esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The DSS Induced Mice Trial  
A total of 32 healthy male C57 BL/6 mice (10 weeks old) were purchased from the Model Animal Research Center of Nanjing University (Nanjing, China, SCXK <JIangsu>20180027). All mice were maintained in Animal Center of Nanjing Agricultural University (SYXK<JIangsu>2011-0037) under the standard light-dark cycle home (25 \({}^{\circ}\)C, humidity of 50%) and supplied with normal diet (D12450K, protein 20%, carbohydrate 70% and fat 10%) and purified water. Following the regulations of the National Guidelines for Experimental Animal Welfare and Ethical Committee of Experimental Animal Center of Nanjing Agricultural University, the mice trials were carried on. After 7 days of adaptation, the mice were weighed (bodyweight 22.01 \(\pm\) 0.51 g) and randomly divided into four groups including negative control (NC), positive control (PC), GP-treated group 1 (GP-1, 100 mg/kg/day) and group 2 (GP-2, 300 mg/kg/day). According to the study of [PERSON] et al. (2018), the colitis mice model was induced by drinking DSS water (1.5%, \(w\)/\(v\), YEASEN Biotechnology, Shanghai, China) for 10 days. For the NC group, the regular diet and purified water were prepared for the whole process. In the PC group, the DSS (1.5%) water was supplied to mice in the first 10 days, and then administered with purified water by intragastric gavage for additional 10 days. In the GP group, the DSS water was supplied the same with PC in the first 10 days, after that, the GP was supplied to mice with 100 or 300 mg/kg/day in GP-1 and GP-2 groups, respectively. During the trial, the body weight, the feces, and the diarrhea condition were recorded every two days until the mice were sacrificed by cervical dislocation at the end of the experiment (20 days). Colon tissue was collected from all animals, the colon length was measured and the fresh feces was collected into sterile tubes. In addition, the fecal samples were collected for intestinal microbe sequencing.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## Appendix C Complexity Analysis of the SC Decoding Algorithm  
In this section, we analyze the number of different operations (e.g., tanh and atanh) required by the LLR-based SC decoding algorithm. The SC decoding algorithm is a recursive algorithm with the following complexity:  
\[\mathrm{T}(N)=2\mathrm{T}\left(\frac{N}{2}\right)+\mathrm{t}(N) \tag{16}\]  
where \(\mathrm{T}(N)\) is the number of operations needed for decoding a length-\(N\) sequence, and \(\mathrm{t}(N)\) is the number of operations needed for preparing data for sub-problems (the merging step). The merging step performs \(N\) tanh operations, \(\frac{N}{2}\) atanh operations, \(\frac{N}{2}\) float additions, and \(\frac{N}{2}\) float multiplications [4]. Thus, \(\mathrm{t}(N)=\frac{5}{2}N\), and Equation (16) turns into  
\[\mathrm{T}(N)=2\mathrm{T}\left(\frac{N}{2}\right)+\frac{5N}{2} \tag{17}\]  
Solving Equation (17), we obtain \(\mathrm{T}(N)=\frac{5}{2}N\log N\). These operations are composed of \(N\log N\) tanh operations, \(\frac{1}{2}N\log N\) atanh operations, \(\frac{1}{2}N\log N\) multiplications, and \(\frac{1}{2}N\log N\) additions.
# The Use of Radio and Telemedicine by TMAS Centers in
## 3 Results
### Communication Channels  
By analyzing the studies that were finally selected, we identified that most seafers contact TMAS centers for medical help through different means of communication. Figure 2 presents the frequency of work associated with distinct communication channels or telemedical technologies that were used by ships to contact onshore doctors. Five studies adopted the telephone [8,17,18,19,20], followed by email [8,18,19,21], radio [22,23], and web application platforms [24].  
Figure 1: PRISMA 2020 flow diagram for new systematic reviews which included searches of databases only (* searched database).  
Figure 2: Different communication technologies are used for requests of medical assistance at sea.
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Threat Impact Analyses  
With our detection and link routing scheme, we can minimize the availability threat impact from an indefinitely long time to a few seconds. Without our defense scheme, the fake base station can deprive the user equipment of cellular services from the legitimate base station for an indefinitely long time, as we describe in Section 4.1. However, with our scheme implemented, the threat impact is significantly less in time duration. The user equipment can restore its RRC connection from the fake base station to the legitimate base station within \(2029.11+897=2926.11\) ms = \(2.93\) s. The restoration time is the sum of detection time duration (\(\tau_{T}=897\) ms, discussed in Section 7.5) and link routing switching time duration (\(2029.11\) ms, discussed in Section 7.6). The detection contributes around \(30\%\) and the link routing contributes around \(70\%\) of the restoration time since the attack launches in our implementation. The threat impact time duration can be minimized further by reducing the switching time duration because the detection time duration depends on radio channel conditions and faulty base station approximation. The switching time duration can be minimized further with engineering effort because, in our implementation, we reset the software radio to start the base station scanning process, which takes a significant amount of time.
## 1 Introduction
For example, studies have confirmed that the shade cast by PV panels can increase edible biomass production in chicory and lettuce [20; 21; 31]. The growth patterns of crops cultivated in agrivoltaic systems exhibit variability, necessitating comprehensive studies to understand the implications for food security in relation to specific locations, crop types, and employed technologies. Certain agronomic crops may prove more suitable for integration with PV panels than others. Therefore, it is crucial to assess the impact of shade generated by PV panels on different crops andlocations, particularly in areas where climate change leads to diminished water availability and an increasing frequency of heatwaves. The aim of this paper is to explore the potential of agrivoltaic systems in optimizing land use efficiency, concurrently addressing energy production, food security, water resource utilization, and human capital investment in the context of climate change. Our research specifically delves into the influence of shade generated by PV panels on tomato. This crop was selected due to its global prevalence and commercial significance, as well as its vital role in human diets. Tomatoes are widely cultivated and consumed across the world, both fresh and in various cooked dishes, sauces, and salads. In Italy, the yellow datterino cultivar has gained widespread popularity in gastronomy due to its sweeter flavor profile compared to traditional red tomatoes. Italy is a major producer and consumer of tomato fruit, which holds significant economic value in the food market, surpassing other fruit-bearing vegetable crops in demand and market appeal. Therefore, integrating tomatoes into agrivoltaic systems presents a compelling area of interest and potential. This study seeks to assess the potential impact of shade provided by agrivoltaic systems on maintaining food security under different water availability conditions, comparing the outcomes with tomatoes grown in open fields within the same areas. Our analysis primarily focuses on evaluating food security by measuring crop productivity, particularly the biomass and the quality of tomato fruits produced under varying conditions of sunlight exposure (whether under full sunlight or PV panels) with different water supplies.
## 3 Field Tests for Energy Slab
### In-Situ Thermal Performance Tests (TPTs)  
As mentioned in Section 3.1, the TRTs cannot be directly applied to energy slabs with the horizontally layered configuration. Moreover, since the TRT was originally developed to estimate the effective thermal conductivity of the ground, it cannot evaluate the actual thermal performance of GHEXs [23]. The in-situ TPT is proposed to evaluate the actual thermal performance of GHEXs because it is simulated under real operational conditions, such as an intermittent operation (i.e., 8 h operation-16 h pause, 12 h operation-12 h pause) [24; 25; 26]. In this test, an artificial heating load is applied by maintaining a regular inlet fluid temperature using a constant-temperature water bath. From the TPTs, the heat exchange amount is calculated from Equation (3):  
\[Q=C\;\dot{m}\;\Delta T=C\;\dot{m}\;(T_{in}-T_{out}) \tag{3}\]  
The heat exchange of the energy slabs according to the pipe materials was evaluated. In the TPTs, test results can be fairly affected by ambient air temperature if there is no thermal insulation layer, especially during the resting period. Therefore, the thermal performance of the energy slabs, including a thermal insulation layer, was also evaluated. Figure 7b shows a schematic description of the TPTs. Figure 8 and Table 4 summarize the experimental set-up and monitoring instruments adopted in the test. In addition, the connecting tube between the heat exchange pipe and the constant-temperature bath was thermally insulated to minimize the influence of ambient air.  
Figure 8: Experimental set-up and monitoring instruments of thermal performance tests (TPTs). (**a**) Time controller; (**b**) Data logger; (**c**) Flow meter; (**d**) Constant Temp water bath; (**e**) Experimental set-up.
# Labeling Peptides with Radioiodine: An Overview of Traditional and Emerging Techniques
## 4 Indirect Radioiodination Methods: The Labelling of the Prosthetic Groups  
### Iododestannylation  
Iododestannylation is a frequently used method for introducing radioiodine, involving the reaction of a stannane precursor with an in situ generated iodinated reagent derived from NaI and an oxidant. The transformation results in radiolabeled derivatives through an ipso S\({}_{\mathrm{E}}\)Ar reaction (Figure 10) [22; 23; 24].  
The reaction starts with a stannane precursor and uses an in situ generated iodinated reagent from NaI and an oxidant. The transformation proceeds smoothly and selectively via an ipso S\({}_{\mathrm{E}}\)Ar reaction, which leads to the radiolabeled derivatives. Aryltialkylstannanes  
Figure 8: Method to prepare radioiodinated tetrazines.  
Figure 10: Iododestannylation through an S\({}_{\mathrm{E}}\)Ar reaction.  
Figure 9: Stannylated precursor to obtain an \({}^{125}\)I-labeled product.  
are typically synthesized from halogenated precursors through metalation or palladium-mediated reactions. The main advantage of iododestannylation is that it proceeds under mild conditions, and even deactivated aromatic compounds can enter these reactions, yielding iodine derivatives stable in vivo. Stannylation and destannylation reactions usually give high product yields, even when small amounts of starting trialkyltin are used. Unfortunately, the major drawback is the potential contamination of the radiotracer with organotin residues, which can hinder its use in clinical settings. Also, aryltrialkylstannanes can have stability and toxicity issues.  
Because contamination with organotin compounds is a significant concern in radioio-dination protocols, several strategies have been developed to minimize residual organotin levels in the final product. One such approach involves the use of fluorine-rich organostannanes to enable solid-phase extraction and efficient removal of tin residues [52]. More recent solutions include the use of fluoride-assisted solid-phase extraction or reactions with stannylated derivatives in ionic liquids, followed by filtration through SiO\({}_{2}\)[53]. Notably, [PERSON] et al. [52] demonstrated that radioiododestannylation using fluorine-rich organostannanes not only facilitates effective purification but also delivers high radiochemical yields. For example, [125]ISIB was synthesized using this approach and isolated with a 67% radiochemical yield (RCY) and 100% radiochemical conversion (RCC), underscoring the utility of this method for preparing small-molecule radiotracers such as [125]IJAGI-5198.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 3 Service Delivery Models in Fog Computing
### Software as a Service (SaaS)  
This type of service is similar to software as a service (SaaS) in cloud computing and means that the consumer of the service accesses the applications offered by the fog node through a thin client or simple interface. The fog provider applications run on a group of federated nodes managed by the fog provider. The consumer of SaaS fog services is exempt from the management and control of the underlying fog infrastructure, including the network, servers, operating systems, storage, or even individual application functionality, except configuring user-specific application settings.  
Smart cities use various software tools in their operations and for data analysis. The SaaS delivery model is becoming attractive for smart cities as it provides them with online software services and various software tools deployed at the fog level and facilitates remote access to them over a network or the Internet. It does not require installing fog-based services locally and makes it easier to access the latest version of the services.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Color (OC)  
OC is of substantial importance in monitoring aquatic environments and in studying the biology, chemistry, and physics of oceans. The main reason for measuring OC is to study phytoplankton. Phytoplankton has a foundational role in forming the oceanic food web and is the initiation element of the food chain for most of the Earth. OC can also represent the health and chemistry of the ocean. Finally, OC information can help the fishing industry by finding suitable fishing spots that are rich in phytoplankton.  
The wide variety of RS systems (e.g., satellites with different spectral, spatial, and temporal resolutions) has facilitated OC studies by developing various algorithms to derive biogeochemical and optical parameters related to OC. This has also helped to efficiently characterize the ocean biosphere properties at high spatiotemporal scales [182; 183]. OC measurement using RS methods is highly required for various oceanographic applications, especially on a global scale. Due to the need for efficient environmental monitoring of the offshore and onshore oceans, there have been considerable developments in spaceborne OC technology and the corresponding algorithms in recent years.  
OC can be mainly studied by analyzing the reflectance data and, thus, optical RS systems are the main instruments that have been used for OC mapping. However, it should be noted that TIR radiometers, MIR radiometers, and SAR systems have also been rarely used for OC estimation. OC mapping using optical satellites is only discussed in this section.
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
## 2 Background Study
### Related Literature  
Several studies cover combining Blockchain with ML to solve security and privacy issues in the literature. We summarize these integrations into three main categories, NN-based integration, partial integration of Blockchain, and Blockchain with Federated Learning. One of the NN-based integration examples is called DeepRing, where authors of [12] designed each NN layer to be presented as a Blockchain block to protect against tampering attacks. Although this integration stood robust against a tampering attack that downgraded a regular CNN performance by 20.71%, its application is limited to NN-based ML models. Other research included the partially separated integration of ML with Blockchain [13]. One example is [14]'s work of combining ML with Blockchain for a more efficient and safer COVID-19 vaccine supply chain. Their solution queried records from the Blockchain to feed them into a separate Long Short Term Memory (LSTM) classifier. The demand forecasting LSTM helped preserve 4% of the vaccine ratio, 6 million vaccine doses.  
Most Blockchain-integrated ML solutions focus on employing the technology to protect the privacy of the ML model. Studies with such scope deployed Federated Learning (FL) [15], or as can be known as Decentralized ML (DML) [16], where a centralized server collects and aggregates learning parameters among participating nodes.  
The majority of found FL-based integration, such as works of [17; 18; 19], relied on off-chain execution of ML training while applying different types of consensus algorithms to manage work among nodes. This partial application of the ML decision process is due to the metered usage and storage of Blockchain, which can result in the prosperous implementation of the whole ML fitting and decision process.  
One other noticeable example is the collaborative deep learning framework called DeepChain. Similar to the previously mentioned studies, [PERSON]. [20] designed DeepChain for nodes to train a global model locally and then upload their gradients through a smart contract. They also relied on consensus algorithms for control updating the model's gradients which are averaged and broadcasted again for the next learning iteration. Table 1 summarises existing related work and their limitations.  
As shown in Table 1, the studies incorporating ML with Blockchain are scarce. At the same time, most of these integrations focused on collaborative learning and partially included Blockchain in the ML decision process. None of the found literature included the decision process to be performed on-chain. To our knowledge, there is still no ML decision integration with Blockchain. This study proposes the further inclusion of the decision function in smart contracts to achieve a more reliable and secure decision process.
## 4 Applications of InSAR in Landslide Monitoring
### Recent Advances in Landslide Monitoring with InSAR
#### 4.2.4 Improvements in Spatial Visibility and Temporal Resolution
As emphasized by [PERSON] et al., a higher sampling frequency of InSAR data can lead to improved prediction accuracy [86; 89], which has also been reported by several other studies [103; 104]. As discussed in Section 4.2.3, failure time prediction is mainly based on creep theory, and for landslides with unclear or short accelerated tertiary phases, higher sampling frequency is crucial for improving prediction accuracy. However, a fundamental limitation of satellite-based InSAR is its relatively low temporal resolution, especially when compared to in situ sensors or GB-InSAR systems, which can easily achieve sampling frequencies at the minute level [105]. Even the widely used Sentinel-1 satellite, for instance, has a revisit period of up to six days. While data fusion may help achieve the goal of improving temporal resolution, it may also introduce challenges such as coherence loss, phase unwrapping errors, and variations in radar geometry. A promising development is that future InSAR constellation missions with a higher temporal resolution, along with the launch of geosynchronous SAR satellites like Ludi Tance-4 (01), are also expected to mitigate these limitations.
## 3 Experiments
### Experimental Results
#### 3.5.4 Model Efficiency Comparison  
To comprehensively evaluate the proposed model in terms of computational efficiency, resource consumption, and classification performance, we conduct a systematic comparison at 100\(\times\) magnification between HoRFNet and several baseline models, including the Backbone, SeONet, RFNet, and the lightweight EfficientNet-lite. The experimental results are presented in Table 6. Compared to the Backbone model, HoRFNet shows significant advantages across multiple dimensions. First, in terms of classification performance, HoRFNet achieves the highest accuracy of 98.56%, significantly outperforming the Backbone and other comparison models. This result demonstrates that HoRFNet possesses stronger feature representation and discriminative capabilities. In terms of training efficiency, HoRFNet completes training in just 41 min, clearly outperforming the Backbone's 64 min. This indicates that its optimized architecture and enhanced feature extraction capability effectively reduce training time. During inference, HoRFNet achieves an average inference time of 7.80 s, which is higher than the Backbone's 3.28 s. However, this difference primarily results from the incorporation of high-order statistical modeling and multi-branch receptive field modules. Specifically, the matrix power normalization in covariance pooling and the use of dilated convolutions enhance feature representation and global perception, but also increase the complexity of the inference path. Despite this, HoRFNet still performs much faster than EfficientNet-lite, which takes 26.40 s, highlighting its strong deployment responsiveness in practical settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Two-Class** & **Four-Class** \\ \hline Backbone & 80.00 & 76.25 \\ HoRFNet & **88.75** & **82.50** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of HoRFNet and baseline model on the BACH dataset under two-class and four-class classification tasks.  
In terms of resource consumption, HoRFNet has 101.72 M parameters, which is only about one-fourth the size of the Backbone's 396.98 M. This enhances the model's compactness and deployment flexibility. Regarding computational complexity, HoRFNet requires 3.57 G FLOPs, which is comparable to the Backbone's 3.45 G FLOPs. This demonstrates that HoRFNet substantially enhances representational capacity without introducing a significant computational burden. In contrast, although EfficientNet-lite has fewer parameters (81.64 M), its computational complexity reaches 6.80G FLOPs, which results in a disadvantage in actual computational efficiency. Overall, HoRFNet maintains excellent classification performance while achieving a favorable balance between computational efficiency and model effectiveness.
## 3 Methods
### Inter-Continental Model Transfer Based on Fine-Tuning and Small Sample Sizes  
Inter-continental model transfer was conducted based on the fine-tuning of the pre-trained model (Model A) and a small sample size. The Shuangcheng District, Songnen Plain, Sanjiang Plain, and Liaohe Plain, which are located in Northeast China, were selected as the target areas for model inter-continental transfer. The dataset for the Shuangcheng District in 2017 was used as the training and validation dataset to fine-tune the model. Considering that the features of rice in Northeast China are significantly different from those in the United States, the pre-trained model was unable to extract effective recognition features of rice in this region (which will be discussed in the next section). We fine-tuned the parameters of both the first layer (temporal feature extraction layer) and the output layer of the pre-trained model with a small sample size; the remaining layers of the pre-trained model were frozen. After the fine-tuning of the model was completed, it was applied to rice mapping in the Shuangcheng District from 2018 to 2020 to test the temporal generalization ability of the model. The fine-tuned model was also applied to the Songnen, Sanjiang, and Liaohe plains to test the spatial generalization ability of the inter-continental transferred model.  
Additionally, the influence of the number of samples used for fine-tuning on the accuracy of inter-continental model transfer was examined to determine the balance point between the number of samples and the rice mapping accuracy during model fine-tuning. In the process of model fine-tuning and transfer, the number of training samples used was increased from 5 to 50 at intervals of 5. Each number of samples was repeatedly trained 10 times. The average value of each accuracy assessment metric in the 10 training samples was taken as the final accuracy result. Its standard deviation was calculated to evaluate the stability of the model transfer with different sample sizes.