esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Data Sources
### Plant-Level Emissions Data  
We sourced facility-level power plant CO2 emissions data reported by the following agencies to compare against estimates derived from our machine learning approach:  
* United States--US EPA Clean Air Markets Program Data (CAMPD) ([[https://campd.epa.gov](https://campd.epa.gov)]([https://campd.epa.gov](https://campd.epa.gov)), accessed on 1 June 2023);
* European Union-European Union Emissions Trading System (EU ETS); ([[https://www.eea.europa.eu/data-and-maps/data/european-union-emissions-trading-scheme-17/e-ets-data-download-latest-version](https://www.eea.europa.eu/data-and-maps/data/european-union-emissions-trading-scheme-17/e-ets-data-download-latest-version)]([https://www.eea.europa.eu/data-and-maps/data/european-union-emissions-trading-scheme-17/e-ets-data-download-latest-version](https://www.eea.europa.eu/data-and-maps/data/european-union-emissions-trading-scheme-17/e-ets-data-download-latest-version)), accessed on 25 October 2023);
* Australia-Clean Energy Regulator (CER) National Greenhouse and Energy Reporting (NGER) Scheme ([[https://www.cleanenergyregulator.gov.au/NGER](https://www.cleanenergyregulator.gov.au/NGER)]([https://www.cleanenergyregulator.gov.au/NGER](https://www.cleanenergyregulator.gov.au/NGER)), accessed on 25 October 2023);
* India-Central Electricity Authority (CEA) ([[https://cea.nic.in](https://cea.nic.in)]([https://cea.nic.in](https://cea.nic.in)), accessed on 25 October 2023).  
Note that, for Australia and India, emissions data are reported on the local fiscal year (1 July-30 June in Australia, 1 April-31 March in India). For comparability to annual-level ML-based predictions, emissions were converted to the Gregorian calendar year by rescaling based on sub-annual electricity data reported from NEM and NPP, respectively:  
\[\text{monthly emissions}=\frac{\text{monthly generation}}{\text{yearly generation}}*\text{yearly emissions}\]
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 4 mmWave and MIMO-Specific Attacks  
### Beam Alignment Jamming  
The BA process outlined for 5G NR has been engineered to be fast and precise in non-malicious radio environments [62]. However, it can represent a potential vulnerability and advantage for smart jammers [80; 81]. They could initiate an attack during the BA phase with the aim of reducing the precision of beam selection. This could negatively affect the overall performance and the quality of service that users experience. This type of attack can also be performed on other communication standards relying on beamforming technology, such as 802.11 ad [82]. The 5G NR BA process is illustrated in Figure 8.  
Figure 8: NR beam-alignment procedure and jamming attack.  
In order to mitigate the effect of the BA jamming attack, the authors of [80] propose a randomized probing technique, which involves transmitting a corrupted probing sequence to enable the user equipment to reject the jamming signal using subspace-based orthogonal projections and jamming cancellation. This method shows promising results in maintaining the accuracy of beam selection and ensuring the quality of service despite the presence of smart jammers.
# Cyber-Storms Cone from Clouds: Security of Cloud Computing in the IoT Era
## 6 Generic Security Issues
### Availability
#### 6.3.1 Network Level Issues  
DoS attacks, as well as Distributed Denial of Service (DDoS) attacks, are the main categories of attacks that can affect availability at the network level. DDoS attacks are more dangerous than SDoS ones [128], since they hide the original attacker, make it difficult to distinguish between a legit overload and a malicious attack, and generate a huge quantity of traffic [129; 130].  
Notably, even though DoS and DDoS attacks affect other paradigms than Cloud computing, researchers demonstrated that these are critical threats for Cloud computing. As a matter of fact, not only Cloud can be the victim of such attacks, but it can be part of the attacking infrastructure; for example, botClouds [131] are DDoS botnets deployed in the Cloud environment.  
[PERSON] et al. [132] identified a growing number of DoS attacks occurrences in Cloud environments, and argue that this relationship may be rooted in the intrinsic characteristics of the Cloud which, in a certain way, support the success of DoS attacks. For the sake of brevity, we are not going to dig into each and every kind of DoS attack. We point out that authors of [93] identified two types of flooding attacks effects which are specific of the Cloud, namely Direct DoS and Indirect DoS.  
To have a clear picture of the impact that DoS attacks can have, it is sufficient to consider that the DDoS Mirai malware infected over 600.000 devices and its DDoS attacks reached traffic peaks of 1 Tbit/s [7; 133].
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### General LPWAN Architecture  
Owing to the different LPWAN technologies available today, it is difficult to present an all-encompassing architecture that unifies all LPWAN technologies. Such an inability to generalize may be obvious since each developer provides a unique architecture for its devices. One such popular LPWAN architecture is the star-of-stars network architecture, which is an architecture widely deployed in both Sigfox and LoRa-based infrastructures [64]. In this section, we identify specific commonalities between different LPWAN architectures in order to describe a generalized LPWAN architecture. This will provide LPWAN developers with an architecture that spurs interoperability. There are several specific architectures identified in the LPWAN literature. For example, a LoRaWAN network architecture was presented in [65] wherein end nodes (IoT devices) typically connect to different gateways/concentrators via LoRa RF and LoRaMAC. The gateway (GW) connects to a network server (NS) via 3G/4G/Ethernet/Fibre back-haul, whereas the NS connects to application servers (AS) via TCP/IP Secured Sockets Layers (SSL) payloads. In another article [66], authors presented a more detailed LPWAN architecture called OpenChirp. The OpenChirp network was built using LoRaWAN, which allows users to register devices, describe transducer properties, transfer data and retrieve historical values. Unlike the architecture in [65], OpenChirp connects its GWs through a publish-subscriber server that decouples the producers and consumers of information according to their timing and availability conditions. LPWAN servers and serializers are both classified within the service domain in the OpenChirp architecture, and they connect to application services through a Hypertext Transfer Protocol (HTTP) server.
## 2 Related Work
### Binary-To-Source Comparison  
In the case of binary-to-source comparison detection, the target programs are binary files and the third-party libraries are source code files.  
Binary Analysis Tool (BAT) [4] extracts the strings from both sources and binaries. It considers longer strings to have more uniqueness and assigns them larger weights. OSSPolice [5] extracts syntactical features, such as strings and exported function names when matching binaries against library sources. It introduces a novel hierarchical indexing scheme to achieve both high scalability and accuracy. BCFinder [6] selects string constants as features to generate a profile for sources and binaries. When extracting strings from sources, it ignores strings that are required by programming syntax such as \"%s\", \"%d\", which are required by printf. B2 SMatcher [9] extracts five kinds of code features: string literals, exported function names, constants in assignments, constant parameters in function calls, and in/out-degrees of a function on call graph. It categorizes these features into program-level features (string literals, exported function names) and function-level features (constants in assignments, constant parameters in function calls, and in/out-degrees of a function on call graph) and proposes a two-stage version identification approach based on the two levels of code features. B2 SFinder [7] is a state-of-the-art method for binary-to-source TPL detection. It extracts seven kinds of code features (String literal, Switch/Case, If/else, Export function name, String Array, Integer Array, Enum Array) to make a separate analysis and detection.
## 4 Experiment
### Case Study  
To verify the effectiveness of DMPL, we compare the prediction results of different methods on a sentence of the test set, as shown in Figure 5. Our model is able to identify all quadruples accurately. In Example 1, extract-classify and MVP have difficulty in the extraction of aspect terms and opinion terms. In Example 2, the extract-classify model has trouble targeting the quadruples (NULL, LAPTOP#QUALITY, negative, terrible). Our model has obvious advantages in sentiment prediction with implicit terms. In addition, the classification of the Laptop-ACOS dataset, such as \"SOFTWARE#GENERAL\"  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Pre** & **Rec** & **F1** \\ \hline \multirow{6}{*}{Restaurant-ACOS} & DLO & 60.47 & 60.4 & 60.43 \\  & DPML & **71.07** & **61.14** & **65.73** \\  & w/o MBML & 72.25 & 58.84 & 64.86 \\  & w/o MCED & 70.35 & 59.06 & 64.21 \\  & w/o \(\mathcal{T}_{OSCA}\) & 61.63 & 61.9 & 61.76 \\  & w/o \(\mathcal{T}_{ACSO}\) & 60.72 & 60.92 & 60.82 \\ \hline \multirow{6}{*}{Laptop-ACOS} & DLO & 44.93 & 44.62 & 44.77 \\  & DPML & **52.26** & **44.81** & **47.06** \\  & w/o MBML & 54.54 & 42.46 & 47.75 \\ \cline{1-1}  & w/o MCED & 52.09 & 41.95 & 46.47 \\ \cline{1-1}  & w/o \(\mathcal{T}_{OSCA}\) & 45.02 & 45.13 & 45.08 \\ \cline{1-1}  & w/o \(\mathcal{T}_{ACSO}\) & 45.20 & 45.05 & 45.13 \\ \hline \hline \end{tabular} Bold numbers represent the best results.  
\end{table}
Table 4: Ablation study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **EAEO** & **EAIO** & **IAEO** & **IAIO** & **Contain-I** \\ \hline \multirow{2}{*}{Restaurant-ACOS} & DPML & 66.45 & 47.88 & 51.79 & 58.73 & 50.37 \\  & w/o MCED & 65.62 & 31.96 & 50.34 & 53.92 & 49.49 \\ \hline \multirow{2}{*}{Laptop-ACOS} & DPML & 47.50 & 37.44 & 58.12 & 39.44 & 46.18 \\  & w/o MCED & 47.2 & 35.25 & 56.44 & 36.00 & 42.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study for implicit sentiment.  
and \"SOFTWARE#GENERAL\", has similar semantics and is diverse, which also brings challenges to the model.
## 4 Results and Discussion
### Laboratory Data  
Waveband measurements of hydroponic grass showed a marked decrease in radiance values as the illumination changed from darkness to lit (Figure 5). These reflectance changes were observed over a period of about 30 min; however, in contrast to earlier findings [10] these changes were observed in both the reference and signal bands.  
Figure 4: Results from goniometer measurements show waveband response across a range of incidence angles. Note the sensor field of view is approximately 140\({}^{\circ}\).  
Figure 5: Change in reflected radiance measured in the two PRI wavebands over time as turf was exposed to approximately 350 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). First series of measurements are shown in the first column. The second series of measurements were acquired after acclimating the turf to darkness for two hours and are shown in the second column.
###### Abstract  
Sonars are essential for underwater sensing as they can operate over extended ranges and in poor visibility conditions. The use of a synthetic aperture is a popular approach to increase the resolution of sonars, i.e., the sonar with its \(N\) transducers is positioned at \(k\) places to generate a virtual sensor with \(kN\) transducers. The state of the art for synthetic aperture sonar (SAS) is strongly coupled to constraints, especially with respect to the trajectory of the placements and the need for good navigation data. In this article, we introduce an approach to SAS using registration of scans from single arrays, i.e., at individual poses of arbitrary trajectories, hence avoiding the need for navigation data of conventional SAS systems. The approach is introduced here for the near field using the coherent phase information of sonar scans. A Delay and Sum (D&S) beamformer (BF) is used, which directly operates on pixel/voxel form on a Cartesian grid supporting the registration. It is shown that this pixel/voxel-based registration and the coherent processing of several scans forming a synthetic aperture yields substantial improvements of the image resolution. The experimental evaluation is done with an advanced simulation tool generating realistic 2D sonar array data, i.e., with simulations of a linear 1D antenna reconstructing 2D images. For the image registration of the raw sonar scans, a robust implementation of a spectral method is presented. Furthermore, analyses with respect to the trajectories of the sensor locations are provided to remedy possible grating lobes due to the gaping positions of the transmitter devices.  
underwater sensing; sonar; synthetic aperture; registration +
Footnote †: journal: Sensors
## 4 Data Processing
### Peak Detection  
Peak detection distinguishes the signals of the analytes from each other and from the background signal, so this step is critical to correctly obtaining the chemical information contained in the chromatographic data. GC\(\times\)GC-MS peak detection methods were first adapted from the ones used in one-dimensional GC. Regardless of the modulation, the \"sample\" is continuously fed to the detector. And the feedback of the detector is always a time series of the signal intensity, so 1D integration approaches were first applied to the vector of detector signals from GC\(\times\)GC, usually with scripts developed by the lab itself. One-dimensional sub-peaks are detected, and then it is manually determined which sub-peaks belong to the same two-dimensional peak. Finally, the sub-peak areas belonging to the same peak will be added [151]. These methods are suitable for the targeted detection of individual analytes. However, when using untargeted analysis, each chromatogram has thousands of subpeaks in one dimension, and manually combining these sub-peaksis not practical. To solve the problem, scripts to automatically recognize and merge sub-peaks in 2D were developed [152; 153]. Although the algorithm can automatically merge sub-peaks, errors may occur on the distorted peaks, and the merging criteria must be carefully chosen for crowded chromatograms. Cluster error can be avoided by performing peak detection directly on the 2D demodulated data. During demodulation, the 1D signal vector is rearranged into a 2D matrix. Consequently, multivariate and graphical methods can be applied directly on the 2D plane. Several 2D peak picking methods have been applied to GC\(\times\)GC-MS data, such as local maximum value combined with parallel factor analysis (PARAFAC), watershed, and peak shape matching [154; 155; 156]. As usual, they all work well with perfect chromatographic data. Unfortunately, a perfect chromatogram cannot be guaranteed. Especially during beverage VOC profiling, the contents of major and minor compounds may differ by a billion times. It is common to see deformed peaks as the result of column or detector saturation. When saturation occurs, these peak-picking strategies show their limitations by producing false peak splitting, which results in incorrect deconvolution. At this stage, these methods can be used in combination, and the results obtained are validated against each other [130]. Another direction is isolating the unique \(m/z\) for each peak, especially when high-resolution mass spectrometry is available, thus eliminating the need to apply complex deconvolution algorithms [157].
# Non-Linear Modeling of Immune System Activation and Lymph Flow Dynamics
## 2 Materials and Models
### The Lymphatic System
#### 2.1.1 Lymph Node  
As previously mentioned, the lymphatic system is a network of lymphoid organs and lymphatic lymph nodes (LNs) that play a crucial role in fluid homeostasis, lipid absorption, and immunity. Primary organs, such as bone marrow and the thymus, create immune cells called lymphocytes, while secondary organs, like LNs, spleen, tonsils, and specialized mucosal tissue, provide immune response sites. The lymph is composed of macromolecule-rich interstitial fluid, circulating antigen-presenting cells (APCs), and lymphocytes. Lymphatic transport is critical for maintaining fluid balance and allowing detection and immune response to harmful pathogens.  
Lymph nodes (LNs) consist of stromal cells and macrophages, which actively combat infections and monitor inflammatory responses to foreign substances. Lymphatic fluid enters the lymph node by afferent lymphatic vessels and is deposited into subcapsular  
Figure 3: Atlas 3D [16].  
sinuses. The contents of the lymph follow several routes, while endothelial cells create a continuous barrier to block the entry of big particles into the LN cortex. Macrophages have the ability to capture and present antigens that are carried by lymph to immune cells that are entering the body. The LN exhibits a high degree of vascularity, as blood capillaries extend from the hilus and create a compact network. Lymphocytes, including B- and T-cells, enter the lymph nodes through specific blood arteries located in the paracortex and exit back into the bloodstream by efferent lymphatic capillaries [17].
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 1 Introduction
We designed the DGIF to integrate global and differential information effectively. This module combines global structural consistency with precise change localization,  
Figure 1: Task definition of change detection in remote sensing images. ensuring that both contextual and differential features contribute to accurate change detection. The fusion mechanism further enhances the model's robustness across various change detection scenarios. 4. Through experiments on three benchmark datasets (LEVIR-CD, GZ-CD, and SYSU-CD), our Siamese-SAM model achieves state-of-the-art performance, demonstrating superior robustness and accuracy. We also conducted extensive ablation studies to validate the effectiveness of each of the proposed modules, confirming their contributions to performance improvement.
## 2 Background of WUE Estimation by Remote Sensing
### Definition of Ecosystem WUE
For specific ecosystems, the optimal value of \(k\) can be found by analyzing the ecosystem carbon-water coupling characteristics at different time scales [1; 17; 26]. Therefore, the selection of T or ET depends on the measurement at the ecosystem scale, while the choice of the WUE Equation, including VPD, is related to the influence of VPD on WUE trend, and changes in the physiological behavior of vegetation for future climate change. To assess data from the research and technology perspective, the calculation of water loss at ecosystems is always different, which has resulted in different understandings on the variability of WUE. In previous studies, the transpiration of vegetation is considered as the water loss from the ecosystem, which is consistent with the concept of Transpiration Use Efficiency (WUEr) [19]. \[\text{WUE}_{\text{T}}=\frac{\text{GPP}}{\text{T}}\ \bigg{(}\text{or WUE}=\frac{\text{ NPP}}{\text{T}},\ \text{WUE}=\frac{\text{NEE}}{\text{T}}\bigg{)}, \tag{1}\]  
where, NEE is the net ecosystem carbon exchange, T is the transpiration. WUE based on this algorithm reflects the ability of the plant population to utilize water. With the progress in observation technology, an increasing number of studies have measured the total ET of the ecosystem (i.e., the sum of vegetation T and soil E) as the water loss of ecosystem [19]. Then, WUE is represented as:  
\[\text{WUE}_{\text{ET}}=\frac{\text{GPP}}{\text{ET}}=\frac{\text{GPP}}{\text {T}}\times\frac{\text{T}}{\text{ET}}\ \bigg{(}\text{or WUE}=\frac{\text{NPP}}{\text{ET}}\bigg{)}, \tag{2}\]  
At the regional scale, due to a lack of measured GPP and ET data, it is difficult to study the spatial pattern of WUE. The Precipitation Use Efficiency (PUE) is also used to analyze the spatial variability of water use along with the climate gradient of the ecosystem, for which more precise remotely sensed GPP and ET or the high resolution WUE remote sensing products are necessary to perform the spatial variability analysis of water loss of the ecosystem. WUE algorithms involve different ecological processes and their controlling mechanisms are also different. It can be seen from Figure 2 that the hydrological processes involved are gradually increasing from WUEr to WUEr and PUE, and the spatial and that the temporal variability is more complicated. In addition, although there may be a relatively stable proportional relationship between GPP, NPP, and NEE on the long-time scale, photosynthetic and respiratory processes are controlled by different factors. A difference in numerator in the WUE definition may also lead to distinct variability and controlling mechanisms on a short time scale. Figure 2: Schematic diagram of the different processes involved in expressing the ecosystem WUE.
# Consistency Models of NoSQL Databases
## 4 NoSQL Databases Background
### Redis  
From the official website, \"Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs and geospatial indexes with radius queries\" [22].  
Redis optimizes data in memory by prioritizing high performance, low computation complexity, high memory space efficiency and low application network traffic [23]. Redis guarantees high availability by extending its architecture and introducing the Redis Cluster. Redis on a single instance configuration is strong consistent. In a cluster configuration, Redis is Eventual Consistent when the client reads from the replica nodes.  
Redis Cluster requirements are the following [24; 25]:  
* High performance and linear scalability up to 1000 nodes.
* Relaxed write guarantees. Redis Cluster tries its best to retain all write operations issued by the application, but some of these operations can be lost.
* Availability. Redis Cluster survives network partitions as long as the majority of the master nodes are reachable and there is at least one reachable slave for every master node that is no longer reachable.