esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 2 Cybersecurity Vulnerabilities in Smart Grids and Mitigation Requirements  
Secure and safe operation of SG is critical for ensuring its effective operation [18]. Cybersecurity for the SG promotes both the grid's reliability and the stability of the information transmitted [19]. SG automatically modifies electrical power and communication systems to optimize their operation. For example, SG is defined as \"The transition from today's power systems to future systems based on information, transmission and communication technologies\" and it monitors all components to prevent its attacks because cybersecurity holds a special place in it [19]. The vital information can be understood in a way that all the security risks in the system can be protected with measures. In this context, it will be useful to examine some studies to understand mitigation requirements. Cybersecurity challenges and existing solutions within the SG environment are reviewed in [13] and [20]. This is classified as the SG communication security studies into software and hardware simulations [21; 22; 23]. Risk definition within the scope of information security can express the loss of integrity, privacy, or continuity in the data by using vulnerabilities in information data by malicious threats [24]. The security aspects, especially the Internet of Things (IoT) and the types of cyber threats facing the SG, are examined in [25], and the environmental conditions related to cybersecurity of the SG are split into three categories:  
* Power grid vulnerabilities at the time of the cyber-attack;
* The facilitate of infraction to the control system;
* Describe the ease of earning control over the management system.  
Cyber-attacks are dissociated into three steps: First step is the attacker has in mind to control the management and communication system. Once the management access is acquired, the attacker should identify the system to initiate a smart and effective malicious attack. In the third step, the attacker launches the control of SG component or tries to influence its operation. These attacks may be directed at power systems equipment [26] or auxiliary systems such as Advanced Metering Infrastructures (AMI) [27; 28]. Security vulnerabilities in power and communication protocols can cause dangerous attacks on the SG system. When the content of the applied standards is examined to prevent this, it can be seen that they are based on authentication, encryption, and confidentiality technologies to ensure SG security. Malicious people may be interested in launching large-scale attacks on the smart grid with potentially unpredictable consequences. In light of these concerns, security is one of the most important issues in the SG's current development and future deployment [14; 29]. Figure 3 illustrates the importance of cyber security in SGs.
# Biochemical Profile and Antioxidant Activity of Fresh Fruits from Apple Genotypes
## 3 Results and Discussion
### The Content of Phenolic Compounds  
Table 2 shows the phenolic compound content in fresh apple fruits, with significant differences observed between the genotypes for all identified parameters (_p_ < 0.05). These variations may result from the complex interactions between cultivar genetics, growing conditions, and post-harvest factors [30]. Regarding the content of bioactive compounds, the concentration of TPC varied among the apple genotypes. TPC, known for its antioxidant properties, varied greatly among the genotypes, with the limits of variation being between 377.63 (H1/28) and 839.08 mg GAE/100 g (H18/6). The apple cultivars 'Valery', 'Florina', and 'George' showed a lower content of phenolic compounds, with values 1.21 to 1.68 times lower compared to the H18-6 genotype. Although the cultivars 'Valery', 'Florina', and 'George' showed a high content of phenolic compounds, some genotypes in the testing period (H18/6, H14/1, H8/6, and H4/42) demonstrated an even higher content, highlighting a significant beneficial potential for health. The concentrations determined for the analyzed genotypes were, however, higher than those reported by [PERSON] and [PERSON] [31] for the combination of peel and pulp in five apple varieties. Also, significant differences between apple cultivars in terms of the content of phenolic compounds were also observed by [PERSON] et al. [32]. In their study, the maximum concentration was 1154.65 \(\upmu\)g EAG/g extract, while the minimum value was 82.63 \(\upmu\)g EAG/g extract. Such genotype differences are commonly reported in the literature, which highlights that polyphenol levels are influenced by factors including geographical origin, genotype, varietal profile, and various abiotic and biotic conditions [32; 33]. Among all genotypes studied, total tannins (TTC) stand out by with the highest percentage of total phenolic compounds (TPC), followed, in descending order, by total flavonoids (TFC) and total anthocyanins (TAC). The differences between these classes of phenolic compounds are significant in terms of order of magnitude. The highest
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 3 LoRa Radio Propagation Model  
This study focuses on finding the data rate and the maximum range of TVWS-based LoRa. To estimate this, the receiver sensitivity for which the signal can be correctly detected or the maximum link budget needs to be computed. An SX1276/77/78/79--137 MHz to 1020 MHz low power long range transceiver is considered for the analysis in this study. The \(SF\) can vary from 6 to 12, and there are different values of bandwidth available for LoRa at TVWS frequencies. We have considered 62.5, 125,250, and 500 kHz. A specific \(SF\) and \(BW\) combination results in a particular data rate and receiver sensitivity [14], as shown in Table 1.  
The data rate \(R_{b}\) in kbps is given as  
\[R_{b}=\frac{(SF)(BW)(CR)}{2^{SF}} \tag{2}\]  
where \(SF\) is the spreading factor, \(CR\) is the coding rate, and \(BW\) is the bandwidth.  
The overall link budget [15] is given as in (3) for any wireless signal transmitted to the receiver by a transmitter.  
\[P_{r}=P_{t}+G_{t}-L_{t}-L_{p}+G_{r}-L_{r} \tag{3}\]  
where \(P_{r}\) and \(P_{t}\) denote the received and transmitted power in dBm, respectively, \(G_{t}\) and \(G_{r}\) are the transmitting and receiving antenna gains in dBi, respectively, \(L_{t}\) and \(L_{r}\) are the cable losses at the transmitter and receiver in dB, respectively--and \(L_{p}\) is the path loss.  
Path loss \(L_{p}\) is the loss that occurs when a wireless signal travels over a distance \(d\) from the transmitter to the receiver, varies with the environment, and is modeled accordingly, as discussed further. Three scenarios, free space, outdoor-urban and rural, and indoor, are considered for estimating the range of LoRa transmissions in the 470-580 MHz TVWS frequency range. The parameters used for path-loss modeling are set to typical values [16] mentioned in Table 2. The main objective is to maximize the distance and still obtain the LoRa signal successfully at the receiver. The receiver sensitivity \(P_{r}\) for the specific combination of \(SF\) and \(BW\) is considered as per Table 1, and path loss is evaluated from (3). The maximum distance for this path loss can then be computed using the path-loss model as per the scenario.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**SF**} & \multicolumn{6}{c}{\(BW\) (kHz)} \\ \cline{2-9}  & \multicolumn{2}{c}{**62.5**} & \multicolumn{2}{c}{**125**} & \multicolumn{2}{c}{**250**} & \multicolumn{2}{c}{**500**} \\ \cline{2-9}  & \(P_{r}\) & \(R_{b}\) & \(P_{r}\) & \(R_{b}\) & \(P_{r}\) & \(R_{b}\) & \(P_{r}\) & \(R_{b}\) \\ \hline
6 & \(-
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.6 Electric Chiller (EC)  
The electric chiller (EC) converts the input electrical energy into a cooling power, and the conversion performance coefficient depends on the ratio of the input electrical power to the output cooling capacity. The mathematical model is as follows:  
\[\begin{split} H_{EC}(t)=COP_{EC}P_{EC}(t)\\ P_{EC}^{\text{min}}\leq P_{EC}(t)\leq P_{EC}^{\text{max}}\end{split} \tag{9}\]  
where \(H_{EC}(t)\) is the output cooling power of EC at time slot \(t\). \(COP_{EC}\) is the coefficient of performance of EC. \(P_{EC}(t)\) is input electric power of EC at time slot \(t\). \(P_{EC}^{\text{min}}\),\(P_{EC}^{\text{max}}\) are minimal and maximal output electric power of EC, respectively.
## 4 Proposed Algorithm: CMWN-DV-HOP
### CMWN-DV-Hop  
#### 4.3.1 Calculation of the Hop Value Based on the Circle Cutting Technique  
Each anchor node floods its grouping information to neighboring nodes, including the initial value of 0 hops and anchor node position coordinates. The segmentation factor is introduced based on the cyclotomic method, and diverse segmentation factors divide the unknown nodes into different groups. For example, when the segmentation factor is 8, the communication area in Figure 7 is divided into A to H. Area A is the anchor node that can receive within a communication radius of \(0.125R\). The main idea is to estimate the distance from a transmitter to a receiver using the power of the received signal, the knowledge of the transmitted power, and the path loss model [45]. The hop count of this area node is recorded as 0.125 when a transmission is made. Area B refers to the area where the anchor node can receive packet information when it broadcasts within a communication radius of \(0.25R\). The hop value of unknown nodes in this geographic region is recorded as 0.25, from C to H, and so on. Based on the round-cutting technique to divide the hop count, recording the minimum number of hops will make the subsequent calculation of the actual hop distance more accurate, and the positioning accuracy will be improved. For example, for the network shown in Figure 3, if the segmentation factor is 4, the average distance per hop is calculated to be 65.40 m. The denominator has been further refined. This is closer to the actual value and makes the subsequent positioning more accurate.  
\[\text{Hopsize}_{1}(\text{a}=4)=\frac{d_{A1,A2}+d_{A1,A3}+d_{A1,A4}}{(0.25+0.75 +0.5)+(0.5+0.75+1)+(0.5+0.75)}=\frac{100+154+73}{5}=65.40 \tag{24}\]  
Figure 6: Flow chart of the weighted recursive least-squares algorithm.
## 2 Materials and Methods  
All chemicals were used as received. HNTs, silver nitrate (AgNO3, 99.9999%), tetrachlorOVAtic acid (HAuCl4.3H2O, 99.9%), sodium citrate dihydrate (99%), (3-Aminopropyl) triethoxysilane (APTES, 99%), and sodium borohydride (NaBH4) were purchased from Sigma-Aldrich. (3-aminopropyl) triethoxysilane (97%) Toluene was obtained from Merck. Prior immobilization of Ag- and Au-NPs the surface of HNTs was APTES-modified through grafting reaction [28]. The reaction was performed under a nitrogen atmosphere using a standard air-free technique (for details, see [22]). In order to prepare an aqueous suspensionof HNTs:Au-NPs, APTES-modified HNTs [24] were added dropwise to colloidal solution of Au-NPs, which were synthesized according to [29]. The obtained suspension was agitated and left to mix on a shaker for 12 h. Next, the samples were centrifuged, washed with de-ionized water twice, and concentrated to 1 mg/mL. The HNTs decorated with Ag-NPs were washed several times with deionized water to remove the excess borohydride and finally dried in air to obtain powder of HNTs:Ag-NPs. Then, the powder with a concentration of about 1 mg/mL was dispersed in deionized water to prepare aqueous suspensions of HNTs:Ag-NPs.  
HNTs before and after immobilization of Au- and Au-NPs were investigated using a transmission electron microscope (TEM: FEI TECNAI G2 F20-ST) operating at 200 kV, after drop-casting a drop of solution of the sample onto a carbon coated copper grid. The size distributions of plasmonic NPs were obtained from their TEM images by using a free ImageJ software.  
In vitro tests were carried out by using the Raman spectroscopy of cells (3T3 fibroblasts) cultivated with HNTs:Ag-NPs. The measurements were carried out by using a confocal scanning Raman microscope Horiba LabRAM HR Evolution (HORIBA Ltd., Kyoto, Japan). All measurements were performed with linearly polarized exciting light at a wavelength of 632.8 nm, 600 lines/mm diffraction grating, and \(\times\)100 objective (N.A. = 0.90), whereas we used unpolarized detection to have a significant signal-to-noise ratio. The spot size was about 0.43 \(\upmu\)m. The Raman spectra were recorded with 7.5 mW incident powers and an integration time of 30 s at each point. The statistics were collected from (at least) 25 points at different parts of the sample. SERS measurements were carried out for four sets of the samples, i.e., (i) a control set with fibroblast cells without with HNTs:Ag-NPs; (ii) cells with bare HNTs (set A); (iii) cells with added HNTs:Ag-NPs of 5 nm mean size (set C); (iv) cells with added HNTs:Ag-NPs of 10 nm mean size (set E).  
Optical extinction spectra and local electric fields in HNTs:Au-NPs were simulated by using Lumerical Finite Difference IDE software (ANSYS, Inc., Canonsburg, PA, USA). The refractive indices of water and HNTs were assumed to be equal to 1.34 and 1.56 in the whole spectral range from 400 to 800 nm. The length, external and internal diameters of HNTs were taken at 1 \(\upmu\)m, 80 nm, and 20 nm, respectively. Au- and Ag-NPs were assumed to be randomly distributed on the outer surface of HNTs.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 7 Open Challenges
### Domain Adaptation  
The performance of sound event classification algorithms often suffers from covariate shift, i.e., a distribution mismatch between training and test datasets. When being deployed in real-world application scenarios, ASC systems usually face novel acoustic conditions that are caused by different recording devices or environmental influences. Domain adaption methods aim to increase the robustness of classification algorithms in such scenarios by adapting them to data from a novel target domain [85]. Depending on whether labels exist for the target domain data, supervised and unsupervised methods are distinguished. Supervised domain adaptation usually involves fine-tuning a model on new target domain data after it was pre-trained on the annotated source domain data.  
One unsupervised domain adaptation strategy is to alter the target domain data such that their distribution becomes closer to that of the source domain data. As an example, [PERSON] used \"spectral correction\" to compensate for different frequency responses of the recording equipment. He estimated a set of frequency-dependent magnitude coefficients from the source domain data and used them for spectrogram equalization of the target domain data [86]. Despite its simplicity, this approach led to the best classification results in Task 1B \"Acoustic Scene Classification with mismatched recording devices\" of the 2019 DCASE Challenge. However, it required time-aligned audio recordings from several microphones during the training phase. [PERSON] and [PERSON] performed an independent domain adaptation of both the source and target domain to an additional domain using a factorized hierarchical variational autoencoder [87].  
As a second unsupervised strategy, [PERSON] et al. used an adversarial training approach such that the intermediate feature mappings of an ASC model followed a similar distribution for both the source and target domain data [85]. This approach was further improved using the Wasserstein generative adversarial networks (WGAN) formulation [88]. When analyzing the best-performing systems in the microphone mismatch ASC Task (1B) of the 2019 DCASE challenge, it becomes apparent that combining approaches for domain adaptation and data augmentation jointly improved the robustness of ASC algorithms against changes of the acoustic conditions.
## 4 Experiments and Results  
We evaluated the ReTrackVLM performance across various datasets and metrics. This section begins with an overview of the datasets and evaluation metrics used, followed by implementation details. Key experiments, including an ablation study that highlights contributions from different components, are presented. We conclude by comparing ReTrackVLM with state-of-the-art methods, showcasing its effectiveness, especially in challenging scenarios involving cross-modal embeddings and zero-shot ReID integration.
## 5 Experimental Evaluation
### Computational Setting  
In order to conduct a fair comparison with these greedy algorithms, all of them were implemented in ANSI C++ using Cygwin GCC 4.4 for compiling the software and carried out on the same computing platform. The experiments were performed on a laptop equipped with a 64-bit 2.5-GHz Intel(r) Core(tm) i5-7200U processor and 8 GB of RAM. Moreover, in order to get an impression about the solution quality provided by IGA-PIDS, we applied the ILP solver ILOG CPLEX 12.10 in single-threaded mode--with a time limit of 2 CPU hours per instance--to all problem instances. Note that the default optimality gaps of CPLEX were used. These default values indicate CPLEX to stop when an integer feasible solution is reached that is within 0.01% of optimality. The experiments with CPLEX were performed on a cluster of machines with two Intel(r) Xeon(r) Silver 4210 CPUs with 10 cores of 2.20 GHz and 92 Gbytes of RAM.
# A Novel Plug-in Board for Remote Insect Monitoring
## 2 Materials and Methods
### Power Supply  
Three LDO (Low-Dropout) regulators are placed on the plug-in board. Two of them are the MIC29312, while the third one is an NCP1117 LP. The MIC29312 is a 3 A regulator with a very-low-dropout voltage. Its output voltage is adjustable between 1.24 V and 15 V. To set the desired output voltage, two external resistors (voltage divider) are used. Its output voltage can be calculated with the following formula, where \(R_{1}\) is the first resistor while \(R_{2}\) is the second resistor of the voltage divider chain:  
\[V_{out}=1.24\times\left(\frac{R_{1}}{R_{2}}+1\right) \tag{1}\]  
The regulator is protected against overcurrent faults, reversed input polarity, and over-temperature operation. Moreover, it has an enable pin, which was especially important for us in order to turn off the regulator if it is not needed.  
One of the MIC29312 regulators is used to create the 5 V supply voltage, which mainly feeds the Raspberry Pi Zero. As shown in Figure 4, the enable input is controlled by a PNP MOSFET, where the gate voltage comes from the on/off timing circuit (next subsection). This means that the Raspberry Pi Zero will be totally switched off if we turn off the regulator.  
The other MIC29312 provides an approximately 3.8 V output. This is used to operate the LTE communication module. The schematic of the second MIC regulator can be seen in Figure 5. Here, the enable input is directly controlled by a GPIO pin of the Raspberry Pi Zero.  
Figure 4: Schematic of the 5 V regulator.  
Figure 5: Schematic of the 3.8 V regulator.  
The NCP1117 LP LDO regulator is capable of providing 1 A output current with a dropout voltage of 1.3 V at full current load. The NPC1117 \"family\" has adjustable and fixed voltage members. On the plug-in board, we used the fixed 3.3 V version. As with the MIC regulator, it also has internal protection features. This regulator feeds more components on the plug-in board, including the RTC. Therefore, it is constantly turned on.
# Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling
## 4 Discussion
### EMG and Quadriceps Forces during Squatting and Kneeling  
The model's prediction of the strongly falling force of the quadriceps muscle at the attainment of the terminal squatting and kneeling positions was confirmed by the EMG recordings and is in line with other studies [35; 36]. The successful stamina tests during deep squatting and kneeling further demonstrate the low muscle activity of the quadriceps (max EMG range: 5 to 11% MVC). The model predicted a quadriceps force of around 1.0 BW for static postures, which is substantially lower than that predicted by other models (Table 3). In deep knee bending by normal subjects [PERSON] et al. [37] determined a quadriceps force of 1.8 \(\pm\) 0.1 BW at a knee flexion of approximately 150\({}^{\circ}\); however, the thigh- calf contact was not taken into consideration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Measure** & **(I) Task** & **(J) Task** & **Mean Difference (I–) *** (95\% CI)** & \(p\) *** \\ \hline
**Tibofemoral** & LR & DK & **2.10** & (1.79, 2.41) & **<0.001** \\ joint force (BW) & & KN & **2.16** & (1.85, 2.47) & **<0.001** \\  & & SQ & **1.92** & (1.64, 2.19) & **<0.001** \\  & DK & KN & 0.06 & (\(-\)0.16, 0.29) & \textgreater{}0.999 \\  & & SQ & **\(-\)0.18** & (\(-\)0.31, 0.06) & **0.003** \\  & KN & SQ & **\(-\)0.25** & (\(-\)0.40, 0.10) & **0.001** \\ \hline Patellofemoral & LR & DK & **2.55** & (2.00, 3.10) & **<0.001** \\ joint force (BW) & & KN & **2.34** & (1.76, 2.91) & **<0.001** \\  & & SQ & **2.47** & (2.03, 2.91) & **<0.001** \\  & DK & KN & \(-\)0.21 & (\(-\)0.59, 0.16) & 0.603 \\  & SQ & \(-\)0.08 & (\(-\)0.27, 0.11) & \textgreater{}0.999 \\ KN & SQ & 0.14 & (\(-\)0.12, 0.39) & 0.741 \\ \hline \end{tabular}  
* Values are based on estimated marginal means. *** The mean difference is significant at the 0.05 level (highlighted in bold). *** Adjustment for multiple comparisons: Bonferroni.  
\end{table}
Table 2: Pairwise comparisons of the max knee joint forces between the different activities and postures (LR: lowering/rising; DK: deep kneeling; KN: kneeling; SQ: squatting).
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function
### Physical Links
#### 3.1.4 Transport Mode 4 (Car)  
To remain consistent with transport mode 2, the travel time of mode 4 \(t_{l}^{4}\) takes into account road traffic congestion and can be modelled as follows:  
\[t_{l}^{4}=t_{l}^{4^{0}}\left[1+\alpha\left(\frac{fv_{l}^{2}+gv_{l}^{4}}{C_{l}} \right)^{\beta}\right] \tag{14}\]  
where \(t_{l}^{4^{0}}\) (min) is the free-flow travel time of mode 4 on physical link \(l\).  
According to the above, the crowd discomfort and travel fare of mode 4 on physical links are obtained as follows:  
\[u_{l}^{4}=\gamma u^{4^{0}}t_{l}^{4} \tag{15}\]  
\[m_{l}^{4}=\gamma mt^{4^{0}}h_{l} \tag{16}\]  
where \(u^{4^{0}}(\min^{-1})\) is the crowd's discomfort per time unit and \(m^{4^{0}}\) (km\({}^{-1}\)) is the travel fare of mode 4 per unit distance.  
Then, the travel cost of transport mode 4 on each link is  
\[c_{l}^{4}=\lambda_{l}t_{l}^{4^{0}}\left[1+\alpha\left(\frac{fv_{l}^{2}+gv_{l}^ {4}}{C_{l}}\right)^{\beta}\right]+\lambda_{m}\tau m^{4^{0}}h_{l} \tag{17}\]
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(P_{laser}\) & \(t_{exposure}\) & \(E\) & \(T_{final}(\mu\pm\sigma)\) [\({}^{\circ}\)C] & \(T_{final}(max)\) [\({}^{\circ}\)C] & \(T_{final}(min)\) [\({}^{\circ}\)C] \\ \hline \multirow{4}{*}{3.4 W} & 24.54 s & 83.4 J & \(56.5\pm 3.2\) & 64.3 & 53.8 \\  & 41.91 s & 142.5 J & \(69.5\pm 7.5\) & 91.2 & 61.6 \\  & 63.26 s & 215.1 J & \(77.5\pm 8.7\) & 97.4 & 66.2 \\ \hline \multirow{4}{*}{6.8 W} & 7.87 s & 53.5 J & \(59.0\pm 4.6\) & 70.1 & 53.1 \\  & 10.93 s & 74.3 J & \(67.8\pm 4.3\) & 74.5 & 60.2 \\  & 15.29 s & 104.0 J & \(81.0\pm 7.9\) & 96.6 & 70.0 \\ \hline \multirow{4}{*}{10.2 W} & 4.09 s & 41.7 J & \(57.5\pm 2.9\) & 61.4 & 52.2 \\  & 6.32 s & 64.5 J & \(70.9\pm 5.5\) & 85.1 & 63.0 \\ \cline{1-1}  & 7.82 s & 79.8 J & \(79.3\pm 6.1\) & 89.4 & 71.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive temperature values for the fixed-dose application: The mean (\(\mu\)), standard deviation (\(\sigma\)), range (\(max-min\)), maximum (\(max\)) and minimum (\(min\)) values of the achieved tissue surface temperature at the end of laser exposure (\(T_{final}\)).