esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Experimental Design  
Eight treatments (four non-contaminated and four contaminated treatments) were conducted (three tank replicates per treatment), where fish were daily fed (\(\sim\)1.5% of their body weight) with a non-contaminated feed (non-CONT) or with a contaminated (MeHg-enriched) feed (CONT; Hg), respectively. Additionally, two temperature and pH levels were simulated, in a full cross-factorial experimental design, i.e., T1 = 19 \({}^{\circ}\)C and T2 = 23 \({}^{\circ}\)C, corresponding to the optimal _S. senegalensis_ rearing temperature and +4 \({}^{\circ}\)C of seawater warming (W), respectively (scenario RPC8.5 [29]), and pH1 = 8.0 units and pH2 = 7.6 units, corresponding to the optimal _S. senegalensis_ rearing pH and \(-\)0.4 pH level of seawater acidification (A), respectively (scenario RPC8.5 [29]). In this way, the eight treatments were defined as follows: (i) non-CONT; (ii) non-CONT + A; (iii) non-CONT + W; (iv) non-CONT + A + W; (v) CONT; (vi) CONT + A; (vii) CONT + W; (viii) CONT + A + W. After 28 days of exposure to the conditions mentioned above, an additional seven days of MeHg clearance phase was also carried out, by feeding all specimens (including those from CONT treatments) with non-CONT feed. As oxygen limitation is one of the fundamental mechanisms determining the biological responses of fish to environmental changes, especially increases in seawater temperature [41], the average concentration for dissolved oxygen during the experiment was 7.29 \(\pm\) 0.02 mg\(\cdot\)L\({}^{-1}\) and 6.91 \(\pm\) 0.02 mg\(\cdot\)L\({}^{-1}\) for T1 = 19 \({}^{\circ}\)C and T2 = 23 \({}^{\circ}\)C, respectively.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 5 Experiments
### System Evaluation
#### 5.2.3 Performance Evaluation of Face Decryption Operations  
We evaluated the performance of face decryption operations for both the image owner and consumers to assess the system's efficiency in decrypting facial data. For the image owner, the decryption process involves directly retrieving the symmetric key for the image and decrypting the facial regions in the shared images. For consumers, the decryption process requires reconstructing the symmetric key from key shares provided by the owner, followed by decryption of the facial regions using the reconstructed key. The results are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Number of** & **Total Key** & **Avg. CSP Split** & **Avg. CSP Peak** & **Key Split Success** \\
**Consumers**1  & **Images** & **Split** & **Time (ms)** & **Memory (MB)** & **Rate (\%)**2  \\ \hline
1 & 996 & 996 & 70.39 \(\pm\) 6.82 & 4333.06 \(\pm\) 500.83 & 100\% \\
2 & 342 & 684 & 69.99 \(\pm\) 5.4 & 4921.35 \(\pm\) 51.68 & 100\% \\
3 & 147 & 441 & 70.05 \(\pm\) 7.61 & 4915.35 \(\pm\) 52.41 & 100\% \\
4 & 128 & 512 & 69.21 \(\pm\) 5.5 & 4915.68 \(\pm\) 51.16 & 100\% \\
5 & 98 & 490 & 69.79 \(\pm\) 10.96 & 4927.14 \(\pm\) 43.45 & 100\% \\
6–10 & 171 & 1275 & 68.94 \(\pm\) 5.3 & 4905.24 \(\pm\) 70.65 & 100\% \\
11–65 & 118 & 2209 & 68.91 \(\pm\) 7.02 & 4988.1 \(\pm\) 19.69 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of key split operations.  
presented in Table 7. Performance metrics include the average decryption time for both the owner and consumers.  
The decryption time for the owner ranged from 215.17 ms to 275.75 ms, while for consumers it ranged from 211.08 ms to 285.57 ms. These results indicate that the system efficiently decrypts facial data for both groups, with minimal variation in decryption time across different face categories.  
The slightly higher decryption time for consumers is attributed to the need for reconstructing the symmetric key from key shares before decrypting the facial regions. However, the minimal difference in decryption time suggests that the system efficiently manages decryption operations for both the image owner and consumers, despite the additional steps required for key reconstruction in the consumer decryption process.
## 4 Results and Discussion
### Capacity Estimation Models
Since the unit and dimensions of \(MSE\) are inconvenient to explain, the arithmetic square root and root mean square error (\(RMSE\)) are introduced, as follows:  
\[RMSE=\sqrt{MSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}} \tag{8}\]  
The relative error is defined as the ratio of \(RMSE\) and initial capacity. The data from storage experiments at 45 \({}^{\circ}\)C and 54 \({}^{\circ}\)C were used to evaluate the capacity decay model. The specific values are listed in Table 4. The results indicate that \(RMSE\) is within 0.4 Ah and the relative error is less than 2%, which are acceptable for ER48690 with 22 Ah. Figure 8: The relationship between \(1/T\) and ln(\(K\)). To construct the models of primary lithium batteries during the whole life cycle of underwater vehicles, the temperature calibration model is proposed based on the capacity decay model and discharge experimental data. It is worth noting that the capacity derived from the capacity decay model in Equation (6) is the maximum available capacity when battery discharges at 25 \({}^{\circ}\)C. Figure 7 indicates that the maximum available capacity is sensitive to the operating temperature. Hence, a scaling factor (_f_), calibrating the maximum available capacity at different operating temperature, is proposed here:  
\[f=Q_{n,T}/Q_{n,25} \tag{9}\]  
where \(Q_{n,T}\) refers to the maximum available capacity at operating temperature \(T\). \(Q_{n,25}\) represents the maximum available capacity when the battery discharges at 25 \({}^{\circ}\)C, which can be obtained from the capacity decay model. Based on the experimental discharge data, the relationship between \(f\) and temperature is fitted, as shown in Figure 9. Herein, the effect of current rate is neglected and only experiment data with a nominal current rate are taken here. The relative error between experimental data and fitted values is less than 0.7%. \[\ln(K)=-11515.24\cdot\frac{1}{T}+32.81 \tag{4}\]0  
Finally, the maximum capacity at the working stage of underwater vehicles can be given as:  
\[\ln(K)=-11515.24\cdot\frac{1}{T}+32.81 \tag{4}\]1  
According to the ampere-hour integral method, the rest capacity of a primary lithium battery during operation can be given as:  
\[\ln(K)=-11515.24\cdot\frac{1}{T}+32.81 \tag{4}\]2  
where \(Q_{n,T}\) is obtained from the temperature calibration model, \(i\) represents discharge current (A), and \(t\) refers to the discharge time (h). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & _MSE_ **(Ah2)** & _RMSE_ **(Ah)** & **Error (\%)** \\ \hline
45 \({}^{\circ}\)C & 0.077926 & 0.279153 & 1.12123 \\
54 \({}^{\circ}\)C & 0.147483 & 0.384036 & 1.542495 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The statistical results of the capacity decay model. Figure 9: The fitted curve of scaling factor_f_ versus temperature.
# On Global Quantum Communication Networking
## 2 Proposed Cluster States-Based Quantum Communications Networks
This can be done with the help of the SDN concept. The SDN has been introduced to separate the control plane and data plane, manage network services through the abstraction of higher-level functionality, and implement new applications and algorithms efficiently. It has already been studied to enable the coexistence of classical and quantum communication channels. Our SDN-based QCN architecture is composed of three layers, namely an application layer, a control layer, and a QCN layer. Users send their requests from the application layer with the help of the northbound interface to the SDN controller. The SDN controller allocates the QCN resources with the help of its global map through the southbound interface. The QCN layer would be composed of dense wavelength-division multiplexing (DWDM) FSO/single-mode fiber (SMF)/few-mode fiber (FMF) links and QCN nodes. Any two nodes in the QCN can communicate through either through a dedicated SMF/FSO/FMF link or through a wavelength channel. The SDN control should also determine sequence of measurements to be performed in order to establish desired EPR pairs. To deal with time-varying channel conditions over heterogeneous links, we should adapt the system configuration based on both application requirement and link condition. Figure 3: Establishing EPR pairs between nodes 1 and 9 as well as between nodes 3 and 7. Figure 2: [PERSON]’s approach to create the \(T\)-shape cluster state.
## 2 Materials and Methods  
### Study Area and Soil Sampling  
The study area is an agricultural land covering ca. 1.5 km\({}^{2}\) in the northwestern section (48\({}^{\circ}\)0\({}^{\prime}\)40\({}^{\prime}\)N, 2\({}^{\circ}\)50\({}^{\prime}\)40\({}^{\prime\prime}\)W) of the Naizin watershed (western France). The fields studied were located on both sides of a small tributary of the Coet-Dan stream with a moderate slope (<5%). Fields in the northern part have the steepest slopes, with slopes of between 3% and 5%, while those in the southern part are less than 2%. The climate is temperate, with mean annual rainfall of ca. 909 mm. Agriculture is mainly intensive, with cereals, maize, and grassland as the main land uses in crop rotations. The soils are developed on a silty substrate derived from weathered schists and Quaternary aeolian deposits. The dominant soil types are Luvic Cambisols and Haplic Albeluvisols of silty texture [62; 63; 64].  
We sampled 83 points within 22 fields in October 2020. These sampling points were selected using a 100 m triangular grid, except in small fields, in which points were selected randomly (Figure 1). Within 5 m of each point, 5 subsamples of soil were collected randomly from the top 5 cm and then pooled. The samples were air-dried, sieved to 2 mm, and divided in half by sample quartering: one half was sent to the national INRAE soil analysis laboratory (LAS Arras, France) for soil analysis, while the other was used to measure reflectance spectra under laboratory conditions. The SOC content was measured via dry combustion according to certified method NF ISO 10694 [65].
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### CPS Hub with Heterogeneous Data Planes
#### 6.4.1 MQTT as the Data Plane Protocol  
The evaluation arrangements related to the MQTT case are shown in Figure 22. The system comprises three MQTT endpoints: one MQTT server and two MQTT clients. MQTT endpoints were equipped with XMPP clients that are capable of data plane negotiation. For XMPP services, there are two federated XMPP servers in the system, local and public. The public XMPP server also hosts the virtual communication spaces of the owners.  
The MQTT server and MQTT subscriber clients run in the NS-3 network simulator environment, being clients of the simulated lr-wpan/6 lowpan network. The MQTT server also has a Local Area Network (LAN) interface to company intranet. The MQTT publisher client runs on company intranet. The XMPP clients running in the NS-3 simulator connect to the virtual communication server (public XMPP server) via the local XMPP server, whereas the MQTT publisher client running on company intranet can register directly to the virtual communication server.  
There are two owners in the system, Company-a and Company-b. Company-a owns the MQTT server and MQTT subscriber client, while Company-b owns the MQTT publisher client. This means that there are two virtual communication spaces in the system. They can be expressed using XMPP JID as Company-a@m2 mgrids.erve.vtt.fi and Company-b@m2 mgrids.erve.vtt.fi, respectively.  
Both MQTT clients are willing to communicate with the MQTT server. The MQTT subscriber client is under the same owner (under the same virtual communication space) as the MQTT server, so they inherently have a trust relationship. However, in order to obtain the MQTT server's IP address and other communication parameters, it needs still to execute data plane protocol negotiation. On the other hand, the MQTT publisher client needs to establish a trust relationship with the MQTT server, as they are under different owners. Naturally, it also needs to perform data plane protocol negotiation.  
The MQTT server was configured to accept Company-b's trust relationship creation request and data plane negotiation request. This is done by adding Company-b's JID (Company-b@m2 mgrids.erve.vtt.fi) into the trusted list.  
After both MQTT client endpoints have successfully performed the data plane negotiation with the MQTT server, they can connect to MQTT server, as they now know the IP address and port to be applied. The evaluation case indicated the successful operation of the data plane negotiation when using MQTT as the data plane protocol.  
Figure 22: Data plane negotiation in the message queue based telemetry transport (MQTT) case.
### Regulation, Standardization, and Conceptualization of Remote Operation of AVs  
In addition to technological leaps forward, legal environments have become more favorable toward using remote operations on public roads as well. For instance, the German Road Traffic Act (\"Strassenverkehrsgesetz\") has been modified last year so it now explicitly permits AV of Level 4 on German roads--as long as they are monitored and controlled, if necessary, by a human operator coined \"Technical Supervisor\" (\"Technische Aufsicht\") [11]. This supervisor can be either on board the vehicle or at another location. Thus, remote vehicle operations are now legally feasible on German roads. Also, in the UK, Sweden, Japan and a few US states, laws and regulations that require remote supervision of highly automated cars without an on-board driver have been passed [12]. Moreover, driverless operation of vehicles on public roads is now possible in major European countries like France and the UK [13] as well as at least 41 states of the US [14].  
The standardization of remote operations has also seen tremendous steps forward. The latest update of the SAE's Taxonomy for Driving Automation Systems [8] includes two conceptualizations of remote vehicle operations: Remote Driving and Remote Assistance. In _Remote Driving_ (RD), a human operator is executing \"real-time performance of [... ] the DDT (i.e., dynamic driving task such as braking, steering, or accelerating)\" ([8] p. 19). Thus, remote driving resembles the conventional way of driving a vehicle: by initiating direct low-level driving maneuvers, including lateral and longitudinal motion control, right when the situation requires them. The Remote Driver, who is the actor to execute Remote Driving, may overrule the vehicle automation's driving tasks.  
In contrast, _Remote Assistance_ (RA) is defined as an \"event-driven provision, by a remotely located human, of information or advice to an ADS (i.e., automated driving system) equipped vehicle in driverless operation in order to facilitate trip continuation when the ADS encounters a situation it cannot manage\" ([8] p. 18). Thus, the Remote Assistant supports the vehicle by providing high-level guidance on how to deal with certain situations that are not part of the automation's ODD. This advice is provided well before the challenging situation and must not be time-critical. In addition, the automation must be capable of processing the high-level information provided and translate it into direct driving maneuvers. Examples for guidance range from simple \"giving clearance\" cases in which the vehicle requests an assessment of the situation from the Remote Assistant on how to proceed in a certain situation, e.g., when it is uncertain whether an identified object is an actual obstacle, to more demanding \"setting trajectories or waypoints\" cases that require the Remote Assistant to determine a pathway or waypoints of a pathway that the AV follows to circumvent an obstacle.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 4 Experimental Results and Discussion  
### Experimental Testbed  
To evaluate the localization accuracy of the proposed algorithm, we employ the \(U|I IndoorLoc\) dataset, which spans over 108,000 m\({}^{2}\) across three to five floors of buildings on the University Jaume I (UJI) campus in Spain [36]. The dataset consists of a training set with 19,937 records and a validation set with 1,111 records, encompassing nine fields that capture location and measurement information Specifically, the nine dimensions include coordinates, floor, building, space, relative position, user ID, device ID and timestamp encapsulating details such as where, who and when the sample was collected. Notably, nine out of the 11 devices used in the validation dataset are distinct from those used in the training dataset, highlighting its robustness for assessing generalization. To ensure statistical independence between the training and validation datasets, the validation data were collected three months after the training data. The localization environment includes 520 wireless APs spread across three buildings (Buildings 0, 1, and 2), each consisting of four to five floors. Each observation in the dataset contains real-world coordinates (longitude, latitude, and floor) of a mobile device, along with 520 RSSI values from all detected APs. Figure 3 displays the floor plans of the three buildings on the UJI campus map, along with their respective buildings, as well as the Spatial distributions of the dataset for LONGITUDE, LATITUDE, and FLOOR.  
The RSSI values range from \(-104\) dBm, representing the weakest signal, to 0 dBm, indicating an exceptionally strong signal. If a mobile device does not detect an AP's signal, it is coded as \(-105\) dBm. For simplicity, we denote \(N=19\),937 and \(R=520\) as training sample size and the number of APs (features), respectively.  
Figure 3: Spatial distributions. (**a**) Buildings on the UJI campus map from Google Earth. (**b**) Spatial distributions of training and validation dataset.  
To enhance the performance of machine learning algorithms, it is essential to clean and preprocess raw data to handle missing values, noise, and improper formats. First, non-detected RSSI values are replaced with \(-105\) dBm, a value lower than the weakest detectable signal. Then, any AP whose RSSI remains constant across the training and/or validation datasets is removed from the analysis [37]. Additionally, we separate the training samples into two folders based on the collection time of the fingerprints. If the RSSI values from the same AP for the same RP show significant fluctuations between folders, we exclude their readings since they suggest the AP's physical location might have changed during data collection. This noise reduction and filtration process reduces the number of RSSI readings \(R\) from 520 to 300, significantly improving both the accuracy and speed of our learning algorithms.  
In Table 2, we provide a summary containing the essential information regarding the experimental testbed.
# Recent Updates on the Synthesis of Bioactive Quinoxaline-Containing Sulfonamides
## 2 Synthesis and Biological Activities
### Quinoxaline Sulfonamide Derivatives with Antileishman Activity  
[PERSON] et al. synthesized quinoxaline sulfonamide derivatives and studied their antileishmanalities. The 3-amino-1,4-di-N-oxide quinoxaline-2-carbonitrile derivative **89** was synthesized in 15-90% yield by the reaction of benzofuranaxene **85** and malononitrile **86**, using triethylamine as catalyst and DMF as solvent. The scaffold **87** was further treated with substituted sulfonyl chlorides **88** at 0 \({}^{\circ}\)C to afford quinoxaline sulfonamide derivatives **89** in low yield (13-15%) (Scheme 8) [134].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Neuropharmacological Effect**} \\ \hline
**Compound** & **Activity** & **Inhibition Effect** & **Reference Compound** & **Inhibition Effect** \\ \hline \multirow{8}{*}{\begin{tabular}{c} 84 \\ \end{tabular} } & Total locomotor activity & \
\begin{tabular}{c} Potent sedative at \\ 40 mg/kg \\ \end{tabular} & - & - \\ \cline{2-4}  & \multirow{4}{*}{Anxiolytic activity} & 67.3\% = time in open arm & \multirow{4}{*}{Diazep} & 53.3\% = time in open arm \\ \cline{3-4}  & & 81.0\% = entry into open arm, 25.9 = index of open arm avoidance & & 82.4\% = entry into open arm, 32.2 = index of open arm avoidance \\ \cline{3-4}  & & Dose = 2.5 mg/kg & Dose = 1 mg/kg \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Anticonvulsant activity \\ \end{tabular} } & Protection effect = 100\% & \multirow{4}{*}{Phenobarbittone sodium} & Protection effect = 90\% against both Leptazol at 80 mg/kg. and Strychnine at 2.0 mg/kg after 60 min & \multirow{4}{*}{Phenobarbittone sodium} \\ \cline{3-4}  & & & 60 min 15.15 \\ \cline{1-1} \cline{3-4}  & Dose = 25 mg/kg dose & & Dose = 20 mg/kg \\ \hline \hline \end{tabular}
\end{table}
Table 7: Neuropharmacological activity of quinoxaline-based substituted benzene sulfonamide derivatives.  
The synthesized derivatives were evaluated against _Leishman amazonensis_ strain MHOM/BR/76/LTB in infected macrophages (Table 8) The quinoxaline sulfonamide **92** displayed a potent antileishmanal activity (IC\({}_{50}\) = 3.1 uM), although 15-fold less active than the standard drug amphotericin B. The quinoxaline sulfonamide analog **91** displayed a moderate antileishmanal activity (IC\({}_{50}\) = 16.3 uM), comparable to that observed for 90 (IC\({}_{50}\) = 20.3 uM). The SAR demonstrated that the presence of a 2-naphthyl moiety on the sulfonamide motif and a methyl group on the quinoxaline in 90 led to a potent antileishmanal activity, while a 2-naphthyl moiety and the electronegative chlorine atom was responsible for the moderate antileishmanal activity of scaffold 91. The highest antileishmanal activity was observed for **92** with the \(o\)-nitrophenyl group and chlorine atom (Figure 13) [134].
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Data
#### 2.2.4 Reference Dataset  
Reference data in mountain environments are often limited and outdated since field surveys in these regions are expensive, time consuming and demanding [20]. For our study, reference data on mountain pines were derived by the photointerpretation of recent aerial images by local experts (foresters working in the Sarntal Valley) and provided by the Forest Service of the Autonomous Province of Bolzano. Overall, reference vector data included 31 polygons indicating mountain pine stands (23.19 ha) and 13 polygons indicating other vegetation classes at the treeline ecotone (e.g., _Pinus cembra, Almus viridis, Rhododend ferrugineum_) (7.06 ha). From these polygons, 562 points were randomly extracted with a minimum distance of 30 m, resulting in 310 points for mountain pine class (class 1) and 252 points for other land cover classes (class 0). To ensure that bare areas in between mountain pine patches were not sampled for the mountain pine class, we applied a threshold based on the Normalized Difference Vegetation Index (NDVI) of the PlanetScope scene from 5 August 2020 and selected only sample points with NDVI values >0.4. Sample points in forest boundary regions were visually verified using an RGB airborne orthophoto (20 cm) from summer 2020 [41]. To also include other land cover classes present in the study area (e.g., grassland, bare soil, rocks and coniferous forest) and to account for the spatial restriction of the foresters' reference data to the northern part of the Sarntal Valley, additional reference data (1654 points: 556 points (class 1) and 1098 points (class 0)) were randomly set and manually labeled over the entire study area with respect to the minimum distance (30 m) using the 2020 orthophoto as a visual interpretation reference. Additionally, an orthophoto from 2006 was used to ensure that no reference points in recently cleared mountain pine areas were included in the reference data set, which could cause confusion due to the outdated CHM. In combination with the randomly extracted reference points, this resulted in a reference dataset with 2216 points (Figure 1): 866 points (class 1) and 1350 points (class 0). The reference dataset was then split into training and validation datasets with a ratio of 70:30.