esa-sceva/satcom-chunk-collection huggingface dataset:
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 4 Experimental Results
### Detectability Comparisons with Prior Works
To achieve the tampering of watermelon, the 5 MSBs of \(\{a_{i}^{\text{W}},\ b_{i}^{\text{W}}\}_{i=0}^{N^{\text{W}- 1}}\) are use to replace that of \(\{\hat{a}_{i},\ \hat{b}_{i}\}_{i=0}^{N^{\text{W}- 1}}\). Figure 9 shows the refined detection results of [25; 26; 27] and the proposed method. Figure 9a-c show that [25; 26; 27] do not detect the tampering of bananas, orange and watermelon, respectively. This is because the generation of authentication codes of [25; 26; 27] is independent of the protected contents, e.g., position information and MSBs of quantized values. However, the proposed method can detect these tampering, as shown in Figure 9d, demonstrating the superiority of our method against other works. Figure 7: The tampered and detection results of [25; 26; 27] and the proposed method. (**a**) The tampered image; (**b**) The tampered region; (**c**) Detection result of [25]; (**d**) Detection result of [26]; (**e**) Detection result of [27]; (**f**) Detection result of the proposed method. The components for generating authentication codes, embedding techniques and detectability of methods [25; 26; 27] and the proposed method are summarized in Table 5. Furthermore, the performance in recovery domain for methods [20; 21] are also compared. Comparisons are made between the various detection methods for \"Detection of bananas\", \"Detection of orange\" and \"Detection of watermelon\" with tampering collages of banana, orange and watermelon, respectively. The table shows that methods [20; 25; 26; 27] cannot detect all these types of tampering. The reason is that the components used for generating authentication codes are not included in all to-be-protected contents. In contrast,  
Figure 8: The tampering of Peppers image. (**a**) The tampered image; (**b**) The tampered regions. Figure 9: Detectability comparisons between [25; 26; 27] and the proposed method. (**a**) Detection result of [25]; (**b**) Detection result of [26]; (**c**) Detection result of [27]; (**d**) Detection result of the proposed method. method [20] and the proposed method use contents that need to be protected to generate authentication codes, and thus they are able to detect these tampering.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
###### Abstract  
Synthetic Aperture Radar (SAR) image ship detection faces challenges such as distinguishing ships from other terrains and structures, especially in specific marine complex environments. The motivation behind this work is to enhance detection accuracy while minimizing false positives, which is crucial for applications like defense vessel monitoring and civilian search and rescue operations. To achieve this goal, we propose several architectural improvements to You Only Look Once version 8 Nano (YOLOv8n) and present Small Target-YOLOv8(ST-YOLOv8)--a novel lightweight SAR ship detection model based on the enhance YOLOv8n framework. The C2f module in the backbone's transition sections is replaced by the Conv_Online Reparameterized Convolution (C_OREPA) module, reducing convolutional complexity and improving efficiency. The Atrous Spatial Pyramid Pooling (ASPP) module is added to the end of the backbone to extract finer features from smaller and more complex ship targets. In the neck network, the Shuffle Attention (SA) module is employed before each upsampling step to improve upsampling quality. Additionally, we replace the Complete Intersection over Union (C-IoU) loss function with the Wise Intersection over Union (W-IoU) loss function, which enhances bounding box precision. We conducted ablation experiments on two widely used multimodal SAR datasets. The proposed model significantly outperforms the YOLOv8n baseline, achieving 94.1% accuracy, 82% recall, and 87.6% F1 score on the SAR Ship Detection Dataset (SSDD), and 92.7% accuracy, 84.5% recall, and 88.1% F1 score on the SAR Ship Dataset_v0 dataset (SSDv0). Furthermore, the ST-YOLOv8 model outperforms several state-of-the-art multi-scale ship detection algorithms on both datasets. In summary, the ST-YOLOv8 model, by integrating advanced neural network architectures and optimization techniques, significantly improves detection accuracy and reduces false detection rates. This makes it highly suitable for complex backgrounds and multi-scale ship detection. Future work will focus on lightweight model optimization for deployment on mobile platforms to broaden its applicability across different scenarios.  
deep learning; SAR ship detection; ST-YOLOv8 +
Footnote †: journal: applied sciences
## 4 Model Implementation and Validation
### Validation of SIC Forecasts  
We have shown the high goodness-of-fit of the proposed ARIMA model. Nevertheless, the forecast SICs need further validation. To this end, SICs are selected from nine CMIP5 experiments [23]. Although the CMIP5 lacks data before 2006, both CMIP5 and ERA5 provide SICs from January 2006 to September 2022. Therefore, this period is chosen to compare the accuracy of the proposed model. Figure 19 identifies the absolute errors of the monthly averages of SICs between CMIP5 and ERA5. Each dot represents an absolute error in a single month, and the color represents the quarter to which the month belongs. Figure 20 compares the RMSE of the monthly average of SICs between CMIP5 and ERA5. The most accurate experiment is listed at the top and the least accurate experiment at the bottom. The top four experiments are selected as the reference to validate the forecast SICs of the proposed ARIMA model.  
Figure 19: The absolute errors of SICs between CMIP5 and ERA5 from January 2006 to September 2020 in sub-region No. 2.  
Figure 18: The quarterly means of reanalysis and forecast SICs in all sub-regions. (**a**) January, February, and March (JFM). (**b**) April, May, and June (AM). (**c**) July, August, and September (JAS). (**d**) October, November, and December (OND).  
In October, November, and December in sub-region No. 2, the monthly averaged SICs of the selected experiments are plotted in Figure 21. The solid blue line represents the mean SICs of these four experiments, surrounded by the 95% confidence interval (green shaded region). The ERA5 SICs are plotted as the solid brown line, and the ARIMA model regressed SICs are represented as the dashed blue line. From 2006 to 2015, the mean experimental SIC deviates from the SICs of ERA5 and the ARIMA model. Between 2015 and 2022, all three lines lie close together. From 2023, the ARIMA model forecast SICs stay close to the mean SICs of the CMIP5 experiments, the differences of which are bounded by 0.1 of the SIC value. Therefore, we can conclude that the proposed ARIMA model can provide the same confidence for forecasts as CMIP5 experiments.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 3 Transceiver-Design
### Software Architecture  
The software architecture was divided into the following layers: Application, Control, Coding, Protocol, OWC-Front-End (OWC-FE) and Hardware Abstraction Layer (HAL).  
At the bottom is the HAL, which provides the basic functionality for, e.g., controlling the LED and reading the photodiode signal, as well as setting the MCU and the OPA to energy saving mode.  
Above this layer, there is a layer for the OWC-Front-End (OWC FE). This is used to directly send and receive individual bits and bytes. Currently, UART configured to 1 Start bit, 8 Data bits and 1 Stop bit without parity (8N1) is used for this.  
Since the GPIO control is not carried out via the fast Advanced High-performance Bus (AHB), but via the Advanced Peripheral Bus (APB), which only allows for the slow control of the pins, the use of the UART enables faster transmission, while at the same time, synchronization is carried out at byte level.  
Furthermore, a layer for coding as well as a small protocol layer were created. The Hamming (7,4) and Reed-Solomon (255, 223) codes are currently implemented. In case one of these codes is used, the resulting message frame size is greater than 1, which is why in these cases an additional synchronization symbol (ASCII: _SYN_, DEC: 22) is sent beforehand for synchronization at message level and to recognize the start of a message at the receiver side. Additionally, within the Reed-Solomon code, the length of the message to be transmitted is added at the beginning. This results into a length of 256 bytes, where only 222 bytes are actual information. In the case of the Hamming code, 3 parity bits are added to 4 bits of information. Thus, one byte (8 bits) is split into two half bytes (2 \(\times\) 4 bits) and encoded independently of each other with 3 parity bits each, resulting into 2 \(\times\) 7 bits. The SYN symbol can also be represented with 7 bits. Therefore, when using the Hamming code,the UART can be configured to 7 data bits. The overall amount of data to be transmitted results into \(3\times 7=21\) bits (1 SYN Byte and 2 half bytes with 3 bits of parity) for 8 bits of information. Due to these extra symbols and redundant information, the coding rates decrease to the rates shown in Table 1.  
At the highest control layer level, a simple Application Programming Interface (API) for data transmission is provided to the user, enabling function calls like is_data_available(), send() or start_receiving(), as well as configuring the baud-rate and coding.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 4 The Adaptive Polling Medium Access Control (APMAC) Protocol
### Dissociation Phase  
After completing data transmission, the VLAP and VLN should decide either to maintain or to terminate the connection. In this context, the choice depends on the state of the two devices. For instance, if the VLN is sending data to the VLAP and after completing its available buffer size the VLAP may have some bits destined to that VLN. Instead of terminating the connection, the VLAP may continue transmitting its data. Likewise, if the VLN is receiving data from the VLAP, after data transmission from the VLAP, the VLN may have some data in its buffer destined to the VLAP. In this case, terminating the existing connection can cause unnecessary new connection request. The two devices can keep transmitting data using existing connection. However, if there is no more data transmission from either device, then the connection should be terminated. In this case, the transmitting node should send the CTR to its counterpart. The CTR contains the communicating nodes' identities, sending time, and the connection termination time. In order to avoid premature termination, the device that sends CTR should wait for the CTA from its counterpart device. If the RTT duration finishes without receiving the CTA, the device can terminate the connection. Moreover, if the device receives CTA within the RTT, it terminates the connection. After connection termination the VLAP updates its polling table by removing the details of VLNs that have completed data transmission.
[11] designed a weakly supervised pedestrian attributes location network based on GoogleNet, and the attributes labels are predicted by the detection results of mid-level attributes-related features instead of directly predicting the whole human sample. In addition, [PERSON] et al. used parts that detect by poselet to integrate with the whole pedestrian, and used human-centric and scene-centric context information, and the deep features are extracted to identify pedestrian attributes [12]. However, this contextual information cannot always be used in monitoring scenarios. In the above research, the recognition of pedestrian attributes in the monitoring scene has some problems. For example, the quality of the image acquired in the dataset is poor, the change of appearance and the attributes may be in different spatial positions, fewer training samples are marked, the attributes usually do not have the same distribution, and there is an imbalance of samplesproblem [13; 14; 15]. These problems affect the network model training and the accuracy of the pedestrian attributes recognition model. Therefore, we present a new pedestrian attributes recognition algorithm for important attributes in video surveillance as shown in Figure 1. This method improves the loss function to reduce the impact of sample imbalance and to achieve multi-attribute and multi-label pedestrian attributes recognition. The main contributions of this paper are as follows:  
(1) Construct a new pedestrian attributes dataset with multi-attribute and multi-label as the same attribute. The built dataset not only has indoor images, but also has more outdoor scenes with pedestrian images. The dataset is much richer and it includes a binary-class label and multiple-class label. (2) Combine multi-tasking learning and multi-label learning. This is different from other existing methods which express and identify the pedestrian attributes in a binary classification way. The proposed method includes both the binary classification problem of the same attribute and the multiple classification problem of the same attribute. Here, we propose a loss function based on the combination of Sigmoid and Softmax loss, which solves the multi-label in the same attribute problem and multi-attribute identification problem at the same time. (3) Aiming at the problem of imbalanced samples in data samples, a dynamic weight in the loss function method has been proposed, which can adaptively adjust the weight proportion of the positive and negative in the data samples.
## 5 Results
### Experimental Findings  
Figure 11 shows examples of the baseline measurements taken on a concrete slab, with gage lengths of either 20 cm (left panel) or 14 cm (right panel). The diagrams report the relative displacement (\(\Delta\)) occurring between each pair of gage points as a function of the applied stress in the slab (\(\sigma_{\mathrm{c}}\)). At each stress level, the relative displacements of the gage point pairs at 14 cm showed better consistency compared with those of the 20 cm gage point pairs. This trend was confirmed in all tests (diagrams not reported for sake of simplicity).  
Examples of typical results of the tests are shown in Figure 12, where the concrete stress estimated by the flat jack in accordance with Equation (1), \(f_{m}\), is reported versus the stress in the concrete slab, \(\sigma_{\mathrm{c}}\), obtained by dividing the applied compressive force by the cross-section area of the slab. In each test, the conversion factor \(K_{m}\) used for estimating \(f_{m}\) was adjusted to the actual flat-jack pressure and cut slot width according to the calibration curves reported in Figure 9.  
Figure 13 presents the results in terms of average values, showing, for each pressure level in the concrete, the stress measurement estimated as the mean across all tests. These averages account for the type of instrument used (25 cm or 33 cm flat jack) and the gage length (14 or 20 cm). Standard deviations are also reported and shown as error bars.  
Figure 11: Baseline measurements taken on sample 3: (**a**) Gage length, 20 cm. (**b**) Gage length, 14 cm.  
Figure 10: Flowchart representing the calculation process of the concrete stress from the flat-jack pressure measurements.  
For ease of comparison, Tables 3 and 4 report the average error \(E\) and the coefficient of variation \(CoV\) (defined as the standard deviation divided by the mean) at each pressure level for both the 25 cm and 33 cm flat jacks and for the 14 cm and 20 cm gage length.
## 1 Introduction  
Currently, cancer is one of the main public health problems worldwide. This disease develops because certain cells escape apoptosis and continue to divide in an uncontrolled manner [1]. In the latest estimates for the year 2020, there were 19.3 million new cases and around 10 million deaths caused by this disease. In addition, these estimates are not going to improve in the coming decades, since up to 30.2 million cases are expected in 2040 [2]. Among the most frequent tumors, and with the highest mortality, we find gastrointestinal tumors [3]. Among gastrointestinal tumors, colon cancer and stomach cancer are in the top five for incidence and mortality, among others. Colon cancer is the third most frequent type of tumor, with an incidence of almost 2 million cases, and has the second highest mortality, with almost 1 million deaths, and stomach cancer is fifth in incidence, with around 1 million cases, and fourth in mortality, with around 800 thousand deaths [2, 4].  
There are different approaches in the treatment of these tumors, such as surgery, radiotherapy, or chemotherapy, and despite them, neither incidence nor mortality is decreasing. This is largely due to the fact that the tumor cells acquire multidrug resistance (MDR) to treatment, which leads to failure in the treatment and to the survival of the tumor cell [5, 6]. Plant-derived natural products have been widely studied for their pharmacological potential to overcome MDR in cancer [7]. These molecules can be classified into several categories, including flavonoids, alkaloids, terpenoids, and coumarins. Flavonoids are known for their antioxidant properties and ability to modulate efflux pumps [8], whereas alkaloids often interfere with microtubule dynamics [9]. Terpenoids have shown promise in inducing apoptosis and reversing drug resistance mechanisms [10], and coumarins exhibit several pharmacological effects, including the modulation of drug-metabolizing enzymes and interference with cancer cell proliferation, contributing to the potential to overcome drug resistance [11].  
This review will explore how these and other plant-derived compounds may enhance the efficacy of chemotherapy in gastrointestinal tumors by modulating MDR phenotypes both in vitro and in vivo.
## 4 Results
To address this research question, a detailed examination is conducted to identify the predominant antenna architectures employed in rectenna-based energy harvesting systems. The analysis is organized into three subsections. The first outlines the principal antenna designs reported in the literature, emphasizing structural typologies and typical use cases. The second subsection explores how these designs correlate with key physical parameters--such as surface area, radiation efficiency, operating frequency, and gain--highlighting trade-offs and performance implications. Finally, the third subsection analyzes \begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Antenna** & **Rectifier** & **Bands frequency** \\ \hline
[72] & Slot insertion & Multi-step & L band, S band, S band \\ \hline
[73] & Slot insertion & Multi-step & UHF/ISM \\ \hline
[74] & Circular patch & Without & S band \\ \hline
[75] & Rectangular patch & Schottky & L band, UHF, UHF/ISM, S band \\ \hline
[76] & Circular patch & Schottky & L band, S band \\ \hline
[77] & Bow-tie & Single-step & UHF, L band, S band, S band \\ \hline
[78] & Rectangular patch & Schottky & S band, C band \\ \hline
[79] & Circular patch & Single-step & UHF, UHF/ISM, S band \\ \hline
[80] & Array & Microwave & UHF/ISM, L band, S band, S band \\ \hline
[81] & Rectangular patch & Schottky & UHF \\ \hline
[83] & Array & Multi-step & S band \\ \hline
[84] & Array & Half-wave & S band, S band \\ \hline
[85] & Slot insertion & Without & S band \\ \hline
[86] & Rectangular patch & Full-wave & UHF/ISM, S band \\ \hline
[87] & Rectangular patch & Schottky & S band \\ \hline
[88] & Array & Without & L band, L band, S band \\ \hline
[89] & Rectangular patch & Without & S band, S band \\ \hline
[90] & Other & Without & UHF/ISM, L band \\ \hline
[91] & Array & Multi-step and Schottky & S band \\ \hline
[92] & Rectangular patch & Schottky & S band \\ \hline
[93] & Other & Schottky & UHF \\ \hline
[94] & Rectangular patch & Schottky & S band, C band \\ \hline
[95] & Spiral & Schottky & UHF, L band, S band \\ \hline
[96] & Spiral & Multi-step & S band \\ \hline
[97] & Rectangular patch & Without & UHF, UHF/ISM, S band, UHF \\ \hline
[98] & Other & Single-step & S band, Ku band \\ \hline
[99] & Slot insertion & Multi-step & S band \\ \hline
[100] & Rectangular patch & Schottky & UHF/ISM, S band \\ \hline
[101] & Circular patch & Multi-step & S band, C band \\ \hline
[102] & Fractal & Multi-step & UHF/ISM, L band, L band, S band, S band, S band \\ \hline
[103] & Circular patch & Schottky & S band, S band–X band \\ \hline
[104] & Circular patch & Multi-step & S band, C band \\ \hline
[105] & Circular patch & Multi-step & S band \\ \hline
[106] & Rectangular patch & Half-wave & S band \\ \hline
[107] & Circular patch & Schottky & S band \\ \hline
[108] & Slot insertion & Multi-step & L band, L band, S band \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._the substrate materials' influence on antenna performance, considering dielectric properties and fabrication constraints relevant to low-power energy harvesting scenarios.
###### Abstract  
Warming is expected to intensify hydrological processes and reshape precipitation regimes, which is closely related to water availability for terrestrial ecosystems. Effects of the inter-annual precipitation changes on plant growth are widely concerned. However, it is not well-known how plant growth responds to intra-annual precipitation regime changes. Here, we compiled reanalysis climate data (ERA5) and four satellite-based vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Solar-induced Chlorophyll Fluorescence (SIF), and the Modified Triangular Vegetation Index (MTVI2), to evaluate the response of alpine grasslands (including alpine meadow and alpine steppe) to the change of precipitation regimes, especially to the intra-annual precipitation regimes on the Tibetan Plateau. We found monthly precipitation over the alpine steppe significantly increased in the growing season (May-September), but precipitation over the alpine meadow significantly increased only in the early growing season (May-June) (MJP) during the past four decades (1979-2019). The inter-annual plant growth (vegetation indices changes) on the alpine meadow was dominated by temperature, but it was driven by precipitation for the alpine steppe. On the intra-annual scale, the temperature sensitivity of the vegetation indices generally decreased but precipitation sensitivity increased during the growing season for both the alpine meadow and steppe. In response to the increase in MJP, we found the temperature sensitivity of the vegetation indices during the mid-growing season (July-August) (MGNDVI, MGEVI, MGSIF, and MGMTVI2) in the alpine meadow significantly increased (\(p<0.01\)) while its precipitation sensitivity is determined (\(p<0.01\)). We infer that more MJP over the meadow may be the result of enhanced evapotranspiration, which is at the expense of soil moisture and even induces soil \"drought\" in the early growing season. This may be to elevate community water acquisition capacity through altering root mass allocation and community composition, consequently regulating the divergent climate sensitivities of vegetation growth in the mid-growing season. Our findings highlight that it is inadequate to regard precipitation as an indicator of water availability conditions for plant growth, which may limit our understanding of the response and acclimatization of plants to climate change.  
precipitation changes; climate sensitivity; remote; water availability; Tibetan Plateau 2022142484. [[https://doi.org/10.3390/rs14102484](https://doi.org/10.3390/rs14102484)]([https://doi.org/10.3390/rs14102484](https://doi.org/10.3390/rs14102484))