esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 5 Plant Extracts and Phytochemicals against Parasites Causing Foodborne Diseases
### Cryptosporidium  
_Cryptosporidium_ spp. belongs to the family, _Cryptosporidiumidae_. The most common species that cause human infection are the _Cryptosporidium parvum_ and _Cryptosporidium hominis_. They can be transmitted from animals, human-to-human, water, food, and tend to cause waterborne outbreaks. The clinical manifestation in immunocompetent patients is self-limiting when compared to immunocompromised individuals where it causes chronic diarrhea not responding to treatment [262]. _Cryptosporidium_ spp. are well recognized as causes of diarrheal disease and is increasingly identified as an important cause of morbidity and mortality worldwide [263]. The ethanolic extract from olive (_Olea europaea_) pomace, after oil pressing and phenol recovery, reproducibly inhibited _Cryptosporidium parvum_ (MIC = 250-500 \(\mu\)g/mL, IC\({}_{50}\) = 361 (279-438) \(\mu\)g/mL, IC\({}_{50}\) = 467 (398-615) \(\mu\)g/mL) [264]. In an in vivo study, the extract of _Curcuma longa_ L. had the highest effect on _Cryptosporidium_ocysts shedding. The inhibitory effect was observed at a rate of 100% on the 7 th day of treatment at 750 mg/kg and on the 5 th day at 1000 mg/kg in the watery extracts. At a rate of 100% on the 4 th day at 1000 mg/kgin alcoholic extracts [265]. Potential cryptosporicidal effects have been observed for blueberry with its polyphenolic compounds, cinnamon with its phenolic compounds and onion with its flavonoids and sulfide compounds, garlic with its allicin, mango with its mangiferin, olive pomace with its oleupropein, pomegranate with its polyphenols and tannins and oregano with its carvacrol especially against _Cryptosporidium parvum_ and _Cryptosporidium hominis_[266]. The anti-_Cryptosporidium_ efficacies of various plant extracts were evaluated in four groups of age-matched neonatal Swiss albino mice. There was a 100% reduction in _Cryptosporidium_ oocyte's excretion in stool and copro-DNA of _O. europaea_-treated infected mice after 2 weeks. Thus, the plant of _O. europaea_ is a promising natural therapeutic for cryptosporidiosis [267]. A detailed list of antiprotozool activity of important medicinal plant extract and phytochemicals against foodborne parasites is provided in Table 1. Chemical structures of some of the plants derived bioactive compounds are given in Figure 1.
## 4 Numerical Results and Discussion
### Water Pool Experiments

Table 4: BER comparison between the two systems. Figure 8: Received signal with different attenuation factors. The shorter spreading code can greatly improve the communication rate, but it slightly sacrifices the reliability.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 5 Amphisbaena Design
### Two-Phase Data Block Classification
#### 5.1.2 Organization of Data Blocks with Roles  
In a real environment, it is expensive to capture and compute the update period of each block. Inspired by the Low Inter-reference Recency Set (LIRS) algorithm [29; 30], the data classifier focuses only on the most recent update information of a block (the interval between the last two updates), which requires two recency-based lists to manage the blocks of different roles separately. For this purpose, the least recently used (LRU)-based filter list and the resident list are used to organize all role blocks. With this design, the data classifier can avoid explicitly recording the update period of each resident block and the maximum value of the update period, thus achieving an acceptable computational and storage overhead.  
As shown in Figure 7, each entry in the filter list and the resident list records the LBA of the role block. Note that the actual size of the HFB is determined by the number of SUP blocks and LUP blocks, where the ratio of the filter list to the HFB is \(\lambda\), and the ratio of the resident list to the HFB is 1-\(\lambda\). The filter list contains only LUP blocks which are the new blocks inserted into the HFB or the SUP blocks degraded from the resident list. For the former, they have no update period information, and the data classifier cannot distinguish whether they are hot blocks or not; for the latter, they have previous update period information and may be hot blocks with frequent updates but long periods, so they need to be observed further. Once an LUP block in the filter list is updated, the data classifier promotes it from the filter list to the most recently used (MRU) end of the resident list and removes its entry from the filter list. Unlike the filter list, the resident list holds the entries of all role blocks and sorts them by recency. To identify whether a data block with a short update period, the bottom of the resident list must be an sup block. In other words, it is the one with the longest update period among the SUP blocks. The data classifier reduces the cost of computing the update period of a role block by comparing the recency of the updated block to that of the bottom SUP block, rather than explicitly searching for a block with the longest update period. Please refer to Section 5.2.2 for a detailed description of the role adjustment for write-hit blocks. In addition, the number of ghost and shadow blocks in the resident list is not unlimited; their maximum update period is the update period of the last SUP block minus 1.
## 3 RPL Routing Attacks
### Worst Parent (WP) Attack  
The creation of a sub-optimal topology is one of the main objectives when targeting RPL network security toward overall network performance degradation. This can be achieved by having malicious RPL nodes select the candidate parents of the highest rank as their preferred parents instead of those with the minimum ranks. Although this violates the standard operation of RPL, this behavior can be easily realized to enable the initiation of the WP attack. The attacking nodes then systematically keep changing their preferred parents to the worst possible without the need for changing their current rank. The main objective is to establish the worst available paths for the attacking node's sub-DODAG. The attack targets the creation of routing sub-optimization which would lead to high transmission delay in addition to occasional routing loops and network isolation.
## 1 Introduction  
Triple-negative breast cancer (TNBC) represents approximately 15-20% of all newly-diagnosed breast cancers, and it remains the most aggressive subtype with the poorest outcome [1]. No molecular targets exist for TNBC, and alternative treatment strategies are required. Radiotherapy (RT) is a standard-of-care treatment and RT-induced DNA damage causes direct tumor cell death. However, RT also induces immune responses through the release of tumor antigens and the generation of a favorable inflammatory cytokine [2]. Immune-checkpoint inhibitors (immunotherapy, or ImT) can augment RT effects by increasing tumor-specific T-cell proliferation, thereby improving outcomes over RT or ImT alone [3,4]. In preclinical studies the combination of RT and anti-PD-1/PD-L1 therapies activated CD8 T-cells, reduced inhibitory mechanisms, and induced abscopal effect [4, 5, 6, 7, 8].  
Quantitative imaging (radiomics) comprises both semantic (volume, shape, etc.) and agnostic (texture and others) tumor features. Texture in particular, is a property commonly used for image classification in the field of pattern recognition. There are three different approaches used in image processing to find the textural feature of a region of interest in an image: first-order features, second-order features, and higher-order features. The first order features use statistical moments of the intensity histogram of the image and do not contain information about the relative position of pixels with respect to each other. Second order features employ the angular nearest-neighbor gray tone spatial-dependence matrices, also known as gray level co-occurrence matrices. Higher order features can be obtained by more complex manipulations of the image gray levels.  
Textures have been used to define tumor response to treatment [9,10]. Recently, radiomics have been applied to patient cohorts treated with immunotherapy alone for metastatic non-small cell lung cancer and they were found helpful in prediction of response to therapy [11, 12]. It has furthermore been demonstrated that when RT is incorporated in the predictive models as a covariate, it adds additional value to radiomics and ImT outcome correlations [13,14]. Other investigators have argued that the combination of ImT and RT has a synergistic effect on tumor response, surpassing the responses to ImT or RT alone [15,16], which further supports the abovementioned findings.  
The purpose of this work is to apply CT and MRI radiomics to combined radio-immunotherapy (RImT) treatments, when applied to treatment naive tumors, and determine if imaging better discriminates between responding and non-responding tumors. To our knowledge, this is the first work where tumor response to RImT regimen is assessed through histopathology evaluation.
# An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
## 4 Discussion  
It is notable from the previous section that there were promising results for both models 1 and 2 with the DL approach. The proposed model can help medical systems to predict seizures in patients with a previous medical history, as well as patients lacking a medical history.  
The work presented in this paper shows the great effect on results when segmenting data into smaller epochs in order to enrich the data fed into the DL model. In Figure 10, the effect of segmentation on the proposed model 1, using a window of L length, where L = 10, 30 s, and 8 min are shown.  
A comparative study was undertaken using prior related work, using the same benchmark dataset (CHB-MIT) and the results are presented in Table 10. As shown in the table, the proposed work outperforms state-of-the-art methods in seizure prediction by testing a larger number of samples and by being a patient-independent model. In [19], a binary single-dimensional CNN (BSDCNN) for epileptic seizure prediction was proposed. Their model uses 1D convolutional kernels in the prediction process. Furthermore, the authors of [20] implemented a one-dimensional capsule network for epileptic seizure prediction that achieved an accuracy of 97.74% using only two EEG signal channels. In addition, in [21], a patient-specific prediction model that uses a temporal-spatial multi-scale CNN was presented. Working with 16 patients, they achieved an accuracy of 93.3%. Finally, a patient-specific epileptic seizure prediction model was presented in [23]. They converted raw signals into images. Their model reached an accuracy of 94.3% when seizure prediction was done for 5 patients. However, the conversion of EEG signals into images could cause the loss of important information that CNN can extract from raw signals. Results showed that the proposed model performed better in different cases, such as when using 6 patients  
Figure 9: (**a**) Training vs. validation accuracy for channel 1, for model 2, where window = 10 s. (**b**) Training vs. validation accuracy for channel 1, for model 1, where window = 10 s. (**c**) Training vs. validation accuracy for channel 6, for model 2, where window = 30 s. (**d**) Training vs. validation accuracy for channel 5, for model 2, where window = 30 s.  
and 10 patients for prediction using DL. The improvements in rates were 0.87% compared to the values in [19], 0.13% compared to values in [20], 0.1% compared to values in [21], and 3.54% compared to the values in [23] respectively. The work presented in this paper provides a generic model that can use patients' historical data to predict seizures for other new patients with a high accuracy and precision.
# Experimental Investigation of Crystal Blocking in Drainage Pipes for Tunnels in the Kast Region
## 4 Results and Analysis
### Influence of the Longitudinal Drainage Pipe  
As shown in Figure 11, on the 5 th day of observation, sediment flowed into the longitudinal drain and began to accumulate, resulting in an increase in the number of crystals in the longitudinal drain. The reason for this phenomenon may be that the slope of the longitudinal drain is low, making it easier for sediment to be deposited in the drainage pipe. Figure 12a shows the crystallization curves of the tunnel drainage pipe versus time after installing the longitudinal drainage pipe. In the first 6 days, the crystal weight at the inner wall of the longitudinal drainage pipe increases rapidly and reaches a short-term peak of 2.67 g/m. On the 7 th day, the amount of crystallization decreased by 6%, while continuing to grow to the maximum value of 3.10 g/m from the 8 th day to the 12 th day. It may be because the unsteady crystals formed are washed away by the large flow of the longitudinal drainage pipe, resulting in a short-term decrease in the amount of crystals. After that, the crystallization rate is greater than the scouring rate, and the crystallization continues to grow steadily.  
The amount of crystallization in the longitudinal drainage pipe is about twice greater than that of the circular drainage pipe (1.50 g/m,1.69 g/m, and 1.68 g/m, respectively, corresponding to A, B, and C) due to the following reasons: Firstly, all the water flowing  
Figure 11: Photo of sediment deposition.  
Figure 12: Variation of crystallization rate and flow rates of drainage pipes with time under different conditions. (**a**) Crystallization rate curves. (**b**) Flow rate curves.  
out of the circular drainage pipe flows into the longitudinal drainage pipe, resulting in a large flow of the longitudinal drainage pipe and a large amount of ions passing through the unit time, as shown in Figure 12b; secondly, due to the effect of circulating water flow, a large amount of crystals falling from the circular drainage pipe flow into the longitudinal drainage pipe; thirdly, the slope of the longitudinal drainage pipe is small compared with the friction force of the crystal body, making the crystal formed at the circular drainage pipe falling off and the inner wall of the longitudinal drainage pipe easily, resulting in a constant amount of crystal.  
The test proved that the longitudinal drainage pipe is easier to form crystals and sediment than the circumferential drainage pipe. However, after the installation of the longitudinal drainage pipes, the crystallization amount and flow of the circular drainage pipes basically did not change significantly compared with the standard conditions, indicating that the addition of the longitudinal drainage pipes has no significant effect on the blocking performance of the circular drainage pipes. Therefore, when considering the long-term operation of the tunnel drainage system, priority should be given to the clogging of the longitudinal drainage pipe.
## 4 Proposed Protocol Design
### Energy Consumption Model  
The communication energy consumption model utilized in the proposed model is similar to that of [24]. The energy consumption for receiving the data packets \((e_{r})\) is measured based on the size of data packet, as given in Equation (5).  
\[e_{r}=k\times e_{dec} \tag{5}\]  
where \(e_{dec}\) is the energy dissipation due to the electronic circuits such as encoder, decoder, multiplexer, and filter, \(k\) is the length of packets. The energy consumption for transmitting the packets \((e_{t})\) is based on the size of packet and distance between the transmitter and receiver \(d\), as given in Equation (6).  
\[e_{t}=k\times e_{dec}+k\times\epsilon\times d^{\alpha} \tag{6}\]  
where \(e\) is the transmitter amplifier energy based on the propagation model (free space \(e_{fs}\) or multi-path \(\epsilon_{mp}\) fading), \(\alpha\) is amplifying factor, for free space model \(\alpha=2\), when \(d\leq d_{0}\) and for multi-path fading model, \(\alpha=4\), when \(d>d_{0}\), where \(d_{0}=\sqrt{\frac{e_{fs}}{\epsilon_{mp}}}\).
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Drought Index  
SPEI can be calculated by fitting the difference between precipitation and potential evapotranspiration based on a log-logistic probability distribution, which combines the multi-temporal characteristic of standardized precipitation index and the sensitivity of palmer drought severity index to changes in evaporation demand. A global gridded dataset of the SPEI was calculated using monthly precipitation and potential evapotranspiration from the CRU TS3.24.01 dataset [29]. This dataset is generated at a 0.5\({}^{\circ}\) spatial resolution and at a monthly time step and covers the period from 1902 to 2015. We used the SPEI dataset from 1982 to 2015 in this study, which contains timescales from 1- to 24-months. A 6-month SPEI value is formulated by the cumulative water deficit or surplus from five months before to the current month. Three drought scenarios including moderate (\(-1.5<\text{SPEI}\leq 1\)), severe (\(-2<\text{SPEI}\leq-1.5\)), and extreme (\(\text{SPEI}\leq-2\)) can be differentiated using a set of SPEI thresholds [29]. The yearly aridity index (AI), which has a spatial resolution of 0.5\({}^{\circ}\), was calculated with [PERSON]'s formula [30]. (Response: we cited the citation 31 in Section 2.3) According to this dataset, global land can be classified into arid (0.05 \(\leq\) AI < 0.2), semiarid (0.2 \(\leq\) AI < 0.5), semihumid (0.5 \(\leq\) AI < 0.65) and humid zones (AI \(\geq\) 0.65).
## 5 A Framework for SDN-SDR-Based Maritime Communications  
SATCOM offers numerous advantages over traditional point-to-point terrestrial communications, with a wide geographical coverage being the most significant advantage. However, SATCOM's potential for providing extensive coverage across global regions, the high cost of implementation, and extended propagation delays pose significant hurdles to its deployment within the maritime sector [1]. The maritime industry largely relies on SATCOM systems, which are expensive and have a low data rate [42]. Frequent maritime activities, on the other hand, require high-speed and reliable data transmission in order to ensure smooth communication between vessels and the control center. Current maritime wireless communication systems, however, do not meet this demand. SDN and SDR have tremendous potential to revolutionize maritime communications. With the proper design, integration, and deployment of SDN-SDR-based wireless communications infrastructure with the existing SATCOM infrastructure, both network and physical layer complexities can be alleviated. Maritime communications can be better managed with the planned deployment of software at these two layers to meet the demands and constraints of constantly changing on- and off-shore maritime communication environmental conditions and jurisdictional regulations.  
None of the current solutions in the literature have considered our proposed approach of leveraging SDN and SDR and the existing SATCOM infrastructure into a unified framework for robust maritime communications, especially offshore. An important element of introducing SDN and SDR into maritime communications is the simplification of the communication infrastructure onboard a vessel [7]. The introduction of SDN on top of SDR further bolsters the robustness of the communication infrastructure onboard a vessel, and together makes the management, maintenance, and troubleshooting much simpler. The remainder of this section focuses on the background of SDN and SDR and their potential to alleviate the challenges of maritime communications, followed by an intuitive use case for a unified SDN-SDR-based maritime communications framework integrated with existing SATCOM infrastructure.
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 2 Parameters and Description
### Bridge Design Parameters
#### 2.1.3. Abutment to Backwall Joint Stiffness  
The abutment to backwall (AB) joint stiffness significantly influences abutment response [15] and, therefore, has been considered as a design parameter. [PERSON] et al. [38]  
Figure 1: Bridge geometry: (**a**) typical cross section, (**b**) backfill height, and (**c**) PSC girder.  
investigated AB joint influence in terms of abutment displacement and rotation and concluded that both pinned and fixed AB joint models exhibited very little differences under uniform temperature rise and fall. Considering that the abutment displacement is mainly determined by the superstructure length [25], this result is reasonable. However, the consideration of AB joint is still necessary to identify forces in the superstructure and substructure because AB joint is a gateway through which force is transmitted from top to bottom.  
The specified reinforcement ratio of bars crossing the joint is often meager. PennDOT DM4 [35] specifies #16@500 mm U-shaped reinforcing bars, resulting in joint reinforcement ratios generally less than 25 percent of the recommended AASHTO LRFD [25] minimum reinforcement ratio. Many other state agencies use similar reinforcement details, but this reinforcement guideline still varies between hinge and rigid connection. This low reinforcing ratio permits rotation in response to superstructure expansion and contraction. In analysis, however, the joint is often ignored with the abutment and backwall modeled as a single monolithic element [8; 11; 36; 37]. Four IABs' field monitoring results [5] revealed differential rotations between the backwall and abutment where this flexible AB joint is present. Because of the significance of the AB joint, three joint stiffnesses were considered: (1) hinged (free rotation); (2) PA (Pennsylvanian) Standard (#16@500 mm U-shaped reinforcing bars); and (3) monolithic (rigid).