esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Pea protein has been examined in this context, and the findings have indicated its potential as a substitute due to its ability to reduce bitterness, astringency, and oxidized tannins [23]. The use of vegetable proteins has been demonstrated to be effective in the clarification of muscatel juices [24] and the prevention of mad cow disease in sweet wines [25]. In the case of white wines, wheat gluten [26], grape seed flour [27], potato protein [28], and pea and soy proteins [29] have demonstrated comparable efficacy. Furthermore, cassava and rice starch have been shown to be effective in clarifying cashew juice [30]. In the purification of apple juice, red lentil protein isolates have exhibited a purification ability that is analogous to that of the conventional gelatin and bentonite pair. However, green lentils and pea protein have demonstrated a lower degree of efficiency [16]. These alternatives are in alignment with present-day consumer trends and have the potential to reduce the probability of allergic reactions or other health risks. Notably, these products are not subject to religious or cultural restrictions, a quality that enhances their adaptability in global markets. The development of advanced technologies, such as membrane filtration, high hydrostatic pressure, and pulsed electric fields, has led to the emergence of non-enzymatic methods for juice stabilization and clarification. These methods can be used in combination with enzymatic treatments to enhance product quality, reduce processing time, and minimize environmental impact [31]. A substantial proportion of the research is conducted within a laboratory setting, wherein the efficacy of the process and the characteristics of the product can be evaluated under controlled circumstances. To illustrate, parameters such as the turbidity (NTU), water-soluble solids (BRIX\({}^{\circ}\)), transmittance (T%), color (CIELab), antioxidant capacity, and polyphenol content of apple juice are subjected to analysis. Nevertheless, it is not always evident that laboratory results accurately reflect the range of variability that may be encountered in an industrial setting. It is of considerable importance to simulate industrial conditions, particularly in the case of mixed or lower-quality raw materials, to identify the operational challenges and economic aspects of the manufacturing process. This guarantees that the final product will be stable and of high quality, even when manufactured using sub-optimal raw materials. The objectives of this research are to evaluate the effectiveness of two types of plant-based clarification agents compared to gelatin in industrial apples and, furthermore, to study the effectiveness of positively charged plant proteins with negatively charged clarification agents. The results may not only provide a detailed insight into the efficiency of the clarifying processes and agents but may also contribute to the development of more sustainable and innovative technologies for the juice industry.
# Effects of Diet on Mercury Bioaccumulation in Farmed Gilhead Seabream (_Sparus aurata_)
###### Abstract  
The administration of nutraceutical substances to fish diet can help to control disease outbreaks in aquaculture practices, thereby promoting sustainability and food safety. In particular, some substances have the potential to alleviate the effects of trace metals toxicity in fish also by reducing metal accumulation in tissues. This study evaluates, for the first time, the effect of nutraceutical substances on bioaccumulation mechanisms of mercury (Hg) in tissues and organs of farmed gilthead seabream (_Sparus aurata_) by mesocosom experimentation. The kinetics of bioaccumulation in muscle, gills, gut, liver and kidney and the detoxification efficiency were also assessed. Fish were fed with three different diets: a commercial diet used as control (CD); a diet enriched with short chain fatty acids (SCFA) and extract of _Castanea sativa_ (D1); a diet enriched with yeast _Saccharomyces cerevisiae_ and extract of _Schinopsis balansae_ (D2). All groups were exposed to sub-lethal concentrations of mercury. After 20 days of exposure, mercury levels in different organs and tissues clearly revealed the effectiveness of yeast and plant extracts in limiting the metal bioaccumulation in fish fed with D2 through mercury absorption and then elimination by feces. In contrast, the D1 seems to not reduce the Hg bioaccumulation in fish tissues. This can be attributed to the high affinity of SCFA for mercury, leading to the formation of organometallic compounds absorbed by the fish tissues. This mechanism potentially counteracts the efficiency of tannins contained in the extract plant on mercury removal. This study clearly demonstrates that the use of diets enriched with yeast and/or plant extracts rich in tannins are a useful bioremediation strategy to reduce trace metals bioaccumulation in farmed fish, thus preserving their health status from intoxication, their commercial values, and consequently the health of consumers.  
mercury; bioaccumulation; nutraceutical substances
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
###### Abstract  
Image segmentation and identification are crucial to modern medical image processing techniques. This research provides a novel and effective method for identifying and segmenting liver tumors from public CT images. Our approach leverages the hybrid ResUNet model, a combination of both the ResNet and UNet models developed by the Monai and PyTorch frameworks. The ResNet deep dense network architecture is implemented on public CT scans using the MSD Task03 Liver dataset. The novelty of our method lies in several key aspects. First, we introduce innovative enhancements to the ResUNet architecture, optimizing its performance, especially for liver tumor segmentation tasks. Additionally, by harassing the capabilities of Monai, we streamline the implementation process, eliminating the need for manual script writing and enabling faster, more efficient model development and optimization. The process of preparing images for analysis by a deep neural network involves several steps: data augmentation, a Hounsfield windowing unit, and image normalization. ResUNet network performance is measured by using the DC metric Dice coefficient. This approach, which utilizes residual connections, has proven to be more reliable than other existing techniques. This approach achieved DC values of 0.98% for detecting liver tumors and 0.87% for segmentation. Both qualitative and quantitative evaluations show promising results regarding model precision and accuracy. The implications of this research are that it could be used to increase the precision and accuracy of liver tumor detection and liver segmentation, reflecting the potential of the proposed method. This could help in the early diagnosis and treatment of liver cancer, which can ultimately improve patient prognosis.  
segmentation; tumor; CT images; deep learning techniques; residual network; public image data; Monai +
Footnote †: journal: Medical Image Processing  
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## 3 Base Station Deployment and Self-Optimization Positioning Method
### Base Station Deployment and Transition Zone Division Method  
In large-scale scenarios, due to the limited high-precision ranging distance of a single base station, a substantial number of fixed base station nodes needs to be deployed with rational spacing and configuration to ensure the positioning accuracy of two-dimensional coordinates. This paper adopts a square grid with a side length of \(x_{b}\) for the extensive deployment of base stations.  
When the mobile beacon operates near the boundary of the square area, on one hand, the accuracy weight of the two-dimensional positioning coordinates obtained through the base station's self-optimization method is relatively low. On the other hand, a basis for determining the mobile beacon's transition to an adjacent area has not yet been established. This paper divides the boundary of the square area into a transitional zone between two adjacent areas and establishes a special method for the optimal selection of base stations within the transitional zone. The division of the transitional zone is shown in Figure 3.  
The division results are depicted in Figure 3, which are divided into an A-type central area, B-type transition area, and C-type transition area. By establishing transition areas and a central area in this manner, the impact of lower accuracy in short-range ranging can be effectively mitigated. Additionally, it facilitates the selection and switching of base stations when crossing regions, thereby effectively enhancing the positioning accuracy in the transition areas and achieving the goal of high-precision positioning coverage across the entire map.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 12 Performance Tradeoffs  
AEDG provides a good throughput rate due to the involvement of GWs and AUV. AUV itself visits the GWs and gathers data from them, thereby, increasing the throughput. This throughput improvement in AEDG is achieved at the cost of higher energy consumption and greater message latency. Both are caused by predefined but non optimized trail followed by AUV in the protocol. On the other hand E2E delay for AEDG is minimized by applying a set of constraints but for AEDG-Dopt the considered network shows degraded PAR. Implication of \(d^{th}\) is visible here. It reduces the number of hops that a packet takes while reaching to sink but it also reduces the eligible neighbors for the sender node. Similarly AEDG-Eopt performs better in terms of energy conservation at the cost of low PAR. For energy optimization Equation (20a) is applied which limits the number of predecessors attached with a node. This saves computational powers at receivers but highly effects the PAR.  
RE-AEDG shows better throughput but depicts higher delays in data packet arrival at the sink. One of the two reasons is the involvement of cooperative communication mechanism. Whereas, the other is that the GWs hold data for more amount of time causing the extended E2E delays. To suppress the above mentioned deficiency, we optimized the RE-AEDG scheme for minimized delay but it pays the cost of lowered packet delivery at the sink. In delay optimization, \(d^{th}\) reduces the network performance in terms of data throughput. Similarly, RE-AEDG-Eopt conservers the overall network energy but enhances the packet drop rate. This is due to the restricted number of hops a data packet could avail on its one way path.  
Two AUVs that aid MNs and GWs in data transmission phase causes them to remain alive for a longer duration. However, in spite of this stated argument the ULVRP's performance is still the worst in terms of throughput achievement. This is because the AUVs that take random sojourn stops, lead towards an interference in the voronoi scope of each other. Their random movement causes repeated selection and reselection of data path for nodes hence, reduced data arrival rate at sink.  
Figure 16: E2E delay in energy optimized and non optimized protocols.  
ULVRP-Dopt achieves advancement in delay at the cost of increased energy consumption because \(d^{th}\) assigned to nodes causes the hops at farther distance.  
RE-AEDG has higher PAR due to incorporation of cooperative communication. RE-AEDG-Eopt has lesser E2E delay because queues for every node are limited in this case. The AEDG has better throughput at the cost of delay. A comprehensive over view of performance tradeoffs is shown in Table 4.
# Revisiting the Detection of Lateral Movement through Sysmon
## 6 Methodology
### Exploitation of Remote Services
#### 6.1.2 Exploitation of EternalBlue  
This attack was performed via Metasploit's _smb_login_ auxiliary module and _psexece_, revealing the targeted server of Figure 1 as prone to the _smb_login_ credential leakage and execution of remote command vulnerability. A _Meterpreter_ session also needs to be activated for the attack to be considered successful. _Discussion:_ With respect to _EternalBlue_ vulnerability via _smb_login_ and _PsExece_, the conducted experiments revealed leveraging of Windows SMB as potentially malicious. Specifically, SMB access with administrative privileges, namely _admin$_, _ip$_ and _c$_, is considered suspicious behavior, which implies the manipulation of the Windows system's binaries. This behavior was identified within the collected logs during the execution of the Metasploit's _smb_login_ module and _PsExec_ tool and prior to the execution of remote services through the _Meterpreter_ session on the targeted host. In the context of this work, we utilized the _net.exe_ service to connect via Metaspoilt to the SMB protocol's sharing environment for privilege escalation. SMB share's enumeration followed, through the mapping of the server's _smb-admin_\(\c$\), generating a number of interesting _Sysmon_ log entries on the targeted host. To begin with, since mapping of the _c$ admin shares_ within the borders of a SOHO or corporate network do not constitute a legitimate user's normal activity, any event generated on Sysmon with _Event ID 1_ related to the _net.exe_ should be identified as potentially malicious. Similar to the age of _net.exe_, a _ProcessCreate_ rule for the _whoami.exe_ was also included. Despite being a Windows native executable, it is uncommon for a legitimate user with basic credential access to run it through PowerShell [22]. This pattern was observed within the collected logs and was integrated as a proactive threat-hunting measure and not as a rule that could serve as a standalone.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm  
A concise description of DED-RL algorithm, fully described in [21], is provided in this section.  
The algorithm is based on a pixelization, i.e., a subdivision of the environment's surfaces into rectangular \"tiles\" of pre-fixed maximum tile area: typical tile area values are \(\Delta\)A = 10 \(\times\) 10 [m\({}^{2}\)] or \(\Delta\)A = 5 \(\times\) 5 [m\({}^{2}\)]. In order to derive a discretization with tiles as close as possible to squares, we first define the desired tile-side length \(\Delta\ell\) as  
\[\Delta\ell=\sqrt{\Delta A} \tag{1}\]  
Then, we divide each wall side \"\(u\)\" by \(\Delta\ell\) and save both the integer quotient \(q\) and the reminder length \(\ell_{R}\), so that \(u=q\Delta\ell+\ell_{R}\). Then, the side \(u\) is subdivided into segments of length \(\Delta{\ell_{u}}^{\prime}\) as follows:  
\[\left\{\begin{array}{ll}\Delta{\ell_{u}}^{\prime}=u/q&\text{if }\ell_{R} \leq\frac{\Delta\ell}{2}\\ \Delta{\ell_{u}}^{\prime}=u/(q+1)&\text{if }\ell_{R}>\frac{\Delta\ell}{2} \end{array}\right. \tag{2}\]  
By doing so we ensure that the actual subdivision length \(\Delta{\ell_{u}}^{\prime}\) is close enough to the desired subdivision length \(\Delta\ell\).  
Tiles become the new basic objects of the environment, whereas real objects (walls, edges) are only recognizable as sets of tiles, with edges being defined by properly tagged \"edge tiles\". Pixelization allows for significantly better computation efficiency, as it opened the way to the algorithmic solutions explained below.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Proposed Mechanism
#### 3.2.1 Initial Configuration  
After the deployment of the network, initially, each node broadcasts control packets to determine its location, neighbors, and CNs and their energy levels. After learning the values of other connected nodes, each node builds its routing table with routes towards a sink through the relay nodes. Since the main concern is to select the optimal route towards the sink, each node \(i\) calculates its \(\lambda\) at time \(t\). This value is then shared with the neighbors. The initial configuration can be seen in Figure 7, where two sample nodes are shown with their recorded parameter values.  
Figure 6: Flowchart of the proposed mechanism.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 2 Principles and Methods
### Signal Extraction  
The cross-correlation adopted in this study is the digital characteristic that describes the statistical relation between two signals [34; 35; 36]. In other words, when the collected signal and the decoded signal are very similar, the target signal can be extracted. The decoded signal is designed to have the same frequency and duration as the target signal without noise [37]. The direct cross-correlation function can be expressed as:  
\[R_{\rm{XH}}(t_{1},t_{2})=E[X(t_{1})H(t_{2})] \tag{2}\]  
Figure 3: The amplitude changes of signals before and after filtering; (**a**) Vibroseis signals and footstep signals before filtering; (**b**) Vibroseis signals and footstep signals after filtering.  
where \(R_{\rm{XH}}(t_{1},t_{2})\) stands for the direct cross-correlation signal; \(X(t_{1})\) represents the collected signal at time \(t_{1}\); \(H(t_{2})\) identifies the decoded signal at time \(t_{2}\). Although cross-correlation technology can effectively extract the target signal and suppress partial surface waves in seismic exploration, it is difficult to get the correct information from the direct cross-correlation. When the save our souls (SOS) signal is correlated directly, the cross-correlation signal will be different from our target signal (SOS), as shown in Figure 4. The vibroseis sources transmit the seismic signal intermittently, so the final signal we get is a discrete signal.  
Based on this reality, the segmented correlation technology is proposed to deal with this problem. According to the rules of Morse code, the collected data are divided into a certain number of segments, and then these fragments are correlated with the decoded signal respectively, and the segmented correlation function is as follows.  
\[\Phi_{\rm{XH}}(u)=\sum_{n=1}^{N}E[X_{\rm{n}}(u)H_{\rm{n}}(u)] \tag{3}\]  
where \(\Phi_{\rm{XH}}(u)\) is the segmented correlation signal; \(X(u)\) shows the nth fragment of the collected signal with length \(u\); \(H(u)\) the nth fragment of the decoded signal with length \(u\).  
If the amplitude of a fragment exceeds a pre-selected threshold value, then that fragment is interpreted as \"1\", otherwise the fragment is interpreted as \"0\". So, we can interpret the complete signal according to the following definition:  
* \"1010\" for the beginning of the message;
* \"1110\" for end of message;
* The signal between the beginning signal and the end signal is considered to be the intermediate signal, which is the message.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 2 Methods  
The focus of our method is to learn the significant features of pedestrians. The overall framework of our model is demonstrated in Figure 1. In this section, we introduce our person ReID model from three aspects: the channel attention module for pedestrian salient features learning as shown in Section 2.1, the multi-scale information fusion module for context information extraction (Section 2.2), and the loss function in Section 2.3.
## 6 Results and Discussions
### The Impact of High Traffic Load on Client Satisfaction
With the proposed RL-based solution, on the other hand, the throughput value significantly increases and hits 4 Mbps under Cernet topology, even if the network size increases, as depicted in Figure 9c; therefore, the client maintains a better viewing experience. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Network Topology** & \multicolumn{2}{c}{**Abilene**} & \multicolumn{2}{c}{**Geant**} & \multicolumn{2}{c}{**Cernet**} \\ \hline  & **Client-Server** & **H5–H1** & **H10–H4** & **H15–H6** & **H18–H9** & **H7–H35** & **H22–H19** \\ \hline
**OSPF-based appraach** & Packets dropped (out of 178,572) & 1176 & 193 & 1264 & 1809 & 196 & 641 \\  & Average packet jitter (in ms) & 0.009 & 0.010 & 0.017 & 0.011 & 0.017 & 0.011 \\ \hline
**RL-based solution** & Packets dropped (out of 178,572) & 826 & 123 & 100 & 122 & 146 & 297 \\  & Average packet jitter (in ms) & 0.022 & 0.026 & 0.025 & 0.007 & 0.008 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of packets dropped and average jitter during video transmission for the high background traffic load under three topologies (includes random client-server from each network topology). Figure 9: Network throughput during video transmission under high traffic loads in three different topologies. (**a**) Abilene; (**b**) Geant; (**c**) Cernet.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Data Collection
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Different Physical State with Various APS Inducements**} \\ \hline Physical & Number of & Number of & Duration & Duration After \\ Activity & Experiments & Subjects & (min) & Class Balancing (min) \\ \hline Resting & 89 & 10 & 3172 & 2141 \\ \hline Treadmill & 57 & 20 & 2164 & 2141 \\ \hline Stationary Bike & 61 & 19 & 1713 & 2141 \\ \hline \multicolumn{5}{c}{Sedentary State Experiments with APS Inducment} \\ \hline APS & Number of & Number of & Duration & Duration After \\ Type & Experiments & Subjects & (min) & Class Balancing (min) \\ \hline Non-Stress & 28 & 6 & 846 & 1057 \\ \hline Excitement & 29 & 9 & 1129 & 1057 \\ \hline Mental Stress & 32 & 6 & 1197 & 1057 \\ \hline \multicolumn{5}{c}{Treadmill Experiments with APS Inducment} \\ \hline APS & Number of & Number of & Duration & Duration After \\ Type & Experiments & Subjects & (min) & Class Balancing (min) \\ \hline Non-Stress & 28 & 20 & 1162 & 407 \\ \hline Excitement & 12 & 12 & 676 & 407 \\ \hline Mental Stress & 17 & 8 & 326 & 407 \\ \hline \multicolumn{5}{c}{Stationary Bike Experiments with APS Inducment} \\ \hline APS & Number of & Number of & Duration & Duration After \\ Type & Experiments & Subjects & (min) & Class Balancing (min) \\ \hline Non-Stress & 29 & 19 & 891 & 296 \\ \hline Excitement & 24 & 12 & 585 & 296 \\ \hline Mental Stress & 8 & 7 & 237 & 296 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiments conducted for physical activity (PA) and acute psychological stress (APS) data collection. Figure 3: Overview of physiological and psychological state estimation using wristband biosignals (two-layer tree classification approach. 1 st layer: Physical state (PS) classification; 2 nd layer: APS classification for each PS). as heart rate. However, estimating EE is a still challenging task, though it is a valuable metric for quantifying the intensity of physical activities. We collected data during TR and SB exercises to develop models that can predict the EE using only wristband biosignals. A subset of the experiments for APS and PS model development was used for collecting data for EE estimation. During these experiments, an indirect calorimeter (COSMED K5 wearable metabolic measurement system, COSMED Srl, Italy [47]) data were collected in addition to wristband data. The indirect calorimeter is a gold standard EE measurement that was used as the target output value for training the ML algorithms with the inputs as the wristband data. Sixteen hours of data were collected from 59 experiments with 15 subjects (Table 2).