable. As the time frozen increases, the protein unfolds and the hydrogen bond weakens, resulting in \(\alpha\)-helix decrease [34]. Therefore, addition of OVA/\(\beta\)CD protected MPs from deterioration by binding with protein molecules through hydrogen bonding.  
[PERSON] et al. [35] reported that the disruption of \(\alpha\)-helix structures would be accompanied by the molecular structure loosening, resulted in increased surface hydrophobicity, which might promote protein aggregation, which would further influence the water retention of the MP gel.
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 3 Experimental Setup and Details
### Dataset and Evaluation Criteria  
The ASVspoof 2019 LA database corresponds to a synthesized deception attack scenario, which includes genuine and spoofed speech data generated by 17 different TTS and VC systems. The speech data in the ASVspoof 2019 LA dataset is based on the VCTK corpus, including natural speech from 107 speakers: 46 males and 61 females. The natural speech all has the same recording configuration without channel and background noise interference. The LA partition is divided into three subsets: training, development, and evaluation, with each subset consisting of genuine and spoofed speech. Six different TTS/VC algorithms are used to generate spoofed speech in the training and development subsets, while thirteen different TTS/VC algorithms are used to generate spoofed speech in the evaluation subset.  
ASVspoof 2021 LA aims to reduce the gap between ideal laboratory conditions and expected field conditions. It includes a collection of genuine and spoofed speech transmitted through various telephone systems, including IP telephony (VoIP) and public switched telephone networks (PSTN). Speech data may undergo a series of unknown changes when transmitted across different telephone systems, which can result in artefacts not only from forgery but also from coding and transmission. Therefore, the challenge in the 2021 LA task is to investigate the robustness against harmful changes caused by artefacts resulting from compression, packet loss, and variations in bandwidth, transmission facilities, and bit rates. The speech data are sourced from the ASVspoof 2019 LA evaluation database, which originates from the VCTK database. The spoofing experiments are generated using 1 of 13 different VC, TTS, or hybrid spoofing attack algorithms.  
ASVspoof 2021 DF extends the detection of spoofed speech to non-ASV scenarios. The DF evaluation data collect genuine and spoofed speech segments generated using different lossy codecs. The evaluation source data are taken from the ASVspoof 2019 LA evaluation set and other sources, and the ASVspoof 2021 DF evaluation data are generated using over 100 different spoofing algorithms.  
According to the rules of the ASVspoof Challenge, the model in this paper was trained on the ASVspoof 2019 LA training and development sets and evaluated on the ASVspoof 2021 LA and ASVspoof 2021 DF evaluation subsets. The evaluation criteria used the tools provided by ASVspoof, including the equal error rate (EER) and the minimum-detection cost function (min t-DCF).
## 4 Experimental Research
### Comparison of Multiple Recognition Methods  
In this section, a comparative study was performed on the same public dataset to prove the effectiveness of the recognition method, i.e., the combination of IMFE and SVM.  
First, the combined recognition of IMFE and SVM was compared with the top three methods in BCI competition II in many aspects [35]. The detailed information is illustrated in Table 3.  
From Table 3, it can be seen that the highest recognition rate of 100% is achieved by using IMFE feature extraction and the SVM classifier; it has increased significantly compared with the top three methods. Furthermore, the average recognition rate with 10-fold CV is higher than the highest recognition rates of the other three methods.  
Next, some research was completed about the combined recognition of IMFE and SVM and the other recognition methods, whose experimental data was from the same dataset III of BCI Competition II [4; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. The detailed information, including the reference numbers, feature extraction methods, classifiers, etc., is shown in Table 4.  
From Table 4, we can see that the proposed recognition method has the highest classification rate (100%) and its average recognition rate (92.14%) with 10-fold CV is higher than the highest recognition rates corresponding to the other methods except references [4; 40; 49].  
Figure 11: The average classification accuracy and standard deviation performed by 10-fold CV for IMFE and multiple classical feature extraction methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extraction** & **Classifier** & **Top Classification Rate (\%)** & **Average Classification Rate with 10-Fold CV (\%)** \\ \hline WT & Bayes & 89.29 & - \\ ERD & LDA & 86.43 & - \\ AR & LDA & 84.29 & - \\ IMFE & SVM & 100 & 92.14 \\ \hline \end{tabular} Note: “-” means that average classification rate with 10-fold CV is not given in the reference.  
\end{table}
Table 3: Comparison with the top three recognition methods in BCI competition II.
## 3 Guard Interval Analyses
### Delay IIR Channel  
Assume that the loop's multipath effect is suppressed and the IIR channel order still is \(l_{g}\). However, the IIR channel coefficients have \(a(i)=0,i=[1,\cdots,l_{g}-1]\) under this special delay IIR channel, which can be regarded as a single-loop IIR channel with a large loop delay. The expression for the guard interval under a special delay IIR channel is the same as (23), which can be expressed as  
\[\overline{s}_{v}(k) =\sum_{i_{0}=0}^{k}a(i_{0})y_{v}(k-i_{0})+\sum_{i_{1}=k+1}^{l_{g} }a(i_{1})y_{v-1}(k-i_{1}+l_{g}) \tag{28}\] \[=a(0)y_{v}(k)+a(1)y_{v-1}(k-1+l_{g}).\]Under the delay IIR channel, the complexity of GI depends not only on the maximum delay of the loop, but also on the number of loops. Therefore, the complexity of the guard interval is \(\mathcal{O}(Nlog_{2}(N)+(l_{g}+1)^{2}+l_{g})\), i.e., \(\mathcal{O}(Nlog_{2}(N)+5)\).  
The expectation of guard interval under a special delay IIR channel is  
\[p_{v}(k) =\frac{\sigma_{d}^{2}}{N}\sum_{n=0}^{N-1}\left[\frac{\sum_{\ell_{ 0}=0}^{k}\sum_{\ell_{1}=0}^{k}a(i_{0})a(i_{1})^{*}e^{2\pi(i_{0}-i_{1})n/N}}{A_ {n}A_{n}^{*}}\right. \tag{29}\] \[\qquad\qquad+\left.\frac{\sum_{\ell_{0}=k+1}^{l_{g}}\sum_{\ell_{1 }=k+1}^{l_{g}}a(i_{0})a(i_{1})^{*}e^{2\pi(i_{0}-i_{1})n/N}}{A_{n}A_{n}^{*}}\right]\] \[=\frac{\sigma_{d}^{2}}{N}\sum_{n=0}^{N-1}\left[\frac{a(0)a(0)^{*} +a(l_{g})a(l_{g})^{*}}{A_{n}A_{n}^{*}}\right]\] \[=\sigma_{d}^{2}\frac{a(0)a(0)^{*}+a(l_{g})a(l_{g})^{*}}{a(0)a(0)^ {*}-a(l_{g})a(l_{g})^{*}}.\]  
Note that the special delay IIR channel has the same expectation although the amplitudes of the protection intervals are different, and the expression is the same as (27). Therefore, this IIR channel should satisfy \(a(0)a(0)^{*}-a(1)a(1)^{*}\gg 0\) for a reasonable guard interval expectation and system stability. It proves that when the loop delay is large, the number of GIs must be greater than or equal to the maximum number of loop delays to maintain the circular convolution of the received signal and the loop channel coefficients. However, the complexity of the guard interval does not increase with the maximum delay of the loop and depends only on the number of loops.
## 4 Table-Based Adaptive DPLL
In that case, the lower bound condition is applied, which sets the integration time as the length of one spreading code period as follows:  
\[T=\left\{ \begin{array}{ll}T,&T>0\\ T_{code},&T=0\end{array} \right. \tag{10}\]  
where \(T_{code}\) is spreading code period [s] (e.g., 1 ms for GPS L1 C/A). The proposed table-based adaptive DPLL algorithm is presented in Algorithm 2. The algorithm corresponds to the one iteration procedure of the carrier tracking loop, repeating for every tracking loop operation interval. The algorithm starts with the reception of the estimated C/N\({}_{0}\) and jerk values from each estimator. The matching index of the optimal bandwidth table is determined, and the corresponding optimal bandwidth value is extracted from the table. Since the optimal bandwidth value is saved only if the minimum RMSE does not exceed the threshold, the existence of the optimal bandwidth value means the receiver can theoretically operate in the current condition. Therefore, the optimal bandwidth is applied to the noise bandwidth through (8); otherwise, the recent value of the noise bandwidth is maintained. Finally, the new noise bandwidth is handed over to the loop filter, and the integration time for the next integration is calculated using (9). ```
1: Get estimated C/N\({}_{0}\) and \(R^{\prime\prime}\)
2:Noise bandwidth transition:
3: Find index for C/N\({}_{0}\) and \(R^{\prime\prime}\)
4: Get \(B_{opt}\) from optimal bandwidth table
5:if\(B_{opt}\) is exist then
6:\(B_{n}[k+1]=\alpha B_{opt}+(1-\alpha)B_{n}[k]\)
7:else
8:\(B_{n}[k+1]=B_{n}[k]\)
9:endif
10: Return \(B_{n}[k+1]\) to the loop filter
11:Integration time calculation:
12:\(T=\Delta T\times BT_{norm}/(\Delta T\times B_{n})\)
13: Set next integration time to \(T\)
```  
**Algorithm 2** Table-based adaptive DPLL algorithm. As one can anticipate, the performance of this algorithm is dependent on the performance of the estimators. If the estimated C/N\({}_{0}\) and jerk are wrong, the resulting noise bandwidth is not optimal anymore. Therefore, the algorithm fits the environment that has slow variations of C/N\({}_{0}\) and/or jerks where the estimators can estimate them without much difficulty. Additionally, scenarios in which the trajectory is fixed so that the receiver can know its approximate location and signal reception characteristics are also appropriate conditions for the algorithm. The Moon exploration mission corresponds to both conditions. Dedicated estimators would be required to apply the algorithm to other conditions, such as having fast variations, etc.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 4 Simulations Environment and Performance Evaluation
### End-to-End Packet Delay  
The end-to-end delay is the time difference between the reception instant of the packet by the sink and its generation instant by the source node. Figures 8-10 present the end-to-end delay according to the number of hops for the scenario of 40 nodes (note that all the results of the other scenarios have the same average tendency). Results show that M-HopCount-Sync end-to-end delay is higher than all other protocols because it uses a synchronization phase for beacon exchange. The synchronization lasts 2 s in order to reach all nodes of the network and during that time data packets are not sent. We also notice that except M-HopCount-Sync, the end-to-end delay of the four other protocols is close with a slight advantage for RPL and CoLBA. Indeed, RPL has a slightly lower delivery rate, which means that more packets are lost and thus packets that are not lost have less competition and waste less time in packet queues. As for CoLBA, its load balancing approach and delay-based metric help reduce the end-to-end delay.  
Figure 6: Queue overflow ratio for 5 pkts/s/node.  
Figure 8: End-to-end delay per hop for 1 pkt/sec/node.  
Figure 10: End-to-end delay per hop for 10 pkts/s/node.  
Figure 7: Queue overflow ratio for 10 pkts/s/node.  
Figure 9: End-to-end delay per hop for 5 pkts/s/node.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 2 Related Work  
In recent years, researchers have developed a number of algorithms for ground-based cloud recognition. The co-occurrence matrix and edge frequency were introduced in [5] to extract local features to describe cloud images, and recognized five different sky conditions. The work [20] extended to classify cloud images into eight sky conditions by utilizing Fourier transformation and statistical features. Since the BoW model is an effective algorithm for texture recognition, some extension methods [21; 22] were proposed. Since the appearance of clouds is a kind of natural texture, [PERSON] et al. [23] employed LBP to classify infrared cloud images. [PERSON] et al. [19] proposed illumination-invariant completed local ternary patterns (ICLTP), which can effectively handle the illumination variations. They soon proposed the salient LBP (SLBP) [13] to capture descriptive cloud information. The desirable property of SLBP is the robustness to noises. However, these features are not robust to view shift for describing cloud images.  
Figure 2: The green and blue indicate two kinds of feature spaces. Then we employ weighted pairwise constraints to the feature spaces. Here, red and black lines denote similar pairs and dissimilar pairs, respectively. The final feature space is learned for cloud recognition.  
Recently, due to the inspiration caused by the success of convolutional neural networks (CNNs) in image recognition [17; 24], [PERSON] et al. [25] first proposed to apply CNNs to ground-based cloud recognition. They employed Fisher Vector (FV) to encode the last conventional layer of CNNs, and they further proposed to extract the deep convolutional visual features to represent cloud images in [26]. [PERSON] et al. [27] employed the deep convolutional activations-based features (DCAFs) to describe cloud images. These aformentioned methods showed promising recognition results when trained and tested on the same feature space. In other words, these features are also not robust to view shift.  
In the recognition procedure to compute similarities or distances between two feature vectors, many predefined metrics cannot show the desirable topology that we are trying to capture. A sought-after alternative is to apply metric learning in place of these predefined metrics. The key idea of metric learning is to conduct a Mahalanobis distance where a transformation matrix is applied to compute the distance between a sample pair. Since metric learning has shown remarkable performance in various fields, such as image retrieval and classification [28], face recognition [29; 30; 31] and human activity recognition [32; 33], we employ the framework of metric learning to ground-based cloud recognition and meanwhile consider the sample imbalance problem.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
### PR Products  
The PR standard products can be classified into Level 1 (1B21, 1C21), Level 2 (2A21, 2A23, 2A25), and Level 3 (3A25, 3A26), in which Level 1 and Level 2 products are data in the instantaneous field of view (FOV) and Level 3 products are data of monthly statistical values for rain parameters  
Figure 1: A schematic diagram of the detection of TRMM (Tropical Rainfall Measuring Mission) including PR (Precipitation Radar) [(1)].  
(Table 1). Due to the TRMM boost from 350 km to 402.5 km in August 2001, major impacts on PR observations included the degradation of PR sensitivity, the increase of the footprint size, and the mismatch between transmission and reception angles for one pulse among onboard averaging pulses, leading to small changes in PR reflectivity and rainfall estimation [11; 12]. Detailed descriptions of PR product algorithms and the changes between versions can be referred to Table 2 and the \"Instruction Manual of TRMM PR Algorithm\" at the website of Japan Aerospace Exploration Agency (JAXA) [13].
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 2 Overview of IEEE 802.15.4 Standard
The CSMA/CA algorithm is used before transmitting data or MAC command frames within the contention access period (CAP). Otherwise, frames are immediately transmitted following the ACK of a data request command. Compared with the beacon mode, the non-beacon mode has a highly simple step for performing the operation and causes a negligible transmission delay. However, it has the disadvantage of high energy consumption because it does not have sleep technology that can save communication energy. In contrast, the beacon mode is a suitable method for sensor networks in terms of energy consumption. This is because it can reduce the energy consumption through an active section where the device can communicate and an inactive section where the device can save energy by switching off the communication module. Figure 2: Slotted CSMA/CA mechanism. However, because this beacon mode cannot transmit data during sleep intervals, there is a delay in data transmission.
# Simplified Routing Mechanism for Capsule Networks
## 2 Theory of Capsule Network  
The capsule network [10; 11; 12] (or CapsNet) is very similar to the classical neural network. The main difference is the basic building block. In the neural network, we use neurons, but in the capsule network, we can find capsules. Figures 1 and 2 show the main differences between the classical artificial neurons and the capsules.  
A capsule is a group of neurons that perform a multitude of internal computation and encapsulate the results of the computations into an n-dimensional vector. This vector is the output of the capsule. The length of this output vector is the probability and the direction of the vector, indicating certain properties about the entity.  
In a capsule-based network, we use routing-by-agreement, where the output vector of any capsule is sent to all higher-level capsules. Each capsule output is compared with the actual output of the higher-level capsules. Where the outputs match, the coupling coefficient between the two capsules are increased.  
Let \(i\) be a lower-level capsule and \(j\) be a higher-level capsule. The prediction vector is calculated as follows:  
\[\hat{u}_{(j\mid i)}=W_{ij}u_{i} \tag{1}\]  
where \(W_{ij}\) is a trainable weighting matrix and \(u_{i}\) is an output pose vector from the \(i\)-th capsule to the \(j\)-th capsule. The coupling coefficients are calculated with a simple SoftMax function, as follows:  
\[c_{ij}=\frac{exp(b_{ij})}{\sum_{k}exp(b_{ik})} \tag{2}\]where \(b_{ij}\) is the log probability of capsule \(i\) coupled with capsule \(j\), and it is initialized with zero values. The total input to capsule \(j\) is a weighted sum over the prediction vectors, calculated as follows:  
\[s_{j}=\sum_{i}c_{ij}\hat{u}_{j|i} \tag{3}\]  
In capsule networks, we use the length of the output vector to represent the probability for the capsule. Therefore, we use a non-linear activation function, which is called the squashing function. The squashing function is the next:  
\[v_{j}=squash\big{(}s_{j}\big{)}=\frac{\|s_{j}\|^{2}}{1+\|s_{j}\|^{2}}\frac{s_ {j}}{\|s_{j}\|} \tag{4}\]  
We can use the dynamic routing algorithm (by [PERSON] et al. [11]) to update the \(c_{ij}\) values in every iteration. In this case, the goal is to optimize the \(v_{j}\) vector. In the dynamic routing algorithm, the \(b_{ij}\) vector is updated in every iteration, as follows:  
\[b_{ij}=b_{ij}+\hat{u}_{j|i}v_{j} \tag{5}\]  
Figure 1: Typical structure of a neuron. (green: inputs, blue: operations, yellow: output, purple: neuron).  
Figure 2: Typical structure of a capsule. (green: inputs, red: prediction vectors, blue: operations, yellow: output, purple: capsule).
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 4 _Cryptosporidium_ spp.
### Cryptosporidium _spp. in Drinking Water_
Pooled detection rates of _Cryptosporidium_ were 19.6% and 13.3% in supply sources and samples, respectively, and a baseline prevalence of _Cryptosporidium_ spp. of 10-20% was estimated in global groundwater supplies. The Greater Middle East presented the highest detection rates for samples and Europe the highest detection rates for supply sources. Private supplies, which are largely unregulated, had higher sample and source contamination levels than public supplies. The highest average oocyst counts were recorded in Saudi Arabia (210 oocyst/L), followed by the USA (25 oocyst/L) and Haiti (12.7 oocyst/L). Pollution sources were of animal origin in 71.4% of cases, of which 53.3% were cattle, and _C. parvum_ was the most prevalent species [5]. The mechanism hypothesized for _Cryptosporidium_ entry was groundwater recharge. Indeed, in situ and ex situ investigations demonstrated the ability ofocysts to migrate through soil columns, despite soil attenuation. Protected groundwater sources exhibited high levels of _Cryptosporidium_ spp. prevalence (21.4%) as a consequence of bedrock percolation, casing/liner cracking, well deterioration, and insufficient or absent wellhead protection, representing a significant public health concern [5]. In a study carried out in northern Italy on a small drinking water treatment plant (DWTP) distributing surface water after filtration, chlorination, and UV treatment, _Cryptosporidium_ oocysts were found in 100% influent and 77.8% effluent samples. Oocysts in the effluent presented intact nuclei, suggesting no inactivation by the high-power UV treatment of the system. The sequence of the 18S ribosomal DNA of the microorganism in the influent was closely related to a genotype found in bank voles, suggesting water contamination by the feces of wild rodents [4]. The global prevalence of _Cryptosporidium_ spp. was estimated to be 25.5% in drinking water, 24.5% in reservoir water, and 18.8% in groundwater [125]. In Minnesota, 40% of the drinking water wells examined were contaminated with _Cryptosporidium_ species [126]. It was reported that _Cryptosporidium_ spp. can go through a whole life cycle and multiply in biofilms in water networks, a finding that must be confirmed [5]. Moreover, biofilms protect oocysts from inactivation by UV light [102].